From b76b7559727710dfb2c2c310b79b43fcdd2e44d3 Mon Sep 17 00:00:00 2001 From: masayukiaino Date: Fri, 13 Dec 2024 08:08:33 +0900 Subject: [PATCH] update vehicle adaptor Signed-off-by: masayukiaino --- .../CMakeLists.txt | 3 +- .../vehicle_adaptor/vehicle_adaptor.hpp | 2 +- .../autoware_vehicle_adaptor.md | 540 +++++++++++ .../calibrator/accel_brake_map_calibrator.py | 248 +---- .../data_analyzer/NN_model_evaluator.py | 29 +- .../include/inputs_prediction.h | 22 +- .../include/linear_regression_compensator.h | 2 +- .../include/transform_vehicle_adaptor_model.h | 133 +-- ...{utils.h => vehicle_adaptor_compensator.h} | 227 ++--- .../include/vehicle_adaptor_utils.h | 33 + .../param/optimization_param.yaml | 68 +- .../python_simulator/python_simulator.py | 19 +- .../run_accel_brake_map_calibrator.py | 70 -- .../src/inputs_prediction.cpp | 222 ----- .../src/transform_vehicle_adaptor_model.cpp | 192 ---- ...ls.cpp => vehicle_adaptor_compensator.cpp} | 905 +++++++----------- .../src/vehicle_adaptor_utils.cpp | 219 +++++ .../training/add_data_from_csv.py | 6 +- .../training/convert_model_to_csv.py | 59 -- .../training/get_initial_hidden_NN.py | 104 -- ...get_initial_hidden_NN_with_offline_data.py | 197 ++-- .../training/inputs_schedule_predictor_NN.py | 634 +++++++++--- ...in_error_prediction_NN_with_memory_bank.py | 641 ------------- ...n_error_prediction_NN_with_offline_data.py | 151 +-- .../training/training_utils.py | 707 ++------------ ...in_error_prediction_NN_with_memory_bank.py | 105 -- ...n_error_prediction_NN_with_offline_data.py | 6 - .../autoware_vehicle_adaptor/build_test.py | 4 +- .../images/bs_nom_steer_lat_dev.png | Bin 0 -> 70286 bytes .../images/bs_nom_vel_acc.png | Bin 0 -> 85779 bytes .../images/bs_va_steer_lat_dev.png | Bin 0 -> 82789 bytes .../images/bs_va_vel_acc.png | Bin 0 -> 116790 bytes .../images/kw_nom_steer_lat_dev.png | Bin 0 -> 76153 bytes .../images/kw_nom_vel_acc.png | Bin 0 -> 75541 bytes .../images/kw_va_steer_lat_dev.png | Bin 0 -> 80243 bytes .../kw_va_steer_lat_dev_param_change.png | Bin 0 -> 83689 bytes .../images/kw_va_vel_acc.png | Bin 0 -> 81747 bytes .../autoware_vehicle_adaptor/setup.py | 8 +- .../bias_acc_encoder_layer_1.csv | 32 +- .../bias_acc_encoder_layer_2.csv | 16 +- .../vehicle_model_1/bias_acc_layer_1.csv | 32 +- .../vehicle_model_1/bias_acc_layer_2.csv | 16 +- .../bias_complimentary_layer.csv | 128 +-- .../vehicle_model_1/bias_final_layer.csv | 8 +- .../bias_initial_hidden_acc_layer_1.csv | 16 - .../bias_initial_hidden_acc_layer_2.csv | 8 - .../bias_initial_hidden_final_layer.csv | 36 - .../bias_initial_hidden_fusion_layer.csv | 36 - .../bias_initial_hidden_gru_hh.csv | 96 -- .../bias_initial_hidden_gru_ih.csv | 96 -- .../bias_initial_hidden_only_online.csv | 36 - .../bias_initial_hidden_steer_layer_1.csv | 16 - .../bias_initial_hidden_steer_layer_2.csv | 8 - .../vehicle_model_1/bias_linear_relu.csv | 32 +- .../bias_lstm_encoder_hh_0.csv | 144 +-- .../bias_lstm_encoder_ih_0.csv | 144 +-- .../vehicle_model_1/bias_lstm_hh.csv | 128 +-- .../vehicle_model_1/bias_lstm_ih.csv | 128 +-- .../bias_steer_encoder_layer_1.csv | 32 +- .../bias_steer_encoder_layer_2.csv | 16 +- .../vehicle_model_1/bias_steer_layer_1.csv | 32 +- .../vehicle_model_1/bias_steer_layer_2.csv | 16 +- .../vehicle_model_1/offline_features.csv | 72 +- .../weight_acc_encoder_layer_1.csv | 32 +- .../weight_acc_encoder_layer_2.csv | 16 +- .../vehicle_model_1/weight_acc_layer_1.csv | 32 +- .../vehicle_model_1/weight_acc_layer_2.csv | 16 +- .../weight_complimentary_layer.csv | 128 +-- .../vehicle_model_1/weight_final_layer.csv | 8 +- .../weight_initial_hidden_acc_layer_1.csv | 16 - .../weight_initial_hidden_acc_layer_2.csv | 8 - .../weight_initial_hidden_final_layer.csv | 36 - .../weight_initial_hidden_fusion_layer.csv | 36 - .../weight_initial_hidden_gru_hh.csv | 96 -- .../weight_initial_hidden_gru_ih.csv | 96 -- .../weight_initial_hidden_only_online.csv | 36 - .../weight_initial_hidden_steer_layer_1.csv | 16 - .../weight_initial_hidden_steer_layer_2.csv | 8 - .../vehicle_model_1/weight_linear_relu.csv | 32 +- .../weight_lstm_encoder_hh_0.csv | 144 +-- .../weight_lstm_encoder_ih_0.csv | 144 +-- .../vehicle_model_1/weight_lstm_hh.csv | 128 +-- .../vehicle_model_1/weight_lstm_ih.csv | 128 +-- .../weight_steer_encoder_layer_1.csv | 32 +- .../weight_steer_encoder_layer_2.csv | 16 +- .../vehicle_model_1/weight_steer_layer_1.csv | 32 +- .../vehicle_model_1/weight_steer_layer_2.csv | 16 +- .../bias_acc_encoder_layer_1.csv | 32 +- .../bias_acc_encoder_layer_2.csv | 16 +- .../vehicle_model_2/bias_acc_layer_1.csv | 32 +- .../vehicle_model_2/bias_acc_layer_2.csv | 16 +- .../bias_complimentary_layer.csv | 128 +-- .../vehicle_model_2/bias_final_layer.csv | 8 +- .../bias_initial_hidden_acc_layer_1.csv | 16 - .../bias_initial_hidden_acc_layer_2.csv | 8 - .../bias_initial_hidden_final_layer.csv | 36 - .../bias_initial_hidden_fusion_layer.csv | 36 - .../bias_initial_hidden_gru_hh.csv | 96 -- 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vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor.md rename vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/{utils.h => vehicle_adaptor_compensator.h} (80%) create mode 100644 vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/vehicle_adaptor_utils.h rename vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/{utils.cpp => vehicle_adaptor_compensator.cpp} (77%) create mode 100644 vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_utils.cpp delete mode 100644 vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN.py delete mode 100644 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+20,13 @@ ament_auto_add_library(actuation_map_converter SHARED ament_auto_add_library(vehicle_adaptor SHARED src/vehicle_adaptor/vehicle_adaptor.cpp - src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/utils.cpp + src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_compensator.cpp src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/inputs_prediction.cpp src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/nominal_dynamics.cpp src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/linear_regression_compensator.cpp src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/inputs_ref_smoother.cpp + src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_utils.cpp ) target_include_directories(vehicle_adaptor PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/include/autoware_raw_vehicle_cmd_converter/vehicle_adaptor/vehicle_adaptor.hpp b/vehicle/autoware_raw_vehicle_cmd_converter/include/autoware_raw_vehicle_cmd_converter/vehicle_adaptor/vehicle_adaptor.hpp index 026216c80cf1f..9cb2ad34cf698 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/include/autoware_raw_vehicle_cmd_converter/vehicle_adaptor/vehicle_adaptor.hpp +++ b/vehicle/autoware_raw_vehicle_cmd_converter/include/autoware_raw_vehicle_cmd_converter/vehicle_adaptor/vehicle_adaptor.hpp @@ -21,7 +21,7 @@ #include #include #include -#include "utils.h" +#include "vehicle_adaptor_compensator.h" namespace autoware::raw_vehicle_cmd_converter { diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor.md b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor.md new file mode 100644 index 0000000000000..f9ad5a35c7f55 --- /dev/null +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor.md @@ -0,0 +1,540 @@ + + +# Autoware Vehicle Adaptor + +## 背景 + +様々な車両において、パラメータの細かいキャリブレーションなしで動くようなadaptiveな制御がしたい。 +コントローラの下段に配置してモデルパラメータのズレに対して、 +学習によってコントローラ内部モデルに基づいた理想的な状態が実現されるように入力を補正したい。 + +## 車両モデル + +まず現在の状態 $x\in \mathbb{R}^{6+M_{\mathrm{acc}} + M_{\mathrm{steer}}}$ +は以下の成分からなる。 + +- $x[0]$: ワールド座標 $x$成分、 $x[1]$: ワールド座標 $y$成分、 $x[2]$: 直進方向速度、 $x[3]$: ワールド相対ヨー角、 $x[4]$: 直進方向加速度実現値、 $x[5]$: ステア実現値 + +- $x[6:6+M_{\mathrm{acc}}]$は制御 $M_{\mathrm{acc}}$ステップ分の加速度入力値の履歴 + +- $x[6+M_{\mathrm{acc}}:6+M_{\mathrm{acc}}+M_{\mathrm{steer}}]$ は制御 $M_{\mathrm{steer}}$ ステップ分のステア入力値の履歴 + +ダイナミクスはノミナルモデルあるいは（メモリを含まない）学習モデル $F$を用いて、 +$$x^+ = F(x,u)$$ +と表され、 $u$は加速度・ステア入力値の変化率である。 +ここで制御時間ステップ $dt_{\mathrm{ctrl}}$を用いて次の時刻の加速度、ステア入力値の履歴の最新値 $x^+[6]$ および $x^+[6+M_{\mathrm{acc}}]$ は $x^+[6] = x[6] + u[0]*dt_{\mathrm{ctrl}}$ および $x^+[6+M_{\mathrm{acc}}] = x[6+M_{\mathrm{acc}}]+u[1]*dt_{\mathrm{ctrl}}$ のように変化している。 +更に、学習モデルがメモリを含む場合、隠れ状態 $h$ およびセル状態 $c$ を用いて +$$(x^+,h^+ ,c^+) = F(x,h,c,u)$$ +の形でダイナミクスが表される。 +次の時刻のはじめの $6$ 成分 $x^+[:6]$ についてはノミナルモデルおよび学習モデルの場合について分けて説明する。 + +### ノミナルモデル + +ノミナル車両モデルとして、以下のようなモデルを使っている。これは典型的な二輪キネマティクスに加え、加速度およびステアのダイナミクスに一次遅れと無駄時間を付け加えたモデルである。 +ノミナルモデルのモデルパラメーターとして定めている加速度・ステア無駄時間を制御ステップで表した $d_{\mathrm{acc}}$ および $d_{\mathrm{steer}}$ を用いて、時間遅れを考慮した場合の加速度・ステア入力を +$\alpha = x^+[6 + d_{\mathrm{acc}}]$ および $\delta = x^+[6 + M_{\mathrm{acc}} + d_{\mathrm{steer}}]$ と表す。 +このとき +$x^+[:6]$ は + +$$ +\begin{equation*} +\begin{split} +x^+[0] =& x[0] + x[2]\cos(x[3])dt_{\mathrm{ctrl}}\\ +x^+[1] =& x[1] + x[2]\sin(x[3])dt_{\mathrm{ctrl}}\\ +x^+[2] =& x[2] + x[4]dt_{\mathrm{ctrl}}\\ +x^+[3] =& x[3] + x[2]\frac{\tan(x[5])}{L}dt_{\mathrm{ctrl}}\\ +x^+[4] =& x[4] - \frac{x[4]-\alpha}{\tau_{\mathrm{acc}}}dt_{\mathrm{ctrl}}\\ +x^+[5] =& x[5] - \frac{x[5]-\delta}{\tau_{\mathrm{steer}}}dt_{\mathrm{ctrl}}\\ +\end{split} +\end{equation*} +$$ + +と表される。 +ここで $L$は車体長さ、 $\tau_{\mathrm{acc}}$は加速度一次遅れ時定数、 $\tau_{\mathrm{steer}}$はステア一次遅れ時定数である。 + +### 学習モデル + +学習モデルではノミナルモデルによる予測値と実現値を比較し誤差を学習する。 +実現値 $x_{\mathrm{obs}}$ との差について入力の履歴を除いた部分 +$$x_{\mathrm{obs}}[:6] - F_{\mathrm{nom}}(x,u) [:6]$$ +をニューラルネットにより近似する（ただし入力の履歴は単に更新されていくだけであるのでこの部分に対するダイナミクスは明らかである）。 +入力の履歴つきの現在の状態 $x\in \mathbb{R}^{6+M_{\mathrm{acc}} + M_{\mathrm{steer}}}$ から回転対称性や位置に対する対称性にもとづいて次元を減らしたもの $x_r\in \mathbb{R}^{3+M_{\mathrm{acc}} + M_{\mathrm{steer}}}$ に対して +実現値とノミナルモデルの車体相対の誤差 $\delta F\in \mathbb{R}^6$ を対応させるような関数を次のlossを用い、以下のようなニューラルネットで近似したものを $\delta F_N$と表したとき、 $F_{\mathrm{nom}} + \delta F_N$ をMPCにおける状態の予測に用いる。 + +$$ +\begin{equation} +\mathrm{loss} = ||\delta F_N - \delta F||_1 + \lambda ||\tanh(a (\delta F_N - \delta F)(\mathrm{steer}))||_1 + \alpha_1||\mathrm{params}||_1 + \alpha_2||\mathrm{params}||_2^2 +\end{equation}\tag{1} +$$ + +2項目はステア誤差の小さいところを拡大している。 + +Early Stoppingに掛かった際、学習率を小さくして学習を継続するといった形で学習率のスケジューリングを行っている。 +あらかじめ指定した学習率のリストが完了したとき学習を停止する。 + + + + +NNモデルは次のようなLSTMエンコーダ・デコーダ構造になっている。 + +```mermaid +graph LR; + A(( + 直進速度 + 1dim + )) + spacerA((" ")) + B(( + 加速度実現値 + 入力履歴 + 16dim + )) + C(( + ステア実現値 + 入力履歴 + 16dim + )) + D(( + 前回加速度誤差 + 1dim + )) + E(( + 前回ステア誤差 + 1dim + )) + F((16dim)) + G((16dim)) + H((8dim)) + I((8dim)) + J(( + セル状態 c + 18dim + )) + K(( + 　隠れ状態 h + 18dim + )) + + subgraph input + direction TB + A + spacerA + B + C + D + E + end + subgraph LSTM + direction LR + J --> K + end + A --> F + A --> G + A --> LSTM + B --> F --> H --> LSTM + C --> G --> I --> LSTM + D --> LSTM + E --> LSTM + + style spacerA fill:none,stroke:none +``` + + +デコーダは次のような構造になっており、エンコーダで圧縮された情報は、デコーダLSTMの初期値および、加速度・ステア隠れ状態として用いられる。 +```mermaid +graph LR; + A(( + 直進速度 + 1dim + )) + spacerA((" ")) + B(( + 加速度実現値 + 入力履歴 + 16dim + )) + spacerB((" ")) + C(( + ステア実現値 + 入力履歴 + 16dim + )) + D(( + 加速度隠れ状態 + 1dim + )) + E(( + ステア隠れ状態 + 1dim + )) + F((16dim)) + G((16dim)) + H((8dim)) + I((8dim)) + J(( + セル状態 c + 16dim + )) + K(( + 　隠れ状態 h + 16dim + )) + L((64dim)) + M((8dim)) + + J_0(( + 速度誤差 + )) + J_1(( + ヨー角誤差 + )) + J_2(( + 加速度誤差 + )) + J_3(( + ステア誤差 + )) + + + subgraph input + direction TB + A ~~~ + spacerA ~~~ + B + spacerB + C + D + E + end + subgraph LSTM + direction LR + J --> K + end + subgraph output + direction TB + J_0 ~~~ J_1 ~~~ J_2 ~~~ J_3 + end + A --> F + A --> G + A --> LSTM + A --> L + B --> F --> H --> LSTM + D --> F + C --> G --> I --> LSTM + E --> G + H --> L + I --> L + LSTM --> M + L --> M + M --> output + style spacerA fill:none,stroke:none + style spacerB fill:none,stroke:none +``` + +またエンコーダLSTMの初期値は次のようにオフラインで収集したデータからアテンション機構により抽出される。 +```mermaid +graph LR; + A(( + 直進速度 + 1dim + )) + B(( + 加速度実現値 + 入力履歴 + 16dim + )) + C(( + ステア実現値 + 入力履歴 + 16dim + )) + D((16dim)) + E((16dim)) + F((8dim)) + G((8dim)) + H(( + 　GRU隠れ状態 h + 32dim + )) + spacerH((" ")) + I(( + 　GRU隠れ状態 h + 32dim + )) + J(( + ・ + ・ + ・ + )) + K(( + 　GRU隠れ状態 h + 32dim + )) + L((128dim)) + M((18dim)) + N((18dim)) + + subgraph input + direction TB + A + B + C + end + subgraph offline_features + direction TB + H + spacerH + I + J + K + end + subgraph LSTMエンコーダ初期値 + direction TB + N + end + A --> D + A --> E + A --> H + B --> D --> F --> H + C --> E --> G --> H + offline_features -->|アテンション機構|L + L --> M + M --> N + style spacerH fill:none,stroke:none + style J fill:none,stroke:none +``` + +## コントローラ入力予定の取得 + +### 入力予定の予測 + +Vehicle Adaptorは $N$ ステップ先までのコントローラ側の加速度・ステア入力の予定 $(\alpha_0,\ldots,\alpha_N)$ および $(\delta_0,\ldots,\delta_N)$ （ただし $\alpha_0$ および $\delta_0$ はコントローラの現在の加速度・ステア入力）が与えられたとき、 +ノミナルダイナミクスに従ってその入力を積分して得られる状態が（ノミナルダイナミクスと実際のダイナミクスのズレがあった場合でも）実現されるように入力を補正する。 +コントローラがMPCで入力の予定がコントローラから与えられている場合は、その入力の予定をそのまま使うが、コントローラの入力の予定が得られない場合、コントローラの過去の入力 $(u_{-h},\ldots,u_0)$ （ここで $u$ は加速度・ステア入力のいずれか）から以下のいずれかの方法で、将来のある $N_0$ ステップ先 $(u_1,\ldots,u_{N_0})$ までコントローラの入力の予定の予測を行う。 +その先においては一定値 $u_{N_0} = u_{N_0+1} = \cdots = u_N$ として定める。 + +- 過去の入力 $(u_{-h},\ldots,u_0)$ を用いて多項式回帰。 +- $u_{-h}$ および $u_0$ から一次外挿。 +- 事前にシミュレータなどで学習したNNモデルにより過去の入力 $(u_{-h},\ldots,u_0)$ および過去の速度履歴 $(v_{-h},\ldots,v_0)$ を用いた予測。 + +コントローラ入力の予定を予測するNNモデルは以下のようなLSTMエンコーダ・デコーダモデルを取る。 + +エンコーダの入力は速度およびコントローラの入力を用いる。 +このエンコーダは直近の数ステップの履歴により更新される。 + +```mermaid +graph LR; + A(( + 直進速度 + 1dim + )) + B(( + コントローラ入力 + 1dim + )) + C(( + セル状態 c0 + 64dim + )) + D(( + 　隠れ状態 h0 + 64dim + )) + E(( + セル状態 c1 + 64dim + )) + F(( + 　隠れ状態 h1 + 64dim + )) + + subgraph input + direction TB + A ~~~ B + end + subgraph LSTM0 + direction LR + C --> D + end + subgraph LSTM1 + direction LR + E --> F + end + input ---> LSTM0 + LSTM0 --> LSTM1 +``` + +デコーダではコントローラ入力の変化率を予測する。 +デコーダ入力は前回のコントローラ入力の変化率とコントローラ入力の履歴を用いる。 +エンコーダにより圧縮された情報はデコーダLSTMの初期値として用いられる。 + +```mermaid +graph LR; + A(( + コントローラ入力の履歴 + 15dim + )) + B(( + コントローラ前回入力の変化率 + 1dim + )) + C(( + セル状態 c0 + 64dim + )) + D(( + 　隠れ状態 h0 + 64dim + )) + E(( + セル状態 c1 + 64dim + )) + F(( + 　隠れ状態 h1 + 64dim + )) + G(( + 16dim + )) + H(( + 16dim + )) + I(( + 入力変化率 + 1dim + )) + + subgraph input + direction TB + A ~~~ B + end + subgraph LSTM0 + direction LR + C --> D + end + subgraph LSTM1 + direction LR + E --> F + end + subgraph output + I + end + input ---> LSTM0 + LSTM0 --> LSTM1 + LSTM1 --> G + G --> H + H --> output +``` +このデコーダを繰り返し用いることで、$N_0$ ステップ先までの入力の予測を得る。 + +### 後処理 + +上記の方法で得られた入力の予定 $(u_1,\ldots,u_N)$ をそのまま用いずに、平滑化して用いることもできる。 +平滑化は最適化問題として表現され、決定変数は入力の変化率 $(d u_0,\ldots,d u_{N-1})$である。 +$\bar u_{i + 1} = \bar u_i + du_i * dt_{\mathrm{ctrl}}$ （ $\bar u_{0} = u_0$ ） +と表したとき、次のようなコスト関数を最小化する。 + +$$ +\sum_{i=0}^{N-1} \lambda_i ||\bar u_i - u_i||_2^2 + \alpha_i ||\bar u_i||_2^2 + \omega_i ||d u_i||_2^2 +$$ + +ここで1項目はもとの予測に近づけるようなコストである。 +2項目は入力値を小さくするようなコストで基本的には終端コストのみ用いる。 +3項目は入力の変化率を小さくするようなコストで、終端に近づくにつれてコストの重みは大きく設定する。 + +こうして得られた平滑化された入力の予測値 $(\bar u_1,\ldots,\bar u_N)$ をVehicle Adaptorに渡す。 + +## 最適化設計 + +MPCでは1ステップ $0.1 s$で $N=12$ステップ先まで予測している。 +MPCにおける決定変数 $U\in \mathbb{R}^{N\times 2}$は各時刻における入力の変化率である。 +ノミナルモデルあるいは学習モデルに基づき $U$を用いて現在の状態 $X[0]= x_0$から将来を予測したものを +$$X = (X[0],X[1],\ldots,X[N])\in \mathbb{R}^{(N+1)\times(6+M_{\mathrm{acc}} + M_{\mathrm{steer}})}$$ +と表す。 +ワールド座標 $xy$成分、直進速度、ワールド相対ヨー角、直進加速度実現値、ステア実現値、直進加速度入力値、ステア入力値の8成分に対する目標軌道 $X_{\mathrm{des}} \in \mathbb{R}^{(N+1)\times8}$ を用いてMPCコストは +$$J(X,U,X_{\mathrm{des}}) =\sum_{t=0}^{N-1} l_t(X[t],U[t],X_{\mathrm{des}}[t]) + l_N(X[N],X_{\mathrm{des}}[N])$$ +のように設計される。ここで $l_t$はステージコスト、 $l_N$は終端コストを表し、 $l_t$および $l_N$には目標値との直進偏差、横偏差、速度偏差、ヨー角偏差、加速度偏差、ステア偏差、加速度入力値偏差、ステア各入力値偏差および、制約条件をコストとして扱ったものが入っている。 +更にステージコスト $l_t$ には $U$ を小さくするコストが入っている。 +入力の変化率 $U$ に対してコストを掛けることで入力の大きな変化を防いでいる。 + +MPCでは $J$ を最小化するような $U$ を求める。 + +## キャリブレーションNN + +アクセル・ブレーキの踏み込み量に対する加速度実現値に対するキャリブレーションをNNベースで行うことができる。 +加速度実現値自体を予測、あるいはベースとなる(精度は低いかもしれない)キャリブレーションマップの予測誤差を補正する形でNNを用いる。 +NNは次のような単純な構造をしている。 + +```mermaid +graph LR; + A(( + 直進速度 + 1dim + )) + B(( + アクセル or ブレーキ + 1dim + )) + C((64dim)) + D((32dim)) + E(( + 加速度実現値 + or + 加速度予測誤差 + 1dim + )) + + + + subgraph input + direction TB + A ~~~ B + end + subgraph output + direction TB + E + end + input --> C --> D --> output +``` + + +## 評価 + +### 実機実験 + +BSでの実験(40 km/h)での実験。 +ノミナル走行では直線部分で20cmくらいの横偏差が出た。 +

+ノミナル走行における速度・加速度のグラフは次の通りで、一部加速度入力と実現値で乖離が見られた。 +

+ +Vehicle adaptor導入ではじめの直線における横偏差が10cmくらいになった。 +

+ +一方で速度・加速度に関しては、特に加速度入力と実現値が乖離していた部分において +Vehicle adaptor導入で振動が発生した。 +

+ + +柏の葉での実験(15km/h)での実験。 +ノミナルでカーブにおいて40cmくらいの横偏差が出た。 + +

+ +ノミナル走行における速度・加速度のグラフは次の通り。 +

+ +Vehicle adaptor導入で20cm以下の横偏差になったカーブもあるが、特に一番はじめのカーブなど良くも悪くもならなかったカーブもあった。 +改善しなかった原因が学習側にあるのか、オンラインでの最適化側にあるのかなどを決定するための解析を今後行う予定である。 +

+ +BS実験の後に加速度入力補正のアルゴリズムの修正を行ったが、速度・加速度は次の通り、Vehicle adaptor導入で特にノミナルに比べて振動が強くなるなどは見られなかった。 +BSコースでも振動しなくなったかは今後の実験にて確認予定。 +

+ +一方で最適化パラメータを変更すると、次のように直線（グラフでは9060 sの停止付近）にてステアの振動が見られた。 +今後このような現象が起きにくくなるような修正を行いたい。 + +

diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/calibrator/accel_brake_map_calibrator.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/calibrator/accel_brake_map_calibrator.py index 156be7b40bb81..7f22791367f21 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/calibrator/accel_brake_map_calibrator.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/calibrator/accel_brake_map_calibrator.py @@ -1,11 +1,8 @@ import numpy as np import scipy.interpolate -from sklearn.preprocessing import PolynomialFeatures -from sklearn.linear_model import ElasticNet from autoware_vehicle_adaptor.calibrator import actuation_map_csv_writer from autoware_vehicle_adaptor.src import actuation_map_2d from autoware_vehicle_adaptor.calibrator import collected_data_counter -import GPy import os import torch from torch import nn @@ -33,19 +30,10 @@ def __init__(self): self.accel_data_output = [] self.brake_data_input = [] self.brake_data_output = [] - self.accel_data_input_for_poly = [] - self.accel_data_output_for_poly = [] - self.brake_data_input_for_poly = [] - self.brake_data_output_for_poly = [] - self.accel_data_input_for_GP = [] - self.accel_data_output_for_GP = [] - self.brake_data_input_for_GP = [] - self.brake_data_output_for_GP = [] self.accel_data_input_for_NN = [] self.accel_data_output_for_NN = [] self.brake_data_input_for_NN = [] self.brake_data_output_for_NN = [] - self.polynomial_regression_performed = False self.dataloader_weights_accel = None self.dataloader_weights_brake = None self.extracted_indices_accel = None @@ -60,31 +48,17 @@ def clear_data(self): self.accel_data_output_residual = [] self.brake_data_output_residual = [] def initialize_for_calibration(self): - # Polynomial - self.accel_data_input_for_poly = self.accel_data_input.copy() - self.accel_data_output_for_poly = self.accel_data_output.copy() - self.brake_data_input_for_poly = self.brake_data_input.copy() - self.brake_data_output_for_poly = self.brake_data_output.copy() + self.accel_data_input_for_NN = self.accel_data_input.copy() + self.accel_data_output_for_NN = self.accel_data_output.copy() + self.brake_data_input_for_NN = self.brake_data_input.copy() + self.brake_data_output_for_NN = self.brake_data_output.copy() self.dataloader_weights_accel = None self.dataloader_weights_brake = None self.extracted_indices_accel = None self.extracted_indices_brake = None - self.reset_GP_and_NN() if self.calc_base_map_error_performed: self.calc_base_map_error_performed = False print("calc_base_map_error is reset") - def reset_GP_and_NN(self): - self.accel_data_input_for_GP = self.accel_data_input_for_poly.copy() - self.accel_data_output_for_GP = self.accel_data_output_for_poly.copy() - self.brake_data_input_for_GP = self.brake_data_input_for_poly.copy() - self.brake_data_output_for_GP = self.brake_data_output_for_poly.copy() - self.accel_data_input_for_NN = self.accel_data_input_for_poly.copy() - self.accel_data_output_for_NN = self.accel_data_output_for_poly.copy() - self.brake_data_input_for_NN = self.brake_data_input_for_poly.copy() - self.brake_data_output_for_NN = self.brake_data_output_for_poly.copy() - if self.polynomial_regression_performed: - self.polynomial_regression_performed = False - print("polynomial regression as a preprocessing for GP and NN is reset") def add_data_from_csv(self, dir_name,smoothing_window = 10, acc_change_threshold=0.2, base_map_dir=None, control_cmd_mode=None): localization_kinematic_state = np.loadtxt( dir_name + "/localization_kinematic_state.csv", delimiter=",", usecols=[0, 1, 4, 5, 7, 8, 9, 10, 47] @@ -275,17 +249,16 @@ def extract_data_for_calibration(self,max_data_num=50): for i in range(len(self.brake_data_input)): self.collected_data_counter.add_data_point(self.brake_data_input[i][0], self.brake_data_output[i], i) self.extracted_indices_brake = self.collected_data_counter.get_extracted_indices(max_data_num) - self.accel_data_input_for_poly = [] - self.accel_data_output_for_poly = [] - self.brake_data_input_for_poly = [] - self.brake_data_output_for_poly = [] + self.accel_data_input_for_NN = [] + self.accel_data_output_for_NN = [] + self.brake_data_input_for_NN = [] + self.brake_data_output_for_NN = [] for i in self.extracted_indices_accel: - self.accel_data_input_for_poly.append(self.accel_data_input[i]) - self.accel_data_output_for_poly.append(self.accel_data_output[i]) + self.accel_data_input_for_NN.append(self.accel_data_input[i]) + self.accel_data_output_for_NN.append(self.accel_data_output[i]) for i in self.extracted_indices_brake: - self.brake_data_input_for_poly.append(self.brake_data_input[i]) - self.brake_data_output_for_poly.append(self.brake_data_output[i]) - self.reset_GP_and_NN() + self.brake_data_input_for_NN.append(self.brake_data_input[i]) + self.brake_data_output_for_NN.append(self.brake_data_output[i]) if self.dataloader_weights_accel is not None: self.dataloader_weights_accel = np.array(self.dataloader_weights_accel)[self.extracted_indices_accel].tolist() if self.dataloader_weights_brake is not None: @@ -296,12 +269,11 @@ def calc_base_map_error(self,base_map_dir): self.base_accel_map = actuation_map_2d.ActuationMap2D(accel_map_path) self.base_brake_map = actuation_map_2d.ActuationMap2D(brake_map_path) for i in range(len(self.accel_data_input)): - accel_data = self.accel_data_input_for_poly[i] - self.accel_data_output_for_poly[i] = self.accel_data_output_for_poly[i] - self.base_accel_map.get_sim_actuation(accel_data[0], accel_data[1]) + accel_data = self.accel_data_input_for_NN[i] + self.accel_data_output_for_NN[i] = self.accel_data_output_for_NN[i] - self.base_accel_map.get_sim_actuation(accel_data[0], accel_data[1]) for i in range(len(self.brake_data_input)): - brake_data = self.brake_data_input_for_poly[i] - self.brake_data_output_for_poly[i] = self.brake_data_output_for_poly[i] - self.base_brake_map.get_sim_actuation(brake_data[0], brake_data[1]) - self.reset_GP_and_NN() + brake_data = self.brake_data_input_for_NN[i] + self.brake_data_output_for_NN[i] = self.brake_data_output_for_NN[i] - self.base_brake_map.get_sim_actuation(brake_data[0], brake_data[1]) self.calc_base_map_error_performed = True def calc_calibration_error(self,map_dir): accel_map_path = map_dir + "/accel_map.csv" @@ -322,13 +294,13 @@ def calc_calibration_error(self,map_dir): print("brake_error: ", brake_error) def plot_training_data(self,save_dir=None): plt.scatter( - np.array(self.accel_data_input_for_poly)[:, 0], - np.array(self.accel_data_output_for_poly), + np.array(self.accel_data_input_for_NN)[:, 0], + np.array(self.accel_data_output_for_NN), label = "accel_data" ) plt.scatter( - np.array(self.brake_data_input_for_poly)[:, 0], - np.array(self.brake_data_output_for_poly), + np.array(self.brake_data_input_for_NN)[:, 0], + np.array(self.brake_data_output_for_NN), label = "brake_data" ) plt.title("vel vs acc plots") @@ -343,7 +315,7 @@ def plot_training_data(self,save_dir=None): fig = plt.figure() ax = fig.add_subplot(111, projection='3d') - ax.scatter(np.array(self.accel_data_input_for_poly)[:, 0], np.array(self.accel_data_input_for_poly)[:, 1], np.array(self.accel_data_output_for_poly)) + ax.scatter(np.array(self.accel_data_input_for_NN)[:, 0], np.array(self.accel_data_input_for_NN)[:, 1], np.array(self.accel_data_output_for_NN)) ax.set_xlabel("vel_obs [m/s]") ax.set_ylabel("accel_input [m/s^2]") ax.set_zlabel("acc_obs [m/s^2]") @@ -355,7 +327,7 @@ def plot_training_data(self,save_dir=None): fig = plt.figure() ax = fig.add_subplot(111, projection='3d') - ax.scatter(np.array(self.brake_data_input_for_poly)[:, 0], np.array(self.brake_data_input_for_poly)[:, 1], np.array(self.brake_data_output_for_poly)) + ax.scatter(np.array(self.brake_data_input_for_NN)[:, 0], np.array(self.brake_data_input_for_NN)[:, 1], np.array(self.brake_data_output_for_NN)) ax.set_xlabel("vel_obs [m/s]") ax.set_ylabel("brake_input [m/s^2]") ax.set_zlabel("acc_obs [m/s^2]") @@ -379,164 +351,6 @@ def map_validation(self, accel_map_matrix, brake_map_matrix, map_vel=default_map print("map validator is applied at vel: ", map_vel[i], "brake: ", map_brake[j]) return accel_map_matrix_valid, brake_map_matrix_valid - - -class CalibratorByPolynomialRegression(AddDataFromCSV): - def __init__(self): - super().__init__() - self.accel_polynomial_model = None - self.brake_polynomial_model = None - self.degree = 3 - self.alpha_1 = 1e-5 - self.alpha_2 = 1e-5 - def calibrate_by_polynomial_regression(self,degree=None, alpha_1=None, alpha_2=None): - if degree is not None: - self.degree = degree - if alpha_1 is not None: - self.alpha_1 = alpha_1 - if alpha_2 is not None: - self.alpha_2 = alpha_2 - alpha = self.alpha_1 + self.alpha_2 - l1_ratio = self.alpha_1 / alpha - self.accel_data_output_for_NN.clear() - self.brake_data_output_for_NN.clear() - accel_data_output_poly_residual = [] - brake_data_output_poly_residual = [] - accel_data_input_poly = PolynomialFeatures(degree=self.degree).fit_transform(self.accel_data_input_for_poly) - brake_data_input_poly = PolynomialFeatures(degree=self.degree).fit_transform(self.brake_data_input_for_poly) - self.accel_polynomial_model = ElasticNet(alpha=alpha,l1_ratio=l1_ratio).fit(accel_data_input_poly, self.accel_data_output_for_poly) - self.brake_polynomial_model = ElasticNet(alpha=alpha,l1_ratio=l1_ratio).fit(brake_data_input_poly, self.brake_data_output_for_poly) - for i in range(len(self.accel_data_input_for_poly)): - accel_data_output_poly_residual.append( - self.accel_data_output_for_poly[i] - self.accel_polynomial_model.predict(accel_data_input_poly[i].reshape(1, -1))[0] - ) - for i in range(len(self.brake_data_input_for_poly)): - brake_data_output_poly_residual.append( - self.brake_data_output_for_poly[i] - self.brake_polynomial_model.predict(brake_data_input_poly[i].reshape(1, -1))[0] - ) - self.accel_data_input_for_GP = self.accel_data_input_for_poly.copy() - self.accel_data_output_for_GP = accel_data_output_poly_residual.copy() - self.brake_data_input_for_GP = self.brake_data_input_for_poly.copy() - self.brake_data_output_for_GP = brake_data_output_poly_residual.copy() - self.accel_data_input_for_NN = self.accel_data_input_for_poly.copy() - self.accel_data_output_for_NN = accel_data_output_poly_residual.copy() - self.brake_data_input_for_NN = self.brake_data_input_for_poly.copy() - self.brake_data_output_for_NN = brake_data_output_poly_residual.copy() - self.polynomial_regression_performed = True - def calc_accel_brake_map_poly(self, map_vel=default_map_vel, map_accel=default_map_accel, map_brake=default_map_brake): - self.accel_map_matrix_poly = np.zeros((len(map_accel), len(map_vel))) - self.brake_map_matrix_poly = np.zeros((len(map_brake), len(map_vel))) - for i in range(len(map_vel)): - for j in range(len(map_accel)): - self.accel_map_matrix_poly[j, i] = self.accel_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_accel[j]]])) - )[0] - if self.calc_base_map_error_performed: - self.accel_map_matrix_poly[j, i] += self.base_accel_map.get_sim_actuation(map_vel[i], map_accel[j]) - for j in range(len(map_brake)): - self.brake_map_matrix_poly[j, i] = self.brake_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_brake[j]]])) - )[0] - if self.calc_base_map_error_performed: - self.brake_map_matrix_poly[j, i] += self.base_brake_map.get_sim_actuation(map_vel[i], map_brake[j]) - self.accel_map_matrix_poly[0] = 0.5 * (self.brake_map_matrix_poly[0] + self.accel_map_matrix_poly[0]) - self.brake_map_matrix_poly[0] = self.accel_map_matrix_poly[0] - def save_accel_brake_map_poly(self,map_vel, map_accel, map_brake, save_dir="."): - if not os.path.isdir(save_dir): - os.mkdir(save_dir) - self.calc_accel_brake_map_poly(map_vel, map_accel, map_brake) - actuation_map_csv_writer.map_csv_writer(map_vel, map_accel, self.accel_map_matrix_poly, save_dir + "/accel_map.csv") - actuation_map_csv_writer.map_csv_writer(map_vel, map_brake, self.brake_map_matrix_poly, save_dir + "/brake_map.csv") - -class CalibratorByGaussianProcessRegression(AddDataFromCSV): - def __init__(self): - super().__init__() - self.accel_GP_model = None - self.brake_GP_model = None - def calibrate_by_GP(self,inducting_num=100,): - induction_index_accel = np.random.choice(len(self.accel_data_input_for_GP), inducting_num, replace=False) - induction_index_brake = np.random.choice(len(self.brake_data_input_for_GP), inducting_num, replace=False) - kernel_accel = GPy.kern.RBF(input_dim=np.array(self.accel_data_input_for_GP)[0].shape[0], ARD=True) - kernel_brake = GPy.kern.RBF(input_dim=np.array(self.brake_data_input_for_GP)[0].shape[0], ARD=True) - X_accel = np.array(self.accel_data_input_for_GP) - Y_accel = np.array(self.accel_data_output_for_GP).reshape(-1, 1) - X_brake = np.array(self.brake_data_input_for_GP) - Y_brake = np.array(self.brake_data_output_for_GP).reshape(-1, 1) - self.accel_GP_model = GPy.models.SparseGPRegression( - X_accel, Y_accel, kernel_accel, Z=X_accel[induction_index_accel] - ) - self.brake_GP_model = GPy.models.SparseGPRegression( - X_brake, Y_brake, kernel_brake, Z=X_brake[induction_index_brake] - ) - self.accel_GP_model.optimize() - self.brake_GP_model.optimize() - def calc_accel_brake_map_GP(self, map_vel=default_map_vel, map_accel=default_map_accel, map_brake=default_map_brake): - self.accel_map_matrix_GP = np.zeros((len(map_accel), len(map_vel))) - self.brake_map_matrix_GP = np.zeros((len(map_brake), len(map_vel))) - for i in range(len(map_vel)): - for j in range(len(map_accel)): - self.accel_map_matrix_GP[j, i] = self.accel_GP_model.predict( - np.array([[map_vel[i], map_accel[j]]]) - )[0] - if self.polynomial_regression_performed: - self.accel_map_matrix_GP[j, i] += self.accel_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_accel[j]]])) - )[0] - if self.calc_base_map_error_performed: - self.accel_map_matrix_GP[j, i] += self.base_accel_map.get_sim_actuation(map_vel[i], map_accel[j]) - for j in range(len(map_brake)): - self.brake_map_matrix_GP[j, i] = self.brake_GP_model.predict( - np.array([[map_vel[i], map_brake[j]]]) - )[0] - if self.polynomial_regression_performed: - self.brake_map_matrix_GP[j, i] += self.brake_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_brake[j]]])) - )[0] - if self.calc_base_map_error_performed: - self.brake_map_matrix_GP[j, i] += self.base_brake_map.get_sim_actuation(map_vel[i], map_brake[j]) - self.accel_map_matrix_GP[0] = 0.5 * (self.accel_map_matrix_GP[0] + self.brake_map_matrix_GP[0]) - self.brake_map_matrix_GP[0] = self.accel_map_matrix_GP[0] - def save_accel_brake_map_GP(self, map_vel=default_map_vel, map_accel=default_map_accel, map_brake=default_map_brake, save_dir="."): - if not os.path.isdir(save_dir): - os.mkdir(save_dir) - self.calc_accel_brake_map_GP(map_vel, map_accel, map_brake) - actuation_map_csv_writer.map_csv_writer(map_vel, map_accel, self.accel_map_matrix_GP, save_dir + "/accel_map.csv") - actuation_map_csv_writer.map_csv_writer(map_vel, map_brake, self.brake_map_matrix_GP, save_dir + "/brake_map.csv") - def data_augmentation_by_GP(self,map_vel=default_map_vel, map_accel=default_map_accel, map_brake=default_map_brake,minimum_cell_number=1): - cell_accel_count = np.zeros((len(map_accel)-1,len(map_vel)-1)) - cell_brake_count = np.zeros((len(map_brake)-1,len(map_vel)-1)) - for i in range(len(self.accel_data_input)): - for j in range(len(map_vel)-1): - for k in range(len(map_accel)-1): - if map_vel[j] <= self.accel_data_input_for_NN[i][0] < map_vel[j+1] and map_accel[k] <= self.accel_data_input_for_NN[i][1] < map_accel[k+1]: - cell_accel_count[k,j] += 1 - for i in range(len(self.brake_data_input)): - for j in range(len(map_vel)-1): - for k in range(len(map_brake)-1): - if map_vel[j] <= self.brake_data_input_for_NN[i][0] < map_vel[j+1] and map_brake[k] <= self.brake_data_input_for_NN[i][1] < map_brake[k+1]: - cell_brake_count[k,j] += 1 - for j in range(len(map_vel)-1): - for k in range(len(map_accel)-1): - if cell_accel_count[k,j] < minimum_cell_number: - for i in range(minimum_cell_number-int(cell_accel_count[k,j])): - new_accel_data_input = [np.random.uniform(map_vel[j],map_vel[j+1]),np.random.uniform(map_accel[k],map_accel[k+1])] - self.accel_data_input_for_NN.append(new_accel_data_input) - self.accel_data_output_for_NN.append( - self.accel_GP_model.predict(np.array(new_accel_data_input).reshape(1,-1))[0][0,0] - ) - if self.dataloader_weights_accel is not None: - self.dataloader_weights_accel.append(np.array(self.dataloader_weights_accel).max()) - for j in range(len(map_vel)-1): - for k in range(len(map_brake)-1): - if cell_brake_count[k,j] < minimum_cell_number: - for i in range(minimum_cell_number-int(cell_brake_count[k,j])): - new_brake_data_input = [np.random.uniform(map_vel[j],map_vel[j+1]),np.random.uniform(map_brake[k],map_brake[k+1])] - self.brake_data_input_for_NN.append(new_brake_data_input) - self.brake_data_output_for_NN.append( - self.brake_GP_model.predict(np.array(new_brake_data_input).reshape(1,-1))[0][0,0] - ) - if self.dataloader_weights_brake is not None: - self.dataloader_weights_brake.append(np.array(self.dataloader_weights_brake).max()) def calc_monotone_constraint_cost(model, test_points, map_matrix, mode, monotone_margin=1e-3): if mode == "accel": @@ -761,20 +575,12 @@ def calc_accel_brake_map_NN(self,map_vel=default_map_vel, map_accel=default_map_ self.accel_map_matrix_NN[j, i] = self.accel_NN_model( torch.tensor([map_vel[i], map_accel[j]], dtype=torch.float32,device=self.device) ).item() - if self.polynomial_regression_performed: - self.accel_map_matrix_NN[j, i] += self.accel_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_accel[j]]])) - )[0] if self.calc_base_map_error_performed: self.accel_map_matrix_NN[j, i] += self.base_accel_map.get_sim_actuation(map_vel[i], map_accel[j]) for j in range(len(map_brake)): self.brake_map_matrix_NN[j, i] = self.brake_NN_model( torch.tensor([map_vel[i], map_brake[j]], dtype=torch.float32,device=self.device) ).item() - if self.polynomial_regression_performed: - self.brake_map_matrix_NN[j, i] += self.brake_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_brake[j]]])) - )[0] if self.calc_base_map_error_performed: self.brake_map_matrix_NN[j, i] += self.base_brake_map.get_sim_actuation(map_vel[i], map_brake[j]) self.accel_map_matrix_NN[0] = 0.5 * (self.accel_map_matrix_NN[0] + self.brake_map_matrix_NN[0]) @@ -871,20 +677,12 @@ def calc_accel_brake_map_ensemble_NN(self,map_vel=default_map_vel, map_accel=def self.accel_map_matrix_ensemble_NN[k, j, i] = self.accel_NN_models[k]( torch.tensor([map_vel[i], map_accel[j]], dtype=torch.float32,device=self.device) ).item() - if self.polynomial_regression_performed: - self.accel_map_matrix_ensemble_NN[k, j, i] += self.accel_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_accel[j]]])) - )[0] if self.calc_base_map_error_performed: self.accel_map_matrix_ensemble_NN[k, j, i] += self.base_accel_map.get_sim_actuation(map_vel[i], map_accel[j]) for j in range(len(map_brake)): self.brake_map_matrix_ensemble_NN[k, j, i] = self.brake_NN_models[k]( torch.tensor([map_vel[i], map_brake[j]], dtype=torch.float32,device=self.device) ).item() - if self.polynomial_regression_performed: - self.brake_map_matrix_ensemble_NN[k, j, i] += self.brake_polynomial_model.predict( - PolynomialFeatures(degree=self.degree).fit_transform(np.array([[map_vel[i], map_brake[j]]])) - )[0] if self.calc_base_map_error_performed: self.brake_map_matrix_ensemble_NN[k, j, i] += self.base_brake_map.get_sim_actuation(map_vel[i], map_brake[j]) self.accel_map_matrix_ensemble_NN[k,0] = 0.5 * (self.accel_map_matrix_ensemble_NN[k,0] + self.brake_map_matrix_ensemble_NN[k,0]) @@ -1075,6 +873,6 @@ def add_dummy_data(self, map_vel=default_map_vel, map_accel=default_map_accel, m plt.savefig(save_dir + "/convex_hull.png") plt.close() -class Calibrator(CalibratorByPolynomialRegression,CalibratorByGaussianProcessRegression,CalibratorByEnsembleNN,DummyDataGenerator): +class Calibrator(CalibratorByEnsembleNN,DummyDataGenerator): def __init__(self): super().__init__() \ No newline at end of file diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/data_analyzer/NN_model_evaluator.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/data_analyzer/NN_model_evaluator.py index 5c7488b793ef9..ca90c210c2f80 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/data_analyzer/NN_model_evaluator.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/data_analyzer/NN_model_evaluator.py @@ -1,17 +1,15 @@ import matplotlib.pyplot as plt from autoware_vehicle_adaptor.training import add_data_from_csv from autoware_vehicle_adaptor.training import training_utils +from autoware_vehicle_adaptor.training import train_error_prediction_NN_with_offline_data from autoware_vehicle_adaptor.param import parameters import numpy as np import simplejson as json import torch past_length = parameters.past_length -class NNModelEvaluator(add_data_from_csv.add_data_from_csv): +class NNModelEvaluator(train_error_prediction_NN_with_offline_data.train_error_prediction_NN_with_offline_data): def __init__(self): super().__init__() - self.model = None - self.model_for_initial_hidden = None - self.models = None def reset_model(self): self.model = None self.model_for_initial_hidden = None @@ -26,7 +24,7 @@ def evaluate(self,window_size=10,json_save_dir=None,save_plt_dir=None,show_plt=F if self.X_test_list is None: print("No test data") return - if self.model is None or self.models is None: + if self.model is None and self.models is None: print("No model") return if self.model_for_initial_hidden is None: @@ -44,27 +42,26 @@ def evaluate(self,window_size=10,json_save_dir=None,save_plt_dir=None,show_plt=F Y_pred_np += Y_pred_np_i / len(self.models) model_prediction_error = training_utils.TrainErrorPredictionNNFunctions.get_models_signed_prediction_error(self.models, X_test, Y_test, window_size) else: - X_test_np, Y_test_np, memory_bank_test_np = training_utils.TrainErrorPredictionNNWithMemoryFunctions.get_sequence_data(self.X_test_list, self.Y_test_list, self.division_indices_test) + X_test_np, Y_test_np, indices_test = training_utils.TrainErrorPredictionNNWithOfflineData.get_sequence_data(self.X_test_list, self.Y_test_list, self.division_indices_test) X_test = torch.tensor(X_test_np, dtype=torch.float32) Y_test = torch.tensor(Y_test_np, dtype=torch.float32) - memory_bank_test = torch.tensor(memory_bank_test_np, dtype=torch.float32) if self.models is None: - Y_pred_np = training_utils.TrainErrorPredictionNNWithMemoryFunctions.get_Y_pred_np(self.model, self.model_for_initial_hidden, X_test, Y_test, memory_bank_test) - model_prediction_error = training_utils.TrainErrorPredictionNNWithMemoryFunctions.get_model_signed_prediction_error(self.model, self.model_for_initial_hidden, X_test, Y_test, memory_bank_test, window_size) + Y_pred_np = training_utils.TrainErrorPredictionNNWithOfflineData.get_Y_pred_np(self.model, self.model_for_initial_hidden, X_test, Y_test,self.offline_data_dict_test, indices_test) + model_prediction_error = training_utils.TrainErrorPredictionNNWithOfflineData.get_model_signed_prediction_error(self.model, self.model_for_initial_hidden, X_test, Y_test, self.offline_data_dict_test, indices_test, window_size) else: - Y_pred_np_0 = training_utils.TrainErrorPredictionNNWithMemoryFunctions.get_Y_pred_np(self.models[0], self.model_for_initial_hidden, X_test, Y_test, memory_bank_test) + Y_pred_np_0 = training_utils.TrainErrorPredictionNNWithOfflineData.get_Y_pred_np(self.models[0], self.model_for_initial_hidden, X_test, Y_test,self.offline_data_dict_test, indices_test) Y_pred_np = Y_pred_np_0 / len(self.models) for i in range(1, len(self.models)): - Y_pred_np_i = training_utils.TrainErrorPredictionNNWithMemoryFunctions.get_Y_pred_np(self.models[i], self.model_for_initial_hidden, X_test, Y_test, memory_bank_test) + Y_pred_np_i = training_utils.TrainErrorPredictionNNWithOfflineData.get_Y_pred_np(self.models[i], self.model_for_initial_hidden, X_test, Y_test,self.offline_data_dict_test, indices_test) Y_pred_np += Y_pred_np_i / len(self.models) - model_prediction_error = training_utils.TrainErrorPredictionNNWithMemoryFunctions.get_models_signed_prediction_error(self.models, self.model_for_initial_hidden, X_test, Y_test, memory_bank_test, window_size) + model_prediction_error = training_utils.TrainErrorPredictionNNWithOfflineData.get_models_signed_prediction_error(self.models, self.model_for_initial_hidden, X_test, Y_test,self.offline_data_dict_test, indices_test, window_size) if self.models is None: state_component_predicted = self.model.state_component_predicted else: state_component_predicted = self.models[0].state_component_predicted - nominal_prediction_error = training_utils.TrainErrorPredictionNNFunctions.get_nominal_signed_prediction_error(X_test_np, window_size) - model_prediction_error = model_prediction_error.detach().to("cpu").numpy() - nominal_prediction_error = nominal_prediction_error.detach().to("cpu").numpy() + nominal_prediction_error = training_utils.get_nominal_signed_prediction_error(X_test, window_size) + #model_prediction_error = model_prediction_error.detach().to("cpu").numpy() + #nominal_prediction_error = nominal_prediction_error.detach().to("cpu").numpy() nominal_mae = np.abs(Y_test_np[:,past_length:]).mean(axis=(0, 1)) model_mae = np.abs(Y_test_np[:,past_length:] - Y_pred_np).mean(axis=(0, 1)) mae_ratio = model_mae / nominal_mae @@ -79,7 +76,7 @@ def evaluate(self,window_size=10,json_save_dir=None,save_plt_dir=None,show_plt=F prediction_mse_ratio = model_prediction_mse / nominal_prediction_mse results_json = {} - for i in len(state_component_predicted): + for i in range(len(state_component_predicted)): results_json[state_component_predicted[i] + "_nominal_mae"] = nominal_mae[i] results_json[state_component_predicted[i] + "_model_mae"] = model_mae[i] results_json[state_component_predicted[i] + "_mae_ratio"] = mae_ratio[i] diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/inputs_prediction.h b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/inputs_prediction.h index 3913e2d780ba3..2f6bf2a81965d 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/inputs_prediction.h +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/inputs_prediction.h @@ -7,27 +7,7 @@ #include #include #include - - -Eigen::VectorXd tanh(const Eigen::VectorXd & v); -Eigen::VectorXd d_tanh(const Eigen::VectorXd & v); -Eigen::VectorXd sigmoid(const Eigen::VectorXd & v); -Eigen::VectorXd relu(const Eigen::VectorXd & x); -Eigen::VectorXd d_relu(const Eigen::VectorXd & x); -Eigen::MatrixXd d_relu_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x); -Eigen::MatrixXd d_tanh_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x); -Eigen::VectorXd d_tanh_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x); -Eigen::MatrixXd d_sigmoid_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x); -Eigen::VectorXd d_sigmoid_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x); -Eigen::VectorXd rotate_data(Eigen::VectorXd states, double yaw); -Eigen::VectorXd vector_power(Eigen::VectorXd vec, int power); -double double_power(double val, int power); -double calc_table_value(double domain_value, std::vector domain_table, std::vector target_table); -std::string get_param_dir_path(); -Eigen::MatrixXd read_csv(std::string file_path); -std::vector read_string_csv(std::string file_path); -Eigen::VectorXd interpolate_eigen(Eigen::VectorXd y, std::vector time_stamp_obs, std::vector time_stamp_new); -std::vector interpolate_vector(std::vector y, std::vector time_stamp_obs, std::vector time_stamp_new); +#include "vehicle_adaptor_utils.h" class InputsSchedulePrediction { diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/linear_regression_compensator.h b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/linear_regression_compensator.h index e742564fab5ad..69bf561de881a 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/linear_regression_compensator.h +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/linear_regression_compensator.h @@ -4,7 +4,7 @@ #include #include #include -#include "inputs_prediction.h" +#include "vehicle_adaptor_utils.h" ///////////////// LinearRegressionCompensator /////////////////////// class LinearRegressionCompensator diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/transform_vehicle_adaptor_model.h b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/transform_vehicle_adaptor_model.h index b532954800022..a9f5a3fdd7635 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/transform_vehicle_adaptor_model.h +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/transform_vehicle_adaptor_model.h @@ -4,10 +4,7 @@ #include #include #include -#include "inputs_prediction.h" -//including inputs_prediction.h is ad-hoc solution to avoid the error of relu etc. not being defined -//such functions should be defined in a separate header file - +#include "vehicle_adaptor_utils.h" class TransformModelToEigen { private: @@ -113,132 +110,4 @@ class TransformModelToEigen Eigen::MatrixXd & dy_dx, Eigen::MatrixXd & dy_dhc, Eigen::MatrixXd & dhc_dhc, Eigen::MatrixXd & dhc_dx); }; -class GetInitialHidden -{ -private: - // attention - Eigen::MatrixXd weight_initial_hidden_acc_layer_1_; - Eigen::MatrixXd weight_initial_hidden_steer_layer_1_; - Eigen::MatrixXd weight_initial_hidden_acc_layer_2_; - Eigen::MatrixXd weight_initial_hidden_steer_layer_2_; - Eigen::MatrixXd weight_initial_hidden_gru_ih_; - Eigen::MatrixXd weight_initial_hidden_gru_hh_; - Eigen::MatrixXd weight_initial_hidden_final_layer_; - Eigen::MatrixXd weight_initial_hidden_fusion_layer_; - Eigen::MatrixXd weight_initial_hidden_only_online_; - - - - Eigen::VectorXd bias_initial_hidden_acc_layer_1_; - Eigen::VectorXd bias_initial_hidden_steer_layer_1_; - Eigen::VectorXd bias_initial_hidden_acc_layer_2_; - Eigen::VectorXd bias_initial_hidden_steer_layer_2_; - Eigen::VectorXd bias_initial_hidden_gru_ih_; - Eigen::VectorXd bias_initial_hidden_gru_hh_; - Eigen::VectorXd bias_initial_hidden_final_layer_; - Eigen::VectorXd bias_initial_hidden_fusion_layer_; - Eigen::VectorXd bias_initial_hidden_only_online_; - - Eigen::VectorXd offline_features_; - bool use_offline_features_ = false; - - int h_gru_dim_; - - int vel_index_ = 0; - int acc_index_ = 1; - int steer_index_ = 2; - int acc_queue_size_ = 15; - int steer_queue_size_ = 15; - int predict_step_ = 3; - int acc_input_start_index_ = 3; - int steer_input_start_index_ = 3 + acc_queue_size_ + predict_step_; - int acc_input_end_index_ = 3 + acc_queue_size_ + predict_step_ - 1; - int steer_input_end_index_ = 3 + acc_queue_size_ + steer_queue_size_ + 2 * predict_step_ - 1; -public: - GetInitialHidden(); - virtual ~GetInitialHidden(); - void set_initial_hidden_params( - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_1, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_1, - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_2, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_2, - const Eigen::MatrixXd & weight_initial_hidden_gru_ih, const Eigen::MatrixXd & weight_initial_hidden_gru_hh, - const Eigen::MatrixXd & weight_initial_hidden_final_layer, - const Eigen::MatrixXd & weight_initial_hidden_fusion_layer, const Eigen::MatrixXd & weight_initial_hidden_only_online, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_1, const Eigen::VectorXd & bias_initial_hidden_steer_layer_1, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_2, const Eigen::VectorXd & bias_initial_hidden_steer_layer_2, - const Eigen::VectorXd & bias_initial_hidden_gru_ih, const Eigen::VectorXd & bias_initial_hidden_gru_hh, - const Eigen::VectorXd & bias_initial_hidden_final_layer, - const Eigen::VectorXd & bias_initial_hidden_fusion_layer, const Eigen::VectorXd & bias_initial_hidden_only_online); - void set_offline_features(const Eigen::VectorXd & offline_features); - void update_gru( - const Eigen::VectorXd & x, const Eigen::VectorXd & h_gru, Eigen::VectorXd & h_gru_next - ); - Eigen::VectorXd get_initial_hidden(Eigen::VectorXd & h_gru); -}; -/* -class GetInitialHidden -{ -private: - // attention - Eigen::MatrixXd weight_initial_hidden_acc_layer_1_; - Eigen::MatrixXd weight_initial_hidden_steer_layer_1_; - Eigen::MatrixXd weight_initial_hidden_acc_layer_2_; - Eigen::MatrixXd weight_initial_hidden_steer_layer_2_; - Eigen::MatrixXd weight_initial_hidden_gru_ih_; - Eigen::MatrixXd weight_initial_hidden_gru_hh_; - Eigen::MatrixXd initial_hidden_query_; - Eigen::MatrixXd weight_initial_hidden_key_layer_; - Eigen::MatrixXd weight_initial_hidden_value_layer_; - Eigen::MatrixXd weight_initial_hidden_final_layer_; - Eigen::VectorXd bias_initial_hidden_acc_layer_1_; - Eigen::VectorXd bias_initial_hidden_steer_layer_1_; - Eigen::VectorXd bias_initial_hidden_acc_layer_2_; - Eigen::VectorXd bias_initial_hidden_steer_layer_2_; - Eigen::VectorXd bias_initial_hidden_gru_ih_; - Eigen::VectorXd bias_initial_hidden_gru_hh_; - Eigen::VectorXd bias_initial_hidden_value_layer_; - Eigen::VectorXd bias_initial_hidden_final_layer_; - int num_heads_; - int key_size_; - int value_size_; - int mean_steps_; - int h_gru_dim_; - std::vector x_queue_ = {}; - std::vector attention_queue_ = {}; - std::vector value_queue_ = {}; - int vel_index_ = 0; - int acc_index_ = 1; - int steer_index_ = 2; - int acc_queue_size_ = 15; - int steer_queue_size_ = 15; - int predict_step_ = 3; - int acc_input_start_index_ = 3; - int steer_input_start_index_ = 3 + acc_queue_size_ + predict_step_; - int acc_input_end_index_ = 3 + acc_queue_size_ + predict_step_ - 1; - int steer_input_end_index_ = 3 + acc_queue_size_ + steer_queue_size_ + 2 * predict_step_ - 1; -public: - GetInitialHidden(); - virtual ~GetInitialHidden(); - void set_attention_params( - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_1, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_1, - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_2, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_2, - const Eigen::MatrixXd & weight_initial_hidden_gru_ih, const Eigen::MatrixXd & weight_initial_hidden_gru_hh, - const Eigen::MatrixXd & initial_hidden_query, const Eigen::MatrixXd & weight_initial_hidden_key_layer, - const Eigen::MatrixXd & weight_initial_hidden_value_layer, const Eigen::MatrixXd & weight_initial_hidden_final_layer, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_1, const Eigen::VectorXd & bias_initial_hidden_steer_layer_1, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_2, const Eigen::VectorXd & bias_initial_hidden_steer_layer_2, - const Eigen::VectorXd & bias_initial_hidden_gru_ih, const Eigen::VectorXd & bias_initial_hidden_gru_hh, - const Eigen::VectorXd & bias_initial_hidden_value_layer, const Eigen::VectorXd & bias_initial_hidden_final_layer, - const int num_heads, const int key_size, const int value_size, const int mean_steps); - void update_gru( - const Eigen::VectorXd & x, const Eigen::VectorXd & h_gru, Eigen::VectorXd & h_gru_next - ); - void initialize_x_queue(); - void update_x_queue(const Eigen::VectorXd & x); - void initialize_attention_info(); - void erase_attention_info_begin(); - void add_weight_and_value(const Eigen::VectorXd & h_gru); - void update_attention_info(); - Eigen::VectorXd get_initial_hidden(); -}; -*/ #endif // TRANSFORM_VEDHICLE_ADAPTOR_MODEL_H \ No newline at end of file diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/utils.h b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/vehicle_adaptor_compensator.h similarity index 80% rename from vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/utils.h rename to vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/vehicle_adaptor_compensator.h index e5310104292ee..60ef9ce312426 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/utils.h +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/vehicle_adaptor_compensator.h @@ -1,5 +1,5 @@ -#ifndef UTILS_H -#define UTILS_H +#ifndef VEHICLE_ADAPTOR_COMPENSATOR_H +#define VEHICLE_ADAPTOR_COMPENSATOR_H #include #include @@ -10,96 +10,31 @@ #include #include "linear_regression_compensator.h" #include "inputs_ref_smoother.h" +#include "vehicle_adaptor_utils.h" Eigen::VectorXd states_vehicle_to_world(Eigen::VectorXd states_vehicle, double yaw); Eigen::VectorXd states_world_to_vehicle(Eigen::VectorXd states_world, double yaw); -/* -Eigen::MatrixXd read_csv(std::string file_path) -{ - std::string build_path = BUILD_PATH; - std::string file_path_abs = build_path + "/" + file_path; - std::vector> csv_data; - std::ifstream ifs(file_path_abs); - if (!ifs) { - std::cerr << "Failed to open file." << std::endl; - return Eigen::MatrixXd(0, 0); - } - std::string line; - while (std::getline(ifs, line)) { - std::istringstream stream(line); - std::string field; - std::vector fields; - while (std::getline(stream, field, ',')) { - fields.push_back(std::stod(field)); - } - csv_data.push_back(fields); - } - ifs.close(); - Eigen::MatrixXd data = Eigen::MatrixXd(csv_data.size(), csv_data[0].size()); - for (int i = 0; i < int(csv_data.size()); i++) { - for (int j = 0; j < int(csv_data[0].size()); j++) { - data(i, j) = csv_data[i][j]; - } - } - return data; -} -*/ -class PolynomialRegression + +class PolynomialRegressionPredictor { private: int degree_ = 2; int num_samples_ = 10; std::vector lambda_ = {0.0, 0.0}; - Eigen::MatrixXd weight_matrix_, coef_matrix_, prediction_matrix_; + Eigen::MatrixXd coef_matrix_, prediction_matrix_; bool ignore_intercept_ = false; - bool predict_ = false; - double minimum_decay_ = 1.0; - // Template function to handle both Eigen::MatrixXd and Eigen::VectorXd - template - std::vector fit_transform_impl(const std::vector& raw_data) { - std::vector result; - for (int i = 0; i < num_samples_; i++) { - T vec_tmp = T::Zero(raw_data[0].rows(), raw_data[0].cols()); - for (int j = 0; j < num_samples_; j++) { - vec_tmp += weight_matrix_(j, i) * raw_data[j]; - } - result.push_back(vec_tmp); - } - return result; - } + double oldest_sample_weight_ = 1.0; public: - PolynomialRegression(); - virtual ~PolynomialRegression(); + PolynomialRegressionPredictor(); + virtual ~PolynomialRegressionPredictor(); void set_params(int degree, int num_samples, std::vector lambda); - void set_minimum_decay(double minimum_decay); + void set_oldest_sample_weight(double oldest_sample_weight); void set_ignore_intercept(); - void set_predict(); void calc_coef_matrix(); void calc_prediction_matrix(int horizon_len); - Eigen::VectorXd fit_transform(Eigen::VectorXd vec); - std::vector fit_transform(const std::vector& raw_data); - std::vector fit_transform(const std::vector& raw_data); Eigen::VectorXd predict(Eigen::VectorXd vec); }; - -class PolynomialFilter -{ -private: - int degree_ = 2; - int num_samples_ = 10; - double lambda_ = 0.0; - double minimum_decay_ = 1.0; - std::vector timestamps_; - std::vector samples_; - -public: - PolynomialFilter(); - virtual ~PolynomialFilter(); - void set_params(int degree, int num_samples, double lambda, double minimum_decay); - double fit_transform(double timestamp, double sample); -}; - class SgFilter { private: @@ -176,37 +111,6 @@ class ButterworthFilter void set_params(); Eigen::VectorXd apply(Eigen::VectorXd input_value); }; -/* -class LinearRegressionCompensator -{ -private: - double lambda_ = 0.0; - double lambda_bias_ = 0.0; - double decay_ = 0.9; - bool fit_yaw_ = false; - int x_index_ = 0; - int y_index_ = 1; - int vel_index_ = 2; - int yaw_index_ = 3; - int acc_index_ = 4; - int steer_index_ = 5; - int state_size_ = 6; - Eigen::MatrixXd XXT_, YXT_, XXT_inv_; - Eigen::MatrixXd regression_matrix_; - std::vector input_queue_ = {}; - bool use_compensator_ = true; - double max_yaw_compensation_, max_acc_compensation_, max_steer_compensation_; -public: - LinearRegressionCompensator(); - virtual ~LinearRegressionCompensator(); - void initialize(); - void update_input_queue(Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat); - void update_regression_matrix(Eigen::VectorXd error_vector); - Eigen::VectorXd predict(Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat); - Eigen::MatrixXd Predict(Eigen::MatrixXd States, Eigen::MatrixXd Acc_input_history_concat, Eigen::MatrixXd Steer_input_history_concat); - Eigen::VectorXd get_bias(); -}; -*/ class TrainedDynamics { private: @@ -304,17 +208,6 @@ class TrainedDynamics Eigen::MatrixXd Prediction_for_compensation( Eigen::MatrixXd States, Eigen::MatrixXd Acc_input_history_concat, Eigen::MatrixXd Steer_input_history_concat, Eigen::MatrixXd H_lstm, Eigen::MatrixXd C_lstm ); - /* - void update_input_queue_for_compensation(Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat); - void update_regression_matrix_for_compensation(Eigen::VectorXd error_vector); - Eigen::VectorXd prediction_for_compensation( - Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat - ); - Eigen::MatrixXd Prediction_for_compensation( - Eigen::MatrixXd States, Eigen::MatrixXd Acc_input_history_concat, Eigen::MatrixXd Steer_input_history_concat - ); - Eigen::VectorXd get_compensation_bias(); - */ Eigen::VectorXd F_with_model_for_calc_controller_prediction_error( const Eigen::VectorXd & states, const Eigen::VectorXd & acc_input_history_concat, const Eigen::VectorXd & steer_input_history_concat, @@ -433,8 +326,25 @@ class AdaptorILQR std::vector state_component_ilqr_index_; int num_state_component_ilqr_; - std::vector acc_input_weight_target_table_, longitudinal_coef_target_table_, longitudinal_coef_by_vel_error_domain_table_, longitudinal_coef_by_acc_error_domain_table_; - std::vector steer_input_weight_target_table_, lateral_coef_target_table_, lateral_coef_by_lateral_error_domain_table_, lateral_coef_by_yaw_error_domain_table_, lateral_coef_by_steer_error_domain_table_; + std::vector controller_acc_input_weight_target_table_, controller_longitudinal_coef_target_table_, controller_vel_error_domain_table_, controller_acc_error_domain_table_; + std::vector controller_steer_input_weight_target_table_, controller_lateral_coef_target_table_, controller_yaw_error_domain_table_, controller_steer_error_domain_table_; + + std::vector vel_for_steer_rate_table_, steer_rate_cost_coef_by_vel_table_; + + + + std::vector acc_rate_input_table_ = {0.001,0.01,0.1}; + std::vector acc_rate_cost_coef_table_ = {100.0,10.0,1.0}; + std::vector x_coef_by_acc_rate_table_, vel_coef_by_acc_rate_table_, acc_coef_by_acc_rate_table_; + + std::vector steer_rate_input_table_ = {0.0001,0.001,0.01}; + std::vector steer_rate_cost_coef_table_ = {100000.0,100.0,1.0}; + std::vector y_coef_by_steer_rate_table_, yaw_coef_by_steer_rate_table_, steer_coef_by_steer_rate_table_; + + std::vector x_error_domain_table_, y_error_domain_table_, vel_error_domain_table_, yaw_error_domain_table_, acc_error_domain_table_, steer_error_domain_table_; + std::vector x_error_target_table_, y_error_target_table_, vel_error_target_table_, yaw_error_target_table_, acc_error_target_table_, steer_error_target_table_; + + public: AdaptorILQR(); virtual ~AdaptorILQR(); @@ -502,28 +412,17 @@ class AdaptorILQR Eigen::VectorXd prediction_for_compensation( Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat, Eigen::VectorXd h_lstm, Eigen::VectorXd c_lstm ); - /* - Eigen::MatrixXd Prediction_for_compensation( - Eigen::MatrixXd States, Eigen::MatrixXd Acc_input_history_concat, Eigen::MatrixXd Steer_input_history_concat, Eigen::MatrixXd H_lstm, Eigen::MatrixXd C_lstm - ); - void update_input_queue_for_compensation(Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat); - void update_regression_matrix_for_compensation(Eigen::VectorXd error_vector); - Eigen::VectorXd prediction_for_compensation( - Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat - ); - Eigen::MatrixXd Prediction_for_compensation( - Eigen::MatrixXd States, Eigen::MatrixXd Acc_input_history_concat, Eigen::MatrixXd Steer_input_history_concat - ); - Eigen::VectorXd get_compensation_bias(); - */ void calc_forward_trajectory_with_cost( Eigen::VectorXd states, Eigen::VectorXd acc_input_history, Eigen::VectorXd steer_input_history, std::vector D_inputs_schedule, const Eigen::VectorXd & h_lstm, const Eigen::VectorXd & c_lstm, const Eigen::VectorXd & previous_error, std::vector & states_prediction, - const std::vector & states_ref, - const std::vector & d_input_ref, - std::vector inputs_ref, double longitudinal_coef, double lateral_coef, double acc_input_weight, double steer_input_weight, + const std::vector & states_ref, const std::vector & d_input_ref, + std::vector inputs_ref, + double x_weight_coef, double y_weight_coef, double vel_weight_coef, + double yaw_weight_coef, double acc_weight_coef, double steer_weight_coef, + double acc_input_weight, double steer_input_weight, + double acc_rate_weight_coef, double steer_rate_weight_coef, Eigen::VectorXd & Cost); void calc_inputs_ref_info( const Eigen::VectorXd & states, const Eigen::VectorXd & acc_input_history, @@ -531,7 +430,15 @@ class AdaptorILQR const Eigen::VectorXd & c_lstm, const Eigen::VectorXd & previous_error, const std::vector & states_ref, const Eigen::VectorXd & acc_controller_input_schedule, const Eigen::VectorXd & steer_controller_input_schedule, - std::vector & inputs_ref, double & longitudinal_coef, double & lateral_coef, double & acc_input_weight, double & steer_input_weight); + const std::vector & states_prediction, + std::vector & inputs_ref, + const double & acc_input_change_rate, const double & steer_input_change_rate, + double & x_weight_coef, double & y_weight_coef, double & vel_weight_coef, + double & yaw_weight_coef, double & acc_weight_coef, double & steer_weight_coef, + double & acc_input_weight, double & steer_input_weight, + double & acc_rate_weight_coef, double & steer_rate_weight_coef, + double & initial_prediction_x_weight_coef, double & initial_prediction_y_weight_coef, double & initial_prediction_vel_weight_coef, + double & initial_prediction_yaw_weight_coef, double & initial_prediction_acc_weight_coef, double & initial_prediction_steer_weight_coef); Eigen::MatrixXd extract_dF_d_state(Eigen::MatrixXd dF_d_state_with_history); Eigen::MatrixXd extract_dF_d_input(Eigen::MatrixXd dF_d_input); Eigen::MatrixXd right_action_by_state_diff_with_history(Eigen::MatrixXd Mat, Eigen::MatrixXd dF_d_state_with_history); @@ -543,14 +450,21 @@ class AdaptorILQR const std::vector & states_prediction, const std::vector & d_inputs_schedule, const std::vector & states_ref, const std::vector & d_input_ref, const double prev_acc_rate, const double prev_steer_rate, - std::vector inputs_ref, double longitudinal_coef, double lateral_coef, double acc_input_weight, double steer_input_weight, const std::vector & inputs_schedule, + std::vector inputs_ref, + double x_weight_coef, double y_weight_coef, double vel_weight_coef, + double yaw_weight_coef, double acc_weight_coef, double steer_weight_coef, + double acc_input_weight, double steer_input_weight, + double acc_rate_weight_coef, double steer_rate_weight_coef, + const std::vector & inputs_schedule, std::vector & K, std::vector & k); std::vector calc_line_search_candidates(std::vector K, std::vector k, std::vector dF_d_states, std::vector dF_d_inputs, std::vector d_inputs_schedule, Eigen::VectorXd ls_points); void compute_optimal_control(Eigen::VectorXd states, Eigen::VectorXd acc_input_history, Eigen::VectorXd steer_input_history, std::vector & d_inputs_schedule, const Eigen::VectorXd & h_lstm, const Eigen::VectorXd & c_lstm, const std::vector & states_ref, - const std::vector & d_input_ref, Eigen::VectorXd & previous_error, + const std::vector & d_input_ref, + double acc_input_change_rate, double steer_input_change_rate, + Eigen::VectorXd & previous_error, std::vector & states_prediction, const Eigen::VectorXd & acc_controller_input_schedule, const Eigen::VectorXd & steer_controller_input_schedule, double & acc_input, double & steer_input); @@ -562,10 +476,9 @@ class VehicleAdaptor private: NominalDynamics nominal_dynamics_controller_; AdaptorILQR adaptor_ilqr_; - PolynomialRegression polynomial_reg_for_predict_acc_input_, polynomial_reg_for_predict_steer_input_; + PolynomialRegressionPredictor polynomial_reg_for_predict_acc_input_, polynomial_reg_for_predict_steer_input_; SgFilter sg_filter_for_d_inputs_schedule_; ButterworthFilter butterworth_filter_; - PolynomialFilter polynomial_filter_for_acc_inputs_, polynomial_filter_for_steer_inputs_; InputsSchedulePrediction acc_input_schedule_prediction_, steer_input_schedule_prediction_; InputsRefSmoother acc_input_ref_smoother_, steer_input_ref_smoother_; // GetInitialHidden get_initial_hidden_; @@ -639,14 +552,6 @@ class VehicleAdaptor int NN_prediction_target_dim_; - - std::vector steer_rate_input_table_ = {0.0001,0.001,0.01}; - std::vector steer_rate_cost_coef_table_ = {100000.0,100.0,1.0}; - //std::vector steer_rate_cost_coef_table = {1.0,1.0,1.0,1.0}; - std::vector acc_rate_input_table_ = {0.001,0.01,0.1}; - std::vector acc_rate_cost_coef_table_ = {100.0,10.0,1.0}; - - bool initialized_ = false; Eigen::VectorXd acc_input_history_; Eigen::VectorXd steer_input_history_; @@ -657,7 +562,7 @@ class VehicleAdaptor int controller_acc_input_history_len_, controller_steer_input_history_len_; int deg_controller_acc_input_history_, deg_controller_steer_input_history_; std::vector lam_controller_acc_input_history_, lam_controller_steer_input_history_; - double minimum_decay_controller_acc_input_history_, minimum_decay_controller_steer_input_history_; + double oldest_sample_weight_controller_acc_input_history_, oldest_sample_weight_controller_steer_input_history_; int acc_polynomial_prediction_len_, steer_polynomial_prediction_len_; bool use_acc_linear_extrapolation_ = false; @@ -701,13 +606,17 @@ class VehicleAdaptor bool acc_input_schedule_prediction_initialized_ = false; bool steer_input_schedule_prediction_initialized_ = false; - //int h_gru_dim_for_initial_hidden_; Eigen::VectorXd offline_features_; bool use_offline_features_ = false; + double past_acc_input_change_, past_steer_input_change_; + double past_acc_input_change_decay_rate_, past_steer_input_change_decay_rate_; + int acc_input_change_window_size_, steer_input_change_window_size_; + double future_acc_input_change_, future_steer_input_change_; + double future_acc_input_change_decay_rate_, future_steer_input_change_decay_rate_; + double past_acc_input_change_weight_, past_steer_input_change_weight_; public: bool use_controller_steer_input_schedule_ = false; bool use_vehicle_adaptor_; - //bool use_nonzero_initial_hidden_autoware_; bool use_offline_features_autoware_; VehicleAdaptor(); virtual ~VehicleAdaptor(); @@ -730,20 +639,8 @@ class VehicleAdaptor const Eigen::VectorXd & bias_complimentary_layer, const Eigen::VectorXd & bias_linear_relu, const Eigen::VectorXd & bias_final_layer, const double vel_scaling, const double vel_bias, const std::vector state_component_predicted); - //void set_initial_hidden_params( - // const Eigen::MatrixXd & weight_initial_hidden_acc_layer_1, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_1, - // const Eigen::MatrixXd & weight_initial_hidden_acc_layer_2, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_2, - // const Eigen::MatrixXd & weight_initial_hidden_gru_ih, const Eigen::MatrixXd & weight_initial_hidden_gru_hh, - // const Eigen::MatrixXd & weight_initial_hidden_final_layer, - // const Eigen::MatrixXd & weight_initial_hidden_fusion_layer, const Eigen::MatrixXd & weight_initial_hidden_only_online, - // const Eigen::VectorXd & bias_initial_hidden_acc_layer_1, const Eigen::VectorXd & bias_initial_hidden_steer_layer_1, - // const Eigen::VectorXd & bias_initial_hidden_acc_layer_2, const Eigen::VectorXd & bias_initial_hidden_steer_layer_2, - // const Eigen::VectorXd & bias_initial_hidden_gru_ih, const Eigen::VectorXd & bias_initial_hidden_gru_hh, - // const Eigen::VectorXd & bias_initial_hidden_final_layer, - // const Eigen::VectorXd & bias_initial_hidden_fusion_layer, const Eigen::VectorXd & bias_initial_hidden_only_online); void set_offline_features(const Eigen::VectorXd & offline_features); void set_NN_params_from_csv(std::string csv_dir); - // void set_initial_hidden_params_from_csv(std::string csv_dir); void set_offline_features_from_csv(std::string csv_dir); void clear_NN_params(); void set_offline_data_set_for_compensation(Eigen::MatrixXd XXT, Eigen::MatrixXd YYT); @@ -764,4 +661,4 @@ class VehicleAdaptor void send_initialized_flag(); }; } -#endif // UTILS_H \ No newline at end of file +#endif // VEHICLE_ADAPTOR_COMPENSATOR_H \ No newline at end of file diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/vehicle_adaptor_utils.h b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/vehicle_adaptor_utils.h new file mode 100644 index 0000000000000..c6ba98d530916 --- /dev/null +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/include/vehicle_adaptor_utils.h @@ -0,0 +1,33 @@ +#ifndef VEHICLE_ADAPTOR_UTILS_H +#define VEHICLE_ADAPTOR_UTILS_H +#include +#include + +#include +#include "inputs_prediction.h" +#include +#include +#include +#include +#include + +Eigen::VectorXd tanh(const Eigen::VectorXd & v); +Eigen::VectorXd d_tanh(const Eigen::VectorXd & v); +Eigen::VectorXd sigmoid(const Eigen::VectorXd & v); +Eigen::VectorXd relu(const Eigen::VectorXd & x); +Eigen::VectorXd d_relu(const Eigen::VectorXd & x); +Eigen::MatrixXd d_relu_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x); +Eigen::MatrixXd d_tanh_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x); +Eigen::VectorXd d_tanh_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x); +Eigen::MatrixXd d_sigmoid_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x); +Eigen::VectorXd d_sigmoid_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x); +Eigen::VectorXd rotate_data(Eigen::VectorXd states, double yaw); +Eigen::VectorXd vector_power(Eigen::VectorXd vec, int power); +double double_power(double val, int power); +double calc_table_value(double domain_value, std::vector domain_table, std::vector target_table); +std::string get_param_dir_path(); +Eigen::MatrixXd read_csv(std::string file_path); +std::vector read_string_csv(std::string file_path); +Eigen::VectorXd interpolate_eigen(Eigen::VectorXd y, std::vector time_stamp_obs, std::vector time_stamp_new); +std::vector interpolate_vector(std::vector y, std::vector time_stamp_obs, std::vector time_stamp_new); +#endif // VEHICLE_ADAPTOR_UTILS_H \ No newline at end of file diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/param/optimization_param.yaml b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/param/optimization_param.yaml index 6271620c9641e..baa1fc6877395 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/param/optimization_param.yaml +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/param/optimization_param.yaml @@ -13,9 +13,9 @@ optimization_parameter: x_terminal_cost: 1e+1 y_terminal_cost: 1e+7 vel_terminal_cost: 1e+4 - yaw_terminal_cost: 1e+5 + yaw_terminal_cost: 1e+8 acc_terminal_cost: 1e+3 - steer_terminal_cost: 1e+10 # if steer -> yaw' dynamics is precise + steer_terminal_cost: 1e+7 # if steer -> yaw' dynamics is precise x_intermediate_cost: 0.0 y_intermediate_cost: 0.0 vel_intermediate_cost: 0.0 @@ -25,19 +25,12 @@ optimization_parameter: intermediate_cost_index_predict_by_polynomial_regression: 6 intermediate_cost_index_controller_d_inputs_schedule: 6 intermediate_cost_index_controller_prediction: 6 - steer_rate_input_table: [0.0001,0.001,0.01] - steer_rate_cost_coef_table: [100000.0,1000.0,1.0] - acc_rate_input_table: [0.001,0.01,0.1] - acc_rate_cost_coef_table: [100.0,10.0,1.0] - longitudinal_coef_by_vel_error_domain_table: [0.1, 0.3, 1.0] - longitudinal_coef_by_acc_error_domain_table: [0.01, 0.1, 0.2] - longitudinal_coef_target_table: [0.01, 0.1, 1.0] - acc_input_weight_target_table: [1e+6, 1e+6, 1e+0] - lateral_coef_by_lateral_error_domain_table: [0.03, 0.07, 0.2] - lateral_coef_by_yaw_error_domain_table: [0.01, 0.03, 0.1] - lateral_coef_by_steer_error_domain_table: [0.001, 0.003, 0.005] - lateral_coef_target_table: [0.01, 0.1, 1.0] - steer_input_weight_target_table: [1e+7, 1e+4, 1.0] + past_steer_input_change_decay_rate: 0.9 + steer_input_change_window_size: 3 + future_steer_input_change_decay_rate: 0.9 + past_acc_input_change_decay_rate: 0.9 + acc_input_change_window_size: 3 + future_acc_input_change_decay_rate: 0.9 setting: control_dt: 0.033 predict_step: 3 @@ -48,13 +41,13 @@ optimization_parameter: controller_acc_input_history_len: 7 deg_controller_acc_input_history: 2 lam_controller_acc_input_history: [1e-4,1e+1] - minimum_decay_controller_acc_input_history: 0.1 + oldest_sample_weight_controller_acc_input_history: 0.1 acc_polynomial_prediction_len: 8 ignore_intercept_acc_prediction: true controller_steer_input_history_len: 7 deg_controller_steer_input_history: 2 lam_controller_steer_input_history: [1e-4,1e+1] - minimum_decay_controller_steer_input_history: 0.1 + oldest_sample_weight_controller_steer_input_history: 0.1 steer_polynomial_prediction_len: 8 ignore_intercept_steer_prediction: true inputs_schedule_prediction_NN: # secondary @@ -100,14 +93,6 @@ optimization_parameter: sg_window_size_for_NN_diff: 5 input_filter: input_filter_mode: "none" - deg_for_acc_inputs_polynomial_filter: 2 - sample_num_for_acc_inputs_polynomial_filter: 5 - lam_for_acc_inputs_polynomial_filter: 1e-5 - minimum_decay_for_acc_inputs_polynomial_filter: 0.1 - deg_for_steer_inputs_polynomial_filter: 2 - sample_num_for_steer_inputs_polynomial_filter: 5 - lam_for_steer_inputs_polynomial_filter: 1e-5 - minimum_decay_for_steer_inputs_polynomial_filter: 0.1 mix_ratio: mix_ratio_vel_target_table: [0.0,0.0,1.0] mix_ratio_vel_domain_table: [0.0,0.5,2.0] @@ -131,6 +116,39 @@ optimization_parameter: lambda_decay_steer_ref_smoother: 1e-8 lambda_terminal_decay_steer_ref_smoother: 1e-3 use_steer_ref_smoother: false + cost_tables: + acc_rate_input_table: [0.001,0.01,0.1] + acc_rate_cost_coef_table: [100.0,10.0,1.0] + x_coef_by_acc_rate_table: [0.1,0.3,1.0] + vel_coef_by_acc_rate_table: [0.1,0.3,1.0] + acc_coef_by_acc_rate_table: [0.1,0.3,1.0] + steer_rate_input_table: [0.0001,0.001,0.01] + steer_rate_cost_coef_table: [100000.0,1000.0,1.0] + vel_for_steer_rate_table: [3.0, 6.0, 10.0] + steer_rate_cost_coef_by_vel_table: [1.0,100.0,1000.0] + y_coef_by_steer_rate_table: [0.1,0.3,1.0] + yaw_coef_by_steer_rate_table: [0.1,0.3,1.0] + steer_coef_by_steer_rate_table: [1.0,1.0,1.0] + controller_vel_error_domain_table: [0.1, 0.3, 1.0] + controller_acc_error_domain_table: [0.01, 0.1, 0.2] + controller_longitudinal_coef_target_table: [0.01, 0.1, 1.0] + controller_acc_input_weight_target_table: [1e+6, 1e+6, 1e+0] + controller_yaw_error_domain_table: [0.01, 0.03, 0.1] + controller_steer_error_domain_table: [0.001, 0.003, 0.005] + controller_lateral_coef_target_table: [0.01, 0.1, 1.0] + controller_steer_input_weight_target_table: [1e+7, 1e+4, 1.0] + x_error_domain_table: [0.05, 0.1, 0.5] + x_error_target_table: [0.1, 0.3, 1.0] + y_error_domain_table: [0.05, 0.1, 0.5] + y_error_target_table: [0.1, 0.3, 1.0] + vel_error_domain_table: [0.1, 0.3, 1.0] + vel_error_target_table: [0.1, 0.3, 1.0] + yaw_error_domain_table: [0.01, 0.03, 0.1] + yaw_error_target_table: [0.1, 0.3, 1.0] + acc_error_domain_table: [0.01, 0.03, 0.1] + acc_error_target_table: [0.1, 0.3, 1.0] + steer_error_domain_table: [0.001, 0.003, 0.005] + steer_error_target_table: [0.1, 0.3, 1.0] autoware_alignment: use_controller_steer_input_schedule: true use_vehicle_adaptor: true diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/python_simulator.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/python_simulator.py index f653cf0752397..f70c817a988b6 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/python_simulator.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/python_simulator.py @@ -3,7 +3,7 @@ import numpy as np import scipy.interpolate from autoware_vehicle_adaptor.param import parameters -from autoware_vehicle_adaptor.src import utils +from autoware_vehicle_adaptor.src import vehicle_adaptor_compensator from autoware_vehicle_adaptor.src import actuation_map_2d #from utils.density_estimation import KinematicStates #from utils.density_estimation import visualize_speed_acc @@ -116,7 +116,7 @@ def __init__(self): self.controller = nominal_ilqr.NominalILQRController() self.pure_pursuit_controller = pure_pursuit_controller.PurePursuitController(self.wheel_base) self.log_updater = data_collection_utils.driving_log_updater() - self.vehicle_adaptor = utils.VehicleAdaptor() + self.vehicle_adaptor = vehicle_adaptor_compensator.VehicleAdaptor() self.perturbed_sim_flag = False self.nominal_setting_dict = {} self.sim_setting_dict = {} @@ -339,9 +339,8 @@ def drive_sim( control_type: ControlType = ControlType.mpc, use_vehicle_adaptor=False, vehicle_adaptor_model_path="vehicle_model.pth", - use_nonzero_initial_hidden=False, - use_offline_features=True, - initial_hidden_dir="vehicle_model", + use_offline_features=False, + offline_feature_dir="vehicle_model", states_ref_mode="predict_by_polynomial_regression", course_csv_data="slalom_course_data.csv", # "slalom_course_data.csv" or "mpc_figure_eight_course_data.csv" offline_data_dir=None, @@ -380,8 +379,8 @@ def drive_sim( if use_vehicle_adaptor: self.vehicle_adaptor.clear_NN_params() self.set_NN_params(vehicle_adaptor_model_path) - if use_nonzero_initial_hidden: - self.set_initial_hidden_params(initial_hidden_dir, use_offline_features) + if use_offline_features: + self.set_offline_features(offline_feature_dir) self.vehicle_adaptor.send_initialized_flag() self.vehicle_adaptor.unset_offline_data_set_for_compensation() self.vehicle_adaptor.unset_projection_matrix_for_compensation() @@ -736,10 +735,8 @@ def set_NN_params_from_model(self, vehicle_model,load_dir): vehicle_model.vel_bias, state_component_predicted, ) - def set_initial_hidden_params(self, csv_dir,use_offline_features): - self.vehicle_adaptor.set_initial_hidden_params_from_csv(csv_dir) - if use_offline_features: - self.vehicle_adaptor.set_use_offline_features_from_csv(csv_dir) + def set_offline_features(self, csv_dir): + self.vehicle_adaptor.set_offline_features_from_csv(csv_dir) def save_pp_eight_record(self, t_current,save_dir): t_current = math.ceil(t_current * 100 / 60) / 100 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/run_accel_brake_map_calibrator.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/run_accel_brake_map_calibrator.py index 073bd531f54b6..ba8031475cd96 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/run_accel_brake_map_calibrator.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/python_simulator/run_accel_brake_map_calibrator.py @@ -33,12 +33,7 @@ train_dir = root_dir + "/test_python_pure_pursuit_train" test_dir = root_dir + "/test_python_pure_pursuit_test" test_nominal_dir = root_dir + "/test_nominal" -test_NN_without_polynomial_dir = root_dir + "/test_NN_without_polynomial" -test_GP_without_polynomial_dir = root_dir + "/test_GP_without_polynomial" -test_polynomial_dir = root_dir + "/test_polynomial" test_NN_dir = root_dir + "/test_NN" -test_GP_dir = root_dir + "/test_GP" -test_NN_augmented_by_GP_dir = root_dir + "/test_NN_augmented_by_GP" correct_map_dir = "../actuation_cmd_maps/accel_brake_maps/default_parameter" low_quality_map_dir = "../actuation_cmd_maps/accel_brake_maps/low_quality_map" sim_setting_dict = {} @@ -77,94 +72,29 @@ calibrator.add_data_from_csv(train_dir) tester.add_data_from_csv(test_dir) - print("calibrate by NN without polynomial") - calibrator.calibrate_by_NN() - calibrator.save_accel_brake_map_NN(map_vel,map_accel,map_brake,test_NN_without_polynomial_dir) - - print("calibrate by GP without polynomial") - calibrator.calibrate_by_GP() - calibrator.save_accel_brake_map_GP(map_vel,map_accel,map_brake,test_GP_without_polynomial_dir) - - print("calibrate by polynomial regression") - calibrator.calibrate_by_polynomial_regression() - calibrator.save_accel_brake_map_poly(map_vel,map_accel,map_brake,test_polynomial_dir) print("calibrate by NN") calibrator.calibrate_by_NN() calibrator.save_accel_brake_map_NN(map_vel,map_accel,map_brake,test_NN_dir) - print("calibrate by GP") - calibrator.calibrate_by_GP() - calibrator.save_accel_brake_map_GP(map_vel,map_accel,map_brake,test_GP_dir) - - print("calibrate by NN augmented") - calibrator.data_augmentation_by_GP(map_vel,map_accel,map_brake) - calibrator.calibrate_by_NN() - calibrator.save_accel_brake_map_NN(map_vel,map_accel,map_brake,test_NN_augmented_by_GP_dir) print("nominal map") print("train data error") calibrator.calc_calibration_error(low_quality_map_dir) print("test data error") tester.calc_calibration_error(low_quality_map_dir) - print("NN without polynomial map") - print("train data error") - calibrator.calc_calibration_error(test_NN_without_polynomial_dir) - print("test data error") - tester.calc_calibration_error(test_NN_without_polynomial_dir) - print("GP without polynomial map") - print("train data error") - calibrator.calc_calibration_error(test_GP_without_polynomial_dir) - print("test data error") - tester.calc_calibration_error(test_GP_without_polynomial_dir) - print("polynomial map") - print("train data error") - calibrator.calc_calibration_error(test_polynomial_dir) - print("test data error") - tester.calc_calibration_error(test_polynomial_dir) print("NN map") print("train data error") calibrator.calc_calibration_error(test_NN_dir) print("test data error") tester.calc_calibration_error(test_NN_dir) - print("GP map") - print("train data error") - calibrator.calc_calibration_error(test_GP_dir) - print("test data error") - tester.calc_calibration_error(test_GP_dir) - print("NN augmented by GP map") - print("train data error") - calibrator.calc_calibration_error(test_NN_augmented_by_GP_dir) - print("test data error") - tester.calc_calibration_error(test_NN_augmented_by_GP_dir) - print("nominal test") simulator.drive_sim(save_dir=test_nominal_dir) -print("NN without polynomial test") -sim_setting_dict["accel_brake_map_control_path"] = test_NN_without_polynomial_dir -simulator.perturbed_sim(sim_setting_dict) -simulator.drive_sim(save_dir=test_NN_without_polynomial_dir) -print("GP without polynomial test") -sim_setting_dict["accel_brake_map_control_path"] = test_GP_without_polynomial_dir -simulator.perturbed_sim(sim_setting_dict) -simulator.drive_sim(save_dir=test_GP_without_polynomial_dir) -print("polynomial test") -sim_setting_dict["accel_brake_map_control_path"] = test_polynomial_dir -simulator.perturbed_sim(sim_setting_dict) -simulator.drive_sim(save_dir=test_polynomial_dir) print("NN test") sim_setting_dict["accel_brake_map_control_path"] = test_NN_dir simulator.perturbed_sim(sim_setting_dict) simulator.drive_sim(save_dir=test_NN_dir) -print("GP test") -sim_setting_dict["accel_brake_map_control_path"] = test_GP_dir -simulator.perturbed_sim(sim_setting_dict) -simulator.drive_sim(save_dir=test_GP_dir) -print("NN augmented by GP test") -sim_setting_dict["accel_brake_map_control_path"] = test_NN_augmented_by_GP_dir -simulator.perturbed_sim(sim_setting_dict) -simulator.drive_sim(save_dir=test_NN_augmented_by_GP_dir) print("Correct map test") sim_setting_dict["accel_brake_map_control_path"] = correct_map_dir simulator.perturbed_sim(sim_setting_dict) diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/inputs_prediction.cpp b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/inputs_prediction.cpp index 57a3eabec41f3..7fa669c9b78c4 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/inputs_prediction.cpp +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/inputs_prediction.cpp @@ -10,228 +10,6 @@ #include namespace py = pybind11; - - -Eigen::VectorXd tanh(const Eigen::VectorXd & v) -{ - return v.array().tanh(); -} -Eigen::VectorXd d_tanh(const Eigen::VectorXd & v) -{ - return 1 / (v.array().cosh() * v.array().cosh()); -} -Eigen::VectorXd sigmoid(const Eigen::VectorXd & v) -{ - return 0.5 * (0.5 * v).array().tanh() + 0.5; -} -Eigen::VectorXd relu(const Eigen::VectorXd & x) -{ - Eigen::VectorXd x_ = x; - for (int i = 0; i < x.size(); i++) { - if (x[i] < 0) { - x_[i] = 0; - } - } - return x_; -} -Eigen::VectorXd d_relu(const Eigen::VectorXd & x) -{ - Eigen::VectorXd result = Eigen::VectorXd::Ones(x.size()); - for (int i = 0; i < x.size(); i++) { - if (x[i] < 0) { - result[i] = 0; - } - } - return result; -} -Eigen::MatrixXd d_relu_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) -{ - Eigen::MatrixXd result = Eigen::MatrixXd::Zero(m.rows(), m.cols()); - for (int i = 0; i < m.cols(); i++) { - if (x[i] >= 0) { - result.col(i) = m.col(i); - } - } - return result; -} -Eigen::MatrixXd d_tanh_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) -{ - Eigen::MatrixXd result = Eigen::MatrixXd(m.rows(), m.cols()); - for (int i = 0; i < m.cols(); i++) { - result.col(i) = m.col(i) / (std::cosh(x[i]) * std::cosh(x[i])); - } - return result; -} -Eigen::VectorXd d_tanh_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x) -{ - Eigen::VectorXd result = Eigen::VectorXd(v.size()); - for (int i = 0; i < v.size(); i++) { - result[i] = v[i] / (std::cosh(x[i]) * std::cosh(x[i])); - } - return result; -} -Eigen::MatrixXd d_sigmoid_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) -{ - Eigen::MatrixXd result = Eigen::MatrixXd(m.rows(), m.cols()); - for (int i = 0; i < m.cols(); i++) { - result.col(i) = 0.25 * m.col(i) / (std::cosh(0.5 * x[i]) * std::cosh(0.5 * x[i])); - } - return result; -} -Eigen::VectorXd d_sigmoid_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x) -{ - Eigen::VectorXd result = Eigen::VectorXd(v.size()); - for (int i = 0; i < v.size(); i++) { - result[i] = 0.25 * v[i] / (std::cosh(0.5 * x[i]) * std::cosh(0.5 * x[i])); - } - return result; -} - -Eigen::VectorXd rotate_data(Eigen::VectorXd states, double yaw) -{ - Eigen::VectorXd states_rotated = states; - double cos_yaw = std::cos(yaw); - double sin_yaw = std::sin(yaw); - states_rotated[0] = states[0] * cos_yaw + states[1] * sin_yaw; - states_rotated[1] = -states[1] * sin_yaw + states[0] * cos_yaw; - return states_rotated; -} - -Eigen::VectorXd vector_power(Eigen::VectorXd vec, int power) -{ - Eigen::VectorXd result = vec; - for (int i = 0; i < power - 1; i++) { - result = result.array() * vec.array(); - } - return result; -} - -double double_power(double val, int power) -{ - double result = val; - for (int i = 0; i < power - 1; i++) { - result = result * val; - } - return result; -} - -double calc_table_value(double domain_value, std::vector domain_table, std::vector target_table) -{ - if (domain_value <= domain_table[0]) { - return target_table[0]; - } - if (domain_value >= domain_table[domain_table.size() - 1]) { - return target_table[target_table.size() - 1]; - } - for (int i = 0; i < int(domain_table.size()) - 1; i++) { - if (domain_value >= domain_table[i] && domain_value <= domain_table[i + 1]) { - return target_table[i] + - (target_table[i + 1] - target_table[i]) / - (domain_table[i + 1] - domain_table[i]) * - (domain_value - domain_table[i]); - } - } - return 0.0; -} - -std::string get_param_dir_path() -{ - std::string build_path = BUILD_PATH; - std::string param_dir_path = build_path + "/autoware_vehicle_adaptor/param"; - return param_dir_path; -} - -Eigen::MatrixXd read_csv(std::string file_path) -{ - std::string build_path = BUILD_PATH; - std::string file_path_abs = build_path + "/" + file_path; - std::vector> csv_data; - std::ifstream ifs(file_path_abs); - if (!ifs) { - std::cerr << "Failed to open file." << std::endl; - return Eigen::MatrixXd(0, 0); - } - std::string line; - while (std::getline(ifs, line)) { - std::istringstream stream(line); - std::string field; - std::vector fields; - while (std::getline(stream, field, ',')) { - fields.push_back(std::stod(field)); - } - csv_data.push_back(fields); - } - ifs.close(); - Eigen::MatrixXd data = Eigen::MatrixXd(csv_data.size(), csv_data[0].size()); - for (int i = 0; i < int(csv_data.size()); i++) { - for (int j = 0; j < int(csv_data[0].size()); j++) { - data(i, j) = csv_data[i][j]; - } - } - return data; -} -std::vector read_string_csv(std::string file_path) -{ - std::string build_path = BUILD_PATH; - std::string file_path_abs = build_path + "/" + file_path; - std::vector csv_data; - std::ifstream ifs(file_path_abs); - if (!ifs) { - std::cerr << "Failed to open file." << std::endl; - return csv_data; - } - std::string line; - while (std::getline(ifs, line)) { - std::stringstream stream(line); - std::string field; - while (std::getline(stream, field, ',')) { - field.erase(field.find_last_not_of(" \n\r\t") + 1); - field.erase(0, field.find_first_not_of(" \n\r\t")); - csv_data.push_back(field); - } - } - ifs.close(); - return csv_data; -} -Eigen::VectorXd interpolate_eigen(Eigen::VectorXd y, std::vector time_stamp_obs, std::vector time_stamp_new) -{ - Eigen::VectorXd y_new = Eigen::VectorXd(time_stamp_new.size()); - int lower_bound_index = 0; - for (int i = 0; i < int(time_stamp_new.size()); i++) { - if (time_stamp_new[i] >= time_stamp_obs[time_stamp_obs.size() - 1]) { - y_new[i] = y[time_stamp_obs.size() - 1] + (y[time_stamp_obs.size() - 1] - y[time_stamp_obs.size() - 2]) / (time_stamp_obs[time_stamp_obs.size() - 1] - time_stamp_obs[time_stamp_obs.size() - 2]) * (time_stamp_new[i] - time_stamp_obs[time_stamp_obs.size() - 1]); - continue; - } - for (int j = lower_bound_index; j < int(time_stamp_obs.size()) - 1; j++) { - if (time_stamp_new[i] >= time_stamp_obs[j] && time_stamp_new[i] <= time_stamp_obs[j + 1]) { - y_new[i] = y[j] + (y[j + 1] - y[j]) / (time_stamp_obs[j + 1] - time_stamp_obs[j]) * (time_stamp_new[i] - time_stamp_obs[j]); - lower_bound_index = j; - break; - } - } - } - return y_new; -} -std::vector interpolate_vector(std::vector y, std::vector time_stamp_obs, std::vector time_stamp_new) -{ - std::vector y_new; - int lower_bound_index = 0; - for (int i = 0; i < int(time_stamp_new.size()); i++) { - if (time_stamp_new[i] >= time_stamp_obs[time_stamp_obs.size() - 1]) { - y_new.push_back(y[time_stamp_obs.size() - 1] + (y[time_stamp_obs.size() - 1] - y[time_stamp_obs.size() - 2]) / (time_stamp_obs[time_stamp_obs.size() - 1] - time_stamp_obs[time_stamp_obs.size() - 2]) * (time_stamp_new[i] - time_stamp_obs[time_stamp_obs.size() - 1])); - continue; - } - for (int j = lower_bound_index; j < int(time_stamp_obs.size()) - 1; j++) { - if (time_stamp_new[i] >= time_stamp_obs[j] && time_stamp_new[i] <= time_stamp_obs[j + 1]) { - y_new.push_back(y[j] + (y[j + 1] - y[j]) / (time_stamp_obs[j + 1] - time_stamp_obs[j]) * (time_stamp_new[i] - time_stamp_obs[j])); - lower_bound_index = j; - break; - } - } - } - return y_new; -} - InputsSchedulePrediction::InputsSchedulePrediction() {} InputsSchedulePrediction::~InputsSchedulePrediction() {} void InputsSchedulePrediction::set_params(int input_step, int output_step, double control_dt, std::string csv_dir, const int & adaptive_scale_index){ diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp index 3f0c13635a709..48ee326a33f3d 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp @@ -367,195 +367,3 @@ dhc_dx.block(0, 0, h_dim_, x.size()) = dh_dx; dhc_dx.block(h_dim_, 0, h_dim_, x.size()) = dc_dx; } - - GetInitialHidden::GetInitialHidden() {} - GetInitialHidden::~GetInitialHidden() {} - void GetInitialHidden::set_initial_hidden_params( - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_1, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_1, - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_2, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_2, - const Eigen::MatrixXd & weight_initial_hidden_gru_ih, const Eigen::MatrixXd & weight_initial_hidden_gru_hh, - const Eigen::MatrixXd & weight_initial_hidden_final_layer, - const Eigen::MatrixXd & weight_initial_hidden_fusion_layer, const Eigen::MatrixXd & weight_initial_hidden_only_online, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_1, const Eigen::VectorXd & bias_initial_hidden_steer_layer_1, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_2, const Eigen::VectorXd & bias_initial_hidden_steer_layer_2, - const Eigen::VectorXd & bias_initial_hidden_gru_ih, const Eigen::VectorXd & bias_initial_hidden_gru_hh, - const Eigen::VectorXd & bias_initial_hidden_final_layer, - const Eigen::VectorXd & bias_initial_hidden_fusion_layer, const Eigen::VectorXd & bias_initial_hidden_only_online) - { - weight_initial_hidden_acc_layer_1_ = weight_initial_hidden_acc_layer_1; - weight_initial_hidden_steer_layer_1_ = weight_initial_hidden_steer_layer_1; - weight_initial_hidden_acc_layer_2_ = weight_initial_hidden_acc_layer_2; - weight_initial_hidden_steer_layer_2_ = weight_initial_hidden_steer_layer_2; - weight_initial_hidden_gru_ih_ = weight_initial_hidden_gru_ih; - weight_initial_hidden_gru_hh_ = weight_initial_hidden_gru_hh; - weight_initial_hidden_final_layer_ = weight_initial_hidden_final_layer; - weight_initial_hidden_fusion_layer_ = weight_initial_hidden_fusion_layer; - weight_initial_hidden_only_online_ = weight_initial_hidden_only_online; - bias_initial_hidden_acc_layer_1_ = bias_initial_hidden_acc_layer_1; - bias_initial_hidden_steer_layer_1_ = bias_initial_hidden_steer_layer_1; - bias_initial_hidden_acc_layer_2_ = bias_initial_hidden_acc_layer_2; - bias_initial_hidden_steer_layer_2_ = bias_initial_hidden_steer_layer_2; - bias_initial_hidden_gru_ih_ = bias_initial_hidden_gru_ih; - bias_initial_hidden_gru_hh_ = bias_initial_hidden_gru_hh; - bias_initial_hidden_final_layer_ = bias_initial_hidden_final_layer; - bias_initial_hidden_fusion_layer_ = bias_initial_hidden_fusion_layer; - bias_initial_hidden_only_online_ = bias_initial_hidden_only_online; - h_gru_dim_ = weight_initial_hidden_gru_hh_.cols(); - } - void GetInitialHidden::set_offline_features(const Eigen::VectorXd & offline_features) - { - offline_features_ = offline_features; - use_offline_features_ = true; - } - void GetInitialHidden::update_gru( - const Eigen::VectorXd & x, const Eigen::VectorXd & h_gru, Eigen::VectorXd & h_gru_next) - { - Eigen::VectorXd acc_sub(acc_queue_size_ + predict_step_ + 2); - Eigen::VectorXd steer_sub(steer_queue_size_ + predict_step_ + 2); - acc_sub << x[vel_index_], x[acc_index_], x.segment(acc_input_start_index_, acc_queue_size_ + predict_step_); - steer_sub << x[vel_index_], x[steer_index_], x.segment(steer_input_start_index_, steer_queue_size_ + predict_step_); - const Eigen::VectorXd acc_layer_1 = relu(weight_initial_hidden_acc_layer_1_ * acc_sub + bias_initial_hidden_acc_layer_1_); - const Eigen::VectorXd steer_layer_1 = relu(weight_initial_hidden_steer_layer_1_ * steer_sub + bias_initial_hidden_steer_layer_1_); - const Eigen::VectorXd acc_layer_2 = relu(weight_initial_hidden_acc_layer_2_ * acc_layer_1 + bias_initial_hidden_acc_layer_2_); - const Eigen::VectorXd steer_layer_2 = relu(weight_initial_hidden_steer_layer_2_ * steer_layer_1 + bias_initial_hidden_steer_layer_2_); - Eigen::VectorXd h1 = Eigen::VectorXd(1 + acc_layer_2.size() + steer_layer_2.size()); - h1 << x[vel_index_], acc_layer_2, steer_layer_2; - Eigen::VectorXd gru_ih_part = weight_initial_hidden_gru_ih_ * h1 + bias_initial_hidden_gru_ih_; - Eigen::VectorXd gru_hh_part = weight_initial_hidden_gru_hh_ * h_gru + bias_initial_hidden_gru_hh_; - Eigen::VectorXd r = sigmoid(gru_ih_part.segment(0, h_gru_dim_) + gru_hh_part.segment(0, h_gru_dim_)); - Eigen::VectorXd z = sigmoid(gru_ih_part.segment(h_gru_dim_, h_gru_dim_) + gru_hh_part.segment(h_gru_dim_, h_gru_dim_)); - Eigen::VectorXd u_n = gru_ih_part.segment(2 * h_gru_dim_, h_gru_dim_).array() + r.array() * gru_hh_part.segment(2 * h_gru_dim_, h_gru_dim_).array(); - Eigen::VectorXd n = tanh(u_n); - h_gru_next = (1 - z.array()) * n.array() + z.array() * h_gru.array(); - } - Eigen::VectorXd GetInitialHidden::get_initial_hidden(Eigen::VectorXd & h_gru) - { - Eigen::VectorXd online_out = tanh(weight_initial_hidden_final_layer_ * h_gru + bias_initial_hidden_final_layer_); - if (use_offline_features_) - { - Eigen::VectorXd fused_out(offline_features_.size() + online_out.size()); - fused_out << offline_features_, online_out; - return tanh(weight_initial_hidden_fusion_layer_ * fused_out + bias_initial_hidden_fusion_layer_); - } - else - { - return tanh(weight_initial_hidden_only_online_ * online_out + bias_initial_hidden_only_online_); - } - } - - //GetInitialHidden - /* - GetInitialHidden::GetInitialHidden() {} - GetInitialHidden::~GetInitialHidden() {} - void GetInitialHidden::set_attention_params( - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_1, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_1, - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_2, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_2, - const Eigen::MatrixXd & weight_initial_hidden_gru_ih, const Eigen::MatrixXd & weight_initial_hidden_gru_hh, - const Eigen::MatrixXd & initial_hidden_query, const Eigen::MatrixXd & weight_initial_hidden_key_layer, - const Eigen::MatrixXd & weight_initial_hidden_value_layer, const Eigen::MatrixXd & weight_initial_hidden_final_layer, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_1, const Eigen::VectorXd & bias_initial_hidden_steer_layer_1, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_2, const Eigen::VectorXd & bias_initial_hidden_steer_layer_2, - const Eigen::VectorXd & bias_initial_hidden_gru_ih, const Eigen::VectorXd & bias_initial_hidden_gru_hh, - const Eigen::VectorXd & bias_initial_hidden_value_layer, const Eigen::VectorXd & bias_initial_hidden_final_layer, - const int num_heads, const int key_size, const int value_size, const int mean_steps - ) - { - weight_initial_hidden_acc_layer_1_ = weight_initial_hidden_acc_layer_1; - weight_initial_hidden_steer_layer_1_ = weight_initial_hidden_steer_layer_1; - weight_initial_hidden_acc_layer_2_ = weight_initial_hidden_acc_layer_2; - weight_initial_hidden_steer_layer_2_ = weight_initial_hidden_steer_layer_2; - weight_initial_hidden_gru_ih_ = weight_initial_hidden_gru_ih; - weight_initial_hidden_gru_hh_ = weight_initial_hidden_gru_hh; - initial_hidden_query_ = initial_hidden_query; - weight_initial_hidden_key_layer_ = weight_initial_hidden_key_layer; - weight_initial_hidden_value_layer_ = weight_initial_hidden_value_layer; - weight_initial_hidden_final_layer_ = weight_initial_hidden_final_layer; - bias_initial_hidden_acc_layer_1_ = bias_initial_hidden_acc_layer_1; - bias_initial_hidden_steer_layer_1_ = bias_initial_hidden_steer_layer_1; - bias_initial_hidden_acc_layer_2_ = bias_initial_hidden_acc_layer_2; - bias_initial_hidden_steer_layer_2_ = bias_initial_hidden_steer_layer_2; - bias_initial_hidden_gru_ih_ = bias_initial_hidden_gru_ih; - bias_initial_hidden_gru_hh_ = bias_initial_hidden_gru_hh; - bias_initial_hidden_value_layer_ = bias_initial_hidden_value_layer; - bias_initial_hidden_final_layer_ = bias_initial_hidden_final_layer; - num_heads_ = num_heads; - key_size_ = key_size; - value_size_ = value_size; - mean_steps_ = mean_steps; - h_gru_dim_ = weight_initial_hidden_gru_hh_.cols(); - } - void GetInitialHidden::update_gru( - const Eigen::VectorXd & x, const Eigen::VectorXd & h_gru, Eigen::VectorXd & h_gru_next) - { - Eigen::VectorXd acc_sub(acc_queue_size_ + predict_step_ + 2); - Eigen::VectorXd steer_sub(steer_queue_size_ + predict_step_ + 2); - acc_sub << x[vel_index_], x[acc_index_], x.segment(acc_input_start_index_, acc_queue_size_ + predict_step_); - steer_sub << x[vel_index_], x[steer_index_], x.segment(steer_input_start_index_, steer_queue_size_ + predict_step_); - const Eigen::VectorXd acc_layer_1 = relu(weight_initial_hidden_acc_layer_1_ * acc_sub + bias_initial_hidden_acc_layer_1_); - const Eigen::VectorXd steer_layer_1 = relu(weight_initial_hidden_steer_layer_1_ * steer_sub + bias_initial_hidden_steer_layer_1_); - const Eigen::VectorXd acc_layer_2 = relu(weight_initial_hidden_acc_layer_2_ * acc_layer_1 + bias_initial_hidden_acc_layer_2_); - const Eigen::VectorXd steer_layer_2 = relu(weight_initial_hidden_steer_layer_2_ * steer_layer_1 + bias_initial_hidden_steer_layer_2_); - Eigen::VectorXd h1 = Eigen::VectorXd(1 + acc_layer_2.size() + steer_layer_2.size()); - h1 << x[vel_index_], acc_layer_2, steer_layer_2; - Eigen::VectorXd gru_ih_part = weight_initial_hidden_gru_ih_ * h1 + bias_initial_hidden_gru_ih_; - Eigen::VectorXd gru_hh_part = weight_initial_hidden_gru_hh_ * h_gru + bias_initial_hidden_gru_hh_; - Eigen::VectorXd r = sigmoid(gru_ih_part.segment(0, h_gru_dim_) + gru_hh_part.segment(0, h_gru_dim_)); - Eigen::VectorXd z = sigmoid(gru_ih_part.segment(h_gru_dim_, h_gru_dim_) + gru_hh_part.segment(h_gru_dim_, h_gru_dim_)); - Eigen::VectorXd u_n = gru_ih_part.segment(2 * h_gru_dim_, h_gru_dim_).array() + r.array() * gru_hh_part.segment(2 * h_gru_dim_, h_gru_dim_).array(); - Eigen::VectorXd n = tanh(u_n); - h_gru_next = (1 - z.array()) * n.array() + z.array() * h_gru.array(); - } - void GetInitialHidden::initialize_x_queue(){ - x_queue_.clear(); - } - void GetInitialHidden::update_x_queue(const Eigen::VectorXd & x){ - x_queue_.push_back(x); - } - void GetInitialHidden::initialize_attention_info(){ - attention_queue_.clear(); - value_queue_.clear(); - } - void GetInitialHidden::erase_attention_info_begin(){ - attention_queue_.erase(attention_queue_.begin()); - value_queue_.erase(value_queue_.begin()); - } - void GetInitialHidden::add_weight_and_value(const Eigen::VectorXd & h_gru){ - Eigen::VectorXd key = weight_initial_hidden_key_layer_ * h_gru; - Eigen::VectorXd attention_weight = Eigen::VectorXd::Zero(num_heads_); - for (int i = 0; i < num_heads_; i++){ - Eigen::VectorXd query = initial_hidden_query_.row(i).transpose(); - attention_weight[i] = query.dot(key.segment(i * key_size_, key_size_)) / std::sqrt(key_size_); - } - Eigen::VectorXd attention_weight_exp = attention_weight.array().exp(); - Eigen::VectorXd value = weight_initial_hidden_value_layer_ * h_gru + bias_initial_hidden_value_layer_; - attention_queue_.push_back(attention_weight_exp); - value_queue_.push_back(value); - } - void GetInitialHidden::update_attention_info(){ - Eigen::VectorXd h_gru_mean = Eigen::VectorXd::Zero(h_gru_dim_); - Eigen::VectorXd h_gru = Eigen::VectorXd::Zero(h_gru_dim_); - for (int i = 0; i < int(x_queue_.size()); i++){ - Eigen::VectorXd h_gru_next; - update_gru(x_queue_[i], h_gru, h_gru_next); - h_gru = h_gru_next; - if (i >= int(x_queue_.size()) - mean_steps_){ - h_gru_mean += h_gru/mean_steps_; - } - } - add_weight_and_value(h_gru_mean); - } - Eigen::VectorXd GetInitialHidden::get_initial_hidden(){ - Eigen::VectorXd context = Eigen::VectorXd::Zero(value_size_ * num_heads_); - Eigen::VectorXd attention_weight_exp_sum = Eigen::VectorXd::Zero(num_heads_); - for (int i = 0; i < int(attention_queue_.size()); i++){ - attention_weight_exp_sum += attention_queue_[i]; - } - for (int i = 0; i < int(attention_queue_.size()); i++){ - for (int j = 0; j < num_heads_; j++){ - context.segment(j * value_size_, value_size_) += attention_queue_[i][j] / attention_weight_exp_sum[j] * value_queue_[i].segment(j * value_size_, value_size_); - } - } - Eigen::VectorXd h_initial_hidden = tanh(weight_initial_hidden_final_layer_ * context + bias_initial_hidden_final_layer_); - return h_initial_hidden; - } - */ \ No newline at end of file diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/utils.cpp b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_compensator.cpp similarity index 77% rename from vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/utils.cpp rename to vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_compensator.cpp index 7f8ad75011d1e..0861bbd898406 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/utils.cpp +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_compensator.cpp @@ -10,7 +10,7 @@ #include #include #include -#include "utils.h" +#include "vehicle_adaptor_compensator.h" #include // for rand() and srand() #include // for time() @@ -36,41 +36,19 @@ Eigen::VectorXd states_world_to_vehicle(Eigen::VectorXd states_world, double yaw ///////////////// Polynomial Regression /////////////////////// - PolynomialRegression::PolynomialRegression() {} - PolynomialRegression::~PolynomialRegression() {} - void PolynomialRegression::set_params(int degree, int num_samples, std::vector lambda) + PolynomialRegressionPredictor::PolynomialRegressionPredictor() {} + PolynomialRegressionPredictor::~PolynomialRegressionPredictor() {} + void PolynomialRegressionPredictor::set_params(int degree, int num_samples, std::vector lambda) { degree_ = degree; num_samples_ = num_samples; lambda_ = lambda; } - void PolynomialRegression::set_minimum_decay(double minimum_decay) { minimum_decay_ = minimum_decay; } - void PolynomialRegression::set_ignore_intercept() { ignore_intercept_ = true; } - void PolynomialRegression::set_predict() { predict_ = true; } - //void PolynomialRegression::set_ignore_intercept() { predict_and_ignore_intercept_ = true; } - void PolynomialRegression::calc_coef_matrix() + void PolynomialRegressionPredictor::set_oldest_sample_weight(double oldest_sample_weight) { oldest_sample_weight_ = oldest_sample_weight; } + void PolynomialRegressionPredictor::set_ignore_intercept() { ignore_intercept_ = true; } + void PolynomialRegressionPredictor::calc_coef_matrix() { - if (!predict_) { - Eigen::VectorXd time_vector = Eigen::VectorXd::LinSpaced(num_samples_, 0.0, num_samples_ - 1); - Eigen::MatrixXd time_matrix = Eigen::MatrixXd::Ones(num_samples_, degree_ + 1); - - for (int i = 1; i < degree_; i++) { - time_matrix.col(i) = time_matrix.col(i - 1).array() * time_vector.array(); - } - Eigen::MatrixXd regularized_matrix = Eigen::MatrixXd::Identity(degree_ + 1, degree_ + 1); - regularized_matrix(0, 0) = 0.0; - for (int i = 0; i < degree_; i++) { - regularized_matrix(i+1, i+1) = lambda_[i]; - } - Eigen::VectorXd decay_vector = Eigen::VectorXd::LinSpaced(num_samples_, 1.0, minimum_decay_); - Eigen::MatrixXd decay_matrix = decay_vector.asDiagonal(); - weight_matrix_ = time_matrix * - (time_matrix.transpose() * decay_matrix *time_matrix + regularized_matrix).inverse() * - time_matrix.transpose() * decay_matrix; - coef_matrix_ = (time_matrix.transpose() * decay_matrix * time_matrix + regularized_matrix).inverse() * - time_matrix.transpose() * decay_matrix; - } - else if (ignore_intercept_) { + if (ignore_intercept_) { Eigen::VectorXd time_vector = Eigen::VectorXd::LinSpaced(num_samples_ -1 , - (num_samples_ - 1), - 1.0); Eigen::MatrixXd time_matrix = Eigen::MatrixXd::Zero(num_samples_ - 1 , degree_); @@ -79,21 +57,16 @@ Eigen::VectorXd states_world_to_vehicle(Eigen::VectorXd states_world, double yaw for (int i = 1; i < degree_; i++) { time_matrix.col(i) = time_matrix.col(i - 1).array() * time_vector.array(); } - Eigen::VectorXd decay_vector = Eigen::VectorXd::LinSpaced(num_samples_ - 1, minimum_decay_, 1.0); - Eigen::MatrixXd decay_matrix = decay_vector.asDiagonal(); + Eigen::VectorXd weight_vector = Eigen::VectorXd::LinSpaced(num_samples_ - 1, oldest_sample_weight_, 1.0); + Eigen::MatrixXd weight_matrix = weight_vector.asDiagonal(); - Eigen::MatrixXd regularized_matrix = Eigen::MatrixXd::Identity(degree_, degree_); + Eigen::MatrixXd regularization_matrix = Eigen::MatrixXd::Identity(degree_, degree_); for (int i = 0; i < degree_; i++) { - regularized_matrix(i, i) = lambda_[i]; + regularization_matrix(i, i) = lambda_[i]; } - weight_matrix_ = time_matrix * - (time_matrix.transpose() * decay_matrix * time_matrix + - regularized_matrix).inverse() * - time_matrix.transpose() * decay_matrix; - - coef_matrix_ = (time_matrix.transpose() * decay_matrix * time_matrix + - regularized_matrix).inverse() * - time_matrix.transpose() * decay_matrix; + coef_matrix_ = (time_matrix.transpose() * weight_matrix * time_matrix + + regularization_matrix).inverse() * + time_matrix.transpose() * weight_matrix; } else{ Eigen::VectorXd time_vector = Eigen::VectorXd::LinSpaced(num_samples_, - (num_samples_ - 1), 0.0); @@ -101,52 +74,38 @@ Eigen::VectorXd states_world_to_vehicle(Eigen::VectorXd states_world, double yaw for (int i = 1; i < degree_; i++) { time_matrix.col(i) = time_matrix.col(i - 1).array() * time_vector.array(); } - Eigen::VectorXd decay_vector = Eigen::VectorXd::LinSpaced(num_samples_, minimum_decay_, 1.0); - Eigen::MatrixXd decay_matrix = decay_vector.asDiagonal(); + Eigen::VectorXd weight_vector = Eigen::VectorXd::LinSpaced(num_samples_, oldest_sample_weight_, 1.0); + Eigen::MatrixXd weight_matrix = weight_vector.asDiagonal(); - Eigen::MatrixXd regularized_matrix = Eigen::MatrixXd::Identity(degree_ + 1, degree_ + 1); - regularized_matrix(0, 0) = 0.0; + Eigen::MatrixXd regularization_matrix = Eigen::MatrixXd::Identity(degree_ + 1, degree_ + 1); + regularization_matrix(0, 0) = 0.0; for (int i = 0; i < degree_; i++) { - regularized_matrix(i+1, i+1) = lambda_[i]; + regularization_matrix(i+1, i+1) = lambda_[i]; } - weight_matrix_ = time_matrix * - (time_matrix.transpose() * decay_matrix * time_matrix + regularized_matrix).inverse() * - time_matrix.transpose() * decay_matrix; - coef_matrix_ = (time_matrix.transpose() * decay_matrix * time_matrix + regularized_matrix).inverse() * - time_matrix.transpose() * decay_matrix; + coef_matrix_ = (time_matrix.transpose() * weight_matrix * time_matrix + regularization_matrix).inverse() * + time_matrix.transpose() * weight_matrix; } - } - void PolynomialRegression::calc_prediction_matrix(int horizon_len) + void PolynomialRegressionPredictor::calc_prediction_matrix(int horizon_len) { Eigen::VectorXd time_vector = Eigen::VectorXd::LinSpaced(horizon_len, 1.0, horizon_len); if (ignore_intercept_){ - Eigen::MatrixXd pre_prediction_matrix = Eigen::MatrixXd::Zero(horizon_len, degree_); - pre_prediction_matrix.col(0) = time_vector; + Eigen::MatrixXd prediction_time_matrix = Eigen::MatrixXd::Zero(horizon_len, degree_); + prediction_time_matrix.col(0) = time_vector; for (int i = 1; i < degree_; i++) { - pre_prediction_matrix.col(i) = pre_prediction_matrix.col(i - 1).array() * time_vector.array(); + prediction_time_matrix.col(i) = prediction_time_matrix.col(i - 1).array() * time_vector.array(); } - prediction_matrix_ = pre_prediction_matrix * coef_matrix_; + prediction_matrix_ = prediction_time_matrix * coef_matrix_; } else{ - Eigen::MatrixXd pre_prediction_matrix = Eigen::MatrixXd::Ones(horizon_len, degree_ + 1); + Eigen::MatrixXd prediction_time_matrix = Eigen::MatrixXd::Ones(horizon_len, degree_ + 1); for (int i = 1; i < degree_ + 1; i++) { - pre_prediction_matrix.col(i) = pre_prediction_matrix.col(i - 1).array() * time_vector.array(); + prediction_time_matrix.col(i) = prediction_time_matrix.col(i - 1).array() * time_vector.array(); } - prediction_matrix_ = pre_prediction_matrix * coef_matrix_; + prediction_matrix_ = prediction_time_matrix * coef_matrix_; } } - Eigen::VectorXd PolynomialRegression::fit_transform(Eigen::VectorXd vec) { return weight_matrix_ * vec; } - // Overloaded function for std::vector - std::vector PolynomialRegression::fit_transform(const std::vector& raw_data) { - return fit_transform_impl(raw_data); - } - - // Overloaded function for std::vector - std::vector PolynomialRegression::fit_transform(const std::vector& raw_data) { - return fit_transform_impl(raw_data); - } - Eigen::VectorXd PolynomialRegression::predict(Eigen::VectorXd vec) + Eigen::VectorXd PolynomialRegressionPredictor::predict(Eigen::VectorXd vec) { if (ignore_intercept_) { Eigen::VectorXd vec_ = Eigen::VectorXd(vec.size() - 1); @@ -163,56 +122,8 @@ Eigen::VectorXd states_world_to_vehicle(Eigen::VectorXd states_world, double yaw prediction = prediction.array() + vec[vec.size() - 1]; return prediction; } - } -///////////////// Polynomial Filter /////////////////////// -PolynomialFilter::PolynomialFilter() { -} -PolynomialFilter::~PolynomialFilter() { -} -void PolynomialFilter::set_params(int degree, int num_samples, double lambda, double minimum_decay) -{ - degree_ = degree; - num_samples_ = num_samples; - lambda_ = lambda; - minimum_decay_ = minimum_decay; -} -double PolynomialFilter::fit_transform(double timestamp, double sample) -{ - timestamps_.push_back(timestamp); - if (int(timestamps_.size()) > num_samples_) { - timestamps_.erase(timestamps_.begin()); - samples_.erase(samples_.begin()); - } - if (int(timestamps_.size()) < num_samples_) { - samples_.push_back(sample); - return sample; - } - Eigen::VectorXd time_vector = Eigen::VectorXd(num_samples_); - for (int i = 0; i < num_samples_; i++) { - time_vector[i] = timestamps_[i] - timestamps_[num_samples_ - 1]; - } - Eigen::MatrixXd time_matrix = Eigen::MatrixXd::Ones(num_samples_, degree_ + 1); - for (int i = 1; i < degree_; i++) { - time_matrix.col(i) = time_matrix.col(i - 1).array() * time_vector.array(); - } - Eigen::MatrixXd regularized_matrix = lambda_ * Eigen::MatrixXd::Identity(degree_ + 1, degree_ + 1); - regularized_matrix(0, 0) = 0.0; - Eigen::VectorXd decay_vector = Eigen::VectorXd::LinSpaced(num_samples_, minimum_decay_, 1.0); - Eigen::MatrixXd decay_matrix = decay_vector.asDiagonal(); - Eigen::MatrixXd weight_matrix = time_matrix * - (time_matrix.transpose() * decay_matrix * time_matrix + regularized_matrix).inverse() * - time_matrix.transpose() * decay_matrix; - Eigen::VectorXd vec = Eigen::VectorXd::Zero(num_samples_); - for (int i = 0; i < num_samples_ - 1; i++) { - vec[i] = samples_[i]; - } - vec[num_samples_ - 1] = sample; - double sample_new = (weight_matrix * vec)[num_samples_ - 1]; - samples_.push_back(sample_new); - return sample_new; -} ///////////////// SgFilter /////////////////////// SgFilter::SgFilter() {} @@ -341,30 +252,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) YAML::Node optimization_param_node = YAML::LoadFile(get_param_dir_path() + "/optimization_param.yaml"); minimum_acc_diff_ = optimization_param_node["optimization_parameter"]["minimum_diff"]["minimum_acc_diff"].as(); minimum_steer_diff_ = optimization_param_node["optimization_parameter"]["minimum_diff"]["minimum_steer_diff"].as(); - /* - YAML::Node trained_model_param_node = YAML::LoadFile(get_param_dir_path() + "/trained_model_param.yaml"); - bool add_position_to_prediction = trained_model_param_node["trained_model_parameter"]["setting"]["add_position_to_prediction"].as(); - - bool add_vel_to_prediction = trained_model_param_node["trained_model_parameter"]["setting"]["add_vel_to_prediction"].as(); - - bool add_yaw_to_prediction = trained_model_param_node["trained_model_parameter"]["setting"]["add_yaw_to_prediction"].as(); - if (add_vel_to_prediction && add_yaw_to_prediction) { - state_component_predicted_ = {"vel", "yaw", "acc", "steer"}; - } - else if (add_vel_to_prediction) { - state_component_predicted_ = {"vel", "acc", "steer"}; - } - else if (add_yaw_to_prediction) { - state_component_predicted_ = {"yaw", "acc", "steer"}; - } - else { - state_component_predicted_ = {"acc", "steer"}; - } - if (add_position_to_prediction) { - state_component_predicted_.insert(state_component_predicted_.begin(), "y"); - state_component_predicted_.insert(state_component_predicted_.begin(), "x"); - } - */ } TrainedDynamics::~TrainedDynamics() {} void TrainedDynamics::set_vehicle_params( @@ -424,7 +311,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) bias_linear_relu, bias_final_layer, vel_scaling, vel_bias); h_dim_full_ = weight_lstm_encoder_hh[0].cols(); h_dim_ = weight_lstm_hh.cols(); - //num_layers_encoder_ = weight_lstm_encoder_ih.size(); state_component_predicted_ = state_component_predicted; state_component_predicted_index_ = std::vector(state_component_predicted_.size()); for (int i = 0; i < int(state_component_predicted_.size()); i++) { @@ -487,14 +373,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) void TrainedDynamics::initialize_for_candidates_compensation(int num_candidates){ linear_regression_compensation_.initialize_for_candidates(num_candidates); } - /* - void TrainedDynamics::update_input_queue_for_compensation(Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat){ - linear_regression_compensation_.update_input_queue(states, acc_input_history_concat, steer_input_history_concat); - } - void TrainedDynamics::update_regression_matrix_for_compensation(Eigen::VectorXd error_vector){ - linear_regression_compensation_.update_regression_matrix(error_vector); - } - */ Eigen::VectorXd TrainedDynamics::prediction_for_compensation( Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat, Eigen::VectorXd h_lstm, Eigen::VectorXd c_lstm ){ @@ -521,11 +399,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) { linear_regression_compensation_.unset_projection_matrix(); } - /* - Eigen::VectorXd TrainedDynamics::get_compensation_bias(){ - return linear_regression_compensation_.get_bias(); - } - */ Eigen::VectorXd TrainedDynamics::F_with_model_for_calc_controller_prediction_error( const Eigen::VectorXd & states, const Eigen::VectorXd & acc_input_history_concat, const Eigen::VectorXd & steer_input_history_concat, @@ -703,16 +576,11 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) C.block(2 * h_dim_ + state_component_predicted_index_[i], 0, 1, 2 * h_dim_) += dF_dhc.row(i) * predict_dt_; } } - // C.block(2 * h_dim_+4, 2 * h_dim_+2, states.size()-4, 1) = dF_d_states_with_history.col(0) * predict_dt_; - // C.block(2*h_dim_+4, 2*h_dim_+3, states.size()-4, states.size() -3-1+acc_queue_size_+steer_queue_size_+2*predict_step_) = dF_d_states_with_history.rightCols(states.size() - 3 - 1 + acc_queue_size_ + steer_queue_size_ + 2 * predict_step_) * predict_dt_; - C.block(0, 0, 2 * h_dim_, 2 * h_dim_) = dhc_dhc; C.block(0, 2 * h_dim_ + 2, 2 * h_dim_, 1) = dhc_d_states_with_history.col(0); C.block(0, 2 * h_dim_ + 4, 2 * h_dim_, states.size() - 3 - 1 + acc_queue_size_ + steer_queue_size_ + 2 * predict_step_) = dhc_d_states_with_history.rightCols(states.size() - 3 - 1 + acc_queue_size_ + steer_queue_size_ + 2 * predict_step_); - //C.block(2 * h_dim_ + 4, 0, states.size() -4, 2 * h_dim_) = dF_dhc; - Eigen::VectorXd d_steer_d_steer_input = C.block(2 * h_dim_ + steer_index_, 2 * h_dim_ + steer_input_start_index_ + predict_step_, 1, steer_queue_size_ + predict_step_).transpose(); d_steer_d_steer_input.head(steer_queue_size_) += A.block(steer_index_, steer_input_start_index_, 1, steer_queue_size_).transpose(); Eigen::VectorXd d_acc_d_acc_input = C.block(2 * h_dim_ + acc_index_, 2 * h_dim_ + states.size(), 1, acc_queue_size_ + predict_step_).transpose(); @@ -896,16 +764,47 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) state_component_ilqr_.insert(state_component_ilqr_.begin(), "y"); state_component_ilqr_.insert(state_component_ilqr_.begin(), "x"); } - acc_input_weight_target_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["acc_input_weight_target_table"].as>(); - longitudinal_coef_target_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["longitudinal_coef_target_table"].as>(); - longitudinal_coef_by_vel_error_domain_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["longitudinal_coef_by_vel_error_domain_table"].as>(); - longitudinal_coef_by_acc_error_domain_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["longitudinal_coef_by_acc_error_domain_table"].as>(); + controller_acc_input_weight_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_acc_input_weight_target_table"].as>(); + controller_longitudinal_coef_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_longitudinal_coef_target_table"].as>(); + controller_vel_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_vel_error_domain_table"].as>(); + controller_acc_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_acc_error_domain_table"].as>(); + + controller_steer_input_weight_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_steer_input_weight_target_table"].as>(); + controller_lateral_coef_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_lateral_coef_target_table"].as>(); + controller_yaw_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_yaw_error_domain_table"].as>(); + controller_steer_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["controller_steer_error_domain_table"].as>(); + - steer_input_weight_target_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["steer_input_weight_target_table"].as>(); - lateral_coef_target_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["lateral_coef_target_table"].as>(); - lateral_coef_by_lateral_error_domain_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["lateral_coef_by_lateral_error_domain_table"].as>(); - lateral_coef_by_yaw_error_domain_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["lateral_coef_by_yaw_error_domain_table"].as>(); - lateral_coef_by_steer_error_domain_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["lateral_coef_by_steer_error_domain_table"].as>(); + acc_rate_input_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["acc_rate_input_table"].as>(); + acc_rate_cost_coef_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["acc_rate_cost_coef_table"].as>(); + x_coef_by_acc_rate_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["x_coef_by_acc_rate_table"].as>(); + vel_coef_by_acc_rate_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["vel_coef_by_acc_rate_table"].as>(); + acc_coef_by_acc_rate_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["acc_coef_by_acc_rate_table"].as>(); + + + steer_rate_input_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["steer_rate_input_table"].as>(); + steer_rate_cost_coef_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["steer_rate_cost_coef_table"].as>(); + y_coef_by_steer_rate_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["y_coef_by_steer_rate_table"].as>(); + yaw_coef_by_steer_rate_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["yaw_coef_by_steer_rate_table"].as>(); + steer_coef_by_steer_rate_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["steer_coef_by_steer_rate_table"].as>(); + + + vel_for_steer_rate_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["vel_for_steer_rate_table"].as>(); + steer_rate_cost_coef_by_vel_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["steer_rate_cost_coef_by_vel_table"].as>(); + + + x_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["x_error_domain_table"].as>(); + x_error_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["x_error_target_table"].as>(); + y_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["y_error_domain_table"].as>(); + y_error_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["y_error_target_table"].as>(); + vel_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["vel_error_domain_table"].as>(); + vel_error_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["vel_error_target_table"].as>(); + yaw_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["yaw_error_domain_table"].as>(); + yaw_error_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["yaw_error_target_table"].as>(); + acc_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["acc_error_domain_table"].as>(); + acc_error_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["acc_error_target_table"].as>(); + steer_error_domain_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["steer_error_domain_table"].as>(); + steer_error_target_table_ = optimization_param_node["optimization_parameter"]["cost_tables"]["steer_error_target_table"].as>(); } void AdaptorILQR::set_states_cost( double x_cost, double y_cost, double vel_cost, double yaw_cost, double acc_cost, double steer_cost @@ -1012,7 +911,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) bias_lstm_encoder_ih, bias_lstm_encoder_hh, bias_lstm_ih, bias_lstm_hh, bias_complimentary_layer, bias_linear_relu, bias_final_layer, vel_scaling, vel_bias, state_component_predicted); h_dim_ = weight_lstm_hh.cols(); - //num_layers_encoder_ = weight_lstm_encoder_ih.size(); num_state_component_ilqr_ = int(state_component_ilqr_.size()); state_component_ilqr_index_ = std::vector(num_state_component_ilqr_); for (int i = 0; i < int(state_component_ilqr_.size()); i++) { @@ -1093,44 +991,35 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) void AdaptorILQR::unset_projection_matrix_for_compensation(){ trained_dynamics_.unset_projection_matrix_for_compensation(); } - /* - Eigen::MatrixXd AdaptorILQR::Prediction_for_compensation( - Eigen::MatrixXd States, Eigen::MatrixXd Acc_input_history_concat, Eigen::MatrixXd Steer_input_history_concat, Eigen::MatrixXd H_lstm, Eigen::MatrixXd C_lstm - ){ - return trained_dynamics_.Prediction_for_compensation(States, Acc_input_history_concat, Steer_input_history_concat, H_lstm, C_lstm); - } - void AdaptorILQR::update_input_queue_for_compensation(Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat){ - trained_dynamics_.update_input_queue_for_compensation(states, acc_input_history_concat, steer_input_history_concat); - } - void AdaptorILQR::update_regression_matrix_for_compensation(Eigen::VectorXd error_vector){ - trained_dynamics_.update_regression_matrix_for_compensation(error_vector); - } - Eigen::VectorXd AdaptorILQR::prediction_for_compensation( - Eigen::VectorXd states, Eigen::VectorXd acc_input_history_concat, Eigen::VectorXd steer_input_history_concat - ){ - return trained_dynamics_.prediction_for_compensation(states, acc_input_history_concat, steer_input_history_concat); - } - Eigen::MatrixXd AdaptorILQR::Prediction_for_compensation( - Eigen::MatrixXd States, Eigen::MatrixXd Acc_input_history_concat, Eigen::MatrixXd Steer_input_history_concat - ){ - return trained_dynamics_.Prediction_for_compensation(States, Acc_input_history_concat, Steer_input_history_concat); - } - Eigen::VectorXd AdaptorILQR::get_compensation_bias(){ - return trained_dynamics_.get_compensation_bias(); - } - */ void AdaptorILQR::calc_forward_trajectory_with_cost( Eigen::VectorXd states, Eigen::VectorXd acc_input_history, Eigen::VectorXd steer_input_history, std::vector D_inputs_schedule, const Eigen::VectorXd & h_lstm, const Eigen::VectorXd & c_lstm, const Eigen::VectorXd & previous_error, std::vector & states_prediction, const std::vector & states_ref, const std::vector & d_input_ref, - std::vector inputs_ref, double longitudinal_coef, double lateral_coef, double acc_input_weight, double steer_input_weight, + std::vector inputs_ref, + double x_weight_coef, double y_weight_coef, double vel_weight_coef, + double yaw_weight_coef, double acc_weight_coef, double steer_weight_coef, + double acc_input_weight, double steer_input_weight, + double acc_rate_weight_coef, double steer_rate_weight_coef, Eigen::VectorXd & Cost) { int sample_size = D_inputs_schedule[0].cols(); trained_dynamics_.initialize_for_candidates_compensation(sample_size); Cost = Eigen::VectorXd::Zero(sample_size); + Eigen::VectorXd X_Cost = Eigen::VectorXd::Zero(sample_size); + Eigen::VectorXd Y_Cost = Eigen::VectorXd::Zero(sample_size); + Eigen::VectorXd Vel_Cost = Eigen::VectorXd::Zero(sample_size); + Eigen::VectorXd Yaw_Cost = Eigen::VectorXd::Zero(sample_size); + Eigen::VectorXd Acc_Cost = Eigen::VectorXd::Zero(sample_size); + Eigen::VectorXd Steer_Cost = Eigen::VectorXd::Zero(sample_size); + Eigen::RowVectorXd Max_X_Diff = Eigen::RowVectorXd::Zero(sample_size); + Eigen::RowVectorXd Max_Y_Diff = Eigen::RowVectorXd::Zero(sample_size); + Eigen::RowVectorXd Max_Vel_Diff = Eigen::RowVectorXd::Zero(sample_size); + Eigen::RowVectorXd Max_Yaw_Diff = Eigen::RowVectorXd::Zero(sample_size); + Eigen::RowVectorXd Max_Acc_Diff = Eigen::RowVectorXd::Zero(sample_size); + Eigen::RowVectorXd Max_Steer_Diff = Eigen::RowVectorXd::Zero(sample_size); + states_prediction = std::vector(horizon_len_+1); states_prediction[0] = states.replicate(1, sample_size); Eigen::MatrixXd Previous_error = previous_error.replicate(1, sample_size); @@ -1156,66 +1045,80 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) } double yaw_target = states_ref[i][yaw_index_]; Eigen::Matrix2d rotation_matrix = Eigen::Rotation2Dd(-yaw_target).toRotationMatrix(); - Eigen::Matrix2d xy_cost_matrix_sqrt = rotation_matrix.transpose() * Eigen::DiagonalMatrix(std::sqrt(longitudinal_coef * x_cost), std::sqrt(lateral_coef * y_cost)) * rotation_matrix; Eigen::MatrixXd xy_diff = states_prediction[i].block(x_index_, 0, 2, sample_size).colwise() - states_ref[i].segment(x_index_, 2); - for (int j = 0; j < num_state_component_ilqr_; j++) { - if (state_component_ilqr_[j] == "x") { - Cost += 0.5 * (xy_cost_matrix_sqrt*xy_diff).colwise().squaredNorm(); - } else if (state_component_ilqr_[j] == "vel") { - Cost += 0.5 * longitudinal_coef * vel_cost * (states_prediction[i].row(vel_index_).array() - states_ref[i][vel_index_]).square().matrix(); - } else if (state_component_ilqr_[j] == "yaw") { - Cost += 0.5 * lateral_coef * yaw_cost * (states_prediction[i].row(yaw_index_).array() - states_ref[i][yaw_index_]).square().matrix(); - } else if (state_component_ilqr_[j] == "acc") { - Cost += 0.5 * longitudinal_coef * acc_cost * (states_prediction[i].row(acc_index_).array() - states_ref[i][acc_index_]).square().matrix(); - } else if (state_component_ilqr_[j] == "steer") { - Cost += 0.5 * lateral_coef * steer_cost * (states_prediction[i].row(steer_index_).array() - states_ref[i][steer_index_]).square().matrix(); - } - } - - //Cost += 0.5 * acc_cost * (states_prediction[i].row(acc_index_).array() - states_ref[i][acc_index_]).square().matrix(); - - //Cost += 0.5 * steer_cost * (states_prediction[i].row(steer_index_).array() - states_ref[i][steer_index_]).square().matrix(); + xy_diff = rotation_matrix * xy_diff; + Max_X_Diff = xy_diff.row(0).cwiseAbs().cwiseMax(Max_X_Diff); + Max_Y_Diff = xy_diff.row(1).cwiseAbs().cwiseMax(Max_Y_Diff); + X_Cost += 0.5 * x_cost * xy_diff.row(0).array().square().matrix().transpose(); + Y_Cost += 0.5 * y_cost * xy_diff.row(1).array().square().matrix().transpose(); + Max_Vel_Diff = (states_prediction[i].row(vel_index_).array() - states_ref[i][vel_index_]).cwiseAbs().matrix().cwiseMax(Max_Vel_Diff); + Vel_Cost += 0.5 * vel_cost * (states_prediction[i].row(vel_index_).array() - states_ref[i][vel_index_]).square().matrix().transpose(); + Max_Yaw_Diff = (states_prediction[i].row(yaw_index_).array() - states_ref[i][yaw_index_]).cwiseAbs().matrix().cwiseMax(Max_Yaw_Diff); + Yaw_Cost += 0.5 * yaw_cost * (states_prediction[i].row(yaw_index_).array() - states_ref[i][yaw_index_]).square().matrix().transpose(); + Max_Acc_Diff = (states_prediction[i].row(acc_index_).array() - states_ref[i][acc_index_]).cwiseAbs().matrix().cwiseMax(Max_Acc_Diff); + Acc_Cost += 0.5 * acc_cost * (states_prediction[i].row(acc_index_).array() - states_ref[i][acc_index_]).square().matrix().transpose(); + Max_Steer_Diff = (states_prediction[i].row(steer_index_).array() - states_ref[i][steer_index_]).cwiseAbs().matrix().cwiseMax(Max_Steer_Diff); + Steer_Cost += 0.5 * steer_cost * (states_prediction[i].row(steer_index_).array() - states_ref[i][steer_index_]).square().matrix().transpose(); - Cost += 0.5 * acc_rate_cost_ * (D_inputs.row(0).array() - d_input_ref[i][0]).square().matrix(); + Cost += 0.5 * acc_rate_weight_coef * acc_rate_cost_ * (D_inputs.row(0).array() - d_input_ref[i][0]).square().matrix().transpose(); - Cost += 0.5 * steer_rate_cost_ * (D_inputs.row(1).array() - d_input_ref[i][1]).square().matrix(); + Cost += 0.5 * steer_rate_weight_coef * steer_rate_cost_ * (D_inputs.row(1).array() - d_input_ref[i][1]).square().matrix().transpose(); if (i == 0) { double prev_acc_rate = (acc_input_history[acc_queue_size_ - 1] - acc_input_history[acc_queue_size_ - 2])/control_dt_; double prev_steer_rate = (steer_input_history[steer_queue_size_ - 1] - steer_input_history[steer_queue_size_ - 2])/control_dt_; - Cost += 0.5 * acc_rate_rate_cost_ * (D_inputs.row(0).array() - prev_acc_rate).square().matrix(); - Cost += 0.5 * steer_rate_rate_cost_ * (D_inputs.row(1).array() - prev_steer_rate).square().matrix(); + Cost += 0.5 * acc_rate_rate_cost_ * (D_inputs.row(0).array() - prev_acc_rate).square().matrix().transpose(); + Cost += 0.5 * steer_rate_rate_cost_ * (D_inputs.row(1).array() - prev_steer_rate).square().matrix().transpose(); } if (i > 0){ - Cost += 0.5 * acc_rate_rate_cost_ * (D_inputs.row(0).array() - D_inputs_schedule[i-1].row(0).array()).square().matrix(); - Cost += 0.5 * steer_rate_rate_cost_ * (D_inputs.row(1).array() - D_inputs_schedule[i-1].row(1).array()).square().matrix(); - Cost += 0.5 * acc_input_weight * (Acc_input_history.row(acc_queue_size_ - predict_step_).array() - inputs_ref[i - 1][0]).square().matrix(); - Cost += 0.5 * steer_input_weight * (Steer_input_history.row(steer_queue_size_ - predict_step_).array() - inputs_ref[i - 1][1]).square().matrix(); + Cost += 0.5 * acc_rate_rate_cost_ * (D_inputs.row(0).array() - D_inputs_schedule[i-1].row(0).array()).square().matrix().transpose(); + Cost += 0.5 * steer_rate_rate_cost_ * (D_inputs.row(1).array() - D_inputs_schedule[i-1].row(1).array()).square().matrix().transpose(); + Cost += 0.5 * acc_input_weight * (Acc_input_history.row(acc_queue_size_ - predict_step_).array() - inputs_ref[i - 1][0]).square().matrix().transpose(); + Cost += 0.5 * steer_input_weight * (Steer_input_history.row(steer_queue_size_ - predict_step_).array() - inputs_ref[i - 1][1]).square().matrix().transpose(); } states_prediction[i + 1] = trained_dynamics_.F_with_model_for_candidates( states_prediction[i], Acc_input_history, Steer_input_history, D_inputs, H_lstm, C_lstm, Previous_error, i); } + for (int i = 0; i < sample_size; i++) { + for (int j = 0; j < num_state_component_ilqr_; j++) { + if (state_component_ilqr_[j] == "x") { + Cost[i] += std::min(x_weight_coef, calc_table_value(Max_X_Diff[i], x_error_domain_table_, x_error_target_table_)) * X_Cost[i]; + } + else if (state_component_ilqr_[j] == "y") { + Cost[i] += std::min(y_weight_coef, calc_table_value(Max_Y_Diff[i], y_error_domain_table_, y_error_target_table_)) * Y_Cost[i]; + } + else if (state_component_ilqr_[j] == "vel") { + Cost[i] += std::min(vel_weight_coef, calc_table_value(Max_Vel_Diff[i], vel_error_domain_table_, vel_error_target_table_)) * Vel_Cost[i]; + } + else if (state_component_ilqr_[j] == "yaw") { + Cost[i] += std::min(yaw_weight_coef, calc_table_value(Max_Yaw_Diff[i], yaw_error_domain_table_, yaw_error_target_table_)) * Yaw_Cost[i]; + } + else if (state_component_ilqr_[j] == "acc") { + Cost[i] += std::min(acc_weight_coef, calc_table_value(Max_Acc_Diff[i], acc_error_domain_table_, acc_error_target_table_)) * Acc_Cost[i]; + } + else if (state_component_ilqr_[j] == "steer") { + Cost[i] += std::min(steer_weight_coef, calc_table_value(Max_Steer_Diff[i], steer_error_domain_table_, steer_error_target_table_)) * Steer_Cost[i]; + } + } + } + double yaw_target = states_ref[horizon_len_][yaw_index_]; Eigen::Matrix2d rotation_matrix = Eigen::Rotation2Dd(-yaw_target).toRotationMatrix(); - Eigen::Matrix2d xy_cost_matrix_sqrt = rotation_matrix.transpose() * Eigen::DiagonalMatrix(std::sqrt(longitudinal_coef * x_terminal_cost_), std::sqrt(lateral_coef * y_terminal_cost_)) * rotation_matrix; + Eigen::Matrix2d xy_cost_matrix_sqrt = rotation_matrix.transpose() * Eigen::DiagonalMatrix(std::sqrt(x_weight_coef * x_terminal_cost_), std::sqrt(y_weight_coef * y_terminal_cost_)) * rotation_matrix; Eigen::MatrixXd xy_diff = states_prediction[horizon_len_].block(x_index_, 0, 2, sample_size).colwise() - states_ref[horizon_len_].segment(x_index_, 2); for (int j = 0; j < num_state_component_ilqr_; j++) { if (state_component_ilqr_[j] == "x") { - Cost += 0.5 * (xy_cost_matrix_sqrt*xy_diff).colwise().squaredNorm(); + Cost += 0.5 * (xy_cost_matrix_sqrt*xy_diff).colwise().squaredNorm().transpose(); } else if (state_component_ilqr_[j] == "vel") { - Cost += 0.5 * longitudinal_coef * vel_terminal_cost_ * (states_prediction[horizon_len_].row(vel_index_).array() - states_ref[horizon_len_][vel_index_]).square().matrix(); + Cost += 0.5 * vel_weight_coef * vel_terminal_cost_ * (states_prediction[horizon_len_].row(vel_index_).array() - states_ref[horizon_len_][vel_index_]).square().matrix().transpose(); } else if (state_component_ilqr_[j] == "yaw") { - Cost += 0.5 *lateral_coef * yaw_terminal_cost_ * (states_prediction[horizon_len_].row(yaw_index_).array() - states_ref[horizon_len_][yaw_index_]).square().matrix(); + Cost += 0.5 * yaw_weight_coef * yaw_terminal_cost_ * (states_prediction[horizon_len_].row(yaw_index_).array() - states_ref[horizon_len_][yaw_index_]).square().matrix().transpose(); } else if (state_component_ilqr_[j] == "acc") { - Cost += 0.5 * longitudinal_coef * acc_terminal_cost_ * (states_prediction[horizon_len_].row(acc_index_).array() - states_ref[horizon_len_][acc_index_]).square().matrix(); + Cost += 0.5 * acc_weight_coef * acc_terminal_cost_ * (states_prediction[horizon_len_].row(acc_index_).array() - states_ref[horizon_len_][acc_index_]).square().matrix().transpose(); } else if (state_component_ilqr_[j] == "steer") { - Cost += 0.5 * lateral_coef * steer_terminal_cost_ * (states_prediction[horizon_len_].row(steer_index_).array() - states_ref[horizon_len_][steer_index_]).square().matrix(); + Cost += 0.5 * steer_weight_coef * steer_terminal_cost_ * (states_prediction[horizon_len_].row(steer_index_).array() - states_ref[horizon_len_][steer_index_]).square().matrix().transpose(); } } - - //Cost += 0.5 * acc_terminal_cost_ * (states_prediction[horizon_len_].row(acc_index_).array() - states_ref[horizon_len_][acc_index_]).square().matrix(); - - //Cost += 0.5 * steer_terminal_cost_ * (states_prediction[horizon_len_].row(steer_index_).array() - states_ref[horizon_len_][steer_index_]).square().matrix(); } void AdaptorILQR::calc_inputs_ref_info( const Eigen::VectorXd & states, const Eigen::VectorXd & acc_input_history, @@ -1223,8 +1126,16 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) const Eigen::VectorXd & c_lstm, const Eigen::VectorXd & previous_error, const std::vector & states_ref, const Eigen::VectorXd & acc_controller_input_schedule, const Eigen::VectorXd & steer_controller_input_schedule, - std::vector & inputs_ref, double & longitudinal_coef, double & lateral_coef, double & acc_input_weight, double & steer_input_weight) - { + const std::vector & states_prediction, + std::vector & inputs_ref, + const double & acc_input_change_rate, const double & steer_input_change_rate, + double & x_weight_coef, double & y_weight_coef, double & vel_weight_coef, + double & yaw_weight_coef, double & acc_weight_coef, double & steer_weight_coef, + double & acc_input_weight, double & steer_input_weight, + double & acc_rate_weight_coef, double & steer_rate_weight_coef, + double & initial_prediction_x_weight_coef, double & initial_prediction_y_weight_coef, double & initial_prediction_vel_weight_coef, + double & initial_prediction_yaw_weight_coef, double & initial_prediction_acc_weight_coef, double & initial_prediction_steer_weight_coef) + {// edit here to change the adaptive cost trained_dynamics_.load_state_queue_for_compensation(); Eigen::VectorXd acc_input_history_concat(acc_queue_size_ + predict_step_); Eigen::VectorXd steer_input_history_concat(steer_queue_size_ + predict_step_); @@ -1239,10 +1150,15 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) Eigen::VectorXd c_lstm_tmp = c_lstm; inputs_ref = std::vector(horizon_len_); double max_vel_error = 0.0; - double max_lateral_error = 0.0; double max_acc_error = 0.0; double max_steer_error = 0.0; double max_yaw_error = 0.0; + double initial_prediction_max_x_error = 0.0; + double initial_prediction_max_y_error = 0.0; + double initial_prediction_max_vel_error = 0.0; + double initial_prediction_max_acc_error = 0.0; + double initial_prediction_max_steer_error = 0.0; + double initial_prediction_max_yaw_error = 0.0; for (int i = 0; i< horizon_len_; i++) { if (i > 0){ acc_input_history_concat.head(acc_queue_size_) = acc_input_history_concat.tail(acc_queue_size_); @@ -1258,16 +1174,10 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) double vel_error = std::abs(states_ref[i+1][vel_index_] - states_prediction_by_controller_inputs[i + 1][vel_index_]); double acc_error = std::abs(states_ref[i+1][acc_index_] - states_prediction_by_controller_inputs[i + 1][acc_index_]); double steer_error = std::abs(states_ref[i+1][steer_index_] - states_prediction_by_controller_inputs[i + 1][steer_index_]); - double yaw = states_ref[i+1][yaw_index_]; - double lateral_error = std::abs(- std::sin(yaw) * (states_ref[i+1][x_index_] - states_prediction_by_controller_inputs[i + 1][x_index_]) - + std::cos(yaw) * (states_ref[i+1][y_index_] - states_prediction_by_controller_inputs[i + 1][y_index_])); double yaw_error = std::abs(states_ref[i+1][yaw_index_] - states_prediction_by_controller_inputs[i + 1][yaw_index_]); if (vel_error > max_vel_error) { max_vel_error = vel_error; } - if (lateral_error > max_lateral_error) { - max_lateral_error = lateral_error; - } if (acc_error > max_acc_error) { max_acc_error = acc_error; } @@ -1277,25 +1187,78 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) if (yaw_error > max_yaw_error) { max_yaw_error = yaw_error; } - } - longitudinal_coef = std::max( - calc_table_value(max_vel_error, longitudinal_coef_by_vel_error_domain_table_, longitudinal_coef_target_table_), - calc_table_value(max_acc_error, longitudinal_coef_by_acc_error_domain_table_, longitudinal_coef_target_table_)); - lateral_coef = std::max( - { - calc_table_value(max_lateral_error, lateral_coef_by_lateral_error_domain_table_, lateral_coef_target_table_), - calc_table_value(max_steer_error, lateral_coef_by_steer_error_domain_table_, lateral_coef_target_table_), - calc_table_value(max_yaw_error, lateral_coef_by_yaw_error_domain_table_, lateral_coef_target_table_) - }); + + double yaw_target = states_ref[i][yaw_index_]; + Eigen::Matrix2d rotation_matrix = Eigen::Rotation2Dd(-yaw_target).toRotationMatrix(); + Eigen::Vector2d xy_diff = rotation_matrix * Eigen::Vector2d(states_prediction[i][x_index_] - states_ref[i][x_index_], states_prediction[i][y_index_] - states_ref[i][y_index_]); + double initial_prediction_x_error = std::abs(xy_diff[0]); + double initial_prediction_y_error = std::abs(xy_diff[1]); + double initial_prediction_vel_error = std::abs(states_prediction[i][vel_index_] - states_ref[i][vel_index_]); + double initial_prediction_yaw_error = std::abs(states_prediction[i][yaw_index_] - states_ref[i][yaw_index_]); + double initial_prediction_acc_error = std::abs(states_prediction[i][acc_index_] - states_ref[i][acc_index_]); + double initial_prediction_steer_error = std::abs(states_prediction[i][steer_index_] - states_ref[i][steer_index_]); + if (initial_prediction_x_error > initial_prediction_max_x_error) { + initial_prediction_max_x_error = initial_prediction_x_error; + } + if (initial_prediction_y_error > initial_prediction_max_y_error) { + initial_prediction_max_y_error = initial_prediction_y_error; + } + if (initial_prediction_vel_error > initial_prediction_max_vel_error) { + initial_prediction_max_vel_error = initial_prediction_vel_error; + } + if (initial_prediction_yaw_error > initial_prediction_max_yaw_error) { + initial_prediction_max_yaw_error = initial_prediction_yaw_error; + } + if (initial_prediction_acc_error > initial_prediction_max_acc_error) { + initial_prediction_max_acc_error = initial_prediction_acc_error; + } + if (initial_prediction_steer_error > initial_prediction_max_steer_error) { + initial_prediction_max_steer_error = initial_prediction_steer_error; + } + } + double longitudinal_coef = std::max( + calc_table_value(max_vel_error, controller_vel_error_domain_table_, controller_longitudinal_coef_target_table_), + calc_table_value(max_acc_error, controller_acc_error_domain_table_, controller_longitudinal_coef_target_table_)); + double lateral_coef = std::max( + calc_table_value(max_steer_error, controller_steer_error_domain_table_, controller_lateral_coef_target_table_), + calc_table_value(max_yaw_error, controller_yaw_error_domain_table_, controller_lateral_coef_target_table_)); + x_weight_coef = std::min( + longitudinal_coef, + calc_table_value(acc_input_change_rate, acc_rate_input_table_, x_coef_by_acc_rate_table_)); + y_weight_coef = std::min( + lateral_coef, + calc_table_value(steer_input_change_rate, steer_rate_input_table_, y_coef_by_steer_rate_table_)); + vel_weight_coef = std::min( + longitudinal_coef, + calc_table_value(acc_input_change_rate, acc_rate_input_table_, vel_coef_by_acc_rate_table_)); + yaw_weight_coef = std::min( + lateral_coef, + calc_table_value(steer_input_change_rate, steer_rate_input_table_, yaw_coef_by_steer_rate_table_)); + acc_weight_coef = std::min( + longitudinal_coef, + calc_table_value(acc_input_change_rate, acc_rate_input_table_, acc_coef_by_acc_rate_table_)); + steer_weight_coef = std::min( + lateral_coef, + calc_table_value(steer_input_change_rate, steer_rate_input_table_, steer_coef_by_steer_rate_table_)); + acc_input_weight = std::min( - calc_table_value(max_vel_error, longitudinal_coef_by_vel_error_domain_table_, acc_input_weight_target_table_), - calc_table_value(max_acc_error, longitudinal_coef_by_acc_error_domain_table_, acc_input_weight_target_table_)); + calc_table_value(max_vel_error, controller_vel_error_domain_table_, controller_acc_input_weight_target_table_), + calc_table_value(max_acc_error, controller_acc_error_domain_table_, controller_acc_input_weight_target_table_)); steer_input_weight = std::min( - { - calc_table_value(max_lateral_error, lateral_coef_by_lateral_error_domain_table_, steer_input_weight_target_table_), - calc_table_value(max_steer_error, lateral_coef_by_steer_error_domain_table_, steer_input_weight_target_table_), - calc_table_value(max_yaw_error, lateral_coef_by_yaw_error_domain_table_, steer_input_weight_target_table_) - }); + calc_table_value(max_steer_error, controller_steer_error_domain_table_, controller_steer_input_weight_target_table_), + calc_table_value(max_yaw_error, controller_yaw_error_domain_table_, controller_steer_input_weight_target_table_)); + acc_rate_weight_coef = calc_table_value(acc_input_change_rate, acc_rate_input_table_, acc_rate_cost_coef_table_)*acc_rate_cost_; + steer_rate_weight_coef = std::max( + calc_table_value(states_ref[0][vel_index_], vel_for_steer_rate_table_, steer_rate_cost_coef_by_vel_table_), + calc_table_value(steer_input_change_rate, steer_rate_input_table_, steer_rate_cost_coef_table_))*steer_rate_cost_; + + + initial_prediction_x_weight_coef = calc_table_value(initial_prediction_max_x_error, x_error_domain_table_, x_error_target_table_); + initial_prediction_y_weight_coef = calc_table_value(initial_prediction_max_y_error, y_error_domain_table_, y_error_target_table_); + initial_prediction_vel_weight_coef = calc_table_value(initial_prediction_max_vel_error, vel_error_domain_table_, vel_error_target_table_); + initial_prediction_yaw_weight_coef = calc_table_value(initial_prediction_max_yaw_error, yaw_error_domain_table_, yaw_error_target_table_); + initial_prediction_acc_weight_coef = calc_table_value(initial_prediction_max_acc_error, acc_error_domain_table_, acc_error_target_table_); + initial_prediction_steer_weight_coef = calc_table_value(initial_prediction_max_steer_error, steer_error_domain_table_, steer_error_target_table_); } Eigen::MatrixXd AdaptorILQR::extract_dF_d_state(Eigen::MatrixXd dF_d_state_with_history) { @@ -1310,30 +1273,15 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) result(2*h_dim_ + i, 2*h_dim_ + j) = dF_d_state_with_history(2*h_dim_ + state_component_ilqr_index_[i], 2*h_dim_ + state_component_ilqr_index_[j]); } } - //result.block(2*h_dim_,0,1,2*h_dim_) = dF_d_state_with_history.block(2*h_dim_+2,0,1,2*h_dim_); - //result.block(2*h_dim_+1,0,dF_d_state_with_history.rows()-2*h_dim_ -3 -1,2*h_dim_) = dF_d_state_with_history.block(2*h_dim_+3+1,0,dF_d_state_with_history.rows()-2*h_dim_ -3 -1,2*h_dim_); - - //result.block(0,2*h_dim_,2*h_dim_,1) = dF_d_state_with_history.block(0,2*h_dim_+2,2*h_dim_,1); - //result.block(2*h_dim_,2*h_dim_,1,1) = dF_d_state_with_history.block(2*h_dim_+2,2*h_dim_+2,1,1); - //result.block(2*h_dim_+1,2*h_dim_,dF_d_state_with_history.rows()-2*h_dim_ -3 -1,1) = dF_d_state_with_history.block(2*h_dim_+3+1,2*h_dim_+2,dF_d_state_with_history.rows()-2*h_dim_ -3 -1,1); - - //result.block(0,2*h_dim_+1,2*h_dim_,dF_d_state_with_history.cols()-2*h_dim_-3-1) = dF_d_state_with_history.block(0,2*h_dim_+3+1,2*h_dim_,dF_d_state_with_history.cols()-2*h_dim_-3-1); - //result.block(2*h_dim_,2*h_dim_+1,1,dF_d_state_with_history.cols()-2*h_dim_-3-1) = dF_d_state_with_history.block(2*h_dim_+2,2*h_dim_+3+1,1,dF_d_state_with_history.cols()-2*h_dim_-3-1); - //result.block(2*h_dim_+1,2*h_dim_+1,dF_d_state_with_history.rows()-2*h_dim_ -3 -1,dF_d_state_with_history.cols()-2*h_dim_-3-1) = dF_d_state_with_history.block(2*h_dim_+3+1,2*h_dim_+3+1,dF_d_state_with_history.rows()-2*h_dim_ -3 -1,dF_d_state_with_history.cols()-2*h_dim_-3-1); - return result; } Eigen::MatrixXd AdaptorILQR::extract_dF_d_input(Eigen::MatrixXd dF_d_input) { Eigen::MatrixXd result = Eigen::MatrixXd::Zero(2*h_dim_ + num_state_component_ilqr_, 2); - //(dF_d_input.rows() -3, 2); result.block(0,0,2*h_dim_,2) = dF_d_input.block(0,0,2*h_dim_,2); for (int i = 0; i < num_state_component_ilqr_; i++) { result.block(2*h_dim_ + i, 0, 1, 2) = dF_d_input.block(2*h_dim_ + state_component_ilqr_index_[i], 0, 1, 2); } - - //result.block(2*h_dim_,0,1,2) = dF_d_input.block(2*h_dim_+2,0,1,2); - //result.block(2*h_dim_+1,0,dF_d_input.rows()-2*h_dim_ -3 -1,2) = dF_d_input.block(2*h_dim_+3+1,0,dF_d_input.rows()-2*h_dim_ -3 -1,2); return result; } @@ -1386,8 +1334,13 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) const std::vector & states_prediction, const std::vector & d_inputs_schedule, const std::vector & states_ref, const std::vector & d_input_ref, const double prev_acc_rate, const double prev_steer_rate, - std::vector inputs_ref, double longitudinal_coef, double lateral_coef, double acc_input_weight, double steer_input_weight, - const std::vector & inputs_schedule, std::vector & K, std::vector & k) + std::vector inputs_ref, + double x_weight_coef, double y_weight_coef, double vel_weight_coef, + double yaw_weight_coef, double acc_weight_coef, double steer_weight_coef, + double acc_input_weight, double steer_input_weight, + double acc_rate_weight_coef, double steer_rate_weight_coef, + const std::vector & inputs_schedule, + std::vector & K, std::vector & k) { K = std::vector(horizon_len_); k = std::vector(horizon_len_); @@ -1395,24 +1348,24 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) Eigen::VectorXd w = Eigen::VectorXd::Zero(num_state_component_ilqr_ + 2*h_dim_ + acc_queue_size_ + steer_queue_size_); double yaw_target = states_ref[horizon_len_][yaw_index_]; Eigen::Matrix2d rotation_matrix = Eigen::Rotation2Dd(-yaw_target).toRotationMatrix(); - Eigen::Matrix2d xy_cost_matrix = rotation_matrix.transpose() * Eigen::DiagonalMatrix(Eigen::Vector2d(longitudinal_coef * x_terminal_cost_, lateral_coef * y_terminal_cost_)) * rotation_matrix; + Eigen::Matrix2d xy_cost_matrix = rotation_matrix.transpose() * Eigen::DiagonalMatrix(Eigen::Vector2d(x_weight_coef * x_terminal_cost_, y_weight_coef * y_terminal_cost_)) * rotation_matrix; Eigen::Vector2d xy_diff = Eigen::Vector2d(states_prediction[horizon_len_][x_index_] - states_ref[horizon_len_][x_index_], states_prediction[horizon_len_][y_index_] - states_ref[horizon_len_][y_index_]); for (int j = 0; j < num_state_component_ilqr_; j++) { if (state_component_ilqr_[j] == "x") { P.block(2*h_dim_ + j, 2*h_dim_ + j, 2, 2) = xy_cost_matrix; w.segment(2*h_dim_+j,2) = xy_cost_matrix * xy_diff; } else if (state_component_ilqr_[j] == "vel") { - P(2*h_dim_ + j, 2*h_dim_ + j) = longitudinal_coef * vel_terminal_cost_; - w(2*h_dim_ + j) = longitudinal_coef * vel_terminal_cost_ * (states_prediction[horizon_len_][vel_index_] - states_ref[horizon_len_][vel_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) = vel_weight_coef * vel_terminal_cost_; + w(2*h_dim_ + j) = vel_weight_coef * vel_terminal_cost_ * (states_prediction[horizon_len_][vel_index_] - states_ref[horizon_len_][vel_index_]); } else if (state_component_ilqr_[j] == "yaw") { - P(2*h_dim_ + j, 2*h_dim_ + j) = lateral_coef * yaw_terminal_cost_; - w(2*h_dim_ + j) = lateral_coef * yaw_terminal_cost_ * (states_prediction[horizon_len_][yaw_index_] - states_ref[horizon_len_][yaw_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) = yaw_weight_coef * yaw_terminal_cost_; + w(2*h_dim_ + j) = yaw_weight_coef * yaw_terminal_cost_ * (states_prediction[horizon_len_][yaw_index_] - states_ref[horizon_len_][yaw_index_]); } else if (state_component_ilqr_[j] == "acc") { - P(2*h_dim_ + j, 2*h_dim_ + j) = longitudinal_coef * acc_terminal_cost_; - w(2*h_dim_ + j) = longitudinal_coef * acc_terminal_cost_ * (states_prediction[horizon_len_][acc_index_] - states_ref[horizon_len_][acc_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) = acc_weight_coef * acc_terminal_cost_; + w(2*h_dim_ + j) = acc_weight_coef * acc_terminal_cost_ * (states_prediction[horizon_len_][acc_index_] - states_ref[horizon_len_][acc_index_]); } else if (state_component_ilqr_[j] == "steer") { - P(2*h_dim_ + j, 2*h_dim_ + j) = lateral_coef * steer_terminal_cost_; - w(2*h_dim_ + j) = lateral_coef * steer_terminal_cost_ * (states_prediction[horizon_len_][steer_index_] - states_ref[horizon_len_][steer_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) = steer_weight_coef * steer_terminal_cost_; + w(2*h_dim_ + j) = steer_weight_coef * steer_terminal_cost_ * (states_prediction[horizon_len_][steer_index_] - states_ref[horizon_len_][steer_index_]); } } @@ -1421,11 +1374,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) w(2*h_dim_ + num_state_component_ilqr_ + acc_queue_size_ - predict_step_) = acc_input_weight * (inputs_schedule[horizon_len_-1][0] - inputs_ref[horizon_len_-1][0]); w(2*h_dim_ + num_state_component_ilqr_ + acc_queue_size_ + steer_queue_size_ - predict_step_) = steer_input_weight * (inputs_schedule[horizon_len_-1][1] - inputs_ref[horizon_len_-1][1]); - //P(2*h_dim_ + 1, 2*h_dim_ + 1) = acc_terminal_cost_; - //P(2*h_dim_ + 2, 2*h_dim_ + 2) = steer_terminal_cost_; - //w(2*h_dim_ + 1) = acc_terminal_cost_ * (states_prediction[horizon_len_][acc_index_] - states_ref[horizon_len_][acc_index_]); - //w(2*h_dim_ + 2) = steer_terminal_cost_ * (states_prediction[horizon_len_][steer_index_] - states_ref[horizon_len_][steer_index_]); - for (int i = horizon_len_ - 1; i >= 0; i--) { Eigen::MatrixXd Pi1Ai = right_action_by_state_diff_with_history(P, extract_dF_d_state(dF_d_states[i])); @@ -1433,8 +1381,8 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) Eigen::MatrixXd H = left_action_by_input_diff(extract_dF_d_input(dF_d_inputs[i]), right_action_by_input_diff(P, extract_dF_d_input(dF_d_inputs[i]))); - H(0,0) += acc_rate_cost_; - H(1,1) += steer_rate_cost_; + H(0,0) += acc_rate_weight_coef * acc_rate_cost_; + H(1,1) += steer_rate_weight_coef * steer_rate_cost_; if (i == 0) { H(0,0) += acc_rate_rate_cost_; H(1,1) += steer_rate_rate_cost_; @@ -1442,8 +1390,8 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) Eigen::MatrixXd invH = H.inverse(); Eigen::VectorXd g_ = left_action_by_input_diff(extract_dF_d_input(dF_d_inputs[i]), w); - g_(0) += acc_rate_cost_ * (d_inputs_schedule[i][0] - d_input_ref[i][0]); - g_(1) += steer_rate_cost_ * (d_inputs_schedule[i][1] - d_input_ref[i][1]); + g_(0) += acc_rate_weight_coef * acc_rate_cost_ * (d_inputs_schedule[i][0] - d_input_ref[i][0]); + g_(1) += steer_rate_weight_coef * steer_rate_cost_ * (d_inputs_schedule[i][1] - d_input_ref[i][1]); if (i == 0) { g_(0) += acc_rate_rate_cost_ * (d_inputs_schedule[i][0] - prev_acc_rate); g_(1) += steer_rate_rate_cost_ * (d_inputs_schedule[i][1] - prev_steer_rate); @@ -1470,24 +1418,24 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) w -= G.transpose() * k[i]; yaw_target = states_ref[i][yaw_index_]; rotation_matrix = Eigen::Rotation2Dd(-yaw_target).toRotationMatrix(); - xy_cost_matrix = rotation_matrix.transpose() * Eigen::DiagonalMatrix(Eigen::Vector2d(longitudinal_coef * x_cost, lateral_coef * y_cost)) * rotation_matrix; + xy_cost_matrix = rotation_matrix.transpose() * Eigen::DiagonalMatrix(Eigen::Vector2d(x_weight_coef * x_cost, y_weight_coef * y_cost)) * rotation_matrix; xy_diff = Eigen::Vector2d(states_prediction[i][x_index_] - states_ref[i][x_index_], states_prediction[i][y_index_] - states_ref[i][y_index_]); for (int j = 0; j < num_state_component_ilqr_; j++){ if (state_component_ilqr_[j] == "x") { P.block(2*h_dim_ + j, 2*h_dim_ + j, 2, 2) += xy_cost_matrix; w.segment(2*h_dim_ + j, 2) += xy_cost_matrix * xy_diff; } else if (state_component_ilqr_[j] == "vel") { - P(2*h_dim_ + j, 2*h_dim_ + j) += longitudinal_coef * vel_cost; - w(2*h_dim_ + j) += longitudinal_coef * vel_cost * (states_prediction[i][vel_index_] - states_ref[i][vel_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) += vel_weight_coef * vel_cost; + w(2*h_dim_ + j) += vel_weight_coef * vel_cost * (states_prediction[i][vel_index_] - states_ref[i][vel_index_]); } else if (state_component_ilqr_[j] == "yaw") { - P(2*h_dim_ + j, 2*h_dim_ + j) += lateral_coef * yaw_cost; - w(2*h_dim_ + j) += lateral_coef * yaw_cost * (states_prediction[i][yaw_index_] - states_ref[i][yaw_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) += yaw_weight_coef * yaw_cost; + w(2*h_dim_ + j) += yaw_weight_coef * yaw_cost * (states_prediction[i][yaw_index_] - states_ref[i][yaw_index_]); } else if (state_component_ilqr_[j] == "acc") { - P(2*h_dim_ + j, 2*h_dim_ + j) += longitudinal_coef * acc_cost; - w(2*h_dim_ + j) += longitudinal_coef * acc_cost * (states_prediction[i][acc_index_] - states_ref[i][acc_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) += acc_weight_coef * acc_cost; + w(2*h_dim_ + j) += acc_weight_coef * acc_cost * (states_prediction[i][acc_index_] - states_ref[i][acc_index_]); } else if (state_component_ilqr_[j] == "steer") { - P(2*h_dim_ + j, 2*h_dim_ + j) += lateral_coef * steer_cost; - w(2*h_dim_ + j) += lateral_coef * steer_cost * (states_prediction[i][steer_index_] - states_ref[i][steer_index_]); + P(2*h_dim_ + j, 2*h_dim_ + j) += steer_weight_coef * steer_cost; + w(2*h_dim_ + j) += steer_weight_coef * steer_cost * (states_prediction[i][steer_index_] - states_ref[i][steer_index_]); } } if (i>0){ @@ -1496,10 +1444,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) w(2*h_dim_ + num_state_component_ilqr_ + acc_queue_size_ - predict_step_) += acc_input_weight * (inputs_schedule[i-1][0] - inputs_ref[i-1][0]); w(2*h_dim_ + num_state_component_ilqr_ + acc_queue_size_ + steer_queue_size_ - predict_step_) += steer_input_weight * (inputs_schedule[i-1][1] - inputs_ref[i-1][1]); } - //P(2*h_dim_ + 1, 2*h_dim_ + 1) += acc_cost; - //P(2*h_dim_ + 2, 2*h_dim_ + 2) += steer_cost; - //w(2*h_dim_ + 1) += acc_cost * (states_prediction[i][acc_index_] - states_ref[i][acc_index_]); - //w(2*h_dim_ + 2) += steer_cost * (states_prediction[i][steer_index_] - states_ref[i][steer_index_]); } } std::vector AdaptorILQR::calc_line_search_candidates(std::vector K, std::vector k, std::vector dF_d_states, std::vector dF_d_inputs, std::vector d_inputs_schedule, Eigen::VectorXd ls_points) @@ -1532,34 +1476,63 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) Eigen::VectorXd steer_input_history, std::vector & d_inputs_schedule, const Eigen::VectorXd & h_lstm, const Eigen::VectorXd & c_lstm, const std::vector & states_ref, - const std::vector & d_input_ref, Eigen::VectorXd & previous_error, + const std::vector & d_input_ref, + double acc_input_change_rate, double steer_input_change_rate, + Eigen::VectorXd & previous_error, std::vector & states_prediction, const Eigen::VectorXd & acc_controller_input_schedule, const Eigen::VectorXd & steer_controller_input_schedule, double & acc_input, double & steer_input) { - std::vector inputs_ref; - double longitudinal_coef, lateral_coef, acc_input_weight, steer_input_weight; - calc_inputs_ref_info( - states, acc_input_history, steer_input_history, h_lstm, c_lstm, previous_error, states_ref, - acc_controller_input_schedule, steer_controller_input_schedule, - inputs_ref, longitudinal_coef, lateral_coef, acc_input_weight, steer_input_weight); std::vector dF_d_states, dF_d_inputs; std::vector inputs_schedule; trained_dynamics_.calc_forward_trajectory_with_diff(states, acc_input_history, steer_input_history, d_inputs_schedule, h_lstm, c_lstm, previous_error, states_prediction, dF_d_states, dF_d_inputs, inputs_schedule); + + std::vector inputs_ref; + double x_weight_coef, y_weight_coef, vel_weight_coef, yaw_weight_coef, acc_weight_coef, steer_weight_coef; + double acc_input_weight, steer_input_weight; + double acc_rate_weight_coef, steer_rate_weight_coef; + double initial_prediction_x_weight_coef, initial_prediction_y_weight_coef, initial_prediction_vel_weight_coef, + initial_prediction_yaw_weight_coef, initial_prediction_acc_weight_coef, initial_prediction_steer_weight_coef; + + calc_inputs_ref_info( + states, acc_input_history, steer_input_history, h_lstm, c_lstm, previous_error, states_ref, + acc_controller_input_schedule, steer_controller_input_schedule, + states_prediction, + inputs_ref, + acc_input_change_rate, steer_input_change_rate, + x_weight_coef, y_weight_coef, vel_weight_coef, yaw_weight_coef, acc_weight_coef, steer_weight_coef, + acc_input_weight, steer_input_weight, + acc_rate_weight_coef, steer_rate_weight_coef, + initial_prediction_x_weight_coef, initial_prediction_y_weight_coef, initial_prediction_vel_weight_coef, + initial_prediction_yaw_weight_coef, initial_prediction_acc_weight_coef, initial_prediction_steer_weight_coef); + std::vector K; std::vector k; double prev_acc_rate = (acc_input_history[acc_queue_size_ - 1] - acc_input_history[acc_queue_size_ - 2])/control_dt_; double prev_steer_rate = (steer_input_history[steer_queue_size_ - 1] - steer_input_history[steer_queue_size_ - 2])/control_dt_; compute_ilqr_coefficients(dF_d_states, dF_d_inputs, states_prediction, d_inputs_schedule, states_ref, d_input_ref, prev_acc_rate, prev_steer_rate, - inputs_ref, longitudinal_coef, lateral_coef, acc_input_weight, steer_input_weight, inputs_schedule, K, k); + inputs_ref, + std::min(x_weight_coef,initial_prediction_x_weight_coef), + std::min(y_weight_coef,initial_prediction_y_weight_coef), + std::min(vel_weight_coef,initial_prediction_vel_weight_coef), + std::min(yaw_weight_coef,initial_prediction_yaw_weight_coef), + std::min(acc_weight_coef,initial_prediction_acc_weight_coef), + std::min(steer_weight_coef,initial_prediction_steer_weight_coef), + acc_input_weight, steer_input_weight, + acc_rate_weight_coef, steer_rate_weight_coef, + inputs_schedule, K, k); Eigen::VectorXd ls_points = Eigen::VectorXd::LinSpaced(11, 0.0, 1.0); std::vector D_inputs_schedule = calc_line_search_candidates(K, k, dF_d_states, dF_d_inputs, d_inputs_schedule, ls_points); Eigen::VectorXd Cost; std::vector States_prediction; calc_forward_trajectory_with_cost(states, acc_input_history, steer_input_history, D_inputs_schedule, h_lstm, c_lstm, previous_error, States_prediction, states_ref, d_input_ref, - inputs_ref, longitudinal_coef, lateral_coef, acc_input_weight, steer_input_weight, Cost); + inputs_ref, + x_weight_coef, y_weight_coef, vel_weight_coef, yaw_weight_coef, acc_weight_coef, steer_weight_coef, + acc_input_weight, steer_input_weight, + acc_rate_weight_coef, steer_rate_weight_coef, + Cost); int min_index; Cost.minCoeff(&min_index); @@ -1580,7 +1553,9 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) steer_input = steer_input_history[steer_queue_size_ - 1] + d_inputs_schedule[0][1] * control_dt_; } - + + + ///////////////// VehicleAdaptor /////////////////////// VehicleAdaptor::VehicleAdaptor(){ @@ -1625,10 +1600,12 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) acc_terminal_cost_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["acc_terminal_cost"].as(); steer_terminal_cost_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["steer_terminal_cost"].as(); - acc_rate_input_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["acc_rate_input_table"].as>(); - steer_rate_input_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["steer_rate_input_table"].as>(); - acc_rate_cost_coef_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["acc_rate_cost_coef_table"].as>(); - steer_rate_cost_coef_table_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["steer_rate_cost_coef_table"].as>(); + past_acc_input_change_decay_rate_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["past_acc_input_change_decay_rate"].as(); + past_steer_input_change_decay_rate_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["past_steer_input_change_decay_rate"].as(); + acc_input_change_window_size_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["acc_input_change_window_size"].as(); + steer_input_change_window_size_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["steer_input_change_window_size"].as(); + future_acc_input_change_decay_rate_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["future_acc_input_change_decay_rate"].as(); + future_steer_input_change_decay_rate_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["future_steer_input_change_decay_rate"].as(); x_intermediate_cost_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["x_intermediate_cost"].as(); y_intermediate_cost_ = optimization_param_node["optimization_parameter"]["weight_parameter"]["y_intermediate_cost"].as(); @@ -1649,8 +1626,8 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) deg_controller_steer_input_history_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["deg_controller_steer_input_history"].as(); lam_controller_acc_input_history_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["lam_controller_acc_input_history"].as>(); lam_controller_steer_input_history_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["lam_controller_steer_input_history"].as>(); - minimum_decay_controller_acc_input_history_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["minimum_decay_controller_acc_input_history"].as(); - minimum_decay_controller_steer_input_history_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["minimum_decay_controller_steer_input_history"].as(); + oldest_sample_weight_controller_acc_input_history_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["oldest_sample_weight_controller_acc_input_history"].as(); + oldest_sample_weight_controller_steer_input_history_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["oldest_sample_weight_controller_steer_input_history"].as(); acc_polynomial_prediction_len_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["acc_polynomial_prediction_len"].as(); steer_polynomial_prediction_len_ = optimization_param_node["optimization_parameter"]["polynomial_regression"]["steer_polynomial_prediction_len"].as(); @@ -1702,9 +1679,8 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) polynomial_reg_for_predict_acc_input_.set_params( deg_controller_acc_input_history_, controller_acc_input_history_len_, lam_controller_acc_input_history_); - polynomial_reg_for_predict_acc_input_.set_minimum_decay(minimum_decay_controller_acc_input_history_); + polynomial_reg_for_predict_acc_input_.set_oldest_sample_weight(oldest_sample_weight_controller_acc_input_history_); //polynomial_reg_for_predict_acc_input_.set_ignore_intercept(); - polynomial_reg_for_predict_acc_input_.set_predict(); bool ignore_intercept_acc_prediction = optimization_param_node["optimization_parameter"]["polynomial_regression"]["ignore_intercept_acc_prediction"].as(); if (ignore_intercept_acc_prediction){ polynomial_reg_for_predict_acc_input_.set_ignore_intercept(); @@ -1713,9 +1689,7 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) polynomial_reg_for_predict_acc_input_.calc_prediction_matrix(predict_step_*horizon_len_-1); polynomial_reg_for_predict_steer_input_.set_params( deg_controller_steer_input_history_, controller_steer_input_history_len_, lam_controller_steer_input_history_); - polynomial_reg_for_predict_steer_input_.set_minimum_decay(minimum_decay_controller_steer_input_history_); - //polynomial_reg_for_predict_steer_input_.set_ignore_intercept(); - polynomial_reg_for_predict_steer_input_.set_predict(); + polynomial_reg_for_predict_steer_input_.set_oldest_sample_weight(oldest_sample_weight_controller_steer_input_history_); bool ignore_intercept_steer_prediction = optimization_param_node["optimization_parameter"]["polynomial_regression"]["ignore_intercept_steer_prediction"].as(); if (ignore_intercept_steer_prediction){ polynomial_reg_for_predict_steer_input_.set_ignore_intercept(); @@ -1725,35 +1699,19 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) adaptor_ilqr_.set_states_cost(x_cost_,y_cost_,vel_cost_,yaw_cost_,acc_cost_,steer_cost_); + adaptor_ilqr_.set_inputs_cost(acc_rate_cost_, steer_rate_cost_); adaptor_ilqr_.set_terminal_cost(x_terminal_cost_,y_terminal_cost_, vel_terminal_cost_,yaw_terminal_cost_,acc_terminal_cost_,steer_terminal_cost_); adaptor_ilqr_.set_intermediate_cost(x_intermediate_cost_,y_intermediate_cost_,vel_intermediate_cost_,yaw_intermediate_cost_,acc_intermediate_cost_,steer_intermediate_cost_,intermediate_cost_index_); adaptor_ilqr_.set_rate_cost(acc_rate_rate_cost,steer_rate_rate_cost); reflect_controller_d_input_ratio_ = optimization_param_node["optimization_parameter"]["ilqr"]["reflect_controller_d_input_ratio"].as(); use_controller_inputs_as_target_ = optimization_param_node["optimization_parameter"]["ilqr"]["use_controller_inputs_as_target"].as(); - int deg_for_acc_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["deg_for_acc_inputs_polynomial_filter"].as(); - int sample_num_for_acc_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["sample_num_for_acc_inputs_polynomial_filter"].as(); - double lam_for_acc_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["lam_for_acc_inputs_polynomial_filter"].as(); - double minimum_decay_for_acc_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["minimum_decay_for_acc_inputs_polynomial_filter"].as(); - int deg_for_steer_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["deg_for_steer_inputs_polynomial_filter"].as(); - int sample_num_for_steer_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["sample_num_for_steer_inputs_polynomial_filter"].as(); - double lam_for_steer_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["lam_for_steer_inputs_polynomial_filter"].as(); - double minimum_decay_for_steer_inputs_polynomial_filter = optimization_param_node["optimization_parameter"]["input_filter"]["minimum_decay_for_steer_inputs_polynomial_filter"].as(); - polynomial_filter_for_acc_inputs_.set_params(deg_for_acc_inputs_polynomial_filter, - sample_num_for_acc_inputs_polynomial_filter, - lam_for_acc_inputs_polynomial_filter, - minimum_decay_for_acc_inputs_polynomial_filter); - polynomial_filter_for_steer_inputs_.set_params(deg_for_steer_inputs_polynomial_filter, - sample_num_for_steer_inputs_polynomial_filter, - lam_for_steer_inputs_polynomial_filter, - minimum_decay_for_steer_inputs_polynomial_filter); input_filter_mode_ = optimization_param_node["optimization_parameter"]["input_filter"]["input_filter_mode"].as(); if (input_filter_mode_ == "butterworth"){ butterworth_filter_.set_params(); } use_controller_steer_input_schedule_ = optimization_param_node["optimization_parameter"]["autoware_alignment"]["use_controller_steer_input_schedule"].as(); use_vehicle_adaptor_ = optimization_param_node["optimization_parameter"]["autoware_alignment"]["use_vehicle_adaptor"].as(); - //use_nonzero_initial_hidden_autoware_ = optimization_param_node["optimization_parameter"]["autoware_alignment"]["use_nonzero_initial_hidden"].as(); use_offline_features_autoware_ = optimization_param_node["optimization_parameter"]["autoware_alignment"]["use_offline_features"].as(); use_acc_input_schedule_prediction_ = optimization_param_node["optimization_parameter"]["inputs_schedule_prediction_NN"]["use_acc_input_schedule_prediction"].as(); @@ -1846,65 +1804,7 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) num_layers_encoder_ = weight_lstm_encoder_ih.size(); NN_prediction_target_dim_ = state_component_predicted.size(); } - /* - void VehicleAdaptor::set_initial_hidden_params( - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_1, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_1, - const Eigen::MatrixXd & weight_initial_hidden_acc_layer_2, const Eigen::MatrixXd & weight_initial_hidden_steer_layer_2, - const Eigen::MatrixXd & weight_initial_hidden_gru_ih, const Eigen::MatrixXd & weight_initial_hidden_gru_hh, - const Eigen::MatrixXd & weight_initial_hidden_final_layer, - const Eigen::MatrixXd & weight_initial_hidden_fusion_layer, const Eigen::MatrixXd & weight_initial_hidden_only_online, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_1, const Eigen::VectorXd & bias_initial_hidden_steer_layer_1, - const Eigen::VectorXd & bias_initial_hidden_acc_layer_2, const Eigen::VectorXd & bias_initial_hidden_steer_layer_2, - const Eigen::VectorXd & bias_initial_hidden_gru_ih, const Eigen::VectorXd & bias_initial_hidden_gru_hh, - const Eigen::VectorXd & bias_initial_hidden_final_layer, - const Eigen::VectorXd & bias_initial_hidden_fusion_layer, const Eigen::VectorXd & bias_initial_hidden_only_online) - { - get_initial_hidden_.set_initial_hidden_params( - weight_initial_hidden_acc_layer_1, weight_initial_hidden_steer_layer_1, - weight_initial_hidden_acc_layer_2, weight_initial_hidden_steer_layer_2, - weight_initial_hidden_gru_ih, weight_initial_hidden_gru_hh, - weight_initial_hidden_final_layer, weight_initial_hidden_fusion_layer, weight_initial_hidden_only_online, - bias_initial_hidden_acc_layer_1, bias_initial_hidden_steer_layer_1, - bias_initial_hidden_acc_layer_2, bias_initial_hidden_steer_layer_2, - bias_initial_hidden_gru_ih, bias_initial_hidden_gru_hh, - bias_initial_hidden_final_layer, bias_initial_hidden_fusion_layer, bias_initial_hidden_only_online); - h_gru_dim_for_initial_hidden_ = weight_initial_hidden_gru_hh.cols(); - use_nonzero_initial_hidden_ = true; - } - void VehicleAdaptor::set_initial_hidden_params_from_csv(std::string csv_dir){ - std::cout << "csv_dir: " << csv_dir << std::endl; - Eigen::MatrixXd weight_initial_hidden_acc_layer_1 = read_csv(csv_dir + "/weight_initial_hidden_acc_layer_1.csv"); - Eigen::MatrixXd weight_initial_hidden_steer_layer_1 = read_csv(csv_dir + "/weight_initial_hidden_steer_layer_1.csv"); - Eigen::MatrixXd weight_initial_hidden_acc_layer_2 = read_csv(csv_dir + "/weight_initial_hidden_acc_layer_2.csv"); - Eigen::MatrixXd weight_initial_hidden_steer_layer_2 = read_csv(csv_dir + "/weight_initial_hidden_steer_layer_2.csv"); - Eigen::MatrixXd weight_initial_hidden_gru_ih = read_csv(csv_dir + "/weight_initial_hidden_gru_ih.csv"); - Eigen::MatrixXd weight_initial_hidden_gru_hh = read_csv(csv_dir + "/weight_initial_hidden_gru_hh.csv"); - Eigen::MatrixXd weight_initial_hidden_final_layer = read_csv(csv_dir + "/weight_initial_hidden_final_layer.csv"); - Eigen::MatrixXd weight_initial_hidden_fusion_layer = read_csv(csv_dir + "/weight_initial_hidden_fusion_layer.csv"); - Eigen::MatrixXd weight_initial_hidden_only_online = read_csv(csv_dir + "/weight_initial_hidden_only_online.csv"); - - Eigen::VectorXd bias_initial_hidden_acc_layer_1 = read_csv(csv_dir + "/bias_initial_hidden_acc_layer_1.csv").col(0); - Eigen::VectorXd bias_initial_hidden_steer_layer_1 = read_csv(csv_dir + "/bias_initial_hidden_steer_layer_1.csv").col(0); - Eigen::VectorXd bias_initial_hidden_acc_layer_2 = read_csv(csv_dir + "/bias_initial_hidden_acc_layer_2.csv").col(0); - Eigen::VectorXd bias_initial_hidden_steer_layer_2 = read_csv(csv_dir + "/bias_initial_hidden_steer_layer_2.csv").col(0); - Eigen::VectorXd bias_initial_hidden_gru_ih = read_csv(csv_dir + "/bias_initial_hidden_gru_ih.csv").col(0); - Eigen::VectorXd bias_initial_hidden_gru_hh = read_csv(csv_dir + "/bias_initial_hidden_gru_hh.csv").col(0); - Eigen::VectorXd bias_initial_hidden_final_layer = read_csv(csv_dir + "/bias_initial_hidden_final_layer.csv").col(0); - Eigen::VectorXd bias_initial_hidden_fusion_layer = read_csv(csv_dir + "/bias_initial_hidden_fusion_layer.csv").col(0); - Eigen::VectorXd bias_initial_hidden_only_online = read_csv(csv_dir + "/bias_initial_hidden_only_online.csv").col(0); - set_initial_hidden_params( - weight_initial_hidden_acc_layer_1, weight_initial_hidden_steer_layer_1, - weight_initial_hidden_acc_layer_2, weight_initial_hidden_steer_layer_2, - weight_initial_hidden_gru_ih, weight_initial_hidden_gru_hh, - weight_initial_hidden_final_layer, weight_initial_hidden_fusion_layer, weight_initial_hidden_only_online, - bias_initial_hidden_acc_layer_1, bias_initial_hidden_steer_layer_1, - bias_initial_hidden_acc_layer_2, bias_initial_hidden_steer_layer_2, - bias_initial_hidden_gru_ih, bias_initial_hidden_gru_hh, - bias_initial_hidden_final_layer, bias_initial_hidden_fusion_layer, bias_initial_hidden_only_online); - } - */ void VehicleAdaptor::set_offline_features(const Eigen::VectorXd & offline_features){ - //get_initial_hidden_.set_offline_features(offline_features); offline_features_ = offline_features; use_offline_features_ = true; } @@ -1914,8 +1814,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) set_offline_features(offline_features); } void VehicleAdaptor::set_NN_params_from_csv(std::string csv_dir){ - // Eigen::MatrixXd weight_acc_layer_1, weight_steer_layer_1, weight_acc_layer_2, weight_steer_layer_2, weight_lstm_ih, weight_lstm_hh, weight_complimentary_layer, weight_linear_relu, weight_final_layer; - // Eigen::VectorXd bias_acc_layer_1, bias_steer_layer_1, bias_acc_layer_2, bias_steer_layer_2, bias_lstm_ih, bias_lstm_hh, bias_complimentary_layer, bias_linear_relu, bias_final_layer; double vel_scaling, vel_bias; std::cout << "csv_dir: " << csv_dir << std::endl; Eigen::MatrixXd model_info = read_csv(csv_dir + "/model_info.csv"); @@ -2108,7 +2006,7 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) { int controller_acc_input_history_len = std::max({controller_acc_input_history_len_, acc_delay_step_ + 1, past_len_for_acc_linear_extrapolation_ + 1}); int controller_steer_input_history_len = std::max({controller_steer_input_history_len_, steer_delay_step_ + 1,past_len_for_steer_linear_extrapolation_ + 1}); - if (!initialized_) { + if (!initialized_) {//initialize start_time_ = time_stamp; if (states_ref_mode_ == "predict_by_polynomial_regression"){ set_params(); @@ -2152,7 +2050,9 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) sg_filter_for_d_inputs_schedule_.calc_sg_filter_weight(); initialized_ = true; - } + past_acc_input_change_ = 0.0; + past_steer_input_change_ = 0.0; + }//end of initialization else{//already initialized case std::vector time_stamp_acc_input_history(acc_queue_size_); @@ -2216,21 +2116,20 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) Eigen::Map acc_controller_input_history_obs_eigen(acc_controller_input_history_obs_.data(), acc_controller_input_history_obs_.size()); Eigen::Map steer_controller_input_history_obs_eigen(steer_controller_input_history_obs_.data(), steer_controller_input_history_obs_.size()); - //acc_controller_input_history_.head(controller_acc_input_history_len_ - 1) = acc_controller_input_history_.tail(controller_acc_input_history_len_ - 1); - //acc_controller_input_history_(controller_acc_input_history_len_ - 1) = acc_controller_input; - acc_controller_input_history_ = interpolate_eigen(acc_controller_input_history_obs_eigen, time_stamp_obs_, time_stamp_controller_acc_input_history); - - //steer_controller_input_history_.head(controller_steer_input_history_len_ - 1) = steer_controller_input_history_.tail(controller_steer_input_history_len_ - 1); - //steer_controller_input_history_(controller_steer_input_history_len_ - 1) = steer_controller_input; steer_controller_input_history_ = interpolate_eigen(steer_controller_input_history_obs_eigen, time_stamp_obs_, time_stamp_controller_steer_input_history); - + past_acc_input_change_ = past_acc_input_change_decay_rate_ * past_acc_input_change_; + past_acc_input_change_ += (1 - past_acc_input_change_decay_rate_) * std::abs(acc_controller_input_history_[acc_controller_input_history_.size()-1] + - acc_controller_input_history_[acc_controller_input_history_.size()-1-acc_input_change_window_size_]) + / acc_input_change_window_size_; + past_steer_input_change_ = past_steer_input_change_decay_rate_ * past_steer_input_change_; + past_steer_input_change_ += (1 - past_steer_input_change_decay_rate_) * std::abs(steer_controller_input_history_[steer_controller_input_history_.size()-1] + - steer_controller_input_history_[steer_controller_input_history_.size()-1-steer_input_change_window_size_]) + / steer_input_change_window_size_; if (use_sg_for_d_inputs_schedule_){ d_inputs_schedule_ = sg_filter_for_d_inputs_schedule_.sg_filter(d_inputs_schedule_); } - //state_history_lstm_.erase(state_history_lstm_.begin()); - //state_history_lstm_.push_back(states); } // update lstm states // @@ -2243,37 +2142,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) Eigen::Vector2d acc_steer_error = Eigen::Vector2d::Zero(); Eigen::VectorXd previous_error_compensator = Eigen::VectorXd::Zero(previous_error_.size()); - /* - if (use_nonzero_initial_hidden_) - { - Eigen::VectorXd h_gru = Eigen::VectorXd::Zero(h_gru_dim_for_initial_hidden_); - for (int i = 0; i states_ref(horizon_len_ + 1); // calculate controller input history with schedule // @@ -2351,16 +2217,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) if (use_acc_input_schedule_prediction_) { std::vector controller_acc_input_prediction_by_NN = acc_input_schedule_prediction_.get_inputs_schedule_predicted(acc_input_schedule_predictor_states, time_stamp); - //for (int i = 0; i < horizon_len_ * predict_step_ - 1; i++) - //{ - // if (i < acc_input_schedule_prediction_len_) - // { - // acc_controller_inputs_prediction_by_NN[i] = controller_acc_input_prediction_by_NN[i]; - // } - // else{ - // acc_controller_inputs_prediction_by_NN[i] = acc_controller_inputs_prediction_by_NN[acc_input_schedule_prediction_len_ - 1]; - // } - //} for (int i = 0; i < acc_input_schedule_prediction_len_; i++) { acc_controller_inputs_prediction_by_NN[i] = controller_acc_input_prediction_by_NN[i]; @@ -2376,16 +2232,6 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) if (use_steer_input_schedule_prediction_) { std::vector controller_steer_input_prediction_by_NN = steer_input_schedule_prediction_.get_inputs_schedule_predicted(steer_input_schedule_predictor_states, time_stamp); - //for (int i = 0; i < horizon_len_ * predict_step_ - 1; i++) - //{ - // if (i < steer_input_schedule_prediction_len_) - // { - // steer_controller_inputs_prediction_by_NN[i] = controller_steer_input_prediction_by_NN[i]; - // } - // else{ - // steer_controller_inputs_prediction_by_NN[i] = steer_controller_inputs_prediction_by_NN[steer_input_schedule_prediction_len_ - 1]; - // } - //} for (int i = 0; i < steer_input_schedule_prediction_len_; i++) { steer_controller_inputs_prediction_by_NN[i] = controller_steer_input_prediction_by_NN[i]; @@ -2404,12 +2250,8 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) d_inputs_schedule_[i][1] = (1 - reflect_controller_d_input_ratio_)*d_inputs_schedule_[i][1]; d_inputs_schedule_[i][1] += reflect_controller_d_input_ratio_*steer_controller_d_inputs_schedule_[i]; } - Eigen::VectorXd acc_controller_input_history_diff = (acc_controller_input_history_.tail(acc_controller_input_history_.size() - 1) - acc_controller_input_history_.head(acc_controller_input_history_.size() - 1))/control_dt_; - Eigen::VectorXd abs_acc_controller_input_history_diff = acc_controller_input_history_diff.cwiseAbs(); - Eigen::VectorXd abs_steer_controller_d_inputs_schedule = steer_controller_d_inputs_schedule_.head(horizon_len_).cwiseAbs(); - - acc_rate_cost = calc_table_value(abs_acc_controller_input_history_diff.maxCoeff(), acc_rate_input_table_, acc_rate_cost_coef_table_)*acc_rate_cost_; - steer_rate_cost = calc_table_value(abs_steer_controller_d_inputs_schedule.maxCoeff(), steer_rate_input_table_, steer_rate_cost_coef_table_)*steer_rate_cost_; + past_acc_input_change_weight_ = 1.0; + past_steer_input_change_weight_ = 0.5; Eigen::VectorXd acc_controller_input_prediction(horizon_len_*predict_step_-1); if (use_acc_input_schedule_prediction_) @@ -2448,10 +2290,9 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) d_inputs_schedule_[i][0] += reflect_controller_d_input_ratio_*acc_controller_d_inputs_schedule_[i]; d_inputs_schedule_[i][1] += reflect_controller_d_input_ratio_*steer_controller_d_inputs_schedule_[i]; } - Eigen::VectorXd abs_acc_controller_d_inputs_schedule = acc_controller_d_inputs_schedule_.head(horizon_len_).cwiseAbs(); - Eigen::VectorXd abs_steer_controller_d_inputs_schedule = steer_controller_d_inputs_schedule_.head(horizon_len_).cwiseAbs(); - acc_rate_cost = calc_table_value(abs_acc_controller_d_inputs_schedule.maxCoeff(), acc_rate_input_table_, acc_rate_cost_coef_table_)*acc_rate_cost_; - steer_rate_cost = calc_table_value(abs_steer_controller_d_inputs_schedule.maxCoeff(), steer_rate_input_table_, steer_rate_cost_coef_table_)*steer_rate_cost_; + past_acc_input_change_weight_ = 0.5; + past_steer_input_change_weight_ = 0.5; + for (int i = 0; i< horizon_len_; i++) { for (int j = 0; j< predict_step_; j++) { if (i != 0 || j != 0) @@ -2464,23 +2305,15 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) } else { - if (states_ref_mode_ == "predict_by_polynomial_regression") - { - Eigen::VectorXd acc_controller_input_history_diff = (acc_controller_input_history_.tail(acc_controller_input_history_.size() - 1) - acc_controller_input_history_.head(acc_controller_input_history_.size() - 1))/control_dt_; - Eigen::VectorXd steer_controller_input_history_diff = (steer_controller_input_history_.tail(steer_controller_input_history_.size() - 1) - steer_controller_input_history_.head(steer_controller_input_history_.size() - 1))/control_dt_; - Eigen::VectorXd abs_acc_controller_input_history_diff = acc_controller_input_history_diff.cwiseAbs(); - Eigen::VectorXd abs_steer_controller_input_history_diff = steer_controller_input_history_diff.cwiseAbs(); - acc_rate_cost = calc_table_value(abs_acc_controller_input_history_diff.maxCoeff(), acc_rate_input_table_, acc_rate_cost_coef_table_)*acc_rate_cost_; - steer_rate_cost = calc_table_value(abs_steer_controller_input_history_diff.maxCoeff(), steer_rate_input_table_, steer_rate_cost_coef_table_)*steer_rate_cost_; + if (states_ref_mode_ == "predict_by_polynomial_regression"){ + past_acc_input_change_weight_ = 1.0; + past_steer_input_change_weight_ = 1.0; } - else{ - Eigen::VectorXd acc_controller_prediction_diff = (acc_controller_prediction_.tail(acc_controller_prediction_.size() - 1) - acc_controller_prediction_.head(acc_controller_prediction_.size() - 1))/predict_dt_; - Eigen::VectorXd steer_controller_prediction_diff = (steer_controller_prediction_.tail(steer_controller_prediction_.size() - 1) - steer_controller_prediction_.head(steer_controller_prediction_.size() - 1))/predict_dt_; - Eigen::VectorXd abs_acc_controller_prediction_diff = acc_controller_prediction_diff.head(horizon_len_).cwiseAbs(); - Eigen::VectorXd abs_steer_controller_prediction_diff = steer_controller_prediction_diff.head(horizon_len_).cwiseAbs(); - acc_rate_cost = calc_table_value(abs_acc_controller_prediction_diff.maxCoeff(), acc_rate_input_table_, acc_rate_cost_coef_table_)*acc_rate_cost_; - steer_rate_cost = calc_table_value(abs_steer_controller_prediction_diff.maxCoeff(), steer_rate_input_table_, steer_rate_cost_coef_table_)*steer_rate_cost_; + else{//states_ref_mode_ == "controller_prediction" + past_acc_input_change_weight_ = 0.5; + past_steer_input_change_weight_ = 0.5; } + Eigen::VectorXd acc_controller_input_prediction(horizon_len_*predict_step_-1); Eigen::VectorXd steer_controller_input_prediction(horizon_len_*predict_step_-1); if (use_acc_input_schedule_prediction_) @@ -2557,29 +2390,9 @@ double PolynomialFilter::fit_transform(double timestamp, double sample) } } - adaptor_ilqr_.set_inputs_cost(acc_rate_cost, - steer_rate_cost); + std::vector d_input_ref(horizon_len_, Eigen::VectorXd::Zero(2)); - /* - Eigen::VectorXd states_tmp_compensation = state_history_lstm_[predict_step_*(update_lstm_len_ - compensation_lstm_len_)]; - Eigen::VectorXd previous_error_tmp = Eigen::VectorXd::Zero(previous_error_.size()); - for (int i = 0; i(m, "NominalDynamics") @@ -2664,7 +2498,6 @@ PYBIND11_MODULE(utils, m) .def(py::init()) .def("set_NN_params", &VehicleAdaptor::set_NN_params) .def("set_NN_params_from_csv", &VehicleAdaptor::set_NN_params_from_csv) - //.def("set_initial_hidden_params_from_csv", &VehicleAdaptor::set_initial_hidden_params_from_csv) .def("set_offline_features_from_csv", &VehicleAdaptor::set_offline_features_from_csv) .def("set_offline_data_set_for_compensation", &VehicleAdaptor::set_offline_data_set_for_compensation) .def("set_offline_data_set_for_compensation_from_csv", &VehicleAdaptor::set_offline_data_set_for_compensation_from_csv) diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_utils.cpp b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_utils.cpp new file mode 100644 index 0000000000000..1cf15bb588b2c --- /dev/null +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/src/vehicle_adaptor_utils.cpp @@ -0,0 +1,219 @@ +#include "vehicle_adaptor_utils.h" + +Eigen::VectorXd tanh(const Eigen::VectorXd & v) +{ + return v.array().tanh(); +} +Eigen::VectorXd d_tanh(const Eigen::VectorXd & v) +{ + return 1 / (v.array().cosh() * v.array().cosh()); +} +Eigen::VectorXd sigmoid(const Eigen::VectorXd & v) +{ + return 0.5 * (0.5 * v).array().tanh() + 0.5; +} +Eigen::VectorXd relu(const Eigen::VectorXd & x) +{ + Eigen::VectorXd x_ = x; + for (int i = 0; i < x.size(); i++) { + if (x[i] < 0) { + x_[i] = 0; + } + } + return x_; +} +Eigen::VectorXd d_relu(const Eigen::VectorXd & x) +{ + Eigen::VectorXd result = Eigen::VectorXd::Ones(x.size()); + for (int i = 0; i < x.size(); i++) { + if (x[i] < 0) { + result[i] = 0; + } + } + return result; +} +Eigen::MatrixXd d_relu_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) +{ + Eigen::MatrixXd result = Eigen::MatrixXd::Zero(m.rows(), m.cols()); + for (int i = 0; i < m.cols(); i++) { + if (x[i] >= 0) { + result.col(i) = m.col(i); + } + } + return result; +} +Eigen::MatrixXd d_tanh_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) +{ + Eigen::MatrixXd result = Eigen::MatrixXd(m.rows(), m.cols()); + for (int i = 0; i < m.cols(); i++) { + result.col(i) = m.col(i) / (std::cosh(x[i]) * std::cosh(x[i])); + } + return result; +} +Eigen::VectorXd d_tanh_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x) +{ + Eigen::VectorXd result = Eigen::VectorXd(v.size()); + for (int i = 0; i < v.size(); i++) { + result[i] = v[i] / (std::cosh(x[i]) * std::cosh(x[i])); + } + return result; +} +Eigen::MatrixXd d_sigmoid_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) +{ + Eigen::MatrixXd result = Eigen::MatrixXd(m.rows(), m.cols()); + for (int i = 0; i < m.cols(); i++) { + result.col(i) = 0.25 * m.col(i) / (std::cosh(0.5 * x[i]) * std::cosh(0.5 * x[i])); + } + return result; +} +Eigen::VectorXd d_sigmoid_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x) +{ + Eigen::VectorXd result = Eigen::VectorXd(v.size()); + for (int i = 0; i < v.size(); i++) { + result[i] = 0.25 * v[i] / (std::cosh(0.5 * x[i]) * std::cosh(0.5 * x[i])); + } + return result; +} + +Eigen::VectorXd rotate_data(Eigen::VectorXd states, double yaw) +{ + Eigen::VectorXd states_rotated = states; + double cos_yaw = std::cos(yaw); + double sin_yaw = std::sin(yaw); + states_rotated[0] = states[0] * cos_yaw + states[1] * sin_yaw; + states_rotated[1] = -states[1] * sin_yaw + states[0] * cos_yaw; + return states_rotated; +} + +Eigen::VectorXd vector_power(Eigen::VectorXd vec, int power) +{ + Eigen::VectorXd result = vec; + for (int i = 0; i < power - 1; i++) { + result = result.array() * vec.array(); + } + return result; +} + +double double_power(double val, int power) +{ + double result = val; + for (int i = 0; i < power - 1; i++) { + result = result * val; + } + return result; +} +double calc_table_value(double domain_value, std::vector domain_table, std::vector target_table) +{ + if (domain_value <= domain_table[0]) { + return target_table[0]; + } + if (domain_value >= domain_table[domain_table.size() - 1]) { + return target_table[target_table.size() - 1]; + } + for (int i = 0; i < int(domain_table.size()) - 1; i++) { + if (domain_value >= domain_table[i] && domain_value <= domain_table[i + 1]) { + return target_table[i] + + (target_table[i + 1] - target_table[i]) / + (domain_table[i + 1] - domain_table[i]) * + (domain_value - domain_table[i]); + } + } + return 0.0; +} + +std::string get_param_dir_path() +{ + std::string build_path = BUILD_PATH; + std::string param_dir_path = build_path + "/autoware_vehicle_adaptor/param"; + return param_dir_path; +} +Eigen::MatrixXd read_csv(std::string file_path) +{ + std::string build_path = BUILD_PATH; + std::string file_path_abs = build_path + "/" + file_path; + std::vector> csv_data; + std::ifstream ifs(file_path_abs); + if (!ifs) { + std::cerr << "Failed to open file." << std::endl; + return Eigen::MatrixXd(0, 0); + } + std::string line; + while (std::getline(ifs, line)) { + std::istringstream stream(line); + std::string field; + std::vector fields; + while (std::getline(stream, field, ',')) { + fields.push_back(std::stod(field)); + } + csv_data.push_back(fields); + } + ifs.close(); + Eigen::MatrixXd data = Eigen::MatrixXd(csv_data.size(), csv_data[0].size()); + for (int i = 0; i < int(csv_data.size()); i++) { + for (int j = 0; j < int(csv_data[0].size()); j++) { + data(i, j) = csv_data[i][j]; + } + } + return data; +} +std::vector read_string_csv(std::string file_path) +{ + std::string build_path = BUILD_PATH; + std::string file_path_abs = build_path + "/" + file_path; + std::vector csv_data; + std::ifstream ifs(file_path_abs); + if (!ifs) { + std::cerr << "Failed to open file." << std::endl; + return csv_data; + } + std::string line; + while (std::getline(ifs, line)) { + std::stringstream stream(line); + std::string field; + while (std::getline(stream, field, ',')) { + field.erase(field.find_last_not_of(" \n\r\t") + 1); + field.erase(0, field.find_first_not_of(" \n\r\t")); + csv_data.push_back(field); + } + } + ifs.close(); + return csv_data; +} +Eigen::VectorXd interpolate_eigen(Eigen::VectorXd y, std::vector time_stamp_obs, std::vector time_stamp_new) +{ + Eigen::VectorXd y_new = Eigen::VectorXd(time_stamp_new.size()); + int lower_bound_index = 0; + for (int i = 0; i < int(time_stamp_new.size()); i++) { + if (time_stamp_new[i] >= time_stamp_obs[time_stamp_obs.size() - 1]) { + y_new[i] = y[time_stamp_obs.size() - 1] + (y[time_stamp_obs.size() - 1] - y[time_stamp_obs.size() - 2]) / (time_stamp_obs[time_stamp_obs.size() - 1] - time_stamp_obs[time_stamp_obs.size() - 2]) * (time_stamp_new[i] - time_stamp_obs[time_stamp_obs.size() - 1]); + continue; + } + for (int j = lower_bound_index; j < int(time_stamp_obs.size()) - 1; j++) { + if (time_stamp_new[i] >= time_stamp_obs[j] && time_stamp_new[i] <= time_stamp_obs[j + 1]) { + y_new[i] = y[j] + (y[j + 1] - y[j]) / (time_stamp_obs[j + 1] - time_stamp_obs[j]) * (time_stamp_new[i] - time_stamp_obs[j]); + lower_bound_index = j; + break; + } + } + } + return y_new; +} +std::vector interpolate_vector(std::vector y, std::vector time_stamp_obs, std::vector time_stamp_new) +{ + std::vector y_new; + int lower_bound_index = 0; + for (int i = 0; i < int(time_stamp_new.size()); i++) { + if (time_stamp_new[i] >= time_stamp_obs[time_stamp_obs.size() - 1]) { + y_new.push_back(y[time_stamp_obs.size() - 1] + (y[time_stamp_obs.size() - 1] - y[time_stamp_obs.size() - 2]) / (time_stamp_obs[time_stamp_obs.size() - 1] - time_stamp_obs[time_stamp_obs.size() - 2]) * (time_stamp_new[i] - time_stamp_obs[time_stamp_obs.size() - 1])); + continue; + } + for (int j = lower_bound_index; j < int(time_stamp_obs.size()) - 1; j++) { + if (time_stamp_new[i] >= time_stamp_obs[j] && time_stamp_new[i] <= time_stamp_obs[j + 1]) { + y_new.push_back(y[j] + (y[j + 1] - y[j]) / (time_stamp_obs[j + 1] - time_stamp_obs[j]) * (time_stamp_new[i] - time_stamp_obs[j])); + lower_bound_index = j; + break; + } + } + } + return y_new; +} diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/add_data_from_csv.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/add_data_from_csv.py index 8b6d988ce4db3..06fb46982b182 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/add_data_from_csv.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/add_data_from_csv.py @@ -4,7 +4,7 @@ import os from pathlib import Path -from autoware_vehicle_adaptor.src import utils +from autoware_vehicle_adaptor.src import vehicle_adaptor_compensator from autoware_vehicle_adaptor.calibrator import get_acc_input_from_csv_via_map import numpy as np import scipy.interpolate @@ -81,7 +81,7 @@ def __init__(self): self.division_indices_val = [] self.division_indices_test = [] self.division_indices_replay = [] - self.nominal_dynamics = utils.NominalDynamics() + self.nominal_dynamics = vehicle_adaptor_compensator.NominalDynamics() self.nominal_dynamics.set_params( wheel_base, acc_time_delay, @@ -334,7 +334,7 @@ def get_interpolated_input(s): predict_error[vel_index] = pseudo_predict_error[vel_index] if not parameters.use_yaw_observation: predict_error[yaw_index] = pseudo_predict_error[yaw_index] - predict_error = utils.rotate_data(predict_error, state[yaw_index]) + predict_error = vehicle_adaptor_compensator.rotate_data(predict_error, state[yaw_index]) if reverse_steer: predict_error[y_index] = -predict_error[y_index] predict_error[yaw_index] = -predict_error[yaw_index] diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/convert_model_to_csv.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/convert_model_to_csv.py index 9c351d02801e4..f6d7fbe8c9b84 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/convert_model_to_csv.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/convert_model_to_csv.py @@ -58,65 +58,6 @@ def convert_model_to_csv(model,save_dir): writer = csv.writer(file) writer.writerow(model.state_component_predicted) -def convert_initial_hidden_model_to_csv(model_for_initial_hidden,save_dir): - model_for_initial_hidden.to("cpu") - - if not os.path.exists(save_dir): - os.makedirs(save_dir) - np.savetxt(save_dir + "/weight_initial_hidden_acc_layer_1.csv", model_for_initial_hidden.acc_layer_1[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_steer_layer_1.csv", model_for_initial_hidden.steer_layer_1[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_acc_layer_2.csv", model_for_initial_hidden.acc_layer_2[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_steer_layer_2.csv", model_for_initial_hidden.steer_layer_2[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_gru_ih.csv", model_for_initial_hidden.gru.weight_ih_l0.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_gru_hh.csv", model_for_initial_hidden.gru.weight_hh_l0.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/initial_hidden_query.csv", model_for_initial_hidden.query.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_key_layer.csv", model_for_initial_hidden.key_layer.weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_value_layer.csv", model_for_initial_hidden.value_layer.weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_final_layer.csv", model_for_initial_hidden.final_layer[0].weight.detach().numpy().astype(np.float64),delimiter=',') - - np.savetxt(save_dir + "/bias_initial_hidden_acc_layer_1.csv",model_for_initial_hidden.acc_layer_1[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_steer_layer_1.csv",model_for_initial_hidden.steer_layer_1[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_acc_layer_2.csv",model_for_initial_hidden.acc_layer_2[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_steer_layer_2.csv",model_for_initial_hidden.steer_layer_2[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_gru_ih.csv",model_for_initial_hidden.gru.bias_ih_l0.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_gru_hh.csv",model_for_initial_hidden.gru.bias_hh_l0.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_value_layer.csv",model_for_initial_hidden.value_layer.bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_final_layer.csv",model_for_initial_hidden.final_layer[0].bias.detach().numpy().astype(np.float64),delimiter=',') - - np.savetxt(save_dir + "/attention_info.csv",np.array([model_for_initial_hidden.num_heads,model_for_initial_hidden.key_size,model_for_initial_hidden.value_size]), fmt='%d', delimiter=',') - np.savetxt(save_dir + "/gru_info.csv",np.array([model_for_initial_hidden.mean_steps]), fmt='%d', delimiter=',') -def convert_initial_hidden_with_offline_data_model_to_csv(preprocessor,model_for_initial_hidden,save_dir): - preprocessor.to("cpu") - model_for_initial_hidden.to("cpu") - if not os.path.exists(save_dir): - os.makedirs(save_dir) - np.savetxt(save_dir + "/weight_initial_hidden_acc_layer_1.csv", preprocessor.acc_layer_1[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_steer_layer_1.csv", preprocessor.steer_layer_1[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_acc_layer_2.csv", preprocessor.acc_layer_2[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_steer_layer_2.csv", preprocessor.steer_layer_2[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_gru_ih.csv", model_for_initial_hidden.gru_online.weight_ih_l0.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_gru_hh.csv", model_for_initial_hidden.gru_online.weight_hh_l0.detach().numpy().astype(np.float64),delimiter=',') - #np.savetxt(save_dir + "/weight_initial_hidden_attn.csv", model_for_initial_hidden.attn_online[0].weight.detach().numpy().astype(np.float64),delimiter=',') - #np.savetxt(save_dir + "/weight_initial_hidden_attn_summarize.csv", model_for_initial_hidden.attn_online[2].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_final_layer.csv", model_for_initial_hidden.final_layer_online[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_fusion_layer.csv", model_for_initial_hidden.fusion[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/weight_initial_hidden_only_online.csv", model_for_initial_hidden.only_online[0].weight.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_acc_layer_1.csv",preprocessor.acc_layer_1[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_steer_layer_1.csv",preprocessor.steer_layer_1[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_acc_layer_2.csv",preprocessor.acc_layer_2[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_steer_layer_2.csv",preprocessor.steer_layer_2[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_gru_ih.csv",model_for_initial_hidden.gru_online.bias_ih_l0.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_gru_hh.csv",model_for_initial_hidden.gru_online.bias_hh_l0.detach().numpy().astype(np.float64),delimiter=',') - #np.savetxt(save_dir + "/bias_initial_hidden_attn.csv",model_for_initial_hidden.attn_online[0].bias.detach().numpy().astype(np.float64),delimiter=',') - #np.savetxt(save_dir + "/bias_initial_hidden_attn_summarize.csv",model_for_initial_hidden.attn_online[2].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_final_layer.csv",model_for_initial_hidden.final_layer_online[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_fusion_layer.csv",model_for_initial_hidden.fusion[0].bias.detach().numpy().astype(np.float64),delimiter=',') - np.savetxt(save_dir + "/bias_initial_hidden_only_online.csv",model_for_initial_hidden.only_online[0].bias.detach().numpy().astype(np.float64),delimiter=',') - - - - - def convert_inputs_schedule_model_to_csv(model,save_dir): if not os.path.exists(save_dir): os.makedirs(save_dir) diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN.py deleted file mode 100644 index 053b5ae6b12f9..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN.py +++ /dev/null @@ -1,104 +0,0 @@ -import numpy as np -import torch -from torch import nn - -acc_queue_size = 15 -steer_queue_size = 15 -prediction_step = 3 - -class GetInitialHiddenNN(nn.Module): - def __init__( - self, - output_size, - acc_hidden_sizes=(16,8), - steer_hidden_sizes=(16,8), - gru_hidden_size=32, - key_size=32, - value_size=32, - num_heads=4, - randomize=0.000001, - mean_steps=3 - ): - super(GetInitialHiddenNN, self).__init__() - self.states_size = 3 # vel, acc, steer - self.vel_index = 0 - self.acc_index = 1 - self.steer_index = 2 - self.key_size = key_size - self.value_size = value_size - self.num_heads = num_heads - acc_input_indices = np.arange(self.states_size, self.states_size + acc_queue_size + prediction_step) - steer_input_indices = np.arange( - self.states_size + acc_queue_size + prediction_step, self.states_size + acc_queue_size + steer_queue_size + 2 * prediction_step - ) - self.acc_layer_input_indices = np.concatenate(([self.vel_index,self.acc_index], acc_input_indices)) - self.steer_layer_input_indices = np.concatenate(([self.vel_index,self.steer_index], steer_input_indices)) - lb = -randomize - ub = randomize - self.acc_layer_1 = nn.Sequential( - nn.Linear(len(self.acc_layer_input_indices), acc_hidden_sizes[0]), - nn.ReLU() - ) - nn.init.uniform_(self.acc_layer_1[0].weight, a=lb, b=ub) - nn.init.uniform_(self.acc_layer_1[0].bias, a=lb, b=ub) - self.acc_layer_2 = nn.Sequential( - nn.Linear(acc_hidden_sizes[0], acc_hidden_sizes[1]), - nn.ReLU() - ) - nn.init.uniform_(self.acc_layer_2[0].weight, a=lb, b=ub) - nn.init.uniform_(self.acc_layer_2[0].bias, a=lb, b=ub) - self.steer_layer_1 = nn.Sequential( - nn.Linear(len(self.steer_layer_input_indices), steer_hidden_sizes[0]), - nn.ReLU() - ) - nn.init.uniform_(self.steer_layer_1[0].weight, a=lb, b=ub) - nn.init.uniform_(self.steer_layer_1[0].bias, a=lb, b=ub) - self.steer_layer_2 = nn.Sequential( - nn.Linear(steer_hidden_sizes[0], steer_hidden_sizes[1]), - nn.ReLU() - ) - nn.init.uniform_(self.steer_layer_2[0].weight, a=lb, b=ub) - nn.init.uniform_(self.steer_layer_2[0].bias, a=lb, b=ub) - combined_input_size = 1 + acc_hidden_sizes[1] + steer_hidden_sizes[1] - self.gru = nn.GRU( - input_size=combined_input_size, - hidden_size=gru_hidden_size, - num_layers=1, - batch_first=True - ) - self.query = nn.Parameter(torch.randn(num_heads, key_size)) - nn.init.uniform_(self.query, a=lb, b=ub) - self.key_layer = nn.Linear(gru_hidden_size, key_size * num_heads,bias=False) # bias is canceled by the softmax - nn.init.uniform_(self.key_layer.weight, a=lb, b=ub) - self.value_layer = nn.Linear(gru_hidden_size, value_size * num_heads) - nn.init.uniform_(self.value_layer.weight, a=lb, b=ub) - nn.init.uniform_(self.value_layer.bias, a=lb, b=ub) - self.mean_steps = mean_steps - self.final_layer = nn.Sequential( - nn.Linear(value_size * num_heads, output_size), - nn.Tanh() - ) - nn.init.uniform_(self.final_layer[0].weight, a=lb, b=ub) - nn.init.uniform_(self.final_layer[0].bias, a=lb, b=ub) - def forward(self, x, only_gru=False): - batch_size, sample_size, seq_len, input_size = x.size() - x_view = x.view(batch_size * sample_size, seq_len, input_size) - acc_input = x_view[:, :, self.acc_layer_input_indices] - steer_input = x_view[:, :, self.steer_layer_input_indices] - acc_layer_1_out = self.acc_layer_1(acc_input) - acc_layer_2_out = self.acc_layer_2(acc_layer_1_out) - steer_layer_1_out = self.steer_layer_1(steer_input) - steer_layer_2_out = self.steer_layer_2(steer_layer_1_out) - combined_input = torch.cat((x_view[:, :, self.vel_index].unsqueeze(2), acc_layer_2_out, steer_layer_2_out), dim=2) - gru_out = self.gru(combined_input)[0][:, -self.mean_steps:].mean(dim=1).view(batch_size, sample_size, -1) - if only_gru: - return gru_out - key = self.key_layer(gru_out).view(batch_size, sample_size, self.num_heads, self.key_size) - value = self.value_layer(gru_out).view(batch_size, sample_size, self.num_heads, self.value_size) - attention = torch.einsum('bsnh,nh->bsn', key, self.query) / np.sqrt(self.key_size) - attention = torch.softmax(attention, dim=1) # (batch_size, sample_size, num_heads) - attention = attention.unsqueeze(3) # (batch_size, sample_size, num_heads, 1) - context = torch.sum(attention * value, dim=1) - context = context.view(batch_size, -1) - return self.final_layer(context) - diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN_with_offline_data.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN_with_offline_data.py index a6857608baaca..cbd07ef8c1445 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN_with_offline_data.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/get_initial_hidden_NN_with_offline_data.py @@ -5,27 +5,40 @@ acc_queue_size = 15 steer_queue_size = 15 prediction_step = 3 - -class Preprocess(nn.Module): + +class GetInitialHiddenNN(nn.Module): def __init__( self, + output_size, # 2 * lstm_encoder_hidden_size acc_hidden_sizes=(16,8), steer_hidden_sizes=(16,8), - randomize=0.01 + gru_hidden_size=32, + key_size=32, + value_size=32, + num_heads=4, + randomize=0.01, + mean_steps=3 ): - super(Preprocess, self).__init__() + super(GetInitialHiddenNN, self).__init__() + lb = -randomize + ub = randomize + self.states_size = 3 # vel, acc, steer self.vel_index = 0 self.acc_index = 1 self.steer_index = 2 + self.key_size = key_size + self.value_size = value_size + self.num_heads = num_heads + + acc_input_indices = np.arange(self.states_size, self.states_size + acc_queue_size + prediction_step) steer_input_indices = np.arange( self.states_size + acc_queue_size + prediction_step, self.states_size + acc_queue_size + steer_queue_size + 2 * prediction_step ) self.acc_layer_input_indices = np.concatenate(([self.vel_index,self.acc_index], acc_input_indices)) self.steer_layer_input_indices = np.concatenate(([self.vel_index,self.steer_index], steer_input_indices)) - lb = -randomize - ub = randomize + # Before the GRU self.acc_layer_1 = nn.Sequential( nn.Linear(len(self.acc_layer_input_indices), acc_hidden_sizes[0]), @@ -51,149 +64,65 @@ def __init__( ) nn.init.uniform_(self.steer_layer_2[0].weight, a=lb, b=ub) nn.init.uniform_(self.steer_layer_2[0].bias, a=lb, b=ub) - self.combined_input_size = 1 + acc_hidden_sizes[1] + steer_hidden_sizes[1] - def forward(self, x): - acc_input = x[:, :, self.acc_layer_input_indices] - acc_layer_1_out = self.acc_layer_1(acc_input) - acc_layer_2_out = self.acc_layer_2(acc_layer_1_out) - steer_input = x[:, :, self.steer_layer_input_indices] - steer_layer_1_out = self.steer_layer_1(steer_input) - steer_layer_2_out = self.steer_layer_2(steer_layer_1_out) - combined_input = torch.cat((x[:, :, self.vel_index].unsqueeze(2), acc_layer_2_out, steer_layer_2_out), dim=2) - return combined_input -class GetOfflineFeatures(nn.Module): - def __init__( - self, - input_size, # combined_input_size - output_size, # 2 * lstm_encoder_hidden_size - gru_hidden_size=32, - key_size=32, - value_size=32, - num_heads=4, - randomize=0.01, - mean_steps=3 - ): - super(GetOfflineFeatures, self).__init__() - lb = -randomize - ub = randomize - input_size = input_size - self.key_size = key_size - self.value_size = value_size - self.num_heads = num_heads - self.gru_offline = nn.GRU( - input_size=input_size, + combined_input_size = 1 + acc_hidden_sizes[1] + steer_hidden_sizes[1] + + self.gru = nn.GRU( + input_size=combined_input_size, hidden_size=gru_hidden_size, num_layers=1, batch_first=True ) - nn.init.uniform_(self.gru_offline.weight_ih_l0, a=lb, b=ub) - nn.init.uniform_(self.gru_offline.weight_hh_l0, a=lb, b=ub) - nn.init.uniform_(self.gru_offline.bias_ih_l0, a=lb, b=ub) - nn.init.uniform_(self.gru_offline.bias_hh_l0, a=lb, b=ub) - self.query_offline = nn.Parameter(torch.randn(num_heads, key_size)) - nn.init.uniform_(self.query_offline, a=lb, b=ub) - self.key_layer_offline = nn.Linear(gru_hidden_size, key_size * num_heads,bias=False) # bias is canceled by the softmax - nn.init.uniform_(self.key_layer_offline.weight, a=lb, b=ub) - self.value_layer_offline = nn.Linear(gru_hidden_size, value_size * num_heads) - nn.init.uniform_(self.value_layer_offline.weight, a=lb, b=ub) - nn.init.uniform_(self.value_layer_offline.bias, a=lb, b=ub) + nn.init.uniform_(self.gru.weight_ih_l0, a=lb, b=ub) + nn.init.uniform_(self.gru.weight_hh_l0, a=lb, b=ub) + nn.init.uniform_(self.gru.bias_ih_l0, a=lb, b=ub) + nn.init.uniform_(self.gru.bias_hh_l0, a=lb, b=ub) + self.query = nn.Parameter(torch.randn(num_heads, key_size)) + nn.init.uniform_(self.query, a=lb, b=ub) + self.key_layer = nn.Linear(gru_hidden_size, key_size * num_heads,bias=False) # bias is canceled by the softmax + nn.init.uniform_(self.key_layer.weight, a=lb, b=ub) + self.value_layer = nn.Linear(gru_hidden_size, value_size * num_heads) + nn.init.uniform_(self.value_layer.weight, a=lb, b=ub) + nn.init.uniform_(self.value_layer.bias, a=lb, b=ub) self.mean_steps = mean_steps - self.final_layer_offline = nn.Sequential( + self.final_layer = nn.Sequential( nn.Linear(value_size * num_heads, output_size), + nn.ReLU(), + nn.Linear(output_size, output_size), nn.Tanh() ) - nn.init.uniform_(self.final_layer_offline[0].weight, a=lb, b=ub) - nn.init.uniform_(self.final_layer_offline[0].bias, a=lb, b=ub) - def forward(self, x, preprocessor): + nn.init.uniform_(self.final_layer[0].weight, a=lb, b=ub) + nn.init.uniform_(self.final_layer[0].bias, a=lb, b=ub) + nn.init.uniform_(self.final_layer[2].weight, a=lb, b=ub) + nn.init.uniform_(self.final_layer[2].bias, a=lb, b=ub) + + def forward(self, x, mask=None): + """ + mask: Tensor of shape (batch_size, 1), 1 if offline data is available, 0 otherwise + """ batch_size, sample_size, seq_len, input_size = x.size() x_view = x.view(batch_size * sample_size, seq_len, input_size) - combined_input = preprocessor(x_view) - gru_out = self.gru_offline(combined_input)[0][:, -self.mean_steps:].mean(dim=1).view(batch_size, sample_size, -1) - key = self.key_layer_offline(gru_out).view(batch_size, sample_size, self.num_heads, self.key_size) - value = self.value_layer_offline(gru_out).view(batch_size, sample_size, self.num_heads, self.value_size) - attention = torch.einsum('bsnh,nh->bsn', key, self.query_offline) / np.sqrt(self.key_size) + + acc_input = x_view[:, :, self.acc_layer_input_indices] + acc_layer_1_out = self.acc_layer_1(acc_input) + acc_layer_2_out = self.acc_layer_2(acc_layer_1_out) + steer_input = x_view[:, :, self.steer_layer_input_indices] + steer_layer_1_out = self.steer_layer_1(steer_input) + steer_layer_2_out = self.steer_layer_2(steer_layer_1_out) + combined_input = torch.cat((x_view[:, :, self.vel_index].unsqueeze(2), acc_layer_2_out, steer_layer_2_out), dim=2) + + gru_out = self.gru(combined_input)[0][:, -self.mean_steps:].mean(dim=1).view(batch_size, sample_size, -1) + key = self.key_layer(gru_out).view(batch_size, sample_size, self.num_heads, self.key_size) + value = self.value_layer(gru_out).view(batch_size, sample_size, self.num_heads, self.value_size) + attention = torch.einsum('bsnh,nh->bsn', key, self.query) / np.sqrt(self.key_size) attention = torch.softmax(attention, dim=1) # (batch_size, sample_size, num_heads) attention = attention.unsqueeze(3) # (batch_size, sample_size, num_heads, 1) context = torch.sum(attention * value, dim=1) context = context.view(batch_size, -1) - return self.final_layer_offline(context) - - -class GetInitialHiddenNN(nn.Module): - def __init__( - self, - input_size, - output_size, - gru_hidden_size=32, - #online_attn_hidden_size=32, - randomize=0.01, - ): - super(GetInitialHiddenNN, self).__init__() - lb = -randomize - ub = randomize - # online data - self.gru_online = nn.GRU( - input_size=input_size, - hidden_size=gru_hidden_size, - num_layers=1, - batch_first=True - ) - nn.init.uniform_(self.gru_online.weight_ih_l0, a=lb, b=ub) - nn.init.uniform_(self.gru_online.weight_hh_l0, a=lb, b=ub) - nn.init.uniform_(self.gru_online.bias_ih_l0, a=lb, b=ub) - nn.init.uniform_(self.gru_online.bias_hh_l0, a=lb, b=ub) - """ - self.attn_online = nn.Sequential( - nn.Linear(gru_hidden_size, online_attn_hidden_size), - nn.Tanh(), - nn.Linear(online_attn_hidden_size, 1) - ) - nn.init.uniform_(self.attn_online[0].weight, a=lb, b=ub) - nn.init.uniform_(self.attn_online[0].bias, a=lb, b=ub) - nn.init.uniform_(self.attn_online[2].weight, a=lb, b=ub) - nn.init.uniform_(self.attn_online[2].bias, a=lb, b=ub) - """ - self.final_layer_online = nn.Sequential( - nn.Linear(gru_hidden_size, output_size), - nn.Tanh() - ) - - # fusion case - self.transform = nn.Sequential( - nn.Linear(output_size, output_size), - nn.Tanh() - ) - nn.init.uniform_(self.transform[0].weight, a=lb, b=ub) - nn.init.uniform_(self.transform[0].bias, a=lb, b=ub) - - # only online case - # self.only_online = nn.Sequential( - # nn.Linear(output_size, output_size), - # nn.Tanh() - # ) - # nn.init.uniform_(self.only_online[0].weight, a=lb, b=ub) - # nn.init.uniform_(self.only_online[0].bias, a=lb, b=ub) - def forward(self, offline_out, mask=None): #online_x, mask, preprocessor): - """ - mask: Tensor of shape (batch_size, 1), 1 if offline data is available, 0 otherwise - """ + final_out = self.final_layer_offline(context) + if mask is None: - return self.transform(offline_out) + return final_out else: mask = mask.float().unsqueeze(1) - return mask * self.transform(offline_out) - #online_combined_input = preprocessor(online_x) - #online_gru_out = self.gru_online(online_combined_input)[1][0] - #online_gru_out = self.gru_online(online_combined_input)[0] - #online_attn = self.attn_online(online_gru_out) # (batch_size, seq_len, 1) - #online_attn = torch.softmax(online_attn, dim=1) - #online_context = torch.sum(online_attn * online_gru_out, dim=1) - #online_out = self.final_layer_online(online_gru_out) - - #mask = mask.float().unsqueeze(1) - #fused_out = self.fusion(torch.cat((offline_out, online_out), dim=1)) - #only_online_out = self.only_online(online_out) - # print(mask.shape, fused_out.shape, only_online_out.shape) - # return mask * fused_out + (1 - mask) * only_online_out - + return mask * final_out diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/inputs_schedule_predictor_NN.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/inputs_schedule_predictor_NN.py index e134ee850013a..b498054bc4434 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/inputs_schedule_predictor_NN.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/inputs_schedule_predictor_NN.py @@ -9,101 +9,159 @@ from autoware_vehicle_adaptor.param import parameters from scipy.ndimage import gaussian_filter import copy +from typing import Tuple, List +# Number of steps in the history used as input to the neural network. +input_step = max(parameters.controller_acc_input_history_len, parameters.controller_steer_input_history_len) +# Number of steps in the future to predict. +output_step = max(parameters.acc_input_schedule_prediction_len, parameters.steer_input_schedule_prediction_len) +control_dt = parameters.control_dt # Time step for the controller +acc_cmd_smoothing_sigma = 10.0 # smoothing parameter for acceleration command +steer_cmd_smoothing_sigma = 10.0 # smoothing parameter for steering command + +def data_smoothing(data: np.ndarray, sigma: float) -> np.ndarray: + """ + Apply a Gaussian filter to smooth the input data. + - data: Input array to smooth. + - sigma: Smoothing parameter. + """ + data_smoothed = gaussian_filter(data, sigma) + return data_smoothed + +# Neural Network definition: Input Schedule Predictor class InputsSchedulePredictorNN(nn.Module): - def __init__(self, dataset_num, lstm_hidden_size=64, - post_decoder_hidden_size=(16,16),control_dt=0.033,target_size=35, input_rate_limit=1.0, - augmented_input_size=15, vel_scaling=0.1, vel_bias=0.0):#,vel_bias=5.0): + """ + LSTM-based model for predicting input schedules (e.g., acceleration and steering). + Output is a sequence of change rates for the input values. + """ + def __init__(self, + dataset_num:int, + lstm_hidden_size:int=64, + post_decoder_hidden_size:Tuple[int, int] =(16,16), + control_dt:float=0.033, + target_size:int=35, + input_rate_limit:float=1.0, + augmented_input_size:int=15, + vel_scaling:float=0.1, + vel_bias:float=0.0 + ): + """ + Constructor + - dataset_num: Number of controller types included in the data + - lstm_hidden_size: Number of hidden units in the LSTM. + - post_decoder_hidden_size: Hidden layer sizes in the post-decoder fully connected network. + - control_dt: Time step for controller. + - target_size: Prediction horizon size. + - input_rate_limit: Maximum limit for the output rate of change. + - augmented_input_size: Size of the augmented input features, derived from the history of inputs for the decoder. + - vel_scaling: Scaling factor for velocity inputs. + - vel_bias: Bias to apply to velocity inputs. + """ super(InputsSchedulePredictorNN, self).__init__() + # Initialize parameters self.dataset_num = dataset_num self.control_dt = control_dt self.target_size = target_size - self.input_size = 2 - self.output_size = 1 + self.input_size = 2 # Input dimensions (velocity and input signals) + self.output_size = 1 # Single output dimension (rate of change) self.augmented_input_size = augmented_input_size self.vel_scaling = vel_scaling self.vel_bias = vel_bias + # Define the LSTM layers for encoding and decoding self.lstm_encoder = nn.LSTM(self.input_size, lstm_hidden_size, num_layers=2, batch_first=True) self.lstm_decoder = nn.LSTM(self.output_size + self.augmented_input_size, lstm_hidden_size, num_layers=2, batch_first=True) + # Fully connected layers after the LSTM decoder self.post_decoder = nn.Sequential( nn.Linear(lstm_hidden_size, post_decoder_hidden_size[0]), nn.ReLU() ) + # Dataset-specific adaptive scaling parameters self.post_decoder_adaptive_scales = nn.ParameterList([nn.Parameter(torch.ones(post_decoder_hidden_size[0])) for _ in range(dataset_num)]) + # Final layers to produce the output self.finalize = nn.Sequential( nn.Linear(post_decoder_hidden_size[0], post_decoder_hidden_size[1]), nn.ReLU(), nn.Linear(post_decoder_hidden_size[1], self.output_size), nn.Tanh() ) - + # Define input transformation matrices for augmented inputs, focusing on updating the history of inputs during decoding. input_transition_matrix = torch.zeros(self.augmented_input_size, self.augmented_input_size) input_transition_matrix[1:, :-1] = torch.eye(self.augmented_input_size - 1) input_transition_matrix[-1, -1] = 1.0 add_new_input = torch.zeros(1,self.augmented_input_size) add_new_input[0,-1] = 1.0 + # Register as non-trainable buffers self.register_buffer("input_transition_matrix", input_transition_matrix) self.register_buffer("add_new_input", add_new_input) + # Input rate limit for the model outputs self.input_rate_limit = input_rate_limit - def forward(self, x, dataset_idx, true_prediction=None): + def forward(self, x: torch.Tensor, dataset_idx: torch.Tensor, true_prediction:torch.Tensor|None=None) -> torch.Tensor: + """ + Forward pass of the model. + - x: Input sequence (batch_size, seq_length, input_size). + - dataset_idx: Index of the dataset for adaptive scaling. + - true_prediction: True values for teacher forcing during training. + """ device = x.device + # Scale the velocity input x_vel_scaled = x.clone() x_vel_scaled[:,:,0] = (x_vel_scaled[:,:,0] - self.vel_bias ) * self.vel_scaling - #x_vel_scaled = self.pre_encoder(x_vel_scaled) + # Retrieve adaptive scales for the dataset post_decoder_scales_tensor = torch.stack(list(self.post_decoder_adaptive_scales)).to(device) - post_decoder_scales = post_decoder_scales_tensor[dataset_idx] - post_decoder_scales = post_decoder_scales.unsqueeze(1) + post_decoder_scales = post_decoder_scales_tensor[dataset_idx].unsqueeze(1) + # Encode the input sequence using the LSTM encoder _, (hidden_raw, cell_raw) = self.lstm_encoder(x_vel_scaled) - #_, (hidden_mean, cell_mean) = self.lstm_encoder_mean(x_vel_scaled_mean) - hidden = hidden_raw#torch.cat([hidden_raw, hidden_mean], dim=2) - cell = cell_raw#torch.cat([cell_raw, cell_mean], dim=2) - #hidden = torch.cat([hidden_raw, hidden_mean], dim=2) - #cell = torch.cat([cell_raw, cell_mean], dim=2) - + hidden = hidden_raw + cell = cell_raw + # Initialize decoder inputs decoder_input_head = x[:,-self.augmented_input_size:,-1] decoder_input_tail = (x[:,[-1],-1] - x[:,[-2],-1])/self.control_dt outputs = [] + # Perform decoding for the target size for i in range(self.target_size): - decoder_output, (hidden, cell) = self.lstm_decoder(torch.cat([decoder_input_head,decoder_input_tail],dim=1).unsqueeze(1), (hidden, cell)) + decoder_output, (hidden, cell) = self.lstm_decoder( + torch.cat([decoder_input_head,decoder_input_tail],dim=1).unsqueeze(1), + (hidden, cell) + ) + # Apply the post-decoder layers decoder_output = self.post_decoder(decoder_output) decoder_output = decoder_output * post_decoder_scales output = self.finalize(decoder_output)[:,:,0] * self.input_rate_limit outputs.append(output) - if true_prediction is not None: - decoder_input_head = decoder_input_head @ self.input_transition_matrix + true_prediction[:,i,:] @ self.add_new_input * self.control_dt + if true_prediction is not None: # Teacher forcing case + decoder_input_head = decoder_input_head @ self.input_transition_matrix + \ + true_prediction[:, i, :] @ self.add_new_input * self.control_dt decoder_input_tail = true_prediction[:,i,:] else: decoder_input_head = decoder_input_head @ self.input_transition_matrix + output @ self.add_new_input * self.control_dt decoder_input_tail = output - outputs = torch.cat(outputs, dim=1).unsqueeze(2) - return outputs - -input_step = max(parameters.controller_acc_input_history_len, parameters.controller_steer_input_history_len) -output_step = max(parameters.acc_input_schedule_prediction_len, parameters.steer_input_schedule_prediction_len) -control_dt = 0.033 -acc_cmd_smoothing_sigma = 10.0 -steer_cmd_smoothing_sigma = 10.0 + # Concatenate outputs + outputs_tensor = torch.cat(outputs, dim=1).unsqueeze(2) + return outputs_tensor -def data_smoothing(data: np.ndarray, sigma: float) -> np.ndarray: - """Apply a Gaussian filter to the data.""" - data_ = gaussian_filter(data, sigma) - return data_ class AddDataFromCsv: - def __init__(self): - self.X_acc_train_list = [] - self.Y_acc_train_list = [] - self.X_steer_train_list = [] - self.Y_steer_train_list = [] - self.indices_train_list = [] - self.X_acc_val_list = [] - self.Y_acc_val_list = [] - self.X_steer_val_list = [] - self.Y_steer_val_list = [] - self.indices_val_list = [] - self.dataset_num = 0 + """ + A helper class for loading and managing training and validation data from CSV files. + """ + def __init__(self) -> None: + # Initialize lists to store data for acceleration and steering + self.X_acc_train_list: List[np.ndarray] = [] # List of NumPy arrays for acceleration training input sequences + self.Y_acc_train_list: List[np.ndarray] = [] # List of NumPy arrays for acceleration training target sequences + self.X_steer_train_list: List[np.ndarray] = [] # List of NumPy arrays for steering training input sequences + self.Y_steer_train_list: List[np.ndarray] = [] # List of NumPy arrays for steering training target sequences + self.indices_train_list: List[int] = [] # List of indices indicating boundaries between datasets in training data + self.X_acc_val_list: List[np.ndarray] = [] # List of NumPy arrays for acceleration validation input sequences + self.Y_acc_val_list: List[np.ndarray] = [] # List of NumPy arrays for acceleration validation target sequences + self.X_steer_val_list: List[np.ndarray] = [] # List of NumPy arrays for steering validation input sequences + self.Y_steer_val_list: List[np.ndarray] = [] # List of NumPy arrays for steering validation target sequences + self.indices_val_list: List[int] = [] + self.dataset_num:int = 0 # Number of controller types included in the data def clear_data(self): + """ + Clear all stored data to reset the class instance. + """ self.X_acc_train_list = [] self.Y_acc_train_list = [] self.X_steer_train_list = [] @@ -115,28 +173,48 @@ def clear_data(self): self.Y_steer_val_list = [] self.indices_val_list = [] self.dataset_num = 0 - def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None,dataset_idx=0,reverse_steer=False): + def add_data_from_csv( + self, + dir_name:str, + add_mode:str="as_train", + control_cmd_mode:str|None=None, + dataset_idx:int=0, + reverse_steer:bool=False + ) -> None: + """ + Load data from CSV files and prepare it for training or validation. + - dir_name: Path to the directory containing the data files. + - add_mode: Specify "as_train" or "as_val" to determine how to store the data. + - control_cmd_mode: Specify which control command CSV to use (e.g., "control_command"). + - dataset_idx: Index of the dataset being added, representing the controller type. + - reverse_steer: If True, reverse the sign of the steering commands. + """ + # Load localization state data + # columns: time_sec, time_nsec, x, y, quaternion_x, quaternion_y, quaternion_z, quaternion_w, linear_velocity_x localization_kinematic_state = np.loadtxt( dir_name + "/localization_kinematic_state.csv", delimiter=",", usecols=[0, 1, 4, 5, 7, 8, 9, 10, 47] ) vel = localization_kinematic_state[:, 8] - if control_cmd_mode == "compensated_control_cmd": + # Load control command based on the specified mode + # columns: time_sec, time_nsec, steering input, acceleration input + if control_cmd_mode == "compensated_control_cmd": # compensated by the vehicle adaptor control_cmd = np.loadtxt( dir_name + "/vehicle_raw_vehicle_cmd_converter_debug_compensated_control_cmd.csv", delimiter=",", usecols=[0, 1, 8, 16] ) - elif control_cmd_mode == "control_command": + elif control_cmd_mode == "control_command": # controller inputs after the filter control_cmd = np.loadtxt( dir_name + "/control_command_control_cmd.csv", delimiter=",", usecols=[0, 1, 8, 16] ) - elif control_cmd_mode == "control_trajectory_follower": + elif control_cmd_mode == "control_trajectory_follower": # controller raw inputs by the trajectory follower node control_cmd = np.loadtxt( dir_name + "/control_trajectory_follower_control_cmd.csv", delimiter=",", usecols=[0, 1, 8, 16] ) - elif control_cmd_mode == "external_selected": + elif control_cmd_mode == "external_selected": # controller inputs by the external selector, e.g., data collecting tool control_cmd = np.loadtxt( dir_name + "/external_selected_control_cmd.csv", delimiter=",", usecols=[0, 1, 8, 16] ) elif control_cmd_mode is None: + # Automatically determine which file to load based on availability if os.path.exists(dir_name + '/control_command_control_cmd.csv'): control_cmd = np.loadtxt( dir_name + "/control_command_control_cmd.csv", delimiter=",", usecols=[0, 1, 8, 16] @@ -159,8 +237,10 @@ def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None, else: print("control_cmd_mode is invalid") return + # Extract acceleration and steering commands acc_cmd = control_cmd[:, 3] steer_cmd = control_cmd[:, 2] + # Load system operation mode data, and determine the operation start and end times system_operation_mode_state = np.loadtxt( dir_name + "/system_operation_mode_state.csv", delimiter=",", usecols=[0, 1, 2] ) @@ -187,7 +267,7 @@ def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None, operation_start_time = system_operation_mode_state[0, 0] + 1e-9 * system_operation_mode_state[0, 1] print("operation_start_time", operation_start_time) print("operation_end_time", operation_end_time) - + # Synchronize timestamps and interpolate data min_time_stamp = max( [ operation_start_time, @@ -204,19 +284,23 @@ def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None, ) data_num = int((max_time_stamp - min_time_stamp)/control_dt) data_time_stamps = min_time_stamp + control_dt * np.arange(data_num) + # Interpolate velocity and control commands vel_interp = scipy.interpolate.interp1d(localization_kinematic_state[:, 0] + 1e-9 * localization_kinematic_state[:, 1], vel)(data_time_stamps) acc_cmd_interp = scipy.interpolate.interp1d(control_cmd[:, 0] + 1e-9 * control_cmd[:, 1], acc_cmd)(data_time_stamps) steer_cmd_interp = scipy.interpolate.interp1d(control_cmd[:, 0] + 1e-9 * control_cmd[:, 1], steer_cmd)(data_time_stamps) + # Apply smoothing to commands acc_cmd_smoothed = data_smoothing(acc_cmd_interp, acc_cmd_smoothing_sigma) steer_cmd_smoothed = data_smoothing(steer_cmd_interp, steer_cmd_smoothing_sigma) + # Prepare data segments for inputs and targets X_acc = [] X_steer = [] Y_acc = [] Y_steer = [] indices = [] for i in range(data_num - input_step - output_step): - if vel_interp[i + input_step] < 0.1: + if vel_interp[i + input_step] < 0.1: # Skip low velocity data continue + # Acceleration input sequence data X_acc.append( np.stack( [ @@ -225,7 +309,14 @@ def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None, ] ).T ) + # Compute the rate of change for acceleration commands + Y_acc.append( + np.array(acc_cmd_smoothed[i + input_step:i + input_step + output_step] - acc_cmd_smoothed[i + input_step - 1:i + input_step + output_step - 1]).reshape(-1, 1)/control_dt + ) + + # Steering input with optional reversal if reverse_steer: + # Reverse the sign of the steering command for compatibility X_steer.append( np.stack( [ @@ -234,10 +325,12 @@ def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None, ] ).T ) + # Compute the rate of change for reversed steering commands Y_steer.append( - np.array(steer_cmd_smoothed[i + input_step:i + input_step + output_step] - steer_cmd_smoothed[i + input_step - 1:i + input_step + output_step - 1]).reshape(-1, 1)/control_dt ) else: + # Use the original steering commands without reversal X_steer.append( np.stack( [ @@ -246,26 +339,27 @@ def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None, ] ).T ) + # Compute the rate of change for original steering commands Y_steer.append( np.array(steer_cmd_smoothed[i + input_step:i + input_step + output_step] - steer_cmd_smoothed[i + input_step - 1:i + input_step + output_step - 1]).reshape(-1, 1)/control_dt ) - Y_acc.append( - np.array(acc_cmd_smoothed[i + input_step:i + input_step + output_step] - acc_cmd_smoothed[i + input_step - 1:i + input_step + output_step - 1]).reshape(-1, 1)/control_dt - ) - - + # Store the dataset index for each data segment, which indicating the controller type indices.append(dataset_idx) + # Increment dataset count if this dataset is new if dataset_idx not in self.indices_train_list and dataset_idx not in self.indices_val_list: self.dataset_num += 1 + # Store the data into the appropriate lists based on the mode if add_mode == "as_train": + # Append to training data self.X_acc_train_list += X_acc self.Y_acc_train_list += Y_acc self.X_steer_train_list += X_steer self.Y_steer_train_list += Y_steer self.indices_train_list += indices elif add_mode == "as_val": + # Append to validation data self.X_acc_val_list += X_acc self.Y_acc_val_list += Y_acc self.X_steer_val_list += X_steer @@ -275,18 +369,78 @@ def add_data_from_csv(self, dir_name, add_mode="as_train",control_cmd_mode=None, print("add_mode is invalid") return -def generate_random_vector(batch_size, seq_len, state_dim, dt, device, vel_scaling): +def generate_random_vector( + batch_size:int, + seq_len:int, + state_dim:int, + dt:float, + device:torch.device, + vel_scaling:float + ) -> torch.Tensor: + """ + Generate a random vector for perturbation purposes. + - batch_size: Number of samples in the batch. + - seq_len: Length of the sequence. + - state_dim: Dimensionality of each state in the sequence. + - dt: Time step for integration. + - device: Torch device (e.g., "cpu" or "cuda"). + - vel_scaling: Scaling factor for velocity. + Returns: Integrated random vector. + """ + # Generate random vector random_vector = torch.randn(batch_size, seq_len, state_dim, device=device) + # Scale and integrate over time random_vector[:,1:,:] = random_vector[:,1:,:] * dt - random_vector[:,:,0] = random_vector[:,:,0] / vel_scaling + random_vector[:,:,0] = random_vector[:,:,0] / vel_scaling # Scale the velocity random_vector_integrated = torch.cumsum(random_vector, dim=1) return random_vector_integrated -def get_loss(criterion,model,X,Y,indices, stage_error_weight=1e-4, prediction_error_weight=100.0,max_small_input_weight= 50.0,small_input_threshold=0.01,second_order_weight=1e-2, integral_points = [0.2,0.5,1.0], integral_weights = [1e-3,1e-2,1.0] ,tanh_gain = 2.0, tanh_weight = 0.1, use_true_prediction=False, alpha_jacobian=None, eps=1e-5): +def get_loss( + criterion: nn.Module, + model: InputsSchedulePredictorNN, + X: torch.Tensor, + Y: torch.Tensor, + indices: torch.Tensor, + stage_error_weight: float = 1e-4, + prediction_error_weight: float = 100.0, + max_small_input_weight: float = 50.0, + small_input_threshold: float = 0.01, + second_order_weight: float = 1e-2, + integral_points: List[float] = [0.2, 0.5, 1.0], + integral_weights: List[float] = [1e-3, 1e-2, 1.0], + tanh_gain: float = 2.0, + tanh_weight: float = 0.1, + use_true_prediction: bool = False, + alpha_jacobian: float|None = None, + eps: float = 1e-5, +) -> torch.Tensor: + """ + Compute the loss function for training the model. + - criterion: Base loss function (e.g., L1 loss). + - model: The prediction model. + - X: Input tensor (batch_size, seq_len, input_dim). + - Y: Target tensor (batch_size, seq_len, output_dim). + - indices: Dataset indices for adaptive scaling. + - stage_error_weight: Weight for stage-wise error in the loss. + - prediction_error_weight: Weight for prediction errors. + - max_small_input_weight: Maximum weight for small input penalties. + - small_input_threshold: Threshold to define "small" inputs. + - second_order_weight: Weight for penalties on second-order derivatives. + - integral_points: Points within the prediction horizon for integral constraints. + - integral_weights: Weights for integral penalties at different points. + - tanh_gain: Gain parameter for the tanh penalty. + - tanh_weight: Weight for the tanh penalty term. + - use_true_prediction: Whether to use teacher-forced predictions. + - alpha_jacobian: Weight for Jacobian penalties (optional). + - eps: Small value for numerical stability. + Returns: Computed loss as a Torch tensor. + """ device = X.device + # Generate random perturbations for Jacobian calculation if alpha_jacobian is not None: random_vector = generate_random_vector(X.size(0),X.size(1),X.size(2),control_dt,device,model.vel_scaling) * eps - if use_true_prediction: + # Forward pass through the model + if use_true_prediction: # Teacher forcing Y_pred = model(X,indices,Y) if alpha_jacobian is not None: Y_perturbed = model(X+random_vector,indices,Y) @@ -294,23 +448,57 @@ def get_loss(criterion,model,X,Y,indices, stage_error_weight=1e-4, prediction_er Y_pred = model(X,indices) if alpha_jacobian is not None: Y_perturbed = model(X+random_vector,indices) + # Compute stage-wise loss loss = stage_error_weight * criterion(Y_pred,Y) + # Add Jacobian penalty if applicable if alpha_jacobian is not None: loss = loss + alpha_jacobian * torch.mean(torch.abs((Y_perturbed - Y_pred) / eps)) + # inputs predicted by the model input_future = torch.mean(torch.abs(X[:,-1,1] + torch.cumsum(Y, dim=1) * control_dt), dim=1) + # weight for emphasizing small inputs small_input_weight = torch.where(input_future > small_input_threshold, torch.tensor(1.0, device=X.device), 1.0/(input_future + 1.0/max_small_input_weight)) + # Compute the prediction error for i in range(len(integral_points)): loss = loss + integral_weights[i] * tanh_weight * torch.mean(torch.abs(torch.tanh(tanh_gain * (Y_pred[:,:int(model.target_size*integral_points[i]),0].sum(dim=1) - Y[:,:int(model.target_size*integral_points[i]),0].sum(dim=1))))) loss = loss + integral_weights[i] * prediction_error_weight * torch.mean(small_input_weight * torch.abs(Y_pred[:,:int(model.target_size*integral_points[i])].sum(dim=1)*control_dt - Y[:,:int(model.target_size*integral_points[i])].sum(dim=1)*control_dt)) - - #loss = loss + second_order_weight * criterion((Y_pred[:,1:] - Y_pred[:,:-1])/control_dt,(Y[:,1:] - Y[:,:-1])/control_dt) - loss = loss + second_order_weight * torch.mean(torch.abs((Y_pred[:,1:] - Y_pred[:,:-1])/control_dt)) #criterion((Y_pred[:,1:] - Y_pred[:,:-1])/control_dt,(Y[:,1:] - Y[:,:-1])/control_dt) + # Add penalty for second-order derivatives + loss = loss + second_order_weight * torch.mean(torch.abs((Y_pred[:,1:] - Y_pred[:,:-1])/control_dt)) + # Add penalty for the final time step. + # This cost assumes the rate of change for the next input is zero. + # To match the scale with other stages, the cost is divided by model.target_size. loss = loss + second_order_weight * torch.mean(torch.abs(Y_pred[:,-1]/control_dt)) / model.target_size return loss -def validate_in_batches(criterion,model,X_val,Y_val,indices,batch_size=1000, tanh_gain = 2.0, tanh_weight = 0.1,max_small_input_weight= 50.0,small_input_threshold=0.01, alpha_jacobian=None): +def validate_in_batches( + criterion: nn.Module, + model: InputsSchedulePredictorNN, + X_val: torch.Tensor, + Y_val: torch.Tensor, + indices: torch.Tensor, + batch_size: int = 1000, + tanh_gain: float = 2.0, + tanh_weight: float = 0.1, + max_small_input_weight: float = 50.0, + small_input_threshold: float = 0.01, + alpha_jacobian: float|None = None, +) -> float: + """ + Validate the model in batches to avoid memory overflow. + - criterion: Base loss function (e.g., L1 loss). + - model: The prediction model. + - X_val: Validation input tensor (batch_size, seq_len, input_dim). + - Y_val: Validation target tensor (batch_size, seq_len, output_dim). + - indices: Dataset indices for adaptive scaling. + - batch_size: Size of each validation batch. + - tanh_gain: Gain parameter for the tanh penalty. + - tanh_weight: Weight for the tanh penalty term. + - max_small_input_weight: Maximum weight for small input penalties. + - small_input_threshold: Threshold to define "small" inputs. + - alpha_jacobian: Weight for Jacobian penalties (optional). + Returns: Average validation loss. + """ model.eval() val_loss = 0.0 num_batches = (X_val.size(0) + batch_size - 1) // batch_size @@ -321,12 +509,38 @@ def validate_in_batches(criterion,model,X_val,Y_val,indices,batch_size=1000, tan X_val_batch = X_val[start:end] Y_val_batch = Y_val[start:end] indices_batch = indices[start:end] + # Compute loss for the current batch loss = get_loss(criterion,model,X_val_batch,Y_val_batch, indices_batch, tanh_gain=tanh_gain, tanh_weight=tanh_weight, max_small_input_weight=max_small_input_weight, small_input_threshold=small_input_threshold, alpha_jacobian=alpha_jacobian).item() - val_loss += loss *(end - start) - val_loss /= X_val.size(0) + val_loss += loss *(end - start) # Scale by batch size + val_loss /= X_val.size(0) # Normalize by total validation size return val_loss class TrainInputsSchedulePredictorNN(AddDataFromCsv): - def __init__(self, max_iter=10000, tol=1e-5, alpha_1=0.1**7, alpha_2=0.1**7, tanh_gain_acc=2.0, tanh_weight_acc=0.1, tanh_gain_steer=10.0, tanh_weight_steer=0.01,alpha_jacobian=None): + """ + A class for training the Inputs Schedule Predictor Neural Network. + Inherits data handling methods from AddDataFromCsv. + """ + def __init__(self, + max_iter: int = 10000, + tol: float = 1e-5, + alpha_1: float = 0.1**7, + alpha_2: float = 0.1**7, + tanh_gain_acc: float = 2.0, + tanh_weight_acc: float = 0.1, + tanh_gain_steer: float = 10.0, + tanh_weight_steer: float = 0.01, + alpha_jacobian: float|None = None) -> None: + """ + Initialize the training class. + - max_iter: Maximum number of iterations for training. + - tol: Tolerance for early stopping. + - alpha_1: L2 regularization weight for model parameters. + - alpha_2: L1 regularization weight for model parameters. + - tanh_gain_acc: Gain parameter for the tanh penalty on acceleration. + - tanh_weight_acc: Weight for the tanh penalty on acceleration. + - tanh_gain_steer: Gain parameter for the tanh penalty on steering. + - tanh_weight_steer: Weight for the tanh penalty on steering. + - alpha_jacobian: Weight for the Jacobian penalty (optional). + """ super().__init__() self.max_iter = max_iter self.tol = tol @@ -338,33 +552,56 @@ def __init__(self, max_iter=10000, tol=1e-5, alpha_1=0.1**7, alpha_2=0.1**7, tan self.tanh_weight_acc = tanh_weight_acc self.tanh_weight_steer = tanh_weight_steer self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.model_acc = None - self.model_steer = None + self.model_acc: InputsSchedulePredictorNN|None= None + self.model_steer: InputsSchedulePredictorNN|None = None def train_model( - self, - model, - X_train, - Y_train, - indices_train, - X_val, - Y_val, - indices_val, - batch_sizes, - learning_rates, - patience, - tanh_gain, - tanh_weight, - max_small_input_weight=50.0, - small_input_threshold=0.01, - ): + self, + model: InputsSchedulePredictorNN, + X_train: torch.Tensor, + Y_train: torch.Tensor, + indices_train: torch.Tensor, + X_val: torch.Tensor, + Y_val: torch.Tensor, + indices_val: torch.Tensor, + batch_sizes: List[int], + learning_rates: List[float], + patience: int, + tanh_gain: float, + tanh_weight: float, + max_small_input_weight: float = 50.0, + small_input_threshold: float = 0.01, + ) -> None: + """ + Train the model using the given dataset. + - model: The Inputs Schedule Predictor Neural Network to train. + - X_train: Training input tensor. + - Y_train: Training target tensor. + - indices_train: Dataset indices for training samples. + - X_val: Validation input tensor. + - Y_val: Validation target tensor. + - indices_val: Dataset indices for validation samples. + - batch_sizes: List of batch sizes to use during training. + - learning_rates: List of learning rates to use during training. + - patience: Number of epochs to wait for improvement before early stopping. + - tanh_gain: Gain parameter for the tanh penalty. + - tanh_weight: Weight for the tanh penalty term. + - max_small_input_weight: Maximum weight for small input penalties. + - small_input_threshold: Threshold to define "small" inputs. + """ print("sample_size:", X_train.shape[0] + X_val.shape[0]) + print("training_size:", X_train.shape[0]) + print("validation_size:", X_val.shape[0]) print("patience:", patience) # Define the loss function. criterion = nn.L1Loss() # Define the optimizer. optimizer = torch.optim.Adam(model.parameters(), lr=learning_rates[0]) - # Define the initial loss. - initial_loss = validate_in_batches(criterion,model,X_val,Y_val,indices_val, tanh_gain=tanh_gain, tanh_weight=tanh_weight, max_small_input_weight=max_small_input_weight, small_input_threshold=small_input_threshold) #,alpha_jacobian=self.alpha_jacobian) + # Compute the initial validation loss + initial_loss = validate_in_batches( + criterion,model,X_val,Y_val,indices_val, + tanh_gain=tanh_gain, tanh_weight=tanh_weight, + max_small_input_weight=max_small_input_weight, + small_input_threshold=small_input_threshold) print("initial_loss:", initial_loss) batch_size = batch_sizes[0] @@ -382,24 +619,36 @@ def train_model( model.train() for X_batch, Y_batch, indices_batch in train_loader: optimizer.zero_grad() - if learning_rates[learning_rate_index] > 5e-3: - use_true_prediction = True - else: - use_true_prediction = False - loss = get_loss(criterion,model,X_batch,Y_batch,indices_batch,use_true_prediction=use_true_prediction, tanh_gain=tanh_gain, tanh_weight=tanh_weight, max_small_input_weight=max_small_input_weight, small_input_threshold=small_input_threshold,alpha_jacobian=self.alpha_jacobian) + # Use true prediction for large learning rates + use_true_prediction = learning_rates[learning_rate_index] > 5e-3 + # Compute loss + loss = get_loss( + criterion,model,X_batch,Y_batch,indices_batch, + use_true_prediction=use_true_prediction, + tanh_gain=tanh_gain, tanh_weight=tanh_weight, + max_small_input_weight=max_small_input_weight, + small_input_threshold=small_input_threshold, + alpha_jacobian=self.alpha_jacobian + ) + # Add L2 and L1 regularization terms for w in model.named_parameters(): - if w[0].find("adaptive_scales") == -1: - loss += self.alpha_1 * torch.sum(w[1]**2) + self.alpha_2 * torch.sum(torch.abs(w[1])) + if "adaptive_scales" not in w[0]: # Exclude adaptive scales from regularization + loss += self.alpha_1 * torch.sum(w[1] ** 2) # L2 regularization + loss += self.alpha_2 * torch.sum(torch.abs(w[1])) # L1 regularization + # Add penalty for adaptive scales for j in range(self.dataset_num): index_rates = (indices_batch == j).sum().item()/indices_batch.size(0) loss += self.alpha_1 * index_rates * torch.mean(torch.abs(model.post_decoder_adaptive_scales[j] - 1.0)) - + # Backpropagation loss.backward() - #torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=max_grad_norm) optimizer.step() - # Validation loss. + # Validation step model.eval() - val_loss = validate_in_batches(criterion,model,X_val,Y_val, indices_val, tanh_gain=tanh_gain, tanh_weight=tanh_weight, max_small_input_weight=max_small_input_weight, small_input_threshold=small_input_threshold) #,alpha_jacobian=self.alpha_jacobian) + val_loss = validate_in_batches( + criterion,model,X_val,Y_val, indices_val, + tanh_gain=tanh_gain, tanh_weight=tanh_weight, + max_small_input_weight=max_small_input_weight, + small_input_threshold=small_input_threshold) if i % 10 == 0: print(val_loss, i) if early_stopping(val_loss): @@ -413,36 +662,61 @@ def train_model( train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) early_stopping.reset() def relearn_model( - self, - model, - X_train, - Y_train, - indices_train, - X_val, - Y_val, - indices_val, - batch_sizes, - learning_rates, - patience, - tanh_gain, - tanh_weight, - max_small_input_weight=50.0, - small_input_threshold=0.01, - randomize=0.001 - ): + self, + model: InputsSchedulePredictorNN, + X_train: torch.Tensor, + Y_train: torch.Tensor, + indices_train: torch.Tensor, + X_val: torch.Tensor, + Y_val: torch.Tensor, + indices_val: torch.Tensor, + batch_sizes: List[int], + learning_rates: List[float], + patience: int, + tanh_gain: float, + tanh_weight: float, + max_small_input_weight: float = 50.0, + small_input_threshold: float = 0.01, + randomize: float = 0.001, + ) -> Tuple[InputsSchedulePredictorNN, bool]: + """ + Fine-tune the model by adding random noise to parameters and retraining. + - model: The initial trained model. + - X_train: Training input tensor. + - Y_train: Training target tensor. + - indices_train: Dataset indices for training samples. + - X_val: Validation input tensor. + - Y_val: Validation target tensor. + - indices_val: Dataset indices for validation samples. + - batch_sizes: List of batch sizes to use during retraining. + - learning_rates: List of learning rates to use during retraining. + - patience: Number of epochs to wait for improvement before early stopping. + - tanh_gain: Gain parameter for the tanh penalty. + - tanh_weight: Weight for the tanh penalty term. + - max_small_input_weight: Maximum weight for small input penalties. + - small_input_threshold: Threshold to define "small" inputs. + - randomize: Amount of random noise added to model parameters. + Returns: The retrained model. + """ + # Flatten LSTM parameters model.lstm_decoder.flatten_parameters() model.lstm_encoder.flatten_parameters() # Define the loss function. criterion = nn.L1Loss() - # Define the optimizer. - # Define the initial loss. - original_val_loss = validate_in_batches(criterion,model,X_val,Y_val,indices_val, tanh_gain=tanh_gain, tanh_weight=tanh_weight, max_small_input_weight=max_small_input_weight, small_input_threshold=small_input_threshold) #,alpha_jacobian=self.alpha_jacobian) + # Compute the original validation loss + original_val_loss = validate_in_batches( + criterion,model,X_val,Y_val,indices_val, + tanh_gain=tanh_gain, tanh_weight=tanh_weight, + max_small_input_weight=max_small_input_weight, + small_input_threshold=small_input_threshold) + # Copy the model and add random noise to its parameters relearned_model = copy.deepcopy(model) relearned_model.lstm_decoder.flatten_parameters() relearned_model.lstm_encoder.flatten_parameters() with torch.no_grad(): for w in relearned_model.parameters(): - w += randomize * torch.randn_like(w) + w.copy_(w + randomize * torch.randn_like(w)) + # Retrain the model self.train_model( relearned_model, X_train, @@ -459,18 +733,56 @@ def relearn_model( max_small_input_weight=max_small_input_weight, small_input_threshold=small_input_threshold ) - relearned_val_loss = validate_in_batches(criterion,relearned_model,X_val,Y_val,indices_val, tanh_gain=tanh_gain, tanh_weight=tanh_weight, max_small_input_weight=max_small_input_weight, small_input_threshold=small_input_threshold) #,alpha_jacobian=self.alpha_jacobian) + # Compute the validation loss after retraining + relearned_val_loss = validate_in_batches( + criterion,relearned_model,X_val,Y_val,indices_val, + tanh_gain=tanh_gain, tanh_weight=tanh_weight, + max_small_input_weight=max_small_input_weight, + small_input_threshold=small_input_threshold) + # Compare the original and retrained validation losse print("original_val_loss:", original_val_loss) print("relearned_val_loss:", relearned_val_loss) + # Return the model with the better validation loss if relearned_val_loss < original_val_loss: - return relearned_model + return relearned_model, True else: - return model + return model, False - def get_trained_model(self,learning_rates=[1e-3,1e-4,1e-5,1e-6],patience=10,batch_sizes=[1000], jerk_limit=1.0, steer_rate_limit=1.0, augmented_input_size=15,max_small_acc_weight=10.0, small_acc_threshold=0.01 , - max_small_steer_weight=30.0, small_steer_threshold=0.005, cmd_mode="both"): + def get_trained_model( + self, + learning_rates: List[float] = [1e-3, 1e-4, 1e-5, 1e-6], + patience: int = 10, + batch_sizes: List[int] = [1000], + jerk_limit: float = 1.0, + steer_rate_limit: float = 1.0, + augmented_input_size: int = 15, + max_small_acc_weight: float = 10.0, + small_acc_threshold: float = 0.01, + max_small_steer_weight: float = 30.0, + small_steer_threshold: float = 0.005, + cmd_mode: str = "both" + ) -> None: + """ + Train the acceleration and/or steering models based on the provided mode. + - learning_rates: List of learning rates to use during training. + - patience: Number of epochs to wait for improvement before early stopping. + - batch_sizes: List of batch sizes to use during training. + - jerk_limit: Input rate limit for acceleration. + - steer_rate_limit: Input rate limit for steering. + - augmented_input_size: Size of augmented input features. + - max_small_acc_weight: Maximum weight for penalties on small acceleration inputs. + - small_acc_threshold: Threshold to define "small" acceleration inputs. + - max_small_steer_weight: Maximum weight for penalties on small steering inputs. + - small_steer_threshold: Threshold to define "small" steering inputs. + - cmd_mode: Mode to specify which model(s) to train ("both", "acc", or "steer"). + """ if cmd_mode == "both" or cmd_mode == "acc": - self.model_acc = InputsSchedulePredictorNN(dataset_num=self.dataset_num,target_size=parameters.acc_input_schedule_prediction_len, input_rate_limit=jerk_limit, augmented_input_size=augmented_input_size).to(self.device) + self.model_acc = InputsSchedulePredictorNN( + dataset_num=self.dataset_num, + target_size=parameters.acc_input_schedule_prediction_len, + input_rate_limit=jerk_limit, + augmented_input_size=augmented_input_size + ).to(self.device) self.train_model( self.model_acc, torch.tensor(np.array(self.X_acc_train_list)[:,output_step - parameters.controller_acc_input_history_len:], dtype=torch.float32,device=self.device), @@ -488,7 +800,12 @@ def get_trained_model(self,learning_rates=[1e-3,1e-4,1e-5,1e-6],patience=10,batc small_input_threshold=small_acc_threshold, ) if cmd_mode == "both" or cmd_mode == "steer": - self.model_steer = InputsSchedulePredictorNN(dataset_num=self.dataset_num,target_size=parameters.steer_input_schedule_prediction_len, input_rate_limit=steer_rate_limit,augmented_input_size=augmented_input_size).to(self.device) + self.model_steer = InputsSchedulePredictorNN( + dataset_num=self.dataset_num, + target_size=parameters.steer_input_schedule_prediction_len, + input_rate_limit=steer_rate_limit, + augmented_input_size=augmented_input_size + ).to(self.device) self.train_model( self.model_steer, torch.tensor(np.array(self.X_steer_train_list)[:,output_step - parameters.controller_steer_input_history_len:], dtype=torch.float32,device=self.device), @@ -505,9 +822,29 @@ def get_trained_model(self,learning_rates=[1e-3,1e-4,1e-5,1e-6],patience=10,batc max_small_input_weight=max_small_steer_weight, small_input_threshold=small_steer_threshold, ) - def relearn_acc(self,learning_rates=[1e-3,1e-4,1e-5,1e-6],patience=10,batch_sizes=[1000], jerk_limit=1.0, max_small_acc_weight=10.0, small_acc_threshold=0.01, randomize=0.001): + def relearn_acc( + self, + learning_rates: List[float] = [1e-3, 1e-4, 1e-5, 1e-6], + patience: int = 10, + batch_sizes: List[int] = [1000], + max_small_acc_weight: float = 10.0, + small_acc_threshold: float = 0.01, + randomize: float = 0.001, + ) -> bool: + """ + Retrain the acceleration model by adding random noise to parameters and retraining. + - learning_rates: List of learning rates to use during retraining. + - patience: Number of epochs to wait for improvement before early stopping. + - batch_sizes: List of batch sizes to use during retraining. + - max_small_acc_weight: Maximum weight for penalties on small acceleration inputs. + - small_acc_threshold: Threshold to define "small" acceleration inputs. + - randomize: Amount of random noise added to model parameters. + """ + if self.model_acc is None: + print("Acceleration model is not trained yet.") + return False self.model_acc.to(self.device) - self.model_acc = self.relearn_model( + self.model_acc, updated = self.relearn_model( self.model_acc, torch.tensor(np.array(self.X_acc_train_list), dtype=torch.float32,device=self.device), torch.tensor(np.array(self.Y_acc_train_list), dtype=torch.float32,device=self.device), @@ -524,9 +861,31 @@ def relearn_acc(self,learning_rates=[1e-3,1e-4,1e-5,1e-6],patience=10,batch_size small_input_threshold=small_acc_threshold, randomize=randomize ) - def relearn_steer(self,learning_rates=[1e-3,1e-4,1e-5,1e-6],patience=10,batch_sizes=[1000], steer_rate_limit=1.0, max_small_steer_weight=30.0, small_steer_threshold=0.005, randomize=0.001): + return updated + def relearn_steer( + self, + learning_rates: List[float] = [1e-3, 1e-4, 1e-5, 1e-6], + patience: int = 10, + batch_sizes: List[int] = [1000], + max_small_steer_weight: float = 30.0, + small_steer_threshold: float = 0.005, + randomize: float = 0.001, + ) -> bool: + """ + Retrain the steering model by adding random noise to parameters and retraining. + - learning_rates: List of learning rates to use during retraining. + - patience: Number of epochs to wait for improvement before early stopping. + - batch_sizes: List of batch sizes to use during retraining. + - steer_rate_limit: Input rate limit for steering. + - max_small_steer_weight: Maximum weight for penalties on small steering inputs. + - small_steer_threshold: Threshold to define "small" steering inputs. + - randomize: Amount of random noise added to model parameters. + """ + if self.model_steer is None: + print("Steering model is not trained yet.") + return False self.model_steer.to(self.device) - self.model_steer = self.relearn_model( + self.model_steer, updated = self.relearn_model( self.model_steer, torch.tensor(np.array(self.X_steer_train_list), dtype=torch.float32,device=self.device), torch.tensor(np.array(self.Y_steer_train_list), dtype=torch.float32,device=self.device), @@ -543,14 +902,29 @@ def relearn_steer(self,learning_rates=[1e-3,1e-4,1e-5,1e-6],patience=10,batch_si small_input_threshold=small_steer_threshold, randomize=randomize ) - def save_model(self,path="inputs_schedule_predictor_model",cmd_mode="both"): + return updated + def save_model(self, path: str = "inputs_schedule_predictor_model", cmd_mode: str = "both") -> None: + """ + Save the trained models to files and export them as CSV for further use. + - path: Directory path where the models will be saved. + - cmd_mode: Mode specifying which model(s) to save ("both", "acc", or "steer"). + """ + # Ensure the directory exists if not os.path.exists(path): os.makedirs(path) + # Save the acceleration model if specified if cmd_mode == "both" or cmd_mode == "acc": + if self.model_acc is None: + print("Acceleration model is not trained yet.") + return self.model_acc.to("cpu") torch.save(self.model_acc, path + "/acc_schedule_predictor.pth") convert_model_to_csv.convert_inputs_schedule_model_to_csv(self.model_acc, path + "/acc_schedule_predictor") + # Save the steering model if specified if cmd_mode == "both" or cmd_mode == "steer": + if self.model_steer is None: + print("Steering model is not trained yet.") + return self.model_steer.to("cpu") torch.save(self.model_steer, path + "/steer_schedule_predictor.pth") convert_model_to_csv.convert_inputs_schedule_model_to_csv(self.model_steer, path + "/steer_schedule_predictor") diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/train_error_prediction_NN_with_memory_bank.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/train_error_prediction_NN_with_memory_bank.py deleted file mode 100644 index ed3f8f64a8988..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/train_error_prediction_NN_with_memory_bank.py +++ /dev/null @@ -1,641 +0,0 @@ -from autoware_vehicle_adaptor.training import add_data_from_csv -from autoware_vehicle_adaptor.training import error_prediction_NN -from autoware_vehicle_adaptor.training import get_initial_hidden_NN -from autoware_vehicle_adaptor.training import convert_model_to_csv -from autoware_vehicle_adaptor.training.training_utils import plot_relearned_vs_original_prediction_error, get_random_memory_bank -from autoware_vehicle_adaptor.training.training_utils import TrainErrorPredictionNNWithMemoryFunctions -from autoware_vehicle_adaptor.training.early_stopping import EarlyStopping -from autoware_vehicle_adaptor.param import parameters -import numpy as np -import torch -from torch import nn -from torch.utils.data import DataLoader -from torch.utils.data import TensorDataset -from torch.utils.data import WeightedRandomSampler -from pathlib import Path -import json -import copy -import matplotlib.pyplot as plt -from sklearn.neighbors import KernelDensity -import yaml -import random -import os -from multiprocessing import Process -import tempfile - -get_loss = TrainErrorPredictionNNWithMemoryFunctions.get_loss -validate_in_batches = TrainErrorPredictionNNWithMemoryFunctions.validate_in_batches -get_each_component_loss = TrainErrorPredictionNNWithMemoryFunctions.get_each_component_loss -get_losses = TrainErrorPredictionNNWithMemoryFunctions.get_losses -get_signed_prediction_error = TrainErrorPredictionNNWithMemoryFunctions.get_signed_prediction_error -get_sequence_data_tensor = TrainErrorPredictionNNWithMemoryFunctions.get_sequence_data_tensor - - -prediction_length = parameters.prediction_length -past_length = parameters.past_length -integration_length = parameters.integration_length -integration_weight = parameters.integration_weight -add_position_to_prediction = parameters.add_position_to_prediction -add_vel_to_prediction = parameters.add_vel_to_prediction -add_yaw_to_prediction = parameters.add_yaw_to_prediction -integrate_states = parameters.integrate_states -integrate_vel = parameters.integrate_vel -integrate_yaw = parameters.integrate_yaw - -state_component_predicted = parameters.state_component_predicted -state_component_predicted_index = parameters.state_component_predicted_index -state_name_to_predicted_index = parameters.state_name_to_predicted_index - -acc_queue_size = parameters.acc_queue_size -steer_queue_size = parameters.steer_queue_size -control_dt = parameters.control_dt -acc_delay_step = parameters.acc_delay_step -steer_delay_step = parameters.steer_delay_step -acc_time_constant = parameters.acc_time_constant -steer_time_constant = parameters.steer_time_constant -wheel_base = parameters.wheel_base -vel_index = 0 -acc_index = 1 -steer_index = 2 -prediction_step = parameters.mpc_predict_step -acc_input_indices_nom = np.arange(3 + acc_queue_size -acc_delay_step, 3 + acc_queue_size -acc_delay_step + prediction_step) -steer_input_indices_nom = np.arange(3 + acc_queue_size + prediction_step + steer_queue_size -steer_delay_step, 3 + acc_queue_size + steer_queue_size -steer_delay_step + 2 * prediction_step) - -prob_update_memory_bank = 0.03 -memory_bank_size = 100 -memory_bank_element_len = 10 - - -class train_error_prediction_NN_with_memory_bank(add_data_from_csv.add_data_from_csv): - """Class for training the error prediction NN.""" - - def __init__( - self, - max_iter=10000, - tol=1e-5, - alpha_1=0.1**7, - alpha_2=0.1**7, - alpha_jacobian=0.1**4, - alpha_jacobian_encoder=0.1**5, - alpha_jacobian_gru_attention=0.1**4, - alpha_hessian=0.1**14, - alpha_hessian_encoder=0.1**14, - alpha_hessian_gru_attention=0.1**14 - ): - super().__init__() - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.max_iter = max_iter - self.tol = tol - self.alpha_1 = alpha_1 - self.alpha_2 = alpha_2 - self.alpha_jacobian = alpha_jacobian - self.alpha_jacobian_encoder = alpha_jacobian_encoder - self.alpha_jacobian_gru_attention = alpha_jacobian_gru_attention - self.alpha_hessian = alpha_hessian - self.alpha_hessian_encoder = alpha_hessian_encoder - self.alpha_hessian_gru_attention = alpha_hessian_gru_attention - - self.models = None - self.model_for_initial_hidden = None - - self.past_length = past_length - self.prediction_length = prediction_length - self.prediction_step = prediction_step - self.acc_queue_size = acc_queue_size - self.steer_queue_size = steer_queue_size - self.adaptive_weight = torch.ones(len(state_component_predicted_index)).to(self.device) - def train_model( - self, - model, - model_for_initial_hidden, - X_train, - Y_train, - memory_bank_train, - batch_sizes: list, - learning_rates: list, - patience: int, - X_val, - Y_val, - memory_bank_val, - freeze_shallow_layers: bool = False, - randomize_remaining_layers: float = 0.001, - integration_prob: float = 0.1, - X_replay=None, - Y_replay=None, - memory_bank_replay=None, - replay_data_rate=0.05 - ): - # Initialize the model - model = model.to(self.device) - model_for_initial_hidden = model_for_initial_hidden.to(self.device) - - print("device:", self.device) - print("total_sample size:", X_train.size(0) + X_val.size(0)) - print("train_sample size:", X_train.size(0)) - criterion = nn.L1Loss() - # freeze the shallow layers - if freeze_shallow_layers: - self.freeze_shallow_layers(model,model_for_initial_hidden,randomize=randomize_remaining_layers) - # save the original adaptive weight - original_adaptive_weight = self.adaptive_weight.clone() - print("original_adaptive_weight: ", original_adaptive_weight) - # Initialize the optimizer - optimizer = torch.optim.Adam(list(model.parameters()) + list(model_for_initial_hidden.parameters()), lr=learning_rates[0]) - #optimizer = torch.optim.Adam(model.parameters(), lr=learning_rates[0]) - - # Initial loss - initial_loss = validate_in_batches(model, model_for_initial_hidden, criterion, X_val, Y_val, memory_bank_val, adaptive_weight=self.adaptive_weight) - print("Initial loss:", initial_loss) - - batch_size = batch_sizes[0] - print("batch_size:", batch_size) - # Initialize the early stopping - early_stopping = EarlyStopping(initial_loss, tol=self.tol, patience=patience) - #Data loader - train_dataset = TensorDataset(X_train, Y_train, memory_bank_train) - train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, pin_memory=True) - # learning_rate index - learning_rate_index = 0 - print("learning rate:", learning_rates[learning_rate_index]) - - # Train the model - for i in range(self.max_iter): - model.train() - model_for_initial_hidden.train() - for X_batch, Y_batch, memory_bank_batch in train_loader: - X_batch = X_batch.to(self.device) - Y_batch = Y_batch.to(self.device) - memory_bank_batch = memory_bank_batch.to(self.device) - optimizer.zero_grad() - loss = get_loss( - criterion, model, model_for_initial_hidden, - X_batch, Y_batch, memory_bank_batch, self.adaptive_weight, - integral_prob=integration_prob, - alpha_jacobian=self.alpha_jacobian,alpha_jacobian_encoder=self.alpha_jacobian_encoder, - alpha_jacobian_gru_attention=self.alpha_jacobian_gru_attention, - alpha_hessian=self.alpha_hessian,alpha_hessian_encoder=self.alpha_hessian_encoder, - alpha_hessian_gru_attention=self.alpha_hessian_gru_attention) - for w in model.parameters(): - loss += self.alpha_1 * torch.norm(w, 1) + self.alpha_2 * torch.norm(w, 2) ** 2 - for w in model_for_initial_hidden.parameters(): - loss += self.alpha_1 * torch.norm(w, 1) + self.alpha_2 * torch.norm(w, 2) ** 2 - if X_replay is not None: - replay_indices = torch.randperm(X_replay.size(0))[:int(replay_data_rate * batch_size)] - X_replay_batch = X_replay[replay_indices].pin_memory().to(self.device) - Y_replay_batch = Y_replay[replay_indices].pin_memory().to(self.device) - memory_bank_replay_batch = memory_bank_replay[replay_indices].pin_memory().to(self.device) - loss += get_loss( - criterion, model, model_for_initial_hidden, - X_replay_batch, Y_replay_batch, memory_bank_replay_batch, self.adaptive_weight, - integral_prob=integration_prob, - alpha_jacobian=self.alpha_jacobian,alpha_jacobian_encoder=self.alpha_jacobian_encoder, - alpha_jacobian_gru_attention=self.alpha_jacobian_gru_attention, - alpha_hessian=self.alpha_hessian,alpha_hessian_encoder=self.alpha_hessian_encoder, - alpha_hessian_gru_attention=self.alpha_hessian_gru_attention) - - loss.backward() - optimizer.step() - # Validate the model - model.eval() - val_loss = validate_in_batches( - model, model_for_initial_hidden, criterion, X_val, Y_val, memory_bank_val, - adaptive_weight=self.adaptive_weight) - val_loss_with_original_weight = validate_in_batches( - model, model_for_initial_hidden, criterion, X_val, Y_val, memory_bank_val, - adaptive_weight=original_adaptive_weight) - if i % 10 == 1: - print("epoch:", i) - print("val_loss_with_original_weight:", val_loss_with_original_weight) - print("val_loss:", val_loss) - if early_stopping(val_loss): - learning_rate_index += 1 - if learning_rate_index >= len(learning_rates): - break - batch_size = batch_sizes[min(learning_rate_index, len(batch_sizes) - 1)] - optimizer = torch.optim.Adam(list(model.parameters()) + list(model_for_initial_hidden.parameters()), lr=learning_rates[learning_rate_index]) - #optimizer = torch.optim.Adam(model.parameters(), lr=learning_rates[learning_rate_index]) - - print("update learning rate to ", learning_rates[learning_rate_index]) - print("batch_size:", batch_size) - train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, pin_memory=True) - early_stopping.reset() - if learning_rates[learning_rate_index - 1] < 3e-4: - self.update_adaptive_weight(model, model_for_initial_hidden, X_train, Y_train, memory_bank_train) - print("adaptive_weight: ", self.adaptive_weight) - - def relearn_model( - self, - model, - model_for_initial_hidden, - X_train, - Y_train, - memory_bank_train, - batch_sizes: list, - learning_rates: list, - patience: int, - X_val, - Y_val, - memory_bank_val, - freeze_shallow_layers: bool = False, - integration_prob: float = 0.1, - randomize=0.001, - reset_weight=False, - X_test=None, - Y_test=None, - memory_bank_test=None, - X_replay=None, - Y_replay=None, - memory_bank_replay=None, - replay_data_rate=0.05, - plt_save_dir=None, - window_size=10, - model_save_path=None, - always_update_model=False - ): - print("randomize:", randomize) - self.update_adaptive_weight(model, model_for_initial_hidden, X_train, Y_train, memory_bank_train) - original_adaptive_weight = self.adaptive_weight.clone() - criterion = nn.L1Loss() - original_train_loss, original_each_component_train_loss, Y_train_pred_origin = get_losses( - model, model_for_initial_hidden, criterion, X_train, Y_train, memory_bank_train, adaptive_weight=original_adaptive_weight) - original_val_loss, original_each_component_val_loss, Y_val_pred_origin = get_losses( - model, model_for_initial_hidden, criterion, X_val, Y_val, memory_bank_val, adaptive_weight=original_adaptive_weight) - if X_test is not None: - original_test_loss, original_each_component_test_loss, Y_test_pred_origin = get_losses( - model, model_for_initial_hidden, criterion, X_test, Y_test, memory_bank_test, adaptive_weight=original_adaptive_weight) - else: - original_test_loss = None - original_each_component_test_loss = None - Y_test_pred_origin = None - if reset_weight: - relearned_model = error_prediction_NN.ErrorPredictionNN( - prediction_length=prediction_length, state_component_predicted=state_component_predicted - ) - relearned_model_for_initial_hidden = get_initial_hidden_NN.GetInitialHiddenNN( - output_size=2*relearned_model.lstm_hidden_total_size - ) - else: - relearned_model = copy.deepcopy(model) - relearned_model.lstm_encoder.flatten_parameters() - relearned_model.lstm.flatten_parameters() - relearned_model_for_initial_hidden = copy.deepcopy(model_for_initial_hidden) - with torch.no_grad(): - if not freeze_shallow_layers: - for w in relearned_model.parameters(): - w += randomize * torch.randn_like(w) - for w in relearned_model_for_initial_hidden.parameters(): - w += randomize * torch.randn_like(w) - self.train_model( - relearned_model, - relearned_model_for_initial_hidden, - X_train, - Y_train, - memory_bank_train, - batch_sizes, - learning_rates, - patience, - X_val, - Y_val, - memory_bank_val, - freeze_shallow_layers=freeze_shallow_layers, - randomize_remaining_layers=randomize, - integration_prob=integration_prob, - X_replay=X_replay, - Y_replay=Y_replay, - memory_bank_replay=memory_bank_replay, - replay_data_rate=replay_data_rate - ) - relearned_train_loss, relearned_each_component_train_loss, Y_train_pred_relearned = get_losses( - relearned_model, relearned_model_for_initial_hidden, criterion, X_train, Y_train, memory_bank_train, adaptive_weight=original_adaptive_weight) - relearned_val_loss, relearned_each_component_val_loss, Y_val_pred_relearned = get_losses( - relearned_model, relearned_model_for_initial_hidden, criterion, X_val, Y_val, memory_bank_val, adaptive_weight=original_adaptive_weight) - if X_test is not None: - relearned_test_loss, relearned_each_component_test_loss, Y_test_pred_relearned = get_losses( - relearned_model, relearned_model_for_initial_hidden, criterion, X_test, Y_test, memory_bank_test, adaptive_weight=original_adaptive_weight) - else: - relearned_test_loss = None - relearned_each_component_test_loss = None - Y_test_pred_relearned = None - - nominal_signed_train_prediction_error, original_signed_train_prediction_error, relearned_signed_train_prediction_error = get_signed_prediction_error( - model, model_for_initial_hidden, relearned_model, relearned_model_for_initial_hidden, - X_train, Y_train, memory_bank_train, window_size - ) - nominal_signed_val_prediction_error, original_signed_val_prediction_error, relearned_signed_val_prediction_error = get_signed_prediction_error( - model, model_for_initial_hidden, relearned_model, relearned_model_for_initial_hidden, - X_val, Y_val, memory_bank_val, window_size - ) - - if X_test is not None: - nominal_signed_test_prediction_error, original_signed_test_prediction_error, relearned_signed_test_prediction_error = get_signed_prediction_error( - model, model_for_initial_hidden, relearned_model, relearned_model_for_initial_hidden, - X_test, Y_test, memory_bank_test, window_size - ) - else: - nominal_signed_test_prediction_error = None - original_signed_test_prediction_error = None - relearned_signed_test_prediction_error = None - plot_relearned_vs_original_prediction_error( - window_size, - original_train_loss, - relearned_train_loss, - original_each_component_train_loss, - relearned_each_component_train_loss, - nominal_signed_train_prediction_error, - original_signed_train_prediction_error, - relearned_signed_train_prediction_error, - Y_train, - Y_train_pred_origin, - Y_train_pred_relearned, - original_val_loss, - relearned_val_loss, - original_each_component_val_loss, - relearned_each_component_val_loss, - nominal_signed_val_prediction_error, - original_signed_val_prediction_error, - relearned_signed_val_prediction_error, - Y_val, - Y_val_pred_origin, - Y_val_pred_relearned, - original_test_loss, - relearned_test_loss, - original_each_component_test_loss, - relearned_each_component_test_loss, - nominal_signed_test_prediction_error, - original_signed_test_prediction_error, - relearned_signed_test_prediction_error, - Y_test, - Y_test_pred_origin, - Y_test_pred_relearned, - plt_save_dir - ) - if model_save_path is not None: - self.save_given_model(relearned_model,relearned_model_for_initial_hidden,model_save_path,model_save_path.replace(".pth","_for_initial_hidden.pth")) - if relearned_val_loss < original_val_loss or always_update_model: - return relearned_model, relearned_model_for_initial_hidden, True - else: - return model, model_for_initial_hidden, False - - def get_trained_model(self, learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience=10, batch_sizes=[100,10,100]): - print("state_component_predicted: ", state_component_predicted) - # Define Time Series Data - X_train, Y_train, memory_bank_train = get_sequence_data_tensor(self.X_train_list, self.Y_train_list,self.division_indices_train) - X_val, Y_val, memory_bank_val = get_sequence_data_tensor(self.X_val_list, self.Y_val_list,self.division_indices_val) - - self.model = error_prediction_NN.ErrorPredictionNN( - prediction_length=prediction_length, state_component_predicted=state_component_predicted - ) - self.model_for_initial_hidden = get_initial_hidden_NN.GetInitialHiddenNN( - output_size=2*self.model.lstm_hidden_total_size - ) - # self.update_adaptive_weight(None,None,X_train,Y_train,memory_bank_train) - self.train_model( - self.model, - self.model_for_initial_hidden, - X_train, - Y_train, - memory_bank_train, - batch_sizes, - learning_rates, - patience, - X_val, - Y_val, - memory_bank_val - ) - def get_relearned_model(self, learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience=10, batch_sizes=[100,10,100], - reset_weight=False, randomize=0.001,plt_save_dir=None,model_save_path=None, - use_replay_data=False, replay_data_rate=0.05, always_update_model=False, freeze_shallow_layers=False): - self.model.to(self.device) - # Define Time Series Data - X_train, Y_train, memory_bank_train = get_sequence_data_tensor(self.X_train_list, self.Y_train_list,self.division_indices_train) - X_val, Y_val, memory_bank_val = get_sequence_data_tensor(self.X_val_list, self.Y_val_list,self.division_indices_val) - if len(self.X_test_list) > 0: - X_test, Y_test, memory_bank_test = get_sequence_data_tensor(self.X_test_list, self.Y_test_list,self.division_indices_test) - else: - X_test = None - Y_test = None - memory_bank_test = None - if use_replay_data and len(self.X_replay_list) > 0: - X_replay, Y_replay, memory_bank_replay = get_sequence_data_tensor(self.X_replay_list, self.Y_replay_list,self.division_indices_replay) - else: - X_replay = None - Y_replay = None - memory_bank_replay = None - - self.model, self.model_for_initial_hidden, updated = self.relearn_model( - self.model, - self.model_for_initial_hidden, - X_train, - Y_train, - memory_bank_train, - batch_sizes, - learning_rates, - patience, - X_val, - Y_val, - memory_bank_val, - randomize=randomize, - X_test=X_test, - Y_test=Y_test, - memory_bank_test=memory_bank_test, - reset_weight=reset_weight, - X_replay=X_replay, - Y_replay=Y_replay, - memory_bank_replay=memory_bank_replay, - replay_data_rate=replay_data_rate, - plt_save_dir=plt_save_dir, - model_save_path=model_save_path, - always_update_model=always_update_model, - freeze_shallow_layers=freeze_shallow_layers - ) - return updated - def initialize_ensemble_models(self): - self.models = [self.model] - self.models_for_initial_hidden = [self.model_for_initial_hidden] - def get_updated_temp_model(self,learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience=10, batch_sizes=[100,10,100], - use_replay_data=False, replay_data_rate=0.05,randomize_remaining_layers=0.0): - self.temp_model = copy.deepcopy(self.model) - self.temp_model_for_initial_hidden = copy.deepcopy(self.model_for_initial_hidden) - X_train, Y_train, memory_bank_train = get_sequence_data_tensor(self.X_train_list, self.Y_train_list,self.division_indices_train) - X_val, Y_val, memory_bank_val = get_sequence_data_tensor(self.X_val_list, self.Y_val_list,self.division_indices_val) - if use_replay_data and len(self.X_replay_list) > 0: - X_replay, Y_replay, memory_bank_replay = get_sequence_data_tensor(self.X_replay_list, self.Y_replay_list,self.division_indices_replay) - else: - X_replay = None - Y_replay = None - memory_bank_replay = None - self.temp_model.to(self.device) - self.update_adaptive_weight(self.temp_model, self.temp_model_for_initial_hidden, - X_train, Y_train, memory_bank_train) - self.train_model(self.temp_model, - X_train, - Y_train, - memory_bank_train, - batch_sizes, - learning_rates, - patience, - X_val, - Y_val, - memory_bank_val, - freeze_shallow_layers=True, - randomize_remaining_layers=randomize_remaining_layers, - X_replay=X_replay, - Y_replay=Y_replay, - memory_bank_replay=memory_bank_replay, - replay_data_rate=replay_data_rate - ) - def relearn_temp_model(self, learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience=10, batch_sizes=[100], - plt_save_dir=None,model_save_path=None, use_replay_data=False, - replay_data_rate=0.05,randomize_remaining_layers=0.0): - self.temp_model.to(self.device) - self.temp_model_for_initial_hidden.to(self.device) - # Define Time Series Data - X_train, Y_train, memory_bank_train = get_sequence_data_tensor(self.X_train_list, self.Y_train_list,self.division_indices_train) - X_val, Y_val, memory_bank_val = get_sequence_data_tensor(self.X_val_list, self.Y_val_list,self.division_indices_val) - if len(self.X_test_list) > 0: - X_test, Y_test, memory_bank_test = get_sequence_data_tensor(self.X_test_list, self.Y_test_list,self.division_indices_test) - else: - X_test = None - Y_test = None - memory_bank_test = None - if use_replay_data and len(self.X_replay_list) > 0: - X_replay, Y_replay, memory_bank_replay = get_sequence_data_tensor(self.X_replay_list, self.Y_replay_list,self.division_indices_replay) - else: - X_replay = None - Y_replay = None - memory_bank_replay = None - self.temp_model, self.temp_model_for_initial_hidden, updated = self.relearn_model( - self.temp_model, - self.temp_model_for_initial_hidden, - X_train, - Y_train, - memory_bank_train, - batch_sizes, - learning_rates, - patience, - X_val, - Y_val, - memory_bank_val, - X_test=X_test, - Y_test=Y_test, - memory_bank_test=memory_bank_test, - X_replay=X_replay, - Y_replay=Y_replay, - memory_bank_replay=memory_bank_replay, - replay_data_rate=replay_data_rate, - plt_save_dir=plt_save_dir, - model_save_path=model_save_path, - freeze_shallow_layers=True, - randomize=randomize_remaining_layers, - ) - return updated - def add_temp_model_to_ensemble(self): - self.models.append(self.temp_model) - self.models_for_initial_hidden.append(self.temp_model_for_initial_hidden) - def save_model(self, path="vehicle_model.pth",path_for_initial_hidden="vehicle_model_for_initial_hidden.pth"): - self.model.to("cpu") - self.model_for_initial_hidden.to("cpu") - torch.save(self.model, path) - torch.save(self.model_for_initial_hidden, path_for_initial_hidden) - save_dir = path.replace(".pth", "") - convert_model_to_csv.convert_model_to_csv(self.model, save_dir) - convert_model_to_csv.convert_initial_hidden_model_to_csv(self.model_for_initial_hidden, save_dir) - def save_given_model(self, model,model_for_initial_hidden, path="vehicle_model.pth",path_for_initial_hidden="vehicle_model_for_initial_hidden.pth"): - model.to("cpu") - model_for_initial_hidden.to("cpu") - torch.save(model, path) - torch.save(model_for_initial_hidden, path_for_initial_hidden) - save_dir = path.replace(".pth", "") - convert_model_to_csv.convert_model_to_csv(model, save_dir) - convert_model_to_csv.convert_initial_hidden_model_to_csv(model_for_initial_hidden, save_dir) - def save_ensemble_models(self, paths): - for i in range(len(paths)): - temp_model = self.models[i] - temp_model.to("cpu") - torch.save(temp_model, paths[i]) - temp_model_for_initial_hidden = self.models_for_initial_hidden[i] - temp_model_for_initial_hidden.to("cpu") - torch.save(temp_model_for_initial_hidden, paths[i].replace(".pth", "_for_initial_hidden.pth")) - save_dir = paths[i].replace(".pth", "") - convert_model_to_csv.convert_model_to_csv(temp_model, save_dir) - convert_model_to_csv.convert_initial_hidden_model_to_csv(temp_model_for_initial_hidden, save_dir) - def get_initial_gru_bank(self,path="gru_bank.csv"): - random_memory_bank_np = get_random_memory_bank(np.array(self.X_train_list)) - random_memory_bank = torch.from_numpy(random_memory_bank_np).to(torch.float32).unsqueeze(0) - if random_memory_bank.device != next(self.model_for_initial_hidden.parameters()).device: - random_memory_bank = random_memory_bank.to(next(self.model_for_initial_hidden.parameters()).device) - gru_bank = self.model_for_initial_hidden(random_memory_bank, only_gru=True).squeeze(0) - gru_bank_np = gru_bank.cpu().detach().numpy() - np.savetxt(path, gru_bank_np, delimiter=",") - def freeze_shallow_layers(self,model,model_for_initial_hidden,randomize=0.001): - # freeze the encoder layers - for param in model.acc_encoder_layer_1.parameters(): - param.requires_grad = False - for param in model.acc_encoder_layer_2.parameters(): - param.requires_grad = False - for param in model.steer_encoder_layer_1.parameters(): - param.requires_grad = False - for param in model.steer_encoder_layer_2.parameters(): - param.requires_grad = False - for param in model.lstm_encoder.parameters(): - param.requires_grad = False - # freeze shallow layers of the decoder - for param in model.acc_layer_1.parameters(): - param.requires_grad = False - for param in model.acc_layer_2.parameters(): - param.requires_grad = False - for param in model.steer_layer_1.parameters(): - param.requires_grad = False - for param in model.steer_layer_2.parameters(): - param.requires_grad = False - for param in model.lstm.parameters(): - param.requires_grad = False - - # freeze model for initial hidden - for param in model_for_initial_hidden.parameters(): - param.requires_grad = False - - # randomize the remaining parameters - with torch.no_grad(): - model.complimentary_layer[0].weight += randomize * torch.randn_like(model.complimentary_layer[0].weight) - model.complimentary_layer[0].bias += randomize * torch.randn_like(model.complimentary_layer[0].bias) - model.linear_relu[0].weight += randomize * torch.randn_like(model.linear_relu[0].weight) - model.linear_relu[0].bias += randomize * torch.randn_like(model.linear_relu[0].bias) - model.final_layer.weight += randomize * torch.randn_like(model.final_layer.weight) - model.final_layer.bias += randomize * torch.randn_like(model.final_layer.bias) - - def update_adaptive_weight(self,model, model_for_initial_hidden, X, Y, memory_bank,batch_size=1000): - if model is not None: - model.to(self.device) - model_for_initial_hidden.to(self.device) - model.eval() - model_for_initial_hidden.eval() - num_batches = (X.size(0) + batch_size - 1) // batch_size - prediction_error = torch.zeros(self.adaptive_weight.shape[0], device=self.device) - with torch.no_grad(): - for i in range(num_batches): - start_idx = i * batch_size - end_idx = min((i + 1) * batch_size, X.size(0)) - if X.device != self.device: - X_batch = X[start_idx:end_idx].pin_memory().to(self.device, non_blocking=True) - Y_batch = Y[start_idx:end_idx].pin_memory().to(self.device, non_blocking=True) - memory_bank_batch = memory_bank[start_idx:end_idx].pin_memory().to(self.device, non_blocking=True) - else: - X_batch = X[start_idx:end_idx] - Y_batch = Y[start_idx:end_idx] - memory_bank_batch = memory_bank[start_idx:end_idx] - if model is not None: - hc_initial_concat = model_for_initial_hidden(memory_bank_batch) - hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") - # Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") - prediction_error += torch.mean(torch.abs(Y_pred - Y_batch[:,past_length:]),dim=(0,1)) * (end_idx - start_idx) - else: - prediction_error += torch.mean(torch.abs(Y_batch[:,past_length:]),dim=(0,1)) * (end_idx - start_idx) - prediction_error /= X.size(0) - print("prediction_error:", prediction_error) - self.adaptive_weight = 1.0 / (prediction_error + 1e-4) - for i in range(len(self.adaptive_weight)): - if self.adaptive_weight[i] > torch.max(self.adaptive_weight[-2:]): - self.adaptive_weight[i] = torch.max(self.adaptive_weight[-2:]) # acc and steer are respected - self.adaptive_weight = self.adaptive_weight / torch.mean(self.adaptive_weight) diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/train_error_prediction_NN_with_offline_data.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/train_error_prediction_NN_with_offline_data.py index 2a1c60d49745d..ccd450eb89711 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/train_error_prediction_NN_with_offline_data.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/train_error_prediction_NN_with_offline_data.py @@ -30,9 +30,9 @@ get_signed_prediction_error = TrainErrorPredictionNNWithOfflineData.get_signed_prediction_error get_sequence_data = TrainErrorPredictionNNWithOfflineData.get_sequence_data -get_offline_feature_dict = TrainErrorPredictionNNWithOfflineData.get_offline_feature_dict -get_offline_features_batch = TrainErrorPredictionNNWithOfflineData.get_offline_features_batch -get_offline_features = TrainErrorPredictionNNWithOfflineData.get_offline_features +get_initial_hidden_dict = TrainErrorPredictionNNWithOfflineData.get_initial_hidden_dict +get_initial_hidden_batch = TrainErrorPredictionNNWithOfflineData.get_initial_hidden_batch +get_initial_hidden = TrainErrorPredictionNNWithOfflineData.get_initial_hidden @@ -68,7 +68,6 @@ -past_initial_hidden_length = past_length offline_data_size = 100 offline_data_len = 10 class CustomDataset(Dataset): @@ -109,11 +108,11 @@ def __init__( self.alpha_hessian_encoder = alpha_hessian_encoder self.alpha_hessian_gru_attention = alpha_hessian_gru_attention + self.model = None self.models = None self.model_for_initial_hidden = None self.past_length = past_length - self.past_initial_hidden_length = past_initial_hidden_length self.prediction_length = prediction_length self.prediction_step = prediction_step self.acc_queue_size = acc_queue_size @@ -161,8 +160,6 @@ def set_offline_data(self,domain="train",target="train",domain_indices=[0],targe def train_model( self, model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X_train, Y_train, @@ -184,8 +181,6 @@ def train_model( ): # Initialize the model model = model.to(self.device) - preprocessor = preprocessor.to(self.device) - model_for_offline_features = model_for_offline_features.to(self.device) model_for_initial_hidden = model_for_initial_hidden.to(self.device) @@ -197,7 +192,7 @@ def train_model( # freeze the shallow layers if freeze_shallow_layers: self.freeze_shallow_layers( - model,preprocessor,model_for_offline_features,model_for_initial_hidden, + model,model_for_initial_hidden, randomize=randomize_remaining_layers) # save the original adaptive weight original_adaptive_weight = self.adaptive_weight.clone() @@ -205,10 +200,6 @@ def train_model( # Initialize the optimizer params_to_optimize = list( param for param in model.parameters() if param.requires_grad - ) + list( - param for param in preprocessor.parameters() if param.requires_grad - ) + list( - param for param in model_for_offline_features.parameters() if param.requires_grad ) + list( param for param in model_for_initial_hidden.parameters() if param.requires_grad ) @@ -217,8 +208,6 @@ def train_model( # Initial loss initial_loss = validate_in_batches( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X_val, @@ -242,8 +231,6 @@ def train_model( # Training loop for i in range(self.max_iter): model.train() - preprocessor.train() - model_for_offline_features.train() model_for_initial_hidden.train() for j in range(len(domains)): self.set_offline_data(domains[j],targets[j],domain_indices[j],target_indices[j]) @@ -252,7 +239,7 @@ def train_model( Y_batch = Y_batch.pin_memory().to(self.device, non_blocking=True) optimizer.zero_grad() loss = get_loss( - criterion,model,preprocessor,model_for_offline_features,model_for_initial_hidden, + criterion,model,model_for_initial_hidden, X_batch,Y_batch,self.offline_data_dict_train,Ind_batch, adaptive_weight=self.adaptive_weight, integral_prob=integration_prob, @@ -269,7 +256,7 @@ def train_model( Y_replay_batch = Y_replay[replay_indices_tensor].pin_memory().to(self.device, non_blocking=True) Ind_replay_batch = indices_replay[replay_indices] loss += get_loss( - criterion,model,preprocessor,model_for_offline_features,model_for_initial_hidden, + criterion,model,model_for_initial_hidden, X_replay_batch,Y_replay_batch,self.offline_data_dict_replay,Ind_replay_batch, adaptive_weight=self.adaptive_weight, integral_prob=integration_prob, @@ -281,13 +268,9 @@ def train_model( optimizer.step() # Validation the model model.eval() - preprocessor.eval() - model_for_offline_features.eval() model_for_initial_hidden.eval() val_loss = validate_in_batches( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X_val, @@ -298,8 +281,6 @@ def train_model( ) val_loss_with_original_weight = validate_in_batches( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X_val, @@ -326,8 +307,6 @@ def train_model( if learning_rates[learning_rate_index - 1] < 3e-4: self.update_adaptive_weight( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X_train, Y_train, @@ -339,8 +318,6 @@ def train_model( def relearn_model( self, model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X_train, Y_train, @@ -371,8 +348,6 @@ def relearn_model( print("randomize: ", randomize) self.update_adaptive_weight( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X_train, Y_train, @@ -383,8 +358,6 @@ def relearn_model( criterion = nn.L1Loss() original_train_loss, original_each_component_train_loss, Y_train_pred_origin = get_losses( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X_train, @@ -394,8 +367,6 @@ def relearn_model( adaptive_weight = original_adaptive_weight) original_val_loss, original_each_component_val_loss, Y_val_pred_origin = get_losses( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X_val, @@ -406,8 +377,6 @@ def relearn_model( if X_test is not None: original_test_loss, original_each_component_test_loss, Y_test_pred_origin = get_losses( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X_test, @@ -423,36 +392,22 @@ def relearn_model( relearned_model = error_prediction_NN.ErrorPredictionNN( prediction_length=prediction_length, state_component_predicted=state_component_predicted ) - relearned_preprocessor = get_initial_hidden_NN_with_offline_data.Preprocess() - relearned_model_for_offline_features = get_initial_hidden_NN_with_offline_data.GetOfflineFeatures( - input_size = relearned_model.combined_input_size, - output_size = 2 * relearned_model.lstm_hidden_total_size, - ).to(self.device) relearned_model_for_initial_hidden = get_initial_hidden_NN_with_offline_data.GetInitialHiddenNN( - input_size = relearned_model.combined_input_size, output_size = 2 * relearned_model.lstm_hidden_total_size, ).to(self.device) else: relearned_model = copy.deepcopy(model) relearned_model.lstm_encoder.flatten_parameters() relearned_model.lstm.flatten_parameters() - relearned_preprocessor = copy.deepcopy(preprocessor) - relearned_model_for_offline_features = copy.deepcopy(model_for_offline_features) relearned_model_for_initial_hidden = copy.deepcopy(model_for_initial_hidden) with torch.no_grad(): if not freeze_shallow_layers: for w in relearned_model.parameters(): w = w + randomize * torch.randn_like(w) - for w in relearned_preprocessor.parameters(): - w = w + randomize * torch.randn_like(w) - for w in relearned_model_for_offline_features.parameters(): - w = w + randomize * torch.randn_like(w) for w in relearned_model_for_initial_hidden.parameters(): w = w + randomize * torch.randn_like(w) self.train_model( relearned_model, - relearned_preprocessor, - relearned_model_for_offline_features, relearned_model_for_initial_hidden, X_train, Y_train, @@ -473,31 +428,31 @@ def relearn_model( domains=domains, targets=targets, domain_indices=domain_indices, target_indices=target_indices ) relearned_train_loss, relearned_each_component_train_loss, Y_train_pred_relearned = get_losses( - relearned_model, relearned_preprocessor, relearned_model_for_offline_features, relearned_model_for_initial_hidden, + relearned_model, relearned_model_for_initial_hidden, criterion, X_train, Y_train, self.offline_data_dict_train, indices_train, adaptive_weight = original_adaptive_weight) relearned_val_loss, relearned_each_component_val_loss, Y_val_pred_relearned = get_losses( - relearned_model, relearned_preprocessor, relearned_model_for_offline_features, relearned_model_for_initial_hidden, + relearned_model, relearned_model_for_initial_hidden, criterion, X_val, Y_val, self.offline_data_dict_val, indices_val, adaptive_weight = original_adaptive_weight) if X_test is not None: relearned_test_loss, relearned_each_component_test_loss, Y_test_pred_relearned = get_losses( - relearned_model, relearned_preprocessor, relearned_model_for_offline_features, relearned_model_for_initial_hidden, + relearned_model, relearned_model_for_initial_hidden, criterion, X_test, Y_test, self.offline_data_dict_test, indices_test, adaptive_weight = original_adaptive_weight) else: relearned_test_loss = None relearned_each_component_test_loss = None Y_test_pred_relearned = None nominal_signed_train_prediction_error, original_signed_train_prediction_error, relearned_signed_train_prediction_error = get_signed_prediction_error( - model, preprocessor, model_for_offline_features, model_for_initial_hidden, - relearned_model, relearned_preprocessor, relearned_model_for_offline_features, relearned_model_for_initial_hidden, + model, model_for_initial_hidden, + relearned_model, relearned_model_for_initial_hidden, X_train, Y_train, self.offline_data_dict_train, indices_train) nominal_signed_val_prediction_error, original_signed_val_prediction_error, relearned_signed_val_prediction_error = get_signed_prediction_error( - model, preprocessor, model_for_offline_features, model_for_initial_hidden, - relearned_model, relearned_preprocessor, relearned_model_for_offline_features, relearned_model_for_initial_hidden, + model, model_for_initial_hidden, + relearned_model, relearned_model_for_initial_hidden, X_val, Y_val, self.offline_data_dict_val, indices_val) if X_test is not None: nominal_signed_test_prediction_error, original_signed_test_prediction_error, relearned_signed_test_prediction_error = get_signed_prediction_error( - model, preprocessor, model_for_offline_features, model_for_initial_hidden, - relearned_model, relearned_preprocessor, relearned_model_for_offline_features, relearned_model_for_initial_hidden, + model, model_for_initial_hidden, + relearned_model, relearned_model_for_initial_hidden, X_test, Y_test, self.offline_data_dict_test, indices_test) else: nominal_signed_test_prediction_error = None @@ -536,16 +491,15 @@ def relearn_model( Y_test_pred_origin, Y_test_pred_relearned, plt_save_dir, - past_length=past_initial_hidden_length + past_length=past_length ) if model_save_path is not None: - self.save_given_model(relearned_model,relearned_preprocessor,relearned_model_for_offline_features,relearned_model_for_initial_hidden, - model_save_path,model_save_path.replace(".pth","_for_preprocessor.pth"), - model_save_path.replace(".pth","_for_offline_features.pth"), model_save_path.replace(".pth","_for_initial_hidden.pth")) + self.save_given_model(relearned_model,relearned_model_for_initial_hidden, + model_save_path.replace(".pth","_for_initial_hidden.pth")) if relearned_val_loss < original_val_loss or always_update_model: - return relearned_model, relearned_preprocessor, relearned_model_for_offline_features, relearned_model_for_initial_hidden, True + return relearned_model, relearned_model_for_initial_hidden, True else: - return model, preprocessor, model_for_offline_features, model_for_initial_hidden, False + return model, model_for_initial_hidden, False @@ -559,21 +513,11 @@ def get_trained_model(self, learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience=10 self.model = error_prediction_NN.ErrorPredictionNN( prediction_length=prediction_length, state_component_predicted=state_component_predicted ).to(self.device) - self.preprocessor = get_initial_hidden_NN_with_offline_data.Preprocess().to(self.device) - combined_input_size = self.preprocessor.combined_input_size - - self.model_for_offline_features = get_initial_hidden_NN_with_offline_data.GetOfflineFeatures( - input_size = combined_input_size, - output_size = 2 * self.model.lstm_hidden_total_size - ).to(self.device) self.model_for_initial_hidden = get_initial_hidden_NN_with_offline_data.GetInitialHiddenNN( - input_size = combined_input_size, output_size = 2 * self.model.lstm_hidden_total_size ).to(self.device) self.train_model( self.model, - self.preprocessor, - self.model_for_offline_features, self.model_for_initial_hidden, X_train, Y_train, @@ -592,8 +536,6 @@ def get_relearned_model(self, learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience= use_replay_data=False, replay_data_rate=0.05, always_update_model=False, freeze_shallow_layers=False, domains=[],targets=[],domain_indices=[],target_indices=[]): self.model.to(self.device) - self.preprocessor.to(self.device) - self.model_for_offline_features.to(self.device) self.model_for_initial_hidden.to(self.device) # Define Time Series Data X_train, Y_train, indices_train = get_sequence_data(self.X_train_list,self.Y_train_list,self.division_indices_train) @@ -610,10 +552,8 @@ def get_relearned_model(self, learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience= X_replay = None Y_replay = None indices_replay = None - self.model, self.preprocessor, self.model_for_offline_features, self.model_for_initial_hidden, updated = self.relearn_model( + self.model, self.model_for_initial_hidden, updated = self.relearn_model( self.model, - self.preprocessor, - self.model_for_offline_features, self.model_for_initial_hidden, X_train, Y_train, @@ -645,8 +585,6 @@ def get_relearned_model(self, learning_rates=[1e-3, 1e-4, 1e-5, 1e-6], patience= def freeze_shallow_layers( self, model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, randomize=0.001 ): @@ -674,13 +612,6 @@ def freeze_shallow_layers( param.requires_grad = False - - # freeze preprocessor - for param in preprocessor.parameters(): - param.requires_grad = False - # freeze model for offline features - for param in model_for_offline_features.parameters(): - param.requires_grad = False # freeze model for initial hidden for param in model_for_initial_hidden.parameters(): param.requires_grad = False @@ -693,33 +624,25 @@ def freeze_shallow_layers( model.final_layer.weight += randomize * torch.randn_like(model.final_layer.weight) model.final_layer.bias += randomize * torch.randn_like(model.final_layer.bias) - def save_model(self, path="vehicle_model.pth", path_for_preprocessor="vehicle_model_for_preprocessor.pth", path_for_offline_features="vehicle_model_for_offline_features.pth", path_for_initial_hidden="vehicle_model_for_initial_hidden.pth"): + def save_model(self, path="vehicle_model.pth", path_for_initial_hidden="vehicle_model_for_initial_hidden.pth"): torch.save(self.model, path) - torch.save(self.preprocessor, path_for_preprocessor) - torch.save(self.model_for_offline_features, path_for_offline_features) torch.save(self.model_for_initial_hidden, path_for_initial_hidden) save_dir = path.replace(".pth","") convert_model_to_csv.convert_model_to_csv(self.model, save_dir) - #convert_model_to_csv.convert_initial_hidden_with_offline_data_model_to_csv(self.preprocessor, self.model_for_initial_hidden, save_dir) - def save_given_model(self,model,preprocessor,model_for_offline_features,model_for_initial_hidden,path="vehicle_model.pth", path_for_preprocessor="vehicle_model_for_preprocessor.pth", path_for_offline_features="vehicle_model_for_offline_features.pth", path_for_initial_hidden="vehicle_model_for_initial_hidden.pth"): + def save_given_model(self,model,model_for_initial_hidden,path="vehicle_model.pth", path_for_initial_hidden="vehicle_model_for_initial_hidden.pth"): torch.save(model, path) - torch.save(preprocessor, path_for_preprocessor) - torch.save(model_for_offline_features, path_for_offline_features) torch.save(model_for_initial_hidden, path_for_initial_hidden) save_dir = path.replace(".pth","") convert_model_to_csv.convert_model_to_csv(model, save_dir) - #convert_model_to_csv.convert_initial_hidden_with_offline_data_model_to_csv(preprocessor, model_for_initial_hidden, save_dir) + def save_offline_features(self, path="offline_features.csv"): - offline_features = get_offline_feature_dict(self.preprocessor,self.model_for_offline_features,self.offline_data_dict_train)[0] - offline_features = self.model_for_initial_hidden(offline_features.unsqueeze(0))[0] - offline_features_np = offline_features.cpu().detach().numpy() - np.savetxt(path, offline_features_np, delimiter=",") + initial_hidden =get_initial_hidden_dict(self.model_for_initial_hidden,self.offline_data_dict_train)[0] + initial_hidden_np = initial_hidden.cpu().detach().numpy() + np.savetxt(path, initial_hidden_np, delimiter=",") def update_adaptive_weight( self, model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X, Y, @@ -732,7 +655,7 @@ def update_adaptive_weight( model_for_initial_hidden.to(self.device) model.eval() model_for_initial_hidden.eval() - offline_feature_dim = 2 * model.lstm_hidden_total_size + initial_hidden_dim = 2 * model.lstm_hidden_total_size num_batches = (X.size(0) + batch_size - 1) // batch_size prediction_error = torch.zeros(self.adaptive_weight.shape[0], device=self.device) @@ -748,22 +671,18 @@ def update_adaptive_weight( X_batch = X[start_idx:end_idx] Y_batch = Y[start_idx:end_idx] if model is not None: - offline_features, mask = get_offline_features( - preprocessor, - model_for_offline_features, + hc_initial_concat = get_initial_hidden( + model_for_initial_hidden, offline_data_dict, indices_batch, - offline_feature_dim + initial_hidden_dim ) - #hc_initial_concat = model_for_initial_hidden(offline_features, X_batch[:,:past_initial_hidden_length],mask,preprocessor) - hc_initial_concat = model_for_initial_hidden(offline_features,mask) - hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - Y_pred, _ = model(X_batch[:,past_length- past_initial_hidden_length:], previous_error=Y_batch[:, past_length- past_initial_hidden_length:past_initial_hidden_length, -2:],hc=hc_initial, mode="get_lstm_states") + Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") # Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") - prediction_error += torch.mean(torch.abs(Y_pred - Y_batch[:,past_initial_hidden_length:]),dim=(0,1)) * (end_idx - start_idx) + prediction_error += torch.mean(torch.abs(Y_pred - Y_batch[:,past_length:]),dim=(0,1)) * (end_idx - start_idx) else: - prediction_error += torch.mean(torch.abs(Y_batch[:,past_initial_hidden_length:]),dim=(0,1)) * (end_idx - start_idx) + prediction_error += torch.mean(torch.abs(Y_batch[:,past_length:]),dim=(0,1)) * (end_idx - start_idx) prediction_error /= X.size(0) print("prediction_error:", prediction_error) self.adaptive_weight = 1.0 / (prediction_error + 1e-4) diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/training_utils.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/training_utils.py index 1087c334dd98a..8ba0656baac54 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/training_utils.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/training_utils.py @@ -26,7 +26,6 @@ #torch.autograd.set_detect_anomaly(True) prediction_length = parameters.prediction_length past_length = parameters.past_length -past_initial_hidden_length = past_length# + 50 integration_length = parameters.integration_length integration_weight = parameters.integration_weight add_position_to_prediction = parameters.add_position_to_prediction @@ -55,10 +54,6 @@ acc_input_indices_nom = np.arange(3 + acc_queue_size -acc_delay_step, 3 + acc_queue_size -acc_delay_step + prediction_step) steer_input_indices_nom = np.arange(3 + acc_queue_size + prediction_step + steer_queue_size -steer_delay_step, 3 + acc_queue_size + steer_queue_size -steer_delay_step + 2 * prediction_step) -prob_update_memory_bank = 0.03 -memory_bank_size = 100 -memory_bank_element_len = 10 - offline_data_size = 300 #100 offline_data_len = 10 @@ -100,38 +95,6 @@ def get_offline_data(X, division_indices, prediction_step, use_division_indices) for i in range(offline_data_size): offline_data.append(X[selected_index[i] + prediction_step * np.arange(offline_data_len)]) return np.array(offline_data) -def transform_to_sequence_data_with_memory_bank(X,seq_size,indices,prediction_step): - X_seq = [] - memory_bank = [] - start_num = 0 - for i in range(len(indices)): - j = 0 - memory_bank_indices = np.random.choice(np.arange(start_num,indices[i] - prediction_step * memory_bank_element_len),memory_bank_size) - #tmp_memory_bank = [] - #for k in range(memory_bank_size): - # tmp_memory_bank.append(X[memory_bank_indices[k] + prediction_step * np.arange(memory_bank_element_len)]) - tmp_memory_bank = np.array([ - X[memory_bank_indices[k] + prediction_step * np.arange(memory_bank_element_len)] - for k in range(memory_bank_size) - ]) - while start_num + j + prediction_step * seq_size <= indices[i]: - X_seq.append(X[start_num + j + prediction_step * np.arange(seq_size)]) - if random.random() < prob_update_memory_bank: - tmp_memory_bank[:-1] = tmp_memory_bank[1:] - tmp_memory_bank[-1] = X[start_num + j + prediction_step * np.arange(seq_size - memory_bank_element_len - 1, seq_size - 1)] - #tmp_memory_bank.pop(0) - #tmp_memory_bank.append(X[start_num + j + prediction_step * np.arange(seq_size - memory_bank_element_len,seq_size)]) - memory_bank.append(np.array(tmp_memory_bank).copy()) - j += 1 - start_num = indices[i] - return np.array(X_seq),np.array(memory_bank) -def get_random_memory_bank(X): - memory_bank_indices = np.random.choice(np.arange(X.shape[0] - prediction_step * memory_bank_element_len),memory_bank_size) - memory_bank = np.array([ - X[memory_bank_indices[k] + prediction_step * np.arange(memory_bank_element_len)] - for k in range(memory_bank_size) - ]) - return memory_bank def get_acc_steer_nominal_prediction(X,window_size=10,past_length=past_length): state_tmp = X[:,past_length, [acc_index, steer_index]] for i in range(window_size): @@ -322,11 +285,11 @@ def get_acc_steer_models_prediction(models,X,Y,window_size=10,batch_size=1000): X_batch = X[k*batch_size:(k+1)*batch_size] Y_batch = Y[k*batch_size:(k+1)*batch_size] - _, hc = model[0](X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") + _, hc = models[0](X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") states_tmp=X_batch[:, past_length, [vel_index, acc_index, steer_index]].unsqueeze(1) for i in range(window_size): states_tmp_with_input_history = torch.cat((states_tmp, X_batch[:,[past_length+i], 3:]), dim=2) - predicted_error_0, hc_next = model[0](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") + predicted_error_0, hc_next = models[0](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") predicted_error = predicted_error_0 / len(models) for j in range(1,len(models)): predicted_error_j, _ = models[j](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") @@ -400,465 +363,54 @@ def get_sequence_data(X, Y, Z, division_indices, acc_threshold=2.0, steer_thresh Z_seq_filtered.append(Z_seq[i]) return np.array(X_seq_filtered), np.array(Y_seq_filtered), np.array(Z_seq_filtered) - - - - -class TrainErrorPredictionNNWithMemoryFunctions: - @staticmethod - def get_loss( - criterion, - model, - model_for_initial_hidden, - X, - Y, - memory_bank, - adaptive_weight, - tanh_gain=10, - tanh_weight=0.1, - first_order_weight=0.01, - second_order_weight=0.01, - randomize_previous_error=[0.5,0.1], - integral_prob=0.0, - alpha_jacobian=0.01, - alpha_jacobian_encoder=0.1, - alpha_jacobian_gru_attention=0.1, - alpha_hessian=0.01, - alpha_hessian_encoder=0.1, - alpha_hessian_gru_attention=0.1, - calc_jacobian_len=10, - eps=1e-6 - ): - """ all tensor and model should be in the same device""" - randomize_scale_tensor = torch.tensor(randomize_previous_error).to(X.device) - previous_error = Y[:, :past_length, -2:] + torch.mean(torch.abs(Y[:, :past_length, -2:]),dim=1).unsqueeze(1) * randomize_scale_tensor * torch.randn_like(Y[:, :past_length, -2:]) - hc_initial_concat = model_for_initial_hidden(memory_bank) - hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), - hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) # initial hidden state - Y_pred, hc = model(X,previous_error=previous_error,hc=hc_initial,mode="get_lstm_states") - #Y_pred, hc = model(X,previous_error=previous_error,mode="get_lstm_states") - - # Calculate loss - loss = criterion(Y_pred * adaptive_weight, Y[:,past_length:] * adaptive_weight) - if alpha_jacobian is not None: - random_vector_memory_bank = torch.randn_like(memory_bank) - hc_initial_perturbed_concat = model_for_initial_hidden(memory_bank + eps * random_vector_memory_bank) - hc_initial_perturbed = (hc_initial_perturbed_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), - hc_initial_perturbed_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - loss += alpha_jacobian_gru_attention * criterion((hc_initial_perturbed_concat - hc_initial_concat) / eps, torch.zeros_like(hc_initial_concat)) - random_vector_previous_error = torch.randn_like(previous_error) - random_vector_X = torch.randn_like(X[:,:past_length+calc_jacobian_len]) - hc_perturbed = model(X[:, :past_length] + eps * random_vector_X[:, :past_length], - previous_error=previous_error + eps * random_vector_previous_error, hc=hc_initial_perturbed, mode="only_encoder") - #hc_perturbed = model(X[:, :past_length] + eps * random_vector_X[:, :past_length],previous_error=previous_error + eps * random_vector_previous_error, mode="only_encoder") - - loss += alpha_jacobian_encoder * criterion((hc_perturbed[0] - hc[0]) / eps, torch.zeros_like(hc_initial[0])) - loss += alpha_jacobian_encoder * criterion((hc_perturbed[1] - hc[1]) / eps, torch.zeros_like(hc_initial[1])) - - - Y_perturbed, _ = model(X[:, past_length: past_length + calc_jacobian_len] + eps * random_vector_X[:,past_length:past_length+calc_jacobian_len], hc=hc_perturbed, mode="predict_with_hc") - loss += alpha_jacobian * criterion((Y_perturbed - Y_pred[:,:calc_jacobian_len]) / eps, torch.zeros_like(Y_perturbed)) - if alpha_hessian is not None: - hc_initial_perturbed_minus_concat = model_for_initial_hidden(memory_bank - eps * random_vector_memory_bank) - hc_initial_perturbed_minus = (hc_initial_perturbed_minus_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), - hc_initial_perturbed_minus_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - loss += alpha_hessian_gru_attention * criterion((hc_initial_perturbed_concat - 2 * hc_initial_concat + hc_initial_perturbed_minus_concat) / eps ** 2, torch.zeros_like(hc_initial_concat)) - hc_perturbed_minus = model(X[:, :past_length] - eps * random_vector_X[:, :past_length], - previous_error=previous_error - eps * random_vector_previous_error,hc=hc_initial_perturbed_minus, mode="only_encoder") - loss += alpha_hessian_encoder * criterion((hc_perturbed[0] - 2 * hc[0] + hc_perturbed_minus[0]) / eps ** 2, torch.zeros_like(hc[0])) - loss += alpha_hessian_encoder * criterion((hc_perturbed[1] - 2 * hc[1] + hc_perturbed_minus[1]) / eps ** 2, torch.zeros_like(hc[1])) - Y_perturbed_minus, _ = model(X[:, past_length: past_length + calc_jacobian_len] - eps * random_vector_X[:,past_length:past_length+calc_jacobian_len], hc=hc_perturbed_minus, mode="predict_with_hc") - loss += alpha_hessian * criterion((Y_perturbed - 2 * Y_pred[:,:calc_jacobian_len] + Y_perturbed_minus) / eps ** 2, torch.zeros_like(Y_perturbed)) - # Calculate the integrated loss - if random.random() < integral_prob: - predicted_states = X[:,past_length, [vel_index,acc_index,steer_index]].unsqueeze(1) - for i in range(integration_length): - if i > 0: - predicted_states[:,0,0] = X[:,past_length + i, vel_index] - - predicted_states_with_input_history = torch.cat((predicted_states, X[:,[past_length + i], 3:]), dim=2) - predicted_error, hc = model(predicted_states_with_input_history, hc=hc, mode="predict_with_hc") - for j in range(prediction_step): - predicted_states[:,0,1] = predicted_states[:,0,1] + (X[:,past_length + i, acc_input_indices_nom[j]] - predicted_states[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) - predicted_states[:,0,2] = predicted_states[:,0,2] + (X[:,past_length + i, steer_input_indices_nom[j]] - predicted_states[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) - predicted_states[:,0,-2] = predicted_states[:,0,-2] + predicted_error[:,0,-2] * control_dt * prediction_step - predicted_states[:,0,-1] = predicted_states[:,0,-1] + predicted_error[:,0,-1] * control_dt * prediction_step - - loss += adaptive_weight[-2] * integration_weight * criterion(predicted_states[:,0,-2], X[:,past_length + integration_length, acc_index]) - loss += adaptive_weight[-1] * integration_weight * criterion(predicted_states[:,0,-1], X[:,past_length + integration_length, steer_index]) - # Calculate the tanh loss - loss += tanh_weight * criterion(torch.tanh(tanh_gain * (Y_pred[:,:,-1]-Y[:,past_length:,-1])),torch.zeros_like(Y_pred[:,:,-1])) - # Calculate the first order loss - first_order_loss = first_order_weight * criterion(Y_pred[:, 1:] - Y_pred[:, :-1], torch.zeros_like(Y_pred[:, 1:])) - # Calculate the second order loss - second_order_loss = second_order_weight * criterion(Y_pred[:, 2:] - 2 * Y_pred[:, 1:-1] + Y_pred[:, :-2], torch.zeros_like(Y_pred[:, 2:])) - # Calculate the total loss - total_loss = loss + first_order_loss + second_order_loss - return total_loss - @staticmethod - def validate_in_batches( - model, - model_for_initial_hidden, - criterion, - X_val, - Y_val, - memory_bank_val, - adaptive_weight, - randomize_previous_error=[0.03,0.03], - batch_size=1000, - alpha_jacobian=None, - alpha_jacobian_encoder=0.1, - alpha_jacobian_gru_attention=0.1, - alpha_hessian=None, - alpha_hessian_encoder=0.1, - alpha_hessian_gru_attention=0.1, - calc_jacobian_len=10, - eps=1e-5 - ): - """ tensors might be in the different device""" - device = next(model.parameters()).device - model.eval() - model_for_initial_hidden.eval() - val_loss = 0.0 - num_batches = (X_val.size(0) + batch_size - 1) // batch_size - - with torch.no_grad(): - for i in range(num_batches): - start_idx = i * batch_size - end_idx = min((i + 1) * batch_size, X_val.size(0)) - if X_val.device != device: - X_batch = X_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - Y_batch = Y_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - memory_bank_batch = memory_bank_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - else: - X_batch = X_val[start_idx:end_idx] - Y_batch = Y_val[start_idx:end_idx] - memory_bank_batch = memory_bank_val[start_idx:end_idx] - - loss = TrainErrorPredictionNNWithMemoryFunctions.get_loss( - criterion, model, model_for_initial_hidden, - X_batch, Y_batch, memory_bank_batch, adaptive_weight, - randomize_previous_error=randomize_previous_error, - alpha_jacobian=alpha_jacobian,alpha_jacobian_encoder=alpha_jacobian_encoder, - alpha_jacobian_gru_attention=alpha_jacobian_gru_attention, - alpha_hessian=alpha_hessian,alpha_hessian_encoder=alpha_hessian_encoder, - alpha_hessian_gru_attention=alpha_hessian_gru_attention, - calc_jacobian_len=calc_jacobian_len,eps=eps - ) - val_loss += loss.item() * (end_idx - start_idx) - val_loss /= X_val.size(0) - return val_loss - @staticmethod - def get_each_component_loss( - model, - model_for_initial_hidden, - X_val, - Y_val, - memory_bank_val, - tanh_gain = 10, - tanh_weight = 0.1, - first_order_weight = 0.01, - second_order_weight = 0.01, - batch_size=1000, - window_size=10 - ): - """ tensors might be in the different device""" - device = next(model.parameters()).device - model.eval() - model_for_initial_hidden.eval() - val_loss = np.zeros(Y_val.size(2)) - tanh_loss = 0.0 - first_order_loss = 0.0 - second_order_loss = 0.0 - num_batches = (X_val.size(0) + batch_size - 1) // batch_size - Y_pred_list = [] - with torch.no_grad(): - for i in range(num_batches): - start_idx = i * batch_size - end_idx = min((i + 1) * batch_size, X_val.size(0)) - if X_val.device != device: - X_batch = X_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - Y_batch = Y_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - memory_bank_batch = memory_bank_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - else: - X_batch = X_val[start_idx:end_idx] - Y_batch = Y_val[start_idx:end_idx] - memory_bank_batch = memory_bank_val[start_idx:end_idx] - hc_initial_concat = model_for_initial_hidden(memory_bank_batch) - hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") - #Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") - - Y_pred_list.append(Y_pred[:,window_size]) - - # Calculate the loss - loss = torch.mean(torch.abs(Y_pred - Y_batch[:,past_length:]),dim=(0,1)) - val_loss += loss.cpu().numpy() * (end_idx - start_idx) - tanh_loss += tanh_weight * torch.mean(torch.abs(torch.tanh(tanh_gain * (Y_pred[:,:,-1]-Y_batch[:,past_length:,-1])))).item() * (end_idx - start_idx) - first_order_loss += first_order_weight * torch.mean(torch.abs((Y_pred[:, 1:] - Y_pred[:, :-1]) - (Y_batch[:, past_length + 1:] - Y_batch[:, past_length:-1]))).item() * (end_idx - start_idx) - second_order_loss += second_order_weight * torch.mean(torch.abs((Y_pred[:, 2:] - 2 * Y_pred[:, 1:-1] + Y_pred[:, :-2]) - (Y_batch[:, past_length + 2:] - 2 * Y_batch[:, past_length + 1:-1] + Y_batch[:, past_length:-2]))).item() * (end_idx - start_idx) - val_loss /= X_val.size(0) - tanh_loss /= X_val.size(0) - first_order_loss /= X_val.size(0) - second_order_loss /= X_val.size(0) - Y_pred_np = torch.cat(Y_pred_list,dim=0).cpu().detach().numpy() - return np.concatenate((val_loss, [tanh_loss, first_order_loss, second_order_loss])), Y_pred_np - @staticmethod - def get_Y_pred_np(model, model_for_initial_hidden, X_val, Y_val, memory_bank_val, batch_size=1000): - """ tensors might be in the different device""" - device = next(model.parameters()).device - model.eval() - model_for_initial_hidden.eval() - num_batches = (X_val.size(0) + batch_size - 1) // batch_size - Y_pred_list = [] - with torch.no_grad(): - for i in range(num_batches): - start_idx = i * batch_size - end_idx = min((i + 1) * batch_size, X_val.size(0)) - if X_val.device != device: - X_batch = X_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - Y_batch = Y_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - memory_bank_batch = memory_bank_val[start_idx:end_idx].pin_memory().to(device, non_blocking=True) - else: - X_batch = X_val[start_idx:end_idx] - Y_batch = Y_val[start_idx:end_idx] - memory_bank_batch = memory_bank_val[start_idx:end_idx] - hc_initial_concat = model_for_initial_hidden(memory_bank_batch) - hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") - #Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") - Y_pred_list.append(Y_pred) - Y_pred_np = torch.cat(Y_pred_list,dim=0).cpu().detach().numpy() - return Y_pred_np - @staticmethod - def get_losses( - model, - model_for_initial_hidden, - criterion, - X, - Y, - memory_bank, - adaptive_weight - ): - loss = TrainErrorPredictionNNWithMemoryFunctions.validate_in_batches( - model, model_for_initial_hidden, criterion, X, Y, memory_bank, adaptive_weight=adaptive_weight - ) - each_component_loss, Y_pred = TrainErrorPredictionNNWithMemoryFunctions.get_each_component_loss( - model, model_for_initial_hidden, X, Y, memory_bank - ) - return loss, each_component_loss, Y_pred - @staticmethod - def get_acc_steer_model_prediction(model,model_for_initial_hidden,X,Y,memory_bank,window_size=10,batch_size=1000): - model.eval() - model_for_initial_hidden.eval() - device = next(model.parameters()).device - num_batches = (X.size(0) + batch_size - 1) // batch_size - prediction = [] - for k in range(num_batches): - if X.device != device: - X_batch = X[k*batch_size:(k+1)*batch_size].pin_memory().to(device, non_blocking=True) - Y_batch = Y[k*batch_size:(k+1)*batch_size].pin_memory().to(device, non_blocking=True) - memory_bank_batch = memory_bank[k*batch_size:(k+1)*batch_size].pin_memory().to(device, non_blocking=True) - else: - X_batch = X[k*batch_size:(k+1)*batch_size] - Y_batch = Y[k*batch_size:(k+1)*batch_size] - memory_bank_batch = memory_bank[k*batch_size:(k+1)*batch_size] - hc_initial_concat = model_for_initial_hidden(memory_bank_batch) - hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - _, hc = model(X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") - - states_tmp=X_batch[:,past_length, [vel_index, acc_index, steer_index]].unsqueeze(1) - for i in range(window_size): - states_tmp_with_input_history = torch.cat((states_tmp, X_batch[:,[past_length+i], 3:]), dim=2) - predicted_error, hc = model(states_tmp_with_input_history, hc=hc, mode="predict_with_hc") - states_tmp[:,0,0] = X_batch[:,past_length + i + 1, 0] - for j in range(prediction_step): - states_tmp[:,0,1] = states_tmp[:,0,1] + (X_batch[:,past_length + i, acc_input_indices_nom[j]] - states_tmp[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) - states_tmp[:,0,2] = states_tmp[:,0,2] + (X_batch[:,past_length + i, steer_input_indices_nom[j]] - states_tmp[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) - states_tmp[:,0,1] = states_tmp[:,0,1] + predicted_error[:,0,state_name_to_predicted_index["acc"]] * control_dt * prediction_step - states_tmp[:,0,2] = states_tmp[:,0,2] + predicted_error[:,0,state_name_to_predicted_index["steer"]] * control_dt * prediction_step - prediction.append(states_tmp[:,0,[1,2]].detach().to("cpu").numpy()) - prediction = np.concatenate(prediction,axis=0) - return prediction - @staticmethod - def get_acc_steer_models_prediction(models,model_for_initial_hidden,X,Y,memory_bank,window_size=10,batch_size=1000): - for model in models: - model.eval() - model_for_initial_hidden.eval() - device = next(model[0].parameters()).device - num_batches = (X.size(0) + batch_size - 1) // batch_size - prediction = [] - for k in range(num_batches): - if X.device != device: - X_batch = X[k*batch_size:(k+1)*batch_size].pin_memory().to(device, non_blocking=True) - Y_batch = Y[k*batch_size:(k+1)*batch_size].pin_memory().to(device, non_blocking=True) - memory_bank_batch = memory_bank[k*batch_size:(k+1)*batch_size].pin_memory().to(device, non_blocking=True) - else: - X_batch = X[k*batch_size:(k+1)*batch_size] - Y_batch = Y[k*batch_size:(k+1)*batch_size] - memory_bank_batch = memory_bank[k*batch_size:(k+1)*batch_size] - hc_initial_concat = model_for_initial_hidden(memory_bank_batch) - hc_initial = (hc_initial_concat[:, :model[0].lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model[0].lstm_hidden_total_size:].unsqueeze(0).contiguous()) - _, hc = model[0](X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") - - states_tmp=X_batch[:,past_length, [vel_index, acc_index, steer_index]].unsqueeze(1) - for i in range(window_size): - states_tmp_with_input_history = torch.cat((states_tmp, X_batch[:,[past_length+i], 3:]), dim=2) - predicted_error_0, hc_next = model[0](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") - predicted_error = predicted_error_0 / len(models) - for j in range(1,len(models)): - predicted_error_j, _ = model[j](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") - predicted_error += predicted_error_j / len(models) - hc = hc_next - states_tmp[:,0,0] = X_batch[:,past_length + i + 1, 0] - for j in range(prediction_step): - states_tmp[:,0,1] = states_tmp[:,0,1] + (X_batch[:,past_length + i, acc_input_indices_nom[j]] - states_tmp[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) - states_tmp[:,0,2] = states_tmp[:,0,2] + (X_batch[:,past_length + i, steer_input_indices_nom[j]] - states_tmp[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) - states_tmp[:,0,1] = states_tmp[:,0,1] + predicted_error[:,0,state_name_to_predicted_index["acc"]] * control_dt * prediction_step - states_tmp[:,0,2] = states_tmp[:,0,2] + predicted_error[:,0,state_name_to_predicted_index["steer"]] * control_dt * prediction_step - prediction.append(states_tmp[:,0,[1,2]].detach().to("cpu").numpy()) - prediction = np.concatenate(prediction,axis=0) - @staticmethod - def get_model_signed_prediction_error(model,model_for_initial_hidden,X,Y,memory_bank,window_size=10,batch_size=1000): - model_prediction = TrainErrorPredictionNNWithMemoryFunctions.get_acc_steer_model_prediction(model,model_for_initial_hidden,X,Y,memory_bank,window_size,batch_size) - model_prediction_error = X[:,past_length+window_size, [acc_index, steer_index]].detach().to("cpu").numpy() - model_prediction - return model_prediction_error - @staticmethod - def get_models_signed_prediction_error(models,model_for_initial_hidden,X,Y,memory_bank,window_size=10,batch_size=1000): - model_prediction = TrainErrorPredictionNNWithMemoryFunctions.get_acc_steer_models_prediction(models,model_for_initial_hidden,X,Y,memory_bank,window_size,batch_size) - model_prediction_error = X[:,past_length+window_size, [acc_index, steer_index]].detach().to("cpu").numpy() - model_prediction - return model_prediction_error - @staticmethod - def get_signed_prediction_error(model,model_for_initial_hidden,relearned_model,relearned_model_for_initial_hidden,X,Y,memory_bank,window_size=10,batch_size=1000): - nominal_prediction_error = get_nominal_signed_prediction_error(X,window_size) - model_prediction_error = TrainErrorPredictionNNWithMemoryFunctions.get_model_signed_prediction_error(model,model_for_initial_hidden,X,Y,memory_bank,window_size,batch_size) - relearned_model_prediction_error = TrainErrorPredictionNNWithMemoryFunctions.get_model_signed_prediction_error(relearned_model,relearned_model_for_initial_hidden,X,Y,memory_bank,window_size,batch_size) - return nominal_prediction_error,model_prediction_error,relearned_model_prediction_error - @staticmethod - def get_sequence_data(X,Y,division_indices, acc_threshold=2.0, steer_threshold=0.7, acc_change_threshold=3.5, steer_change_threshold=0.8, acc_change_window=10, steer_change_window=10): - X_seq, memory_bank = transform_to_sequence_data_with_memory_bank( - np.array(X), - past_length+prediction_length, - division_indices, - prediction_step - ) - Y_seq = transform_to_sequence_data( - np.array(Y)[:, state_component_predicted_index], - past_length + prediction_length, - division_indices, - prediction_step, - ) - X_seq_filtered = [] - Y_seq_filtered = [] - memory_bank_filtered = [] - for i in range(X_seq.shape[0]): - acc = X_seq[i, :, 1] - steer = X_seq[i, :, 2] - acc_input = X_seq[i, :, acc_input_indices_nom] - steer_input = X_seq[i, :, steer_input_indices_nom] - acc_change = (acc[acc_change_window:] - acc[:-acc_change_window]) / (acc_change_window * control_dt) - steer_change = (steer[steer_change_window:] - steer[:-steer_change_window]) / (steer_change_window * control_dt) - if ( - (np.abs(acc).max() < acc_threshold) and - (np.abs(steer).max() < steer_threshold) and - (np.abs(acc_input).max() < acc_threshold) and - (np.abs(steer_input).max() < steer_threshold) and - (np.abs(acc_change).max() < acc_change_threshold) and - (np.abs(steer_change).max() < steer_change_threshold) - ): - X_seq_filtered.append(X_seq[i]) - Y_seq_filtered.append(Y_seq[i]) - memory_bank_filtered.append(memory_bank[i]) - return np.array(X_seq_filtered), np.array(Y_seq_filtered), np.array(memory_bank_filtered) - @staticmethod - def get_sequence_data_as_file(X,Y,division_indices,file_path): - X_seq, Y_seq, memory_bank = TrainErrorPredictionNNWithMemoryFunctions.get_sequence_data(X,Y,division_indices) - np.savez(file_path, X=X_seq, Y=Y_seq, memory_bank=memory_bank) - @staticmethod - def get_sequence_data_tensor(X,Y,division_indices): - with tempfile.NamedTemporaryFile(suffix=".npz", delete=False) as tmp_file: - file_path = tmp_file.name - p = Process(target=TrainErrorPredictionNNWithMemoryFunctions.get_sequence_data_as_file, args=(X,Y,division_indices,file_path)) - p.start() - p.join() - # Retry mechanism to ensure the file is completely written before loading - retries = 5 - while retries > 0: - try: - with np.load(file_path) as data: - X_tensor = torch.from_numpy(data['X']).to(torch.float32) - Y_tensor = torch.from_numpy(data['Y']).to(torch.float32) - memory_bank_tensor = torch.from_numpy(data['memory_bank']).to(torch.float32) - # X_tensor = torch.tensor(data['X'], dtype=torch.float32) - # Y_tensor = torch.tensor(data['Y'], dtype=torch.float32) - # memory_bank_tensor = torch.tensor(data['memory_bank'], dtype=torch.float32) - break # Exit loop if loading is successful - except (IOError, ValueError): - time.sleep(0.1) # Wait briefly before retrying - retries -= 1 - else: - raise IOError("Failed to load data from file.") - - # Remove the file after successful loading - os.remove(file_path) - return X_tensor, Y_tensor, memory_bank_tensor - - - # with offline data class TrainErrorPredictionNNWithOfflineData: @staticmethod - def get_offline_feature_dict( - preprocessor, - model_for_offline_features, + def get_initial_hidden_dict( + model_for_initial_hidden, offline_data_dict - ): # get offline features dict ind -> offline features - offline_feature_dict = {} + ): # get initial_hidden dict ind -> offline features + initial_hidden_dict = {} for key in offline_data_dict.keys(): - if next(model_for_offline_features.parameters()).device == "cpu": + if next(model_for_initial_hidden.parameters()).device == "cpu": offline_data_tensor = torch.tensor(offline_data_dict[key],dtype=torch.float32).unsqueeze(0) else: - offline_data_tensor = torch.tensor(offline_data_dict[key],dtype=torch.float32,device=next(model_for_offline_features.parameters()).device).unsqueeze(0) + offline_data_tensor = torch.tensor(offline_data_dict[key],dtype=torch.float32,device=next(model_for_initial_hidden.parameters()).device).unsqueeze(0) - offline_feature_dict[key] = model_for_offline_features(offline_data_tensor,preprocessor)[0] - return offline_feature_dict + initial_hidden_dict[key] = model_for_initial_hidden(offline_data_tensor)[0] + return initial_hidden_dict @staticmethod - def get_offline_features_batch( - offline_feature_dict, + def get_initial_hidden_batch( + initial_hidden_dict, indices_batch, # index as np array indicating which division index is used device, - offline_feature_dim - ): # get offline features as torch tensor from dict ind -> offline features - offline_features_batch = torch.zeros( - (len(indices_batch),offline_feature_dim), + initial_hidden_dim + ): # get initial_hidden as torch tensor from dict ind -> initial_hidden + initial_hidden_batch = torch.zeros( + (len(indices_batch),initial_hidden_dim), dtype=torch.float32, device=device ) - mask_batch = torch.zeros(len(indices_batch),dtype=torch.bool,device=device) for i in range(indices_batch.shape[0]): - if indices_batch[i] in offline_feature_dict.keys(): - offline_features_batch[i] = offline_feature_dict[indices_batch[i]] - mask_batch[i] = True - return offline_features_batch, mask_batch + if indices_batch[i] in initial_hidden_dict.keys(): + initial_hidden_batch[i] = initial_hidden_dict[indices_batch[i]] + return initial_hidden_batch @staticmethod - def get_offline_features( - preprocessor, - model_for_offline_features, + def get_initial_hidden( + model_for_initial_hidden, offline_data_dict, indices_batch, # index as np array indicating which division index is used - offline_feature_dim - ): # get offline features as torch tensor from dict ind -> offline data (before calculating offline features) - offline_feature_dict = TrainErrorPredictionNNWithOfflineData.get_offline_feature_dict(preprocessor,model_for_offline_features,offline_data_dict) - offline_features, mask = TrainErrorPredictionNNWithOfflineData.get_offline_features_batch(offline_feature_dict,indices_batch,next(model_for_offline_features.parameters()).device,offline_feature_dim) - return offline_features, mask - + initial_hidden_dim + ): # get initial_hidden as torch tensor from dict ind -> initial_hidden (before calculating initial_hidden) + initial_hidden_dict = TrainErrorPredictionNNWithOfflineData.get_initial_hidden_dict(model_for_initial_hidden,offline_data_dict) + initial_hidden = TrainErrorPredictionNNWithOfflineData.get_initial_hidden_batch(initial_hidden_dict,indices_batch,next(model_for_initial_hidden.parameters()).device,initial_hidden_dim) + return initial_hidden + @staticmethod def get_loss( criterion, model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X, Y, @@ -881,91 +433,72 @@ def get_loss( eps=1e-6 ): """ all tensor and model should be in the same device""" # index as np array indicating which division index is used # index as np array indicating which division index is used - offline_feature_dim = 2 * model.lstm_hidden_total_size - offline_features, mask = TrainErrorPredictionNNWithOfflineData.get_offline_features( - preprocessor, - model_for_offline_features, + initial_hidden_dim = 2 * model.lstm_hidden_total_size + hc_initial_concat = TrainErrorPredictionNNWithOfflineData.get_initial_hidden( + model_for_initial_hidden, offline_data_dict, indices, - offline_feature_dim + initial_hidden_dim ) randomize_scale_tensor = torch.tensor(randomize_previous_error).to(X.device) - Y_for_lstm_encoder = Y[:,past_initial_hidden_length-past_length:past_initial_hidden_length,-2:] + Y_for_lstm_encoder = Y[:,:past_length,-2:] previous_error = Y_for_lstm_encoder + torch.mean(torch.abs(Y_for_lstm_encoder),dim=1).unsqueeze(1) * randomize_scale_tensor * torch.randn_like(Y_for_lstm_encoder) - # hc_initial_concat = model_for_initial_hidden(offline_features, X[:, :past_initial_hidden_length], mask, preprocessor) - hc_initial_concat = model_for_initial_hidden(offline_features, mask) + hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) # initial hidden state - X_for_lstm = X[:, past_initial_hidden_length-past_length:] - Y_pred, hc = model(X_for_lstm,previous_error=previous_error,hc=hc_initial,mode="get_lstm_states") - #Y_pred, hc = model(X,previous_error=previous_error,mode="get_lstm_states") + Y_pred, hc = model(X,previous_error=previous_error,hc=hc_initial,mode="get_lstm_states") # Calculate loss - loss = criterion(Y_pred * adaptive_weight, Y[:,past_initial_hidden_length:] * adaptive_weight) + loss = criterion(Y_pred * adaptive_weight, Y[:,past_length:] * adaptive_weight) if alpha_jacobian is not None: - random_vector_offline_features = torch.randn_like(offline_features) + random_vector_hc_initial = torch.randn_like(hc_initial_concat) random_vector_previous_error = torch.randn_like(previous_error) - random_vector_X = torch.randn_like(X[:,:past_initial_hidden_length+calc_jacobian_len]) + random_vector_X = torch.randn_like(X[:,:past_length+calc_jacobian_len]) - #hc_initial_perturbed_concat = model_for_initial_hidden( - # offline_features + eps * random_vector_offline_features, - # X[:, :past_initial_hidden_length] + eps * random_vector_X[:, :past_initial_hidden_length], - # mask, - # preprocessor) - hc_initial_perturbed_concat = model_for_initial_hidden( - offline_features + eps * random_vector_offline_features, - mask) + hc_initial_perturbed_concat = hc_initial_concat + eps * random_vector_hc_initial hc_initial_perturbed = (hc_initial_perturbed_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_perturbed_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) loss += alpha_jacobian_gru_attention * criterion((hc_initial_perturbed_concat - hc_initial_concat) / eps, torch.zeros_like(hc_initial_concat)) - hc_perturbed = model(X_for_lstm[:, :past_length] + eps * random_vector_X[:, past_initial_hidden_length-past_length:past_initial_hidden_length], + hc_perturbed = model(X[:, :past_length] + eps * random_vector_X[:, :past_length], previous_error=previous_error + eps * random_vector_previous_error, hc=hc_initial_perturbed, mode="only_encoder") - #hc_perturbed = model(X[:, :past_length] + eps * random_vector_X[:, :past_length],previous_error=previous_error + eps * random_vector_previous_error, mode="only_encoder") loss += alpha_jacobian_encoder * criterion((hc_perturbed[0] - hc[0]) / eps, torch.zeros_like(hc_initial[0])) loss += alpha_jacobian_encoder * criterion((hc_perturbed[1] - hc[1]) / eps, torch.zeros_like(hc_initial[1])) - Y_perturbed, _ = model(X_for_lstm[:, past_length: past_length + calc_jacobian_len] + eps * random_vector_X[:,past_initial_hidden_length:past_initial_hidden_length+calc_jacobian_len], hc=hc_perturbed, mode="predict_with_hc") + Y_perturbed, _ = model(X[:, past_length: past_length + calc_jacobian_len] + eps * random_vector_X[:,past_length:past_length+calc_jacobian_len], hc=hc_perturbed, mode="predict_with_hc") loss += alpha_jacobian * criterion((Y_perturbed - Y_pred[:,:calc_jacobian_len]) / eps, torch.zeros_like(Y_perturbed)) if alpha_hessian is not None: - #hc_initial_perturbed_minus_concat = model_for_initial_hidden( - # offline_features - eps * random_vector_offline_features, - # X[:, :past_initial_hidden_length] - eps * random_vector_X[:, :past_initial_hidden_length], - # mask, - # preprocessor) - hc_initial_perturbed_minus_concat = model_for_initial_hidden( - offline_features - eps * random_vector_offline_features, - mask) + hc_initial_perturbed_minus_concat = hc_initial_concat - eps * random_vector_hc_initial hc_initial_perturbed_minus = (hc_initial_perturbed_minus_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_perturbed_minus_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) loss += alpha_hessian_gru_attention * criterion((hc_initial_perturbed_concat - 2 * hc_initial_concat + hc_initial_perturbed_minus_concat) / eps ** 2, torch.zeros_like(hc_initial_concat)) - hc_perturbed_minus = model(X_for_lstm[:, :past_length] - eps * random_vector_X[:, past_initial_hidden_length-past_length:past_initial_hidden_length], + hc_perturbed_minus = model(X[:, :past_length] - eps * random_vector_X[:, :past_length], previous_error=previous_error - eps * random_vector_previous_error,hc=hc_initial_perturbed_minus, mode="only_encoder") loss += alpha_hessian_encoder * criterion((hc_perturbed[0] - 2 * hc[0] + hc_perturbed_minus[0]) / eps ** 2, torch.zeros_like(hc[0])) loss += alpha_hessian_encoder * criterion((hc_perturbed[1] - 2 * hc[1] + hc_perturbed_minus[1]) / eps ** 2, torch.zeros_like(hc[1])) - Y_perturbed_minus, _ = model(X_for_lstm[:, past_length: past_length + calc_jacobian_len] - eps * random_vector_X[:,past_initial_hidden_length:past_initial_hidden_length+calc_jacobian_len], hc=hc_perturbed_minus, mode="predict_with_hc") + Y_perturbed_minus, _ = model(X[:, past_length: past_length + calc_jacobian_len] - eps * random_vector_X[:,past_length:past_length+calc_jacobian_len], hc=hc_perturbed_minus, mode="predict_with_hc") loss += alpha_hessian * criterion((Y_perturbed - 2 * Y_pred[:,:calc_jacobian_len] + Y_perturbed_minus) / eps ** 2, torch.zeros_like(Y_perturbed)) # Calculate the integrated loss if random.random() < integral_prob: - predicted_states = X_for_lstm[:,past_length, [vel_index,acc_index,steer_index]].unsqueeze(1) + predicted_states = X[:,past_length, [vel_index,acc_index,steer_index]].unsqueeze(1) for i in range(integration_length): if i > 0: - predicted_states[:,0,0] = X_for_lstm[:,past_length + i, vel_index] + predicted_states[:,0,0] = X[:,past_length + i, vel_index] - predicted_states_with_input_history = torch.cat((predicted_states, X_for_lstm[:,[past_length + i], 3:]), dim=2) + predicted_states_with_input_history = torch.cat((predicted_states, X[:,[past_length + i], 3:]), dim=2) predicted_error, hc = model(predicted_states_with_input_history, hc=hc, mode="predict_with_hc") for j in range(prediction_step): - predicted_states[:,0,1] = predicted_states[:,0,1] + (X_for_lstm[:,past_length + i, acc_input_indices_nom[j]] - predicted_states[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) - predicted_states[:,0,2] = predicted_states[:,0,2] + (X_for_lstm[:,past_length + i, steer_input_indices_nom[j]] - predicted_states[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) + predicted_states[:,0,1] = predicted_states[:,0,1] + (X[:,past_length + i, acc_input_indices_nom[j]] - predicted_states[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) + predicted_states[:,0,2] = predicted_states[:,0,2] + (X[:,past_length + i, steer_input_indices_nom[j]] - predicted_states[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) predicted_states[:,0,-2] = predicted_states[:,0,-2] + predicted_error[:,0,-2] * control_dt * prediction_step predicted_states[:,0,-1] = predicted_states[:,0,-1] + predicted_error[:,0,-1] * control_dt * prediction_step - loss += adaptive_weight[-2] * integration_weight * criterion(predicted_states[:,0,-2], X_for_lstm[:,past_length + integration_length, acc_index]) - loss += adaptive_weight[-1] * integration_weight * criterion(predicted_states[:,0,-1], X_for_lstm[:,past_length + integration_length, steer_index]) + loss += adaptive_weight[-2] * integration_weight * criterion(predicted_states[:,0,-2], X[:,past_length + integration_length, acc_index]) + loss += adaptive_weight[-1] * integration_weight * criterion(predicted_states[:,0,-1], X[:,past_length + integration_length, steer_index]) # Calculate the tanh loss - loss += tanh_weight * criterion(torch.tanh(tanh_gain * (Y_pred[:,:,-1]-Y[:,past_initial_hidden_length:,-1])),torch.zeros_like(Y_pred[:,:,-1])) + loss += tanh_weight * criterion(torch.tanh(tanh_gain * (Y_pred[:,:,-1]-Y[:,past_length:,-1])),torch.zeros_like(Y_pred[:,:,-1])) # Calculate the first order loss first_order_loss = first_order_weight * criterion(Y_pred[:, 1:] - Y_pred[:, :-1], torch.zeros_like(Y_pred[:, 1:])) # Calculate the second order loss @@ -976,8 +509,6 @@ def get_loss( @staticmethod def validate_in_batches( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X_val, @@ -999,8 +530,6 @@ def validate_in_batches( """ tensors might be in the different device""" device = next(model.parameters()).device model.eval() - preprocessor.eval() - model_for_offline_features.eval() model_for_initial_hidden.eval() val_loss = 0.0 num_batches = (X_val.size(0) + batch_size - 1) // batch_size @@ -1018,7 +547,7 @@ def validate_in_batches( Y_batch = Y_val[start_idx:end_idx] loss = TrainErrorPredictionNNWithOfflineData.get_loss( - criterion, model, preprocessor, model_for_offline_features, model_for_initial_hidden, + criterion, model, model_for_initial_hidden, X_batch, Y_batch, offline_data_dict_val, indices_batch, adaptive_weight, randomize_previous_error=randomize_previous_error, alpha_jacobian=alpha_jacobian,alpha_jacobian_encoder=alpha_jacobian_encoder, @@ -1033,8 +562,6 @@ def validate_in_batches( @staticmethod def get_each_component_loss( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X_val, Y_val, @@ -1050,10 +577,8 @@ def get_each_component_loss( """ tensors might be in the different device""" device = next(model.parameters()).device model.eval() - preprocessor.eval() - model_for_offline_features.eval() model_for_initial_hidden.eval() - offline_feature_dim = 2 * model.lstm_hidden_total_size + initial_hidden_dim = 2 * model.lstm_hidden_total_size val_loss = np.zeros(Y_val.size(2)) tanh_loss = 0.0 @@ -1072,26 +597,21 @@ def get_each_component_loss( else: X_batch = X_val[start_idx:end_idx] Y_batch = Y_val[start_idx:end_idx] - offline_features_batch, mask_batch = TrainErrorPredictionNNWithOfflineData.get_offline_features( - preprocessor, - model_for_offline_features, + hc_initial_concat = TrainErrorPredictionNNWithOfflineData.get_initial_hidden( + model_for_initial_hidden, offline_data_val, indices_batch, - offline_feature_dim + initial_hidden_dim ) - #hc_initial_concat = model_for_initial_hidden(offline_features_batch, X_batch[:, :past_initial_hidden_length], mask_batch, preprocessor) - hc_initial_concat = model_for_initial_hidden(offline_features_batch, mask_batch) - hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - Y_pred, _ = model(X_batch[:,past_initial_hidden_length-past_length:], previous_error=Y_batch[:, past_initial_hidden_length-past_length:past_initial_hidden_length, -2:],hc=hc_initial, mode="get_lstm_states") - #Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") + Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") Y_pred_list.append(Y_pred[:,window_size]) # Calculate the loss - loss = torch.mean(torch.abs(Y_pred - Y_batch[:,past_initial_hidden_length:]),dim=(0,1)) + loss = torch.mean(torch.abs(Y_pred - Y_batch[:,past_length:]),dim=(0,1)) val_loss += loss.cpu().numpy() * (end_idx - start_idx) - tanh_loss += tanh_weight * torch.mean(torch.abs(torch.tanh(tanh_gain * (Y_pred[:,:,-1]-Y_batch[:,past_initial_hidden_length:,-1])))).item() * (end_idx - start_idx) + tanh_loss += tanh_weight * torch.mean(torch.abs(torch.tanh(tanh_gain * (Y_pred[:,:,-1]-Y_batch[:,past_length:,-1])))).item() * (end_idx - start_idx) first_order_loss += first_order_weight * torch.mean(torch.abs(Y_pred[:, 1:] - Y_pred[:, :-1])).item() * (end_idx - start_idx) second_order_loss += second_order_weight * torch.mean(torch.abs(Y_pred[:, 2:] - 2 * Y_pred[:, 1:-1] + Y_pred[:, :-2])).item() * (end_idx - start_idx) val_loss /= X_val.size(0) @@ -1103,8 +623,6 @@ def get_each_component_loss( @staticmethod def get_Y_pred_np( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X_val, Y_val, @@ -1115,10 +633,8 @@ def get_Y_pred_np( """ tensors might be in the different device""" device = next(model.parameters()).device model.eval() - preprocessor.eval() - model_for_offline_features.eval() model_for_initial_hidden.eval() - offline_feature_dim = 2 * model.lstm_hidden_total_size + initial_hidden_dim = 2 * model.lstm_hidden_total_size num_batches = (X_val.size(0) + batch_size - 1) // batch_size Y_pred_list = [] with torch.no_grad(): @@ -1132,19 +648,16 @@ def get_Y_pred_np( else: X_batch = X_val[start_idx:end_idx] Y_batch = Y_val[start_idx:end_idx] - offline_features_batch, mask_batch = TrainErrorPredictionNNWithOfflineData.get_offline_features( - preprocessor, - model_for_offline_features, + hc_initial_concat = TrainErrorPredictionNNWithOfflineData.get_offline_features( + model_for_initial_hidden, offline_data_val, indices_batch, - offline_feature_dim + initial_hidden_dim ) - # hc_initial_concat = model_for_initial_hidden(offline_features_batch, X_batch[:, :past_initial_hidden_length], mask_batch, preprocessor) - hc_initial_concat = model_for_initial_hidden(offline_features_batch, mask_batch) hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - Y_pred, _ = model(X_batch[:,past_initial_hidden_length-past_length:], previous_error=Y_batch[:,past_initial_hidden_length-past_length:past_initial_hidden_length,-2:],hc=hc_initial, mode="get_lstm_states") + Y_pred, _ = model(X_batch, previous_error=Y_batch[:,:past_length,-2:],hc=hc_initial, mode="get_lstm_states") #Y_pred, _ = model(X_batch, previous_error=Y_batch[:, :past_length, -2:], mode="get_lstm_states") Y_pred_list.append(Y_pred) Y_pred_np = torch.cat(Y_pred_list,dim=0).cpu().detach().numpy() @@ -1152,8 +665,6 @@ def get_Y_pred_np( @staticmethod def get_losses( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, criterion, X, @@ -1163,19 +674,17 @@ def get_losses( adaptive_weight ): loss = TrainErrorPredictionNNWithOfflineData.validate_in_batches( - model, preprocessor, model_for_offline_features, model_for_initial_hidden, criterion, X, Y, + model, model_for_initial_hidden, criterion, X, Y, offline_data, indices, adaptive_weight=adaptive_weight ) each_component_loss, Y_pred = TrainErrorPredictionNNWithOfflineData.get_each_component_loss( - model, preprocessor, model_for_offline_features, model_for_initial_hidden, X, Y, + model, model_for_initial_hidden, X, Y, offline_data, indices ) return loss, each_component_loss, Y_pred @staticmethod def get_acc_steer_model_prediction( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X, Y, @@ -1186,10 +695,8 @@ def get_acc_steer_model_prediction( ): device = next(model.parameters()).device model.eval() - preprocessor.eval() - model_for_offline_features.eval() model_for_initial_hidden.eval() - offline_feature_dim = 2 * model.lstm_hidden_total_size + initial_hidden_dim = 2 * model.lstm_hidden_total_size num_batches = (X.size(0) + batch_size - 1) // batch_size prediction = [] for k in range(num_batches): @@ -1202,26 +709,23 @@ def get_acc_steer_model_prediction( else: X_batch = X[start_idx:end_idx] Y_batch = Y[start_idx:end_idx] - offline_features_batch, mask_batch = TrainErrorPredictionNNWithOfflineData.get_offline_features( - preprocessor, - model_for_offline_features, + hc_initial_concat = TrainErrorPredictionNNWithOfflineData.get_initial_hidden( + model_for_initial_hidden, offline_data, indices_batch, - offline_feature_dim + initial_hidden_dim ) - # hc_initial_concat = model_for_initial_hidden(offline_features_batch, X_batch[:, :past_initial_hidden_length], mask_batch, preprocessor) - hc_initial_concat = model_for_initial_hidden(offline_features_batch, mask_batch) hc_initial = (hc_initial_concat[:, :model.lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model.lstm_hidden_total_size:].unsqueeze(0).contiguous()) - _, hc = model(X_batch, previous_error=Y_batch[:,past_initial_hidden_length-past_length:past_initial_hidden_length,-2:],hc=hc_initial, mode="get_lstm_states") + _, hc = model(X_batch, previous_error=Y_batch[:,:past_length,-2:],hc=hc_initial, mode="get_lstm_states") - states_tmp=X_batch[:,past_initial_hidden_length, [vel_index, acc_index, steer_index]].unsqueeze(1) + states_tmp=X_batch[:,past_length, [vel_index, acc_index, steer_index]].unsqueeze(1) for i in range(window_size): - states_tmp_with_input_history = torch.cat((states_tmp, X_batch[:,[past_initial_hidden_length+i], 3:]), dim=2) + states_tmp_with_input_history = torch.cat((states_tmp, X_batch[:,[past_length+i], 3:]), dim=2) predicted_error, hc = model(states_tmp_with_input_history, hc=hc, mode="predict_with_hc") - states_tmp[:,0,0] = X_batch[:,past_initial_hidden_length + i + 1, 0] + states_tmp[:,0,0] = X_batch[:,past_length + i + 1, 0] for j in range(prediction_step): - states_tmp[:,0,1] = states_tmp[:,0,1] + (X_batch[:,past_initial_hidden_length + i, acc_input_indices_nom[j]] - states_tmp[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) - states_tmp[:,0,2] = states_tmp[:,0,2] + (X_batch[:,past_initial_hidden_length + i, steer_input_indices_nom[j]] - states_tmp[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) + states_tmp[:,0,1] = states_tmp[:,0,1] + (X_batch[:,past_length + i, acc_input_indices_nom[j]] - states_tmp[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) + states_tmp[:,0,2] = states_tmp[:,0,2] + (X_batch[:,past_length + i, steer_input_indices_nom[j]] - states_tmp[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) states_tmp[:,0,1] = states_tmp[:,0,1] + predicted_error[:,0,state_name_to_predicted_index["acc"]] * control_dt * prediction_step states_tmp[:,0,2] = states_tmp[:,0,2] + predicted_error[:,0,state_name_to_predicted_index["steer"]] * control_dt * prediction_step prediction.append(states_tmp[:,0,[1,2]].detach().to("cpu").numpy()) @@ -1230,8 +734,6 @@ def get_acc_steer_model_prediction( @staticmethod def get_acc_steer_models_prediction( models, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X, Y, @@ -1242,10 +744,8 @@ def get_acc_steer_models_prediction( device = next(models[0].parameters()).device for model in models: model.eval() - preprocessor.eval() - model_for_offline_features.eval() model_for_initial_hidden.eval() - offline_feature_dim = 2 * models[0].lstm_hidden_total_size + initial_hidden_dim = 2 * models[0].lstm_hidden_total_size num_batches = (X.size(0) + batch_size - 1) // batch_size prediction = [] for k in range(num_batches): @@ -1258,32 +758,29 @@ def get_acc_steer_models_prediction( else: X_batch = X[start_idx:end_idx] Y_batch = Y[start_idx:end_idx] - offline_features_batch, mask_batch = TrainErrorPredictionNNWithOfflineData.get_offline_features( - preprocessor, - model_for_offline_features, + hc_initial_concat = TrainErrorPredictionNNWithOfflineData.get_initial_hidden( + model_for_initial_hidden, offline_data, indices_batch, - offline_feature_dim + initial_hidden_dim, ) - # hc_initial_concat = model_for_initial_hidden(offline_features_batch, X_batch[:, :past_initial_hidden_length], mask_batch, preprocessor) - hc_initial_concat = model_for_initial_hidden(offline_features_batch, mask_batch) - hc_initial = (hc_initial_concat[:, :model[0].lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, model[0].lstm_hidden_total_size:].unsqueeze(0).contiguous()) - _, hc = model[0](X_batch[:,past_initial_hidden_length-past_length:], previous_error=Y_batch[:, past_initial_hidden_length-past_length:past_initial_hidden_length, -2:],hc=hc_initial, mode="get_lstm_states") + hc_initial = (hc_initial_concat[:, :models[0].lstm_hidden_total_size].unsqueeze(0).contiguous(), hc_initial_concat[:, models[0].lstm_hidden_total_size:].unsqueeze(0).contiguous()) + _, hc = models[0](X_batch, previous_error=Y_batch[:, :past_length, -2:],hc=hc_initial, mode="get_lstm_states") - states_tmp=X_batch[:,past_initial_hidden_length, [vel_index, acc_index, steer_index]].unsqueeze(1) + states_tmp=X_batch[:,past_length, [vel_index, acc_index, steer_index]].unsqueeze(1) for i in range(window_size): - states_tmp_with_input_history = torch.cat((states_tmp, X_batch[:,[past_initial_hidden_length+i], 3:]), dim=2) - predicted_error_0, hc_next = model[0](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") + states_tmp_with_input_history = torch.cat((states_tmp, X_batch[:,[past_length+i], 3:]), dim=2) + predicted_error_0, hc_next = models[0](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") predicted_error = predicted_error_0 / len(models) for j in range(1,len(models)): - predicted_error_j, _ = model[j](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") + predicted_error_j, _ = models[j](states_tmp_with_input_history, hc=hc, mode="predict_with_hc") predicted_error += predicted_error_j / len(models) hc = hc_next states_tmp[:,0,0] = X_batch[:,past_length + i + 1, 0] for j in range(prediction_step): - states_tmp[:,0,1] = states_tmp[:,0,1] + (X_batch[:,past_initial_hidden_length + i, acc_input_indices_nom[j]] - states_tmp[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) - states_tmp[:,0,2] = states_tmp[:,0,2] + (X_batch[:,past_initial_hidden_length + i, steer_input_indices_nom[j]] - states_tmp[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) + states_tmp[:,0,1] = states_tmp[:,0,1] + (X_batch[:,past_length + i, acc_input_indices_nom[j]] - states_tmp[:,0,1]) * (1 - np.exp(- control_dt / acc_time_constant)) + states_tmp[:,0,2] = states_tmp[:,0,2] + (X_batch[:,past_length + i, steer_input_indices_nom[j]] - states_tmp[:,0,2]) * (1 - np.exp(- control_dt / steer_time_constant)) states_tmp[:,0,1] = states_tmp[:,0,1] + predicted_error[:,0,state_name_to_predicted_index["acc"]] * control_dt * prediction_step states_tmp[:,0,2] = states_tmp[:,0,2] + predicted_error[:,0,state_name_to_predicted_index["steer"]] * control_dt * prediction_step prediction.append(states_tmp[:,0,[1,2]].detach().to("cpu").numpy()) @@ -1291,8 +788,6 @@ def get_acc_steer_models_prediction( @staticmethod def get_model_signed_prediction_error( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X, Y, @@ -1302,15 +797,13 @@ def get_model_signed_prediction_error( batch_size=1000 ): model_prediction = TrainErrorPredictionNNWithOfflineData.get_acc_steer_model_prediction( - model, preprocessor, model_for_offline_features, model_for_initial_hidden, X, Y, offline_data, indices, window_size=window_size, batch_size=batch_size + model, model_for_initial_hidden, X, Y, offline_data, indices, window_size=window_size, batch_size=batch_size ) - model_prediction_error = X[:,past_initial_hidden_length+window_size, [acc_index, steer_index]].detach().to("cpu").numpy() - model_prediction + model_prediction_error = X[:,past_length+window_size, [acc_index, steer_index]].detach().to("cpu").numpy() - model_prediction return model_prediction_error @staticmethod def get_models_signed_prediction_error( models, - preprocessor, - model_for_offline_features, model_for_initial_hidden, X, Y, @@ -1320,19 +813,15 @@ def get_models_signed_prediction_error( batch_size=1000 ): model_prediction = TrainErrorPredictionNNWithOfflineData.get_acc_steer_models_prediction( - models, preprocessor, model_for_offline_features, model_for_initial_hidden, X, Y, offline_data, indices, window_size, batch_size + models, model_for_initial_hidden, X, Y, offline_data, indices, window_size, batch_size ) - model_prediction_error = X[:,past_initial_hidden_length+window_size, [acc_index, steer_index]].detach().to("cpu").numpy() - model_prediction + model_prediction_error = X[:,past_length+window_size, [acc_index, steer_index]].detach().to("cpu").numpy() - model_prediction return model_prediction_error @staticmethod def get_signed_prediction_error( model, - preprocessor, - model_for_offline_features, model_for_initial_hidden, relearned_model, - relearned_preprocessor, - relearned_model_for_offline_features, relearned_model_for_initial_hidden, X, Y, @@ -1341,25 +830,25 @@ def get_signed_prediction_error( window_size=10, batch_size=1000 ): - nominal_prediction_error = get_nominal_signed_prediction_error(X,window_size,past_length=past_initial_hidden_length) + nominal_prediction_error = get_nominal_signed_prediction_error(X,window_size,past_length=past_length) model_prediction_error = TrainErrorPredictionNNWithOfflineData.get_model_signed_prediction_error( - model,preprocessor, model_for_offline_features,model_for_initial_hidden, + model,model_for_initial_hidden, X,Y, offline_data, indices, window_size,batch_size) relearned_model_prediction_error = TrainErrorPredictionNNWithOfflineData.get_model_signed_prediction_error( - relearned_model,relearned_preprocessor, relearned_model_for_offline_features, - relearned_model_for_initial_hidden,X,Y,offline_data, indices,window_size,batch_size) + relearned_model, relearned_model_for_initial_hidden, + X,Y,offline_data, indices,window_size,batch_size) return nominal_prediction_error,model_prediction_error,relearned_model_prediction_error @staticmethod def get_sequence_data(X, Y, division_indices, acc_threshold=2.0, steer_threshold=0.7, acc_change_threshold=3.5, steer_change_threshold=0.8, acc_change_window=10, steer_change_window=10): X_seq, indices =transform_to_sequence_data_with_index( np.array(X), - past_initial_hidden_length + prediction_length, + past_length + prediction_length, division_indices, prediction_step, ) Y_seq = transform_to_sequence_data( np.array(Y)[:, state_component_predicted_index], - past_initial_hidden_length + prediction_length, + past_length + prediction_length, division_indices, prediction_step, ) diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/wrapper_train_error_prediction_NN_with_memory_bank.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/wrapper_train_error_prediction_NN_with_memory_bank.py deleted file mode 100644 index 6583b3f86bd56..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/wrapper_train_error_prediction_NN_with_memory_bank.py +++ /dev/null @@ -1,105 +0,0 @@ -from multiprocessing import Process -from autoware_vehicle_adaptor.training import train_error_prediction_NN_with_memory_bank -import torch - -def get_trained_model_wrapper(learning_rates, batch_sizes, train_dirs, val_dirs, train_reverse_steers, val_reverse_steers, model_save_dir): - model_trainer = train_error_prediction_NN_with_memory_bank.train_error_prediction_NN_with_memory_bank() - for i in range(len(train_dirs)): - model_trainer.add_data_from_csv(train_dirs[i], add_mode = "as_train", reverse_steer=train_reverse_steers[i]) - for i in range(len(val_dirs)): - model_trainer.add_data_from_csv(val_dirs[i], add_mode = "as_val", reverse_steer=val_reverse_steers[i]) - model_trainer.get_trained_model(learning_rates=learning_rates, batch_sizes=batch_sizes) - model_trainer.save_model(path=model_save_dir+"/vehicle_model.pth", path_for_initial_hidden=model_save_dir+"/vehicle_model_for_initial_hidden.pth") - -def get_relearned_model_wrapper( - learning_rates, - batch_sizes, - train_dirs, - val_dirs, - test_dirs, - replay_dirs, - train_reverse_steers, - val_reverse_steers, - test_reverse_steers, - replay_reverse_steers, - model_load_dir, - model_save_dir, - randomize=0.01, - patience=10, - replay_data_rate=0.05, - reset_weight=False, - always_update_model=False, - freeze_shallow_layers=False): - model_trainer = train_error_prediction_NN_with_memory_bank.train_error_prediction_NN_with_memory_bank() - for i in range(len(train_dirs)): - model_trainer.add_data_from_csv(train_dirs[i], add_mode = "as_train", reverse_steer=train_reverse_steers[i]) - for i in range(len(val_dirs)): - model_trainer.add_data_from_csv(val_dirs[i], add_mode = "as_val", reverse_steer=val_reverse_steers[i]) - for i in range(len(test_dirs)): - model_trainer.add_data_from_csv(test_dirs[i], add_mode = "as_test", reverse_steer=test_reverse_steers[i]) - if replay_dirs is not None: - for i in range(len(replay_dirs)): - model_trainer.add_data_from_csv(replay_dirs[i], add_mode = "as_replay", reverse_steer=replay_reverse_steers[i]) - use_replay_data = True - else: - use_replay_data = False - model_trainer.model = torch.load(model_load_dir+"/vehicle_model.pth") - model_trainer.model_for_initial_hidden = torch.load(model_load_dir+"/vehicle_model_for_initial_hidden.pth") - model_trainer.get_relearned_model( - learning_rates=learning_rates, batch_sizes=batch_sizes, patience=patience, - use_replay_data=use_replay_data, randomize=randomize, - replay_data_rate=replay_data_rate, reset_weight=reset_weight, - always_update_model=always_update_model, - freeze_shallow_layers=freeze_shallow_layers, - plt_save_dir=model_save_dir, model_save_path=model_save_dir+"/tmp_vehicle_model.pth") - model_trainer.save_model(path=model_save_dir+"/vehicle_model.pth", path_for_initial_hidden=model_save_dir+"/vehicle_model_for_initial_hidden.pth") -def get_trained_model(learning_rates, batch_sizes, train_dirs, val_dirs, train_reverse_steers, val_reverse_steers, model_save_dir): - p = Process(target=get_trained_model_wrapper, args=(learning_rates, batch_sizes, train_dirs, val_dirs, train_reverse_steers, val_reverse_steers, model_save_dir)) - p.start() - p.join() -def get_relearned_model( - learning_rates, - batch_sizes, - train_dirs, - val_dirs, - test_dirs, - replay_dirs, - train_reverse_steers, - val_reverse_steers, - test_reverse_steers, - replay_reverse_steers, - model_load_dir, - model_save_dir, - randomize=0.01, - patience=10, - replay_data_rate=0.05, - reset_weight=False, - always_update_model=False, - freeze_shallow_layers=False): - p = Process(target=get_relearned_model_wrapper, args=( - learning_rates, - batch_sizes, - train_dirs, - val_dirs, - test_dirs, - replay_dirs, - train_reverse_steers, - val_reverse_steers, - test_reverse_steers, - replay_reverse_steers, - model_load_dir, - model_save_dir, - randomize, - patience, - replay_data_rate, - reset_weight, - always_update_model, - freeze_shallow_layers)) - p.start() - p.join() - - - - - - diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/wrapper_train_error_prediction_NN_with_offline_data.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/wrapper_train_error_prediction_NN_with_offline_data.py index ca19a00030fec..dd6c88b11c435 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/wrapper_train_error_prediction_NN_with_offline_data.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/autoware_vehicle_adaptor/training/wrapper_train_error_prediction_NN_with_offline_data.py @@ -24,8 +24,6 @@ def get_trained_model_wrapper(learning_rates, batch_sizes, train_dirs, val_dirs, model_trainer.get_trained_model(learning_rates=learning_rates, batch_sizes=batch_sizes, domains=domains_reset, targets=targets_reset, domain_indices=domain_indices_reset, target_indices=target_indices_reset) model_trainer.save_model(path=model_save_dir+"/vehicle_model.pth", - path_for_preprocessor=model_save_dir+"/vehicle_model_for_preprocessor.pth", - path_for_offline_features=model_save_dir+"/vehicle_model_for_offline_features.pth", path_for_initial_hidden=model_save_dir+"/vehicle_model_for_initial_hidden.pth") if save_with_train_0: model_trainer.save_offline_features(path=model_save_dir + "/offline_features.csv") @@ -75,8 +73,6 @@ def get_relearned_model_wrapper( for i in range(len(domains)): model_trainer.set_offline_data(domains[i], targets[i], domain_indices[i], target_indices[i]) model_trainer.model = torch.load(model_load_dir+"/vehicle_model.pth") - model_trainer.preprocessor = torch.load(model_load_dir+"/vehicle_model_for_preprocessor.pth") - model_trainer.model_for_offline_features = torch.load(model_load_dir+"/vehicle_model_for_offline_features.pth") model_trainer.model_for_initial_hidden = torch.load(model_load_dir+"/vehicle_model_for_initial_hidden.pth") model_trainer.get_relearned_model( learning_rates=learning_rates, batch_sizes=batch_sizes, patience=patience, @@ -87,8 +83,6 @@ def get_relearned_model_wrapper( plt_save_dir=model_save_dir, model_save_path=model_save_dir+"/tmp_vehicle_model.pth", domains=domains_reset, targets=targets_reset, domain_indices=domain_indices_reset, target_indices=target_indices_reset) model_trainer.save_model(path=model_save_dir+"/vehicle_model.pth", - path_for_preprocessor=model_save_dir+"/vehicle_model_for_preprocessor.pth", - path_for_offline_features=model_save_dir+"/vehicle_model_for_offline_features.pth", path_for_initial_hidden=model_save_dir+"/vehicle_model_for_initial_hidden.pth") if save_with_train_0: model_trainer.save_offline_features(path=model_save_dir + "/offline_features.csv") diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/build_test.py b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/build_test.py index 1b5a1d9d88b72..6e86252c06412 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/build_test.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/build_test.py @@ -6,9 +6,9 @@ "g++ -Ofast -Wall -shared -std=c++23 -fPIC $(python3 -m pybind11 --includes) " ) build_cpp_command += "-DBUILD_PATH=\\\"" + str(Path(__file__).parent) + "\\\" " -build_cpp_command += "autoware_vehicle_adaptor/src/utils.cpp autoware_vehicle_adaptor/src/inputs_prediction.cpp autoware_vehicle_adaptor/src/nominal_dynamics.cpp autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp autoware_vehicle_adaptor/src/linear_regression_compensator.cpp autoware_vehicle_adaptor/src/inputs_ref_smoother.cpp " +build_cpp_command += "autoware_vehicle_adaptor/src/vehicle_adaptor_compensator.cpp autoware_vehicle_adaptor/src/inputs_prediction.cpp autoware_vehicle_adaptor/src/nominal_dynamics.cpp autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp autoware_vehicle_adaptor/src/linear_regression_compensator.cpp autoware_vehicle_adaptor/src/inputs_ref_smoother.cpp " build_cpp_command += ( - "-o autoware_vehicle_adaptor/src/utils$(python3-config --extension-suffix) " + "-o 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    7_s88J)A`iGm}YW+2)9 z^VMsWB>u98?`q2Fi$X)UvPVA92#>rZC32!&xxXcuvVTM1t^Wv>-rybIUTzmoX=^ib z`QhF{2A0eERy=ufjF1k5)a<9EIx6)~T}ars;vX>HGuvSy=|7X&6XC2N9zwF3`PGzF zR(Af3u`!vD#a4fZKfadHH#9R7#|BdZ?;9Jn?ChQrbLoWC_VN0u<#&kr%#H8I!`Ku(QZMQA9$5O*q}{ACn5N0ueE> zohMZnA0^4y{Ufza!{JAn@ybtOHD>~w3w$tj zVz!4effs=JyIsttQj_rX4|7DpzJP9#AUl8l{29h{=CiOD6uaTFJB@?iR0HoTY-@K6iQe{MMbCM2~5acn)p@(U2ry29uT_! z=tVRDxrdDSzZWld&J`LEb9|B3rW|2MU0kFkD}Halz!4;xThisa71~hU+X0xjK4ims~OL#}bz} zhWr3n!|f6A+qJnyi>dkVTDWRUh2Czig2 zTZFY_h%nd&l9*8#(fsACU{&PD&9oUkMg1|^86ZC~k42Xub}0zny0FWVu(ILRHpBHu zsr-e3Kt&2LDRtfFR5{DW$;nBerOiOG5!fd7AYrH28JU|m)8>1K4I|Z|B2O8|qx9jj zf0K1%0y9&1>2Ow5ib`J2HHTxfDfW4NIPzGG(3Z#>2vcWt7r{6YXYR-3=5ma4i4MmN z9izIP5E|n@$`iLrR*)`sjKJ_CW`|Imfm)fU{{H^XFEQ|uH)rkflE2B58sq>gVLYth zss66D>PlZma?Xwcg;j2wuuq(_d%|=-wHdXsp6k~(+~hhyP1^kBtoF$~(i&h)kJHwV^=v;Zl~47$IjHq}@mb3t<|6!u zRwRcES@{J7zV`GqjLlj}aY!Yf!146(Tgfpo082nmK|uk4a^u5~ZwqZ(HoTS=6r{VY zt%0|8Ha8x2ue7XeXqL#3tA$>~7MDP~qHm|mmsS;3y}x-4;;c-m-M8TdF9k5<89hCf 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/setup.py +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/setup.py @@ -18,9 +18,9 @@ "g++ -Ofast -Wall -shared -std=c++23 -fPIC $(python3 -m pybind11 --includes) " ) build_cpp_command += "-DBUILD_PATH=\\\"" + str(Path(__file__).parent) + "\\\" " - build_cpp_command += "autoware_vehicle_adaptor/src/utils.cpp autoware_vehicle_adaptor/src/inputs_prediction.cpp autoware_vehicle_adaptor/src/nominal_dynamics.cpp autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp autoware_vehicle_adaptor/src/linear_regression_compensator.cpp autoware_vehicle_adaptor/src/inputs_ref_smoother.cpp " + build_cpp_command += "autoware_vehicle_adaptor/src/vehicle_adaptor_compensator.cpp autoware_vehicle_adaptor/src/inputs_prediction.cpp autoware_vehicle_adaptor/src/nominal_dynamics.cpp autoware_vehicle_adaptor/src/transform_vehicle_adaptor_model.cpp autoware_vehicle_adaptor/src/linear_regression_compensator.cpp autoware_vehicle_adaptor/src/inputs_ref_smoother.cpp " build_cpp_command += ( - "-o autoware_vehicle_adaptor/src/utils$(python3-config --extension-suffix) " + "-o autoware_vehicle_adaptor/src/vehicle_adaptor_compensator$(python3-config --extension-suffix) " ) build_cpp_command += "-lrt -I$(pwd)/autoware_vehicle_adaptor/include -I/usr/include/eigen3 -lyaml-cpp" print(build_cpp_command) @@ -61,7 +61,7 @@ print(build_inputs_prediction_command) subprocess.run(build_inputs_prediction_command, shell=True) -utils_so_path = "src/" + glob.glob("autoware_vehicle_adaptor/src/utils.*.so")[0].split("/")[-1] +vehicle_adaptor_compensator_so_path = "src/" + glob.glob("autoware_vehicle_adaptor/src/vehicle_adaptor_compensator.*.so")[0].split("/")[-1] actuation_map_so_path = "src/" + glob.glob("autoware_vehicle_adaptor/src/actuation_map_2d.*.so")[0].split("/")[-1] nominal_ilqr_so_path = "controller/" + glob.glob("autoware_vehicle_adaptor/controller/nominal_ilqr.*.so")[0].split("/")[-1] inputs_prediction_so_path = "src/" + glob.glob("autoware_vehicle_adaptor/src/inputs_prediction.*.so")[0].split("/")[-1] @@ -71,6 +71,6 @@ version="1.0.0", packages=find_packages(), package_data={ - "autoware_vehicle_adaptor": ["package_path.json", utils_so_path, actuation_map_so_path, nominal_ilqr_so_path, inputs_prediction_so_path], + "autoware_vehicle_adaptor": ["package_path.json", vehicle_adaptor_compensator_so_path, actuation_map_so_path, nominal_ilqr_so_path, inputs_prediction_so_path], }, ) diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_1.csv index f73ddf1687d3e..e263d89f594a4 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --5.687703490257263184e-01 -1.572953760623931885e-01 -5.714529380202293396e-02 -1.085763052105903625e-01 -9.483638405799865723e-02 -1.503358874469995499e-02 --6.868886295706033707e-03 --4.978453516960144043e-01 -1.244671717286109924e-01 -1.060194298624992371e-01 -1.586544699966907501e-02 -5.790882557630538940e-02 -8.724521845579147339e-02 -8.154500126838684082e-01 -3.643409162759780884e-02 -1.652323454618453979e-01 +9.447958320379257202e-02 +-3.904920443892478943e-02 +-4.699527025222778320e-01 +1.458566039800643921e-01 +-3.368199765682220459e-01 +-3.077215850353240967e-01 +-7.432874292135238647e-02 +-3.889969587326049805e-01 +7.842382043600082397e-02 +-1.985292583703994751e-01 +7.554553151130676270e-01 +-4.822994768619537354e-02 +-4.119287729263305664e-01 +2.234724313020706177e-01 +5.568625032901763916e-02 +9.671768546104431152e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_2.csv index 2321f21b9d8a8..6bc053b95a062 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -8.771612308919429779e-03 -1.144464537501335144e-01 -6.942238658666610718e-02 -6.198814511299133301e-02 --2.246745675802230835e-02 --7.646840810775756836e-02 -1.091548949480056763e-01 -2.548992633819580078e-01 +2.911694943904876709e-01 +1.167204603552818298e-01 +1.205120310187339783e-01 +3.711816072463989258e-01 +4.805700480937957764e-01 +2.870769798755645752e-01 +5.219599604606628418e-01 +6.262990832328796387e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_1.csv index 025c220dd25d3..2f0413c7e4822 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_1.csv @@ -1,16 +1,16 @@ -2.375463582575321198e-02 -1.337547302246093750e-01 -2.418657951056957245e-02 -7.714676111936569214e-02 -1.790988594293594360e-01 -7.039796561002731323e-02 -4.970551654696464539e-02 -1.304802894592285156e-01 --3.642752394080162048e-02 --1.260660216212272644e-02 -9.186952561140060425e-02 -7.274972647428512573e-02 -1.017989963293075562e-01 -7.828899472951889038e-02 -1.771877259016036987e-01 -1.412725597620010376e-01 +-4.330429807305335999e-02 +-4.956674203276634216e-02 +5.305194482207298279e-02 +-9.294666349887847900e-03 +7.941994816064834595e-02 +8.546566963195800781e-02 +6.983704864978790283e-02 +5.682964250445365906e-02 +1.107634603977203369e-01 +7.904335856437683105e-02 +2.872206084430217743e-02 +8.025071769952774048e-02 +1.396385300904512405e-02 +7.349329441785812378e-02 +-1.042479351162910461e-01 +1.730972528457641602e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_2.csv index 40f2a127a4f33..993c5dccd103f 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.024103090167045593e-01 -1.508852094411849976e-01 -9.780577383935451508e-03 -4.319138452410697937e-02 -9.238595515489578247e-02 -1.059526875615119934e-01 -9.110169857740402222e-02 -1.054998338222503662e-01 +5.389664322137832642e-02 +3.577540814876556396e-02 +3.646123688668012619e-03 +4.657787457108497620e-02 +4.009292274713516235e-02 +-1.234907656908035278e-01 +-9.528395719826221466e-03 +8.088576793670654297e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_complimentary_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_complimentary_layer.csv index bb1b5346932b2..a9dc3efd0edaf 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_complimentary_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_complimentary_layer.csv @@ -1,64 +1,64 @@ -1.511160749942064285e-02 -1.398833189159631729e-02 -1.167307570576667786e-01 -4.545302689075469971e-02 -1.077463570982217789e-02 -1.412830781191587448e-03 -2.155422978103160858e-02 -9.623280493542551994e-04 -4.561380296945571899e-02 -1.059380993247032166e-01 --9.806581586599349976e-02 --4.837276786565780640e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_acc_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 6a4f3acd10344..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.700384832936663315e-10 -5.574749906145370915e-08 --1.639053834878723137e-07 -2.375204388727070182e-07 -5.790519352899536898e-08 -5.913284439884591848e-08 --1.567347993614021107e-07 --3.626800904044102936e-08 -2.247556807333239703e-07 -2.873030524597197655e-08 -5.794816004822678224e-08 --4.090935590284061618e-08 --2.924792141811849433e-08 --2.869897031132495613e-08 --2.662483211679500528e-08 --1.567348562048209715e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_acc_layer_2.csv deleted file mode 100644 index 62f75cb43506c..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --8.786349070533105987e-08 --2.436062018773554883e-08 --7.991299355580849806e-08 --1.391345278989319922e-07 --3.481732591126274201e-08 --2.435033650272089290e-07 --6.396118124030181207e-08 --2.479723377746267943e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_final_layer.csv deleted file mode 100644 index b082a7a89c611..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_final_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.333507336676120758e-02 -6.083920970559120178e-02 --1.834504422731697559e-03 --3.571275621652603149e-02 -2.946369349956512451e-01 --1.427265582606196404e-03 --3.688247501850128174e-02 -6.005700305104255676e-02 --1.222376897931098938e-02 --3.048365712165832520e-01 --4.184846207499504089e-02 -1.948376744985580444e-02 -4.896391183137893677e-02 -3.152998397126793861e-03 --2.637754976749420166e-01 -1.586769241839647293e-03 --6.010877341032028198e-02 --2.591094970703125000e-01 -2.363688647747039795e-01 -6.578605622053146362e-03 -7.574999239295721054e-03 -2.695792615413665771e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_gru_hh.csv deleted file mode 100644 index 8057bd867e52d..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ -1.545770733635265515e-10 --8.302931320258721826e-08 --7.699894943868912378e-09 --2.921688491142049315e-08 -1.020158979514462771e-08 --1.473583746701478958e-03 --7.846460212022066116e-04 --1.084701466425030958e-07 --5.566524663436211995e-08 --1.647779015456762863e-07 -2.786956443401322758e-08 -1.140102057206604513e-07 -1.460361964689127490e-08 --1.444058312927154475e-07 -8.503373294388438808e-08 --1.640689930582084344e-07 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--1.957485125103630708e-07 --1.044421349405411092e-08 --4.099469631910324097e-02 --5.203025565947427822e-09 -4.812124032582687505e-08 --2.186568221077322960e-03 -6.986832013353705406e-04 -2.696507871150970459e-01 -5.738455429673194885e-02 --3.137570619583129883e-01 -2.364166826009750366e-01 --2.259131222963333130e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_only_online.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_only_online.csv deleted file mode 100644 index 0418c7e532bb9..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_only_online.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.383439749479293823e-01 --2.788069546222686768e-01 -1.486504375934600830e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_steer_layer_1.csv deleted file mode 100644 index 614901c941b0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.602122381427761866e-07 -1.594512042402129737e-07 --1.383567216350911622e-08 -2.870719839620505809e-08 --4.115875640309241135e-08 --1.629571784178551752e-07 -6.326867207917530322e-08 -6.130223795253186836e-08 -2.572974278081119337e-09 --1.623540981654514326e-07 --3.618814403694159409e-08 --5.186216256447551132e-08 -1.592173859421563975e-08 -2.550376621002214961e-08 --5.809684111568458320e-08 --6.100456317881253199e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_steer_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_steer_layer_2.csv deleted file mode 100644 index fc1f85f18f28b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --1.495364330139636877e-07 --9.433398506786261350e-10 --9.447958149166879593e-08 -6.568057209221933590e-09 --2.248099519874813268e-07 --1.070967527994071133e-07 --9.932387712296986138e-08 --7.939953405866617686e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_linear_relu.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_linear_relu.csv index 6c7ba3b020e52..b40d15d15387e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_linear_relu.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_linear_relu.csv @@ -1,16 +1,16 @@ --3.609813749790191650e-02 --3.190746530890464783e-02 -4.359308257699012756e-02 -8.269068202935159206e-04 -4.143001139163970947e-02 -7.497583515942096710e-03 -7.427339255809783936e-02 --2.295951917767524719e-02 --2.274018079042434692e-01 -8.252906054258346558e-02 -7.786637544631958008e-02 -1.048227921128273010e-01 --4.350556991994380951e-04 --6.249625235795974731e-02 -1.322524547576904297e-01 --3.382597863674163818e-02 +-2.442883513867855072e-02 +5.057684332132339478e-02 +3.201630339026451111e-02 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-1,72 +1,72 @@ --2.799303233623504639e-01 -8.258733898401260376e-02 -5.214651674032211304e-02 --2.117088623344898224e-02 --9.740439057350158691e-02 --1.514682471752166748e-01 --1.841652244329452515e-01 --1.662612110376358032e-01 --2.394026368856430054e-01 -6.085600331425666809e-02 -1.038094758987426758e-01 -1.195812672376632690e-01 --1.226203665137290955e-01 --1.429048180580139160e-01 -6.019178405404090881e-02 --1.727182716131210327e-01 --2.290469259023666382e-01 --3.691007494926452637e-01 -4.015429019927978516e-01 -1.827716976404190063e-01 -1.926851719617843628e-01 -1.571784615516662598e-01 -1.860545575618743896e-01 -2.430029809474945068e-01 -4.373685643076896667e-02 -1.225811541080474854e-01 --1.930130422115325928e-01 --9.093154966831207275e-02 -2.350523173809051514e-01 -5.819558352231979370e-02 -1.559833139181137085e-01 -2.092577219009399414e-01 -2.149106264114379883e-01 -2.697693407535552979e-01 -2.954715490341186523e-01 -6.605675816535949707e-01 --9.321784228086471558e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_encoder_ih_0.csv index bd71a79542f43..2a250786995d9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ --2.799263000488281250e-01 -8.330401778221130371e-02 -5.212230980396270752e-02 --2.048002928495407104e-02 --9.690331667661666870e-02 --1.525534093379974365e-01 --1.841630488634109497e-01 --1.662613004446029663e-01 --2.396262139081954956e-01 -6.080040335655212402e-02 -1.034230291843414307e-01 -1.198021844029426575e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_hh.csv @@ -1,64 +1,64 @@ -1.863418668508529663e-01 --1.841402947902679443e-01 -4.871492087841033936e-02 -6.630924344062805176e-01 -3.120891004800796509e-02 -5.130470544099807739e-02 -3.066616505384445190e-02 -3.965241834521293640e-02 -3.464382141828536987e-02 --1.669278144836425781e-01 --9.552434831857681274e-02 --2.999459765851497650e-02 --6.102378945797681808e-03 -1.027578711509704590e-01 -1.095576584339141846e-01 -1.344114840030670166e-01 -2.494055032730102539e-01 -1.547644734382629395e-01 -1.317952722311019897e-01 --2.324226498603820801e-01 --2.754482328891754150e-01 -1.100205332040786743e-01 --1.426737289875745773e-03 --3.385674655437469482e-01 -1.660625338554382324e-01 -5.306183919310569763e-02 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031398bdfd2a3..8511ea9e3ab32 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_lstm_ih.csv @@ -1,64 +1,64 @@ -1.862874925136566162e-01 --1.859310567378997803e-01 -4.900135472416877747e-02 -6.631961464881896973e-01 -3.110241889953613281e-02 -5.119629949331283569e-02 -2.894839085638523102e-02 -3.989761322736740112e-02 -3.549076244235038757e-02 --1.642386913299560547e-01 --9.745972603559494019e-02 --3.339832648634910583e-02 --2.647247863933444023e-03 -9.829391539096832275e-02 -1.122935935854911804e-01 -1.338122785091400146e-01 -2.497808933258056641e-01 -1.542556881904602051e-01 -1.318080276250839233e-01 --2.323063462972640991e-01 --2.763138115406036377e-01 -1.098379045724868774e-01 --1.424718881025910378e-03 --3.388048410415649414e-01 -1.662753224372863770e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_encoder_layer_1.csv index 707a956dc7533..ca1b020a82ede 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_encoder_layer_1.csv @@ -1,16 +1,16 @@ -2.288375049829483032e-02 -4.156160056591033936e-01 -1.447749207727611065e-03 --1.900681381812319160e-04 -3.408987540751695633e-03 -3.183485008776187897e-03 -4.051115512847900391e-01 --1.631803363561630249e-01 -2.039906978607177734e-01 -3.064470831304788589e-03 -2.237636968493461609e-02 -1.526999026536941528e-01 -1.990733891725540161e-01 --1.465275436639785767e-01 --4.737891629338264465e-02 -1.408819258213043213e-01 +-2.332334361199173145e-06 +1.387599296867847443e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ --4.465385898947715759e-02 --2.514035440981388092e-02 --4.148573800921440125e-02 -1.449349820613861084e-01 -9.823747910559177399e-03 --1.016079541295766830e-02 --1.387605676427483559e-03 --8.763826638460159302e-02 +2.186003774404525757e-01 +-9.245735406875610352e-02 +-2.528463006019592285e-01 +-7.972517609596252441e-02 +6.500462442636489868e-02 +2.470089346170425415e-01 +2.000899612903594971e-02 +1.971768885850906372e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_layer_1.csv index d8e5af208e79c..dc762d230ac56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/bias_steer_layer_1.csv @@ -1,16 +1,16 @@ --5.880776420235633850e-02 -1.072910651564598083e-01 -1.927932947874069214e-01 -1.416619420051574707e-01 -3.611433552578091621e-03 --2.359812110662460327e-01 -1.126473490148782730e-02 --1.914175972342491150e-02 -1.322983503341674805e-01 --9.606996551156044006e-03 --4.914920404553413391e-02 -5.091565847396850586e-02 -2.208182960748672485e-02 -4.450881108641624451e-02 -5.511307157576084137e-03 -9.871846064925193787e-03 +-1.267773564904928207e-03 +1.681745648384094238e-01 +3.152760118246078491e-02 +-3.042486310005187988e-02 +2.783328667283058167e-02 +1.784413307905197144e-01 +3.829433768987655640e-02 +-1.098260059952735901e-01 +8.191139250993728638e-02 +4.840236902236938477e-02 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-1.211080327630043030e-01 +-2.150738146156072617e-03 +4.411975294351577759e-02 +1.444365680217742920e-01 +5.844632163643836975e-02 +6.778846494853496552e-03 +5.850554630160331726e-02 +2.264000841023516841e-06 +1.272178292274475098e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/offline_features.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/offline_features.csv index 1bcdd512dea6f..fa6d9d8875c56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/offline_features.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/offline_features.csv @@ -1,36 +1,36 @@ --1.365479675996539299e-07 -4.500528660855707130e-08 --2.888707228976272745e-07 --3.052138808357085509e-08 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_acc_encoder_layer_1.csv index 5eb1c7c33213a..09a6b18c285fa 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_acc_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --6.779996305704116821e-02,3.508124351501464844e-01,2.439208775758743286e-01,2.227861583232879639e-01,2.015045583248138428e-01,1.802337467670440674e-01,1.594138443470001221e-01,1.380567848682403564e-01,1.166481673717498779e-01,9.721208363771438599e-02,7.622497528791427612e-02,5.588675662875175476e-02,3.574046865105628967e-02,1.916410401463508606e-02,1.854662410914897919e-02,1.806927099823951721e-02,1.663219928741455078e-02,1.390712894499301910e-02,8.530278690159320831e-03,-9.777169907465577126e-04 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -3.336199373006820679e-02,2.459247857332229614e-01,-6.924922019243240356e-02,-3.334385901689529419e-02,2.794704735279083252e-01,-1.098308246582746506e-02,-3.318902477622032166e-02,-2.375750094652175903e-01,-6.978246849030256271e-03,-7.694390369579195976e-04,3.750725463032722473e-02,-1.894451864063739777e-02,-1.075058132410049438e-01,1.155056133866310120e-01,-3.716661781072616577e-02,4.225961863994598389e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.827372312545776367e-01,1.442782729864120483e-01,1.350615918636322021e-01,1.406146585941314697e-01,7.798003405332565308e-02,1.429848670959472656e-01,1.125954836606979370e-01,5.638980120420455933e-02,8.144410699605941772e-02,-5.328862462192773819e-03,-2.268474362790584564e-02,4.206001386046409607e-02,1.457983851432800293e-01,-5.146942660212516785e-02,-6.503234058618545532e-02,-3.310494497418403625e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_final_layer.csv @@ -1,4 +1,4 @@ --4.076786339282989502e-03,-1.227277144789695740e-02,3.244567289948463440e-02,1.793841016478836536e-03,4.501522984355688095e-03,-2.283791452646255493e-02,-1.217357907444238663e-02,-2.098338445648550987e-03,-2.394610270857810974e-02,3.298125788569450378e-02,3.658420871943235397e-03,3.390965238213539124e-02,-1.559238694608211517e-02,-1.272494345903396606e-02,1.682596048340201378e-03,-1.867336407303810120e-02 --1.042943261563777924e-02,-4.991219844669103622e-03,1.183264539577066898e-03,5.007501225918531418e-03,-6.344680878100916743e-05,2.041139872744679451e-03,7.769418880343437195e-03,-3.926411271095275879e-03,-1.374095008941367269e-04,-7.521932013332843781e-03,2.816032152622938156e-03,5.137700587511062622e-03,-1.068992633372545242e-02,-1.806749030947685242e-03,1.107970019802451134e-03,2.244758186861872673e-03 --2.750689350068569183e-02,-3.015980422496795654e-01,8.099721074104309082e-01,5.499200895428657532e-02,1.074275374412536621e-01,-5.590542554855346680e-01,-3.127891719341278076e-01,-1.049866527318954468e-01,-5.508310794830322266e-01,8.068168163299560547e-01,1.146488413214683533e-01,8.553577661514282227e-01,-3.928754031658172607e-01,-3.463063836097717285e-01,3.211103007197380066e-02,-4.625632762908935547e-01 --1.714976727962493896e-01,-3.647217899560928345e-02,1.288588275201618671e-03,-1.639245077967643738e-02,-3.204738348722457886e-02,4.737513139843940735e-03,4.795992001891136169e-02,-2.080518007278442383e-01,9.203262743540108204e-04,-4.152239300310611725e-03,4.169252887368202209e-02,-4.800185561180114746e-03,1.465110201388597488e-02,-2.583358809351921082e-02,5.423993244767189026e-02,-1.011096313595771790e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 09b35a7fc4e0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --5.795665813934647304e-08,2.448358884521439904e-07,-3.380812785280795651e-08,-2.136942289787668869e-10,4.084210303290092270e-08,5.576080042146713822e-08,1.567351972653341363e-07,3.467380693678023817e-08,6.475600144995041774e-08,7.276236857478579623e-08,-1.776304969780539977e-07,3.134217507749781362e-08,1.528735111833157134e-07,1.598162242544276523e-07,4.906196338083645969e-08,5.687583737312706944e-08,2.663517761902767234e-08,-1.778388565298882895e-07,9.540057988033368019e-08,-1.728522325272763283e-10 --2.865231785165178735e-08,1.085090843844227493e-07,4.934348041274461139e-08,6.418773779159892001e-08,1.228051171864308344e-08,1.398261240126430494e-08,-5.777479472612867539e-08,-1.366190183205162612e-08,-7.060171469674969558e-08,-2.162425261076350580e-08,5.675544301197987807e-08,-3.080576860270411998e-08,1.389905257553891715e-08,5.775637745841777360e-08,5.794838742190222547e-08,1.389194714818131615e-08,5.799671143336126988e-08,1.682496844068737119e-07,-5.566382554889059975e-08,1.608615036730043357e-07 --2.546920541135477833e-08,-1.084940137729972776e-07,-2.864933357216159493e-08,-1.399994964401685138e-08,-1.849883024540588394e-10,5.802163727253173420e-08,6.589188217276387149e-08,5.794140278680970368e-08,-1.919061715094372289e-10,-1.567351404219152755e-07,-1.567356520126850228e-07,-1.874498991583095631e-07,3.460730013671309280e-08,-2.833207801700154960e-08,-5.565841121324410778e-08,2.246823527229935280e-07,7.060046414153475780e-08,-4.112459350835706573e-08,1.538483758167785709e-07,4.987273527490287961e-08 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--2.572746904405676105e-09,1.086709744413383305e-07,1.400614557667267945e-08,7.936692192345162766e-09,4.102665940308725112e-08,-8.246642124731806689e-08,5.573721040263990290e-08,6.818912368089513620e-08,4.132279229906998808e-08,-1.598831147475721082e-07,2.749714345107179270e-10,-4.084756000111156027e-08,2.869085591328257578e-08,1.387898684868105192e-08,-6.484240344661884592e-08,-6.196152213533423492e-08,-5.634582800340126596e-10,-1.612109485904511530e-07,2.844504010113269032e-08,2.246597858857057872e-07 --2.837610324490924540e-08,-1.612319806554296520e-07,6.102120408968403353e-08,2.872587145930083352e-08,-2.865953518949027057e-08,2.850439884127808909e-08,1.778415992248483235e-07,2.926793030155749875e-08,-1.032509047149687831e-07,1.884557510045681283e-10,2.864214287967570272e-08,-4.828538990864217340e-08,1.726883596120387665e-07,-6.240391314804583089e-08,5.785573975458646601e-08,1.387097192662167799e-08,-1.379480174534819525e-08,2.865901116422264749e-08,-2.918067565360615845e-08,-2.918391572848122451e-08 --2.864954673498232296e-08,-1.368725399686354649e-08,1.085663257072155830e-07,-2.883944461018472794e-08,1.085741132555995136e-07,-5.121204438296445005e-08,6.429602450452875928e-08,5.809104308696078078e-08,-4.156912325470329961e-08,-3.439327400656111422e-08,-5.183908413641802326e-08,1.530865603172060219e-07,8.827194619698275346e-08,-5.001684755256974313e-08,1.873391681783687091e-07,1.567354246390095796e-07,5.798773017318126222e-08,1.020795323825041123e-07,1.085405756384716369e-07,1.529302977587576606e-07 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--2.852652158935597981e-08,-9.824449875850405078e-08,-1.754762308792123804e-07,5.789251744658940879e-08,4.112624196750402916e-08,5.801046754072558542e-08,6.086136750127479900e-08,-2.864963200011061417e-08,-1.392582582582235773e-08,1.740867645594335045e-07,-1.605755812761344714e-07,-3.050213948085911397e-08,-2.212133978218844277e-07,1.637957893763086759e-07,1.566371992112181033e-07,1.566785812201487715e-07,1.622258025690825889e-07,5.108397616027104959e-08,1.567347993614021107e-07,2.550069666540366597e-08 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--3.479204124801071885e-08,-6.744752312215496204e-08,-6.184050960200693225e-08,2.946307043824591965e-12,-6.112707495731228846e-08,1.314774333849300092e-08,-9.540066514546197141e-08,-5.567803640360580175e-08,1.567590146578368149e-07,-6.107648431452616933e-08,1.636276465433184057e-07,2.845211288793336735e-10,-2.862939751935300592e-08,2.852742042591671634e-08,-4.092018457413360011e-08,-4.101761419406102505e-08,1.497509884984538076e-07,1.882291797983270953e-07,1.623722880594868911e-07,6.816661368702625623e-08 --1.567573093552709906e-07,5.190680241184963961e-08,-4.090967564707170823e-08,-2.676181409810851619e-08,1.796450210811428860e-10,-2.870267934440562385e-08,1.629214239073917270e-07,1.625622587653197115e-07,5.780725231829819677e-08,-6.437543476067730808e-08,1.078759979122878576e-07,-5.816283632498198131e-08,-2.863949966069867514e-08,1.598080103804022656e-07,1.085153371604974382e-07,-3.626300681958127825e-08,2.851320957120151434e-08,-7.937203783114910038e-09,-1.778377480832205038e-07,1.665710347431570426e-10 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--2.448340410410310142e-07,1.380423775287908938e-08,1.561118456727683679e-10,-5.812466596921694872e-08,-3.100375423059631430e-08,-1.527012405411198870e-10,-2.919994557259997237e-08,-1.069702619815871003e-07,-1.525875537033982710e-10,2.856971192954915750e-08,-3.033363005222611264e-10,-8.849530530596894096e-08,1.624740946226665983e-07,-1.728069349837824120e-07,1.750106615183710801e-09,5.574175787614876754e-08,-1.683244903460945352e-07,-1.400569082932179299e-08,2.154006040200329153e-08,-2.080925725067572785e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_acc_layer_2.csv deleted file mode 100644 index b48ef5ed5516a..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -2.044039248971785128e-08,2.853378688882912684e-08,1.634649606785387732e-07,-2.863657222462734353e-08,1.586338811421228456e-07,-3.615636856579840241e-08,4.099652528566366527e-08,-2.866763537667793571e-08,-1.085351186702609994e-07,-5.566445082649806864e-08,-1.587467721719804103e-07,1.566090617188820033e-07,-2.175595814613018320e-10,-1.384351655531190772e-08,-2.042933644474942412e-08,4.782882356835216342e-08 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_fusion_layer.csv deleted file mode 100644 index da8778e4363e8..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_fusion_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_gru_hh.csv deleted file mode 100644 index ebda9420438b0..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ 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--1.575083672378241317e-07,-2.456893177793517680e-08,1.299299183301627636e-03,4.583574853000982330e-09,7.148290137592994142e-07,-1.196986180730164051e-03,-3.440467698965221643e-04,9.724246774567291141e-05,1.484971789977862500e-07,1.561929110494020279e-07,1.268110796809196472e-02,1.301198644796386361e-04,1.105297007597982883e-03,-9.569925023242831230e-04,-3.483079746365547180e-02,4.560764878988265991e-02,-2.130093790242426621e-08,-2.800469189878640464e-08,2.677923534065485001e-03,-1.244230009615421295e-02,-1.687294997054777923e-07,1.886606071366259130e-08,-9.817488025873899460e-04,5.614008102838852210e-08,1.669344698029817664e-07,1.194582000607624650e-04,2.625588967930525541e-04,2.482417039573192596e-03,1.514875143766403198e-02,-7.238288526423275471e-04,2.977170515805482864e-03,-3.903895150870084763e-03 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--1.053751930157886818e-07,-2.511040122499252902e-07,2.483378048054873943e-04,3.056874575690926576e-08,-3.617716356529854238e-05,-5.083134965389035642e-05,-3.177255392074584961e-02,-8.462891855742782354e-05,1.506569304865479353e-07,1.389682040553452680e-07,4.094154108315706253e-03,-9.500762098468840122e-05,-1.690746139502152801e-04,-1.181037150672636926e-04,1.810444295406341553e-01,-5.167514551430940628e-03,-5.528867674797766085e-08,-1.995898770701387548e-07,1.678826212882995605e-01,-7.984144613146781921e-04,5.774685618575858825e-08,-1.090582202323275851e-07,-1.362478506052866578e-04,-2.869202875288578980e-09,9.864854177976667415e-08,-9.314462658949196339e-05,-7.239124533953145146e-05,3.248244465794414282e-04,2.916221274062991142e-03,4.665430262684822083e-02,-6.928963214159011841e-02,-1.553085516206920147e-03 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_gru_ih.csv deleted file mode 100644 index e27e7136113f4..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_gru_ih.csv +++ /dev/null @@ -1,96 +0,0 @@ -2.246665502525502234e-07,7.937374313371492462e-09,1.875921213922993047e-07,-5.788109191939838638e-08,-2.136287946541415295e-08,-1.567381531231148983e-07,1.882228133354146848e-07,-2.598505943751661107e-08,-3.420910132945209625e-08,-1.567461680451742723e-07,-1.786740000397912809e-07,-2.247293053869725554e-07,-3.625288869102405442e-08,1.567380394362771767e-07,-7.059875883896893356e-08,-1.627963115424790885e-07,1.566299800970227807e-07 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--1.215396798215806484e-03,5.976485084602245479e-08,1.600115666633428191e-07,1.141837469020856588e-08,1.607542259307592758e-07,9.066708273053336598e-09,5.679105896660985309e-08,1.958212081376586866e-08,-5.138260661396998330e-08,1.638554607552578091e-07,1.604297921176112141e-07,1.590367446624441072e-07,5.910016653842831147e-08,-2.827723299958506686e-08,-3.570564288679634046e-08,-1.010003884971411026e-07,-1.589997964401845820e-07 --6.777899805456399918e-03,-2.999029291572696820e-08,-4.067278780439664843e-08,-4.132837361225938366e-08,1.150064079524781846e-07,-5.908596989456782467e-08,5.002346453730766029e-11,8.926747341320151463e-08,1.633947164236815297e-07,3.595106434772787907e-08,-1.779992153672083077e-09,-4.192965263882797444e-08,-7.076450003751233453e-08,1.377628588983270674e-08,2.150428635161460988e-08,-6.205691249761002837e-08,1.360416046480850127e-08 --4.967793287846689054e-08,2.150854783167233109e-08,4.158178512625454459e-08,-1.634528246086119907e-07,-5.678036885115034238e-08,-5.813501147144961578e-08,5.776328393380936177e-08,-1.566364602467729128e-07,-2.866544868140863400e-08,-1.388251114065042202e-08,-3.617606481043367239e-08,1.567220948572867201e-07,2.928720022055131267e-08,2.929911779858684895e-08,2.864901915700102109e-08,8.405371687558726990e-08,3.625237710025430715e-08 -8.412970231574945501e-08,-1.656615400413841144e-10,1.629579173823003657e-07,5.797545199470732769e-08,-5.806660041685063334e-08,6.418881781655727536e-08,5.807552483361178020e-08,-2.868460313720788690e-08,1.078672440257832932e-07,-1.686742412410069392e-10,-5.781333456411630323e-08,-1.638241542423202191e-07,2.925599318359672907e-08,1.597442036427310086e-07,5.808584191413501685e-08,1.628393988539755810e-07,-6.706456190386234084e-08 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--3.725880980491638184e-01,2.458690744333580369e-07,5.973591044039494591e-08,1.018822288756382477e-07,-1.559959343921946129e-07,-1.629242376566253370e-07,-2.616832794899437431e-08,1.366417734516289784e-07,2.215908807556843385e-07,1.500245332408667309e-07,5.155551718871720368e-08,-1.559374709358962718e-07,-9.040774351376512641e-09,-1.607899235978038632e-07,5.084858401005476480e-08,-1.145307777505877311e-07,-2.697387557759611809e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_only_online.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_only_online.csv deleted file mode 100644 index d1cd6de226089..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_only_online.csv +++ /dev/null @@ -1,36 +0,0 @@ -8.279625326395034790e-03,3.381546586751937866e-03,2.164445631206035614e-02,8.621551096439361572e-03,-3.941265493631362915e-02,3.542314842343330383e-02,-4.984164610505104065e-03,2.074131276458501816e-03,1.995079033076763153e-02,3.991886973381042480e-02,-2.352407574653625488e-02,-2.274756785482168198e-03,1.613918878138065338e-02,-1.945486105978488922e-02,2.679401077330112457e-02,-1.906441152095794678e-02,-1.385917142033576965e-02,2.443172410130500793e-02,-2.863631211221218109e-02,-1.984162628650665283e-02,-2.116794139146804810e-02,-4.520586878061294556e-02,8.199180010706186295e-04,-1.936784014105796814e-02,-7.391568273305892944e-02,7.122860755771398544e-03,2.108660340309143066e-02,2.120924182236194611e-02,-3.055499168112874031e-03,1.603046059608459473e-02,-1.856614463031291962e-02,1.623449660837650299e-02,2.594424784183502197e-02,2.137184143066406250e-02,1.489730551838874817e-02,2.175739407539367676e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_steer_layer_1.csv deleted file mode 100644 index b3a8b6028a2ba..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -5.784385948004455713e-08,4.115653950975683983e-08,-5.804627889460789447e-08,2.864728720908260584e-08,-3.634495371329649060e-08,-6.166982302602264099e-08,-2.448331599680386716e-07,1.567348846265304019e-07,-8.307063836809902568e-08,3.079690391594169796e-10,-1.396914051099429344e-08,-2.550808630985557102e-08,-6.109024042189048487e-08,-1.834535301448170230e-10,-5.804912106555093487e-08,-5.804991687341498618e-08 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_linear_relu.csv @@ -1,16 +1,16 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_hh_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_hh_0.csv index e72b7cca0f098..7537a9ee9c0b9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_hh_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_hh_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_ih_0.csv index a03b7b141eb63..1190c0ccb7332 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_hh.csv index 2852c6e4eff0d..99d0e5eab9e2c 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_hh.csv @@ -1,64 +1,64 @@ 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_ih.csv index 1502ee1d4acf2..1541f6535f3ad 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_lstm_ih.csv @@ -1,64 +1,64 @@ -1.354664843529462814e-02,5.303078889846801758e-02,-5.030304193496704102e-02,3.654263541102409363e-02,3.604745492339134216e-02,5.328396707773208618e-02,5.226656422019004822e-02,4.835975542664527893e-02,5.317721515893936157e-02,4.100683704018592834e-02,1.429468486458063126e-02,5.063319113105535507e-03,1.167685911059379578e-01,-1.364066172391176224e-02,1.782777607440948486e-01,-6.149024516344070435e-02,-2.606809139251708984e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_steer_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ -1.443694811314344406e-03,-2.542864158749580383e-02,4.027282074093818665e-02,-7.667397707700729370e-02,4.094406217336654663e-02,2.776812203228473663e-02,8.039279282093048096e-02,-1.509349465370178223e+00,1.310569643974304199e-01,4.136419668793678284e-02,1.308051943778991699e-01,2.373549044132232666e-01,-4.637687802314758301e-01,-2.480286872014403343e-03,2.196998000144958496e-01,1.672886610031127930e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_steer_layer_1.csv @@ -1,16 +1,16 @@ -1.337991125183179975e-04,1.429468512535095215e+00,-2.060595005750656128e-01,-1.311620473861694336e-01,-9.247932583093643188e-02,-5.962440371513366699e-02,-1.770549826323986053e-02,8.904102258384227753e-03,1.928523369133472443e-02,2.661564759910106659e-02,5.267998669296503067e-03,-3.306114301085472107e-02,-7.490258663892745972e-02,-1.124373152852058411e-01,-1.150781363248825073e-01,-1.032200455665588379e-01,-8.375178277492523193e-02,-6.815285235643386841e-02,-8.122835308313369751e-02,-1.426265239715576172e-01,-2.786263525485992432e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_steer_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_1/weight_steer_layer_2.csv @@ -1,8 +1,8 @@ --5.466060340404510498e-02,-3.131796419620513916e-01,4.896617680788040161e-02,7.235971093177795410e-01,-2.694058604538440704e-02,-4.396100994199514389e-03,-3.990970551967620850e-02,1.204489588737487793e+00,1.400408148765563965e-01,-2.412422001361846924e-02,4.019365608692169189e-01,4.560724198818206787e-01,-7.040619254112243652e-01,6.191220879554748535e-01,2.303922623395919800e-01,-8.090059161186218262e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --5.687703490257263184e-01 -1.572953760623931885e-01 -5.714529380202293396e-02 -1.085763052105903625e-01 -9.483638405799865723e-02 -1.503358874469995499e-02 --6.868886295706033707e-03 --4.978453516960144043e-01 -1.244671717286109924e-01 -1.060194298624992371e-01 -1.586544699966907501e-02 -5.790882557630538940e-02 -8.724521845579147339e-02 -8.154500126838684082e-01 -3.643409162759780884e-02 -1.652323454618453979e-01 +9.447958320379257202e-02 +-3.904920443892478943e-02 +-4.699527025222778320e-01 +1.458566039800643921e-01 +-3.368199765682220459e-01 +-3.077215850353240967e-01 +-7.432874292135238647e-02 +-3.889969587326049805e-01 +7.842382043600082397e-02 +-1.985292583703994751e-01 +7.554553151130676270e-01 +-4.822994768619537354e-02 +-4.119287729263305664e-01 +2.234724313020706177e-01 +5.568625032901763916e-02 +9.671768546104431152e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_acc_encoder_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_acc_encoder_layer_2.csv index 2321f21b9d8a8..6bc053b95a062 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -8.771612308919429779e-03 -1.144464537501335144e-01 -6.942238658666610718e-02 -6.198814511299133301e-02 --2.246745675802230835e-02 --7.646840810775756836e-02 -1.091548949480056763e-01 -2.548992633819580078e-01 +2.911694943904876709e-01 +1.167204603552818298e-01 +1.205120310187339783e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.024103090167045593e-01 -1.508852094411849976e-01 -9.780577383935451508e-03 -4.319138452410697937e-02 -9.238595515489578247e-02 -1.059526875615119934e-01 -9.110169857740402222e-02 -1.054998338222503662e-01 +5.389664322137832642e-02 +3.577540814876556396e-02 +3.646123688668012619e-03 +4.657787457108497620e-02 +4.009292274713516235e-02 +-1.234907656908035278e-01 +-9.528395719826221466e-03 +8.088576793670654297e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_complimentary_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_complimentary_layer.csv index bb1b5346932b2..a9dc3efd0edaf 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_complimentary_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_complimentary_layer.csv @@ -1,64 +1,64 @@ -1.511160749942064285e-02 -1.398833189159631729e-02 -1.167307570576667786e-01 -4.545302689075469971e-02 -1.077463570982217789e-02 -1.412830781191587448e-03 -2.155422978103160858e-02 -9.623280493542551994e-04 -4.561380296945571899e-02 -1.059380993247032166e-01 --9.806581586599349976e-02 --4.837276786565780640e-02 -3.910212963819503784e-02 --4.298238456249237061e-02 -7.735941559076309204e-02 --2.475859364494681358e-03 -2.536228299140930176e-02 --6.351507455110549927e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_final_layer.csv index 7f69ecaf304d4..e312c241bbb6e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_final_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_final_layer.csv @@ -1,4 +1,4 @@ --1.411965698935091496e-03 -9.323665290139615536e-04 --3.581988811492919922e-02 -5.641778465360403061e-03 +-2.463300246745347977e-03 +-6.471379892900586128e-04 +-6.751611828804016113e-02 +-5.644071847200393677e-03 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_acc_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 6a4f3acd10344..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.700384832936663315e-10 -5.574749906145370915e-08 --1.639053834878723137e-07 -2.375204388727070182e-07 -5.790519352899536898e-08 -5.913284439884591848e-08 --1.567347993614021107e-07 --3.626800904044102936e-08 -2.247556807333239703e-07 -2.873030524597197655e-08 -5.794816004822678224e-08 --4.090935590284061618e-08 --2.924792141811849433e-08 --2.869897031132495613e-08 --2.662483211679500528e-08 --1.567348562048209715e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_acc_layer_2.csv deleted file mode 100644 index 62f75cb43506c..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --8.786349070533105987e-08 --2.436062018773554883e-08 --7.991299355580849806e-08 --1.391345278989319922e-07 --3.481732591126274201e-08 --2.435033650272089290e-07 --6.396118124030181207e-08 --2.479723377746267943e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_final_layer.csv deleted file mode 100644 index b082a7a89c611..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_final_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.333507336676120758e-02 -6.083920970559120178e-02 --1.834504422731697559e-03 --3.571275621652603149e-02 -2.946369349956512451e-01 --1.427265582606196404e-03 --3.688247501850128174e-02 -6.005700305104255676e-02 --1.222376897931098938e-02 --3.048365712165832520e-01 --4.184846207499504089e-02 -1.948376744985580444e-02 -4.896391183137893677e-02 -3.152998397126793861e-03 --2.637754976749420166e-01 -1.586769241839647293e-03 --6.010877341032028198e-02 --2.591094970703125000e-01 -2.363688647747039795e-01 -6.578605622053146362e-03 -7.574999239295721054e-03 -2.695792615413665771e-01 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--7.104340195655822754e-02 --2.075286209583282471e-02 -1.169870570302009583e-01 --2.211910635232925415e-01 --8.285447955131530762e-02 --6.559275090694427490e-03 -2.289427518844604492e-01 --5.724086612462997437e-02 --2.474612742662429810e-01 -6.690966337919235229e-02 --3.198300674557685852e-02 -2.926952540874481201e-01 --3.303932026028633118e-02 --1.297835707664489746e-01 --1.295106951147317886e-02 -5.919522419571876526e-02 -5.894464626908302307e-02 -3.509227558970451355e-02 --2.393457889556884766e-01 --2.499583642929792404e-03 --3.512866795063018799e-03 --1.666042804718017578e-01 --1.854570060968399048e-01 -8.786503970623016357e-02 --9.355223178863525391e-02 -1.098060086369514465e-01 --1.124663949012756348e-01 --2.270619757473468781e-02 --4.248482640832662582e-03 -4.918081313371658325e-02 -2.424361556768417358e-02 --1.200262010097503662e-01 -2.896825373172760010e-01 --4.990265145897865295e-02 --5.451077967882156372e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_gru_hh.csv deleted file mode 100644 index 8057bd867e52d..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ -1.545770733635265515e-10 --8.302931320258721826e-08 --7.699894943868912378e-09 --2.921688491142049315e-08 -1.020158979514462771e-08 --1.473583746701478958e-03 --7.846460212022066116e-04 --1.084701466425030958e-07 --5.566524663436211995e-08 --1.647779015456762863e-07 -2.786956443401322758e-08 -1.140102057206604513e-07 -1.460361964689127490e-08 --1.444058312927154475e-07 -8.503373294388438808e-08 --1.640689930582084344e-07 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-2.848630538210272789e-04 -1.822299510240554810e-01 -4.630837123841047287e-03 --1.818964928388595581e-01 -2.066866159439086914e-01 --2.953495271503925323e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_gru_ih.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_gru_ih.csv deleted file mode 100644 index 9416f9f158008..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_gru_ih.csv +++ /dev/null @@ -1,96 +0,0 @@ --5.804929159580751730e-08 --8.302931320258721826e-08 --1.416650974306321586e-07 --3.625823197239697038e-08 --7.970431425974311423e-08 --1.473613083362579346e-03 --7.847624947316944599e-04 -5.631300581399045768e-08 --5.566524663436211995e-08 -5.795373780870249902e-08 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--1.957485125103630708e-07 --1.044421349405411092e-08 --4.099469631910324097e-02 --5.203025565947427822e-09 -4.812124032582687505e-08 --2.186568221077322960e-03 -6.986832013353705406e-04 -2.696507871150970459e-01 -5.738455429673194885e-02 --3.137570619583129883e-01 -2.364166826009750366e-01 --2.259131222963333130e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_only_online.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_only_online.csv deleted file mode 100644 index 0418c7e532bb9..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_only_online.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.383439749479293823e-01 --2.788069546222686768e-01 -1.486504375934600830e-01 -2.100807130336761475e-01 --3.237645328044891357e-01 --2.319215536117553711e-01 -1.512393504381179810e-01 -2.785775065422058105e-01 --1.205068901181221008e-01 --5.426710471510887146e-02 -1.322433911263942719e-02 --3.126586675643920898e-01 -2.621484696865081787e-01 -1.820376664400100708e-01 --1.614455431699752808e-01 --2.637141570448875427e-02 -6.637267023324966431e-02 -1.449829190969467163e-01 -5.794738978147506714e-02 --2.220921069383621216e-01 --3.295454382896423340e-02 --1.984102465212345123e-02 --1.371687352657318115e-01 --2.934356629848480225e-01 --5.533078685402870178e-02 --1.244134381413459778e-01 -7.511977106332778931e-02 -7.495564967393875122e-02 --9.701989591121673584e-02 --9.739152342081069946e-02 -7.872800924815237522e-04 -1.701286584138870239e-01 --8.328914642333984375e-03 -1.629772633314132690e-01 --1.352069899439811707e-02 -7.753793150186538696e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_steer_layer_1.csv deleted file mode 100644 index 614901c941b0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.602122381427761866e-07 -1.594512042402129737e-07 --1.383567216350911622e-08 -2.870719839620505809e-08 --4.115875640309241135e-08 --1.629571784178551752e-07 -6.326867207917530322e-08 -6.130223795253186836e-08 -2.572974278081119337e-09 --1.623540981654514326e-07 --3.618814403694159409e-08 --5.186216256447551132e-08 -1.592173859421563975e-08 -2.550376621002214961e-08 --5.809684111568458320e-08 --6.100456317881253199e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_steer_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_steer_layer_2.csv deleted file mode 100644 index fc1f85f18f28b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --1.495364330139636877e-07 --9.433398506786261350e-10 --9.447958149166879593e-08 -6.568057209221933590e-09 --2.248099519874813268e-07 --1.070967527994071133e-07 --9.932387712296986138e-08 --7.939953405866617686e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_linear_relu.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_linear_relu.csv index 6c7ba3b020e52..b40d15d15387e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_linear_relu.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_linear_relu.csv @@ -1,16 +1,16 @@ --3.609813749790191650e-02 --3.190746530890464783e-02 -4.359308257699012756e-02 -8.269068202935159206e-04 -4.143001139163970947e-02 -7.497583515942096710e-03 -7.427339255809783936e-02 --2.295951917767524719e-02 --2.274018079042434692e-01 -8.252906054258346558e-02 -7.786637544631958008e-02 -1.048227921128273010e-01 --4.350556991994380951e-04 --6.249625235795974731e-02 -1.322524547576904297e-01 --3.382597863674163818e-02 +-2.442883513867855072e-02 +5.057684332132339478e-02 +3.201630339026451111e-02 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-1,72 +1,72 @@ --2.799303233623504639e-01 -8.258733898401260376e-02 -5.214651674032211304e-02 --2.117088623344898224e-02 --9.740439057350158691e-02 --1.514682471752166748e-01 --1.841652244329452515e-01 --1.662612110376358032e-01 --2.394026368856430054e-01 -6.085600331425666809e-02 -1.038094758987426758e-01 -1.195812672376632690e-01 --1.226203665137290955e-01 --1.429048180580139160e-01 -6.019178405404090881e-02 --1.727182716131210327e-01 --2.290469259023666382e-01 --3.691007494926452637e-01 -4.015429019927978516e-01 -1.827716976404190063e-01 -1.926851719617843628e-01 -1.571784615516662598e-01 -1.860545575618743896e-01 -2.430029809474945068e-01 -4.373685643076896667e-02 -1.225811541080474854e-01 --1.930130422115325928e-01 --9.093154966831207275e-02 -2.350523173809051514e-01 -5.819558352231979370e-02 -1.559833139181137085e-01 -2.092577219009399414e-01 -2.149106264114379883e-01 -2.697693407535552979e-01 -2.954715490341186523e-01 -6.605675816535949707e-01 --9.321784228086471558e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_encoder_ih_0.csv index bd71a79542f43..2a250786995d9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ --2.799263000488281250e-01 -8.330401778221130371e-02 -5.212230980396270752e-02 --2.048002928495407104e-02 --9.690331667661666870e-02 --1.525534093379974365e-01 --1.841630488634109497e-01 --1.662613004446029663e-01 --2.396262139081954956e-01 -6.080040335655212402e-02 -1.034230291843414307e-01 -1.198021844029426575e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_hh.csv @@ -1,64 +1,64 @@ -1.863418668508529663e-01 --1.841402947902679443e-01 -4.871492087841033936e-02 -6.630924344062805176e-01 -3.120891004800796509e-02 -5.130470544099807739e-02 -3.066616505384445190e-02 -3.965241834521293640e-02 -3.464382141828536987e-02 --1.669278144836425781e-01 --9.552434831857681274e-02 --2.999459765851497650e-02 --6.102378945797681808e-03 -1.027578711509704590e-01 -1.095576584339141846e-01 -1.344114840030670166e-01 -2.494055032730102539e-01 -1.547644734382629395e-01 -1.317952722311019897e-01 --2.324226498603820801e-01 --2.754482328891754150e-01 -1.100205332040786743e-01 --1.426737289875745773e-03 --3.385674655437469482e-01 -1.660625338554382324e-01 -5.306183919310569763e-02 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031398bdfd2a3..8511ea9e3ab32 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_lstm_ih.csv @@ -1,64 +1,64 @@ -1.862874925136566162e-01 --1.859310567378997803e-01 -4.900135472416877747e-02 -6.631961464881896973e-01 -3.110241889953613281e-02 -5.119629949331283569e-02 -2.894839085638523102e-02 -3.989761322736740112e-02 -3.549076244235038757e-02 --1.642386913299560547e-01 --9.745972603559494019e-02 --3.339832648634910583e-02 --2.647247863933444023e-03 -9.829391539096832275e-02 -1.122935935854911804e-01 -1.338122785091400146e-01 -2.497808933258056641e-01 -1.542556881904602051e-01 -1.318080276250839233e-01 --2.323063462972640991e-01 --2.763138115406036377e-01 -1.098379045724868774e-01 --1.424718881025910378e-03 --3.388048410415649414e-01 -1.662753224372863770e-01 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-1.202719435095787048e-01 +-9.739692509174346924e-02 +2.492910809814929962e-02 +-1.838845312595367432e-01 +-2.135178744792938232e-01 +-2.200213447213172913e-02 +-1.861061155796051025e-01 +1.671033054590225220e-01 +9.593650698661804199e-02 +3.913679718971252441e-02 +3.075380437076091766e-02 +8.047340437769889832e-03 +-2.656531631946563721e-01 +1.129456683993339539e-01 +-3.222428858280181885e-01 +1.091994494199752808e-01 +9.096560627222061157e-02 +2.707954943180084229e-01 +2.616540491580963135e-01 +1.402111947536468506e-01 +7.889152318239212036e-02 +-3.627977669239044189e-01 +1.870117783546447754e-01 +-6.229979172348976135e-02 +3.609412349760532379e-03 +1.729766875505447388e-01 +-8.216486126184463501e-02 +4.784847795963287354e-02 +2.171274870634078979e-01 +-2.092107012867927551e-02 +2.288868129253387451e-01 +1.596487313508987427e-01 +1.025586947798728943e-01 +-4.004389420151710510e-02 +-1.315923184156417847e-01 +9.082223474979400635e-02 +-5.278126150369644165e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_encoder_layer_1.csv index 707a956dc7533..ca1b020a82ede 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_encoder_layer_1.csv @@ -1,16 +1,16 @@ -2.288375049829483032e-02 -4.156160056591033936e-01 -1.447749207727611065e-03 --1.900681381812319160e-04 -3.408987540751695633e-03 -3.183485008776187897e-03 -4.051115512847900391e-01 --1.631803363561630249e-01 -2.039906978607177734e-01 -3.064470831304788589e-03 -2.237636968493461609e-02 -1.526999026536941528e-01 -1.990733891725540161e-01 --1.465275436639785767e-01 --4.737891629338264465e-02 -1.408819258213043213e-01 +-2.332334361199173145e-06 +1.387599296867847443e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ --4.465385898947715759e-02 --2.514035440981388092e-02 --4.148573800921440125e-02 -1.449349820613861084e-01 -9.823747910559177399e-03 --1.016079541295766830e-02 --1.387605676427483559e-03 --8.763826638460159302e-02 +2.186003774404525757e-01 +-9.245735406875610352e-02 +-2.528463006019592285e-01 +-7.972517609596252441e-02 +6.500462442636489868e-02 +2.470089346170425415e-01 +2.000899612903594971e-02 +1.971768885850906372e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_1.csv index d8e5af208e79c..dc762d230ac56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_1.csv @@ -1,16 +1,16 @@ --5.880776420235633850e-02 -1.072910651564598083e-01 -1.927932947874069214e-01 -1.416619420051574707e-01 -3.611433552578091621e-03 --2.359812110662460327e-01 -1.126473490148782730e-02 --1.914175972342491150e-02 -1.322983503341674805e-01 --9.606996551156044006e-03 --4.914920404553413391e-02 -5.091565847396850586e-02 -2.208182960748672485e-02 -4.450881108641624451e-02 -5.511307157576084137e-03 -9.871846064925193787e-03 +-1.267773564904928207e-03 +1.681745648384094238e-01 +3.152760118246078491e-02 +-3.042486310005187988e-02 +2.783328667283058167e-02 +1.784413307905197144e-01 +3.829433768987655640e-02 +-1.098260059952735901e-01 +8.191139250993728638e-02 +4.840236902236938477e-02 +7.292035222053527832e-02 +1.257531065493822098e-02 +1.193764209747314453e-01 +1.460036914795637131e-02 +1.528179645538330078e-01 +4.296629875898361206e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_2.csv index 987b36427e772..7c8e42e5dc1dc 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/bias_steer_layer_2.csv @@ -1,8 +1,8 @@ -1.204485148191452026e-01 -5.225432664155960083e-02 --3.464022651314735413e-03 -1.128473356366157532e-01 -1.898873150348663330e-01 -1.061795502901077271e-01 -6.685238331556320190e-02 -1.211080327630043030e-01 +-2.150738146156072617e-03 +4.411975294351577759e-02 +1.444365680217742920e-01 +5.844632163643836975e-02 +6.778846494853496552e-03 +5.850554630160331726e-02 +2.264000841023516841e-06 +1.272178292274475098e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/offline_features.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/offline_features.csv index 1bcdd512dea6f..fa6d9d8875c56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/offline_features.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/offline_features.csv @@ -1,36 +1,36 @@ --1.365479675996539299e-07 -4.500528660855707130e-08 --2.888707228976272745e-07 --3.052138808357085509e-08 --3.403145854008471360e-08 --1.366562401017290540e-07 -9.226326369571324904e-09 --1.461385323864305974e-07 -2.401009169261669740e-07 -3.847664586942300957e-08 --4.707507983425784914e-08 -4.440217082901654067e-09 --3.469495624131013756e-08 -3.053523656149081944e-08 -4.850487655971846834e-08 --4.678990350726053293e-08 --1.465268173461709011e-08 --3.929526570800589980e-08 --1.382004199967923341e-07 --3.059484043888005544e-08 -1.119208832278673071e-07 -4.225704230975679820e-09 --1.164682288390395115e-07 --1.629832553362575709e-07 -3.149804683744150680e-08 --1.490812984172862343e-08 -5.521310342260221660e-08 --6.760313198128642398e-08 -1.837763718981477723e-08 --1.734968861910601845e-07 -1.474870856554844067e-07 --1.383132399723763228e-07 -3.487329980345066360e-09 -8.957044173030226375e-08 -2.936226373151384905e-08 --1.337812438606533760e-08 +5.170850083231925964e-02 +-2.384004145860671997e-01 +2.417143583297729492e-01 +3.888960182666778564e-01 +-2.718242108821868896e-01 +-5.412524193525314331e-02 +1.712931394577026367e-01 +1.944515109062194824e-01 +-1.523365378379821777e-01 +1.768556088209152222e-01 +-3.439860641956329346e-01 +4.308066964149475098e-01 +2.342039048671722412e-01 +2.263623923063278198e-01 +8.685183525085449219e-02 +-3.498302400112152100e-02 +7.183667272329330444e-02 +-1.743947416543960571e-01 +6.060919165611267090e-02 +-7.453544437885284424e-02 +3.309508860111236572e-01 +1.058077290654182434e-01 +-9.985291212797164917e-02 +2.645366787910461426e-01 +6.488673090934753418e-01 +1.053337156772613525e-01 +-1.421345621347427368e-01 +-4.722569137811660767e-02 +-3.759335875511169434e-01 +-1.368334740400314331e-01 +2.373346388339996338e-01 +2.330867946147918701e-02 +-7.088837027549743652e-02 +-4.330652952194213867e-01 +5.272904038429260254e-01 +-8.399436622858047485e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_encoder_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_encoder_layer_1.csv index 5eb1c7c33213a..09a6b18c285fa 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --6.779996305704116821e-02,3.508124351501464844e-01,2.439208775758743286e-01,2.227861583232879639e-01,2.015045583248138428e-01,1.802337467670440674e-01,1.594138443470001221e-01,1.380567848682403564e-01,1.166481673717498779e-01,9.721208363771438599e-02,7.622497528791427612e-02,5.588675662875175476e-02,3.574046865105628967e-02,1.916410401463508606e-02,1.854662410914897919e-02,1.806927099823951721e-02,1.663219928741455078e-02,1.390712894499301910e-02,8.530278690159320831e-03,-9.777169907465577126e-04 --1.337423324584960938e-01,-8.315050601959228516e-02,-2.004925161600112915e-01,-1.865487545728683472e-01,-1.720552295446395874e-01,-1.579438298940658569e-01,-1.453569382429122925e-01,-1.358291804790496826e-01,-1.300119906663894653e-01,-1.207905784249305725e-01,-1.113225594162940979e-01,-9.820590913295745850e-02,-8.600991219282150269e-02,-7.682002335786819458e-02,-6.521806120872497559e-02,-5.824101716279983521e-02,-4.736216366291046143e-02,-3.631968051195144653e-02,-2.911020442843437195e-02,-2.347799576818943024e-02 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--1.621379517018795013e-02,6.511135725304484367e-04,-3.536446020007133484e-02,-3.627477958798408508e-02,-3.721936792135238647e-02,-3.808093816041946411e-02,-3.901234269142150879e-02,-3.985518589615821838e-02,-4.090359061956405640e-02,-4.165715724229812622e-02,-4.242743179202079773e-02,-4.329784959554672241e-02,-4.404501989483833313e-02,-4.490786790847778320e-02,-4.554320126771926880e-02,-4.622422531247138977e-02,-4.683774337172508240e-02,-4.749411344528198242e-02,-4.818574711680412292e-02,-4.867927357554435730e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -3.336199373006820679e-02,2.459247857332229614e-01,-6.924922019243240356e-02,-3.334385901689529419e-02,2.794704735279083252e-01,-1.098308246582746506e-02,-3.318902477622032166e-02,-2.375750094652175903e-01,-6.978246849030256271e-03,-7.694390369579195976e-04,3.750725463032722473e-02,-1.894451864063739777e-02,-1.075058132410049438e-01,1.155056133866310120e-01,-3.716661781072616577e-02,4.225961863994598389e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.827372312545776367e-01,1.442782729864120483e-01,1.350615918636322021e-01,1.406146585941314697e-01,7.798003405332565308e-02,1.429848670959472656e-01,1.125954836606979370e-01,5.638980120420455933e-02,8.144410699605941772e-02,-5.328862462192773819e-03,-2.268474362790584564e-02,4.206001386046409607e-02,1.457983851432800293e-01,-5.146942660212516785e-02,-6.503234058618545532e-02,-3.310494497418403625e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_final_layer.csv @@ -1,4 +1,4 @@ --4.076786339282989502e-03,-1.227277144789695740e-02,3.244567289948463440e-02,1.793841016478836536e-03,4.501522984355688095e-03,-2.283791452646255493e-02,-1.217357907444238663e-02,-2.098338445648550987e-03,-2.394610270857810974e-02,3.298125788569450378e-02,3.658420871943235397e-03,3.390965238213539124e-02,-1.559238694608211517e-02,-1.272494345903396606e-02,1.682596048340201378e-03,-1.867336407303810120e-02 --1.042943261563777924e-02,-4.991219844669103622e-03,1.183264539577066898e-03,5.007501225918531418e-03,-6.344680878100916743e-05,2.041139872744679451e-03,7.769418880343437195e-03,-3.926411271095275879e-03,-1.374095008941367269e-04,-7.521932013332843781e-03,2.816032152622938156e-03,5.137700587511062622e-03,-1.068992633372545242e-02,-1.806749030947685242e-03,1.107970019802451134e-03,2.244758186861872673e-03 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 09b35a7fc4e0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --5.795665813934647304e-08,2.448358884521439904e-07,-3.380812785280795651e-08,-2.136942289787668869e-10,4.084210303290092270e-08,5.576080042146713822e-08,1.567351972653341363e-07,3.467380693678023817e-08,6.475600144995041774e-08,7.276236857478579623e-08,-1.776304969780539977e-07,3.134217507749781362e-08,1.528735111833157134e-07,1.598162242544276523e-07,4.906196338083645969e-08,5.687583737312706944e-08,2.663517761902767234e-08,-1.778388565298882895e-07,9.540057988033368019e-08,-1.728522325272763283e-10 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--2.572746904405676105e-09,1.086709744413383305e-07,1.400614557667267945e-08,7.936692192345162766e-09,4.102665940308725112e-08,-8.246642124731806689e-08,5.573721040263990290e-08,6.818912368089513620e-08,4.132279229906998808e-08,-1.598831147475721082e-07,2.749714345107179270e-10,-4.084756000111156027e-08,2.869085591328257578e-08,1.387898684868105192e-08,-6.484240344661884592e-08,-6.196152213533423492e-08,-5.634582800340126596e-10,-1.612109485904511530e-07,2.844504010113269032e-08,2.246597858857057872e-07 --2.837610324490924540e-08,-1.612319806554296520e-07,6.102120408968403353e-08,2.872587145930083352e-08,-2.865953518949027057e-08,2.850439884127808909e-08,1.778415992248483235e-07,2.926793030155749875e-08,-1.032509047149687831e-07,1.884557510045681283e-10,2.864214287967570272e-08,-4.828538990864217340e-08,1.726883596120387665e-07,-6.240391314804583089e-08,5.785573975458646601e-08,1.387097192662167799e-08,-1.379480174534819525e-08,2.865901116422264749e-08,-2.918067565360615845e-08,-2.918391572848122451e-08 --2.864954673498232296e-08,-1.368725399686354649e-08,1.085663257072155830e-07,-2.883944461018472794e-08,1.085741132555995136e-07,-5.121204438296445005e-08,6.429602450452875928e-08,5.809104308696078078e-08,-4.156912325470329961e-08,-3.439327400656111422e-08,-5.183908413641802326e-08,1.530865603172060219e-07,8.827194619698275346e-08,-5.001684755256974313e-08,1.873391681783687091e-07,1.567354246390095796e-07,5.798773017318126222e-08,1.020795323825041123e-07,1.085405756384716369e-07,1.529302977587576606e-07 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-2.866891612995914329e-08,-2.193380339576833649e-07,-1.533985454216235667e-07,-1.635322632864699699e-07,2.861279924104564998e-08,2.814341470980252780e-08,-4.094303562851564493e-08,1.627933556846983265e-07,7.935441637130224990e-09,-2.871069426646499778e-08,1.594537053506428492e-07,-5.806688463394493738e-08,-1.085669509848230518e-07,-1.718574726972121880e-10,-1.398409033015468594e-08,-3.620758448619199044e-08,-1.398062288160417665e-08,6.922468998027397902e-08,-3.616870358769119775e-08,1.628658310437458567e-07 --2.852652158935597981e-08,-9.824449875850405078e-08,-1.754762308792123804e-07,5.789251744658940879e-08,4.112624196750402916e-08,5.801046754072558542e-08,6.086136750127479900e-08,-2.864963200011061417e-08,-1.392582582582235773e-08,1.740867645594335045e-07,-1.605755812761344714e-07,-3.050213948085911397e-08,-2.212133978218844277e-07,1.637957893763086759e-07,1.566371992112181033e-07,1.566785812201487715e-07,1.622258025690825889e-07,5.108397616027104959e-08,1.567347993614021107e-07,2.550069666540366597e-08 -1.628367272132891230e-07,1.778454077339119976e-07,2.857147407553384255e-08,-5.112677925467323803e-08,6.183482526012085145e-08,1.639484707993688062e-07,-1.148689463548180356e-07,-1.397482485288037424e-08,-4.146149024109035963e-08,-5.801484448397786764e-08,-1.394867688020440255e-08,-1.758933554363295571e-10,4.105030626533334726e-08,-1.391860671162703511e-08,3.104235091200280294e-08,-1.567348562048209715e-07,1.909966768076643007e-10,5.804411884469118377e-08,-4.906690875827734999e-08,6.098650118246951024e-08 --3.479204124801071885e-08,-6.744752312215496204e-08,-6.184050960200693225e-08,2.946307043824591965e-12,-6.112707495731228846e-08,1.314774333849300092e-08,-9.540066514546197141e-08,-5.567803640360580175e-08,1.567590146578368149e-07,-6.107648431452616933e-08,1.636276465433184057e-07,2.845211288793336735e-10,-2.862939751935300592e-08,2.852742042591671634e-08,-4.092018457413360011e-08,-4.101761419406102505e-08,1.497509884984538076e-07,1.882291797983270953e-07,1.623722880594868911e-07,6.816661368702625623e-08 --1.567573093552709906e-07,5.190680241184963961e-08,-4.090967564707170823e-08,-2.676181409810851619e-08,1.796450210811428860e-10,-2.870267934440562385e-08,1.629214239073917270e-07,1.625622587653197115e-07,5.780725231829819677e-08,-6.437543476067730808e-08,1.078759979122878576e-07,-5.816283632498198131e-08,-2.863949966069867514e-08,1.598080103804022656e-07,1.085153371604974382e-07,-3.626300681958127825e-08,2.851320957120151434e-08,-7.937203783114910038e-09,-1.778377480832205038e-07,1.665710347431570426e-10 -1.085244889509340283e-07,-5.769888034024006629e-08,2.921932917843150790e-08,6.484848569243695238e-08,-3.524516856145964994e-08,-8.309299204256603844e-08,-2.873326110375273856e-08,-2.872246085416918504e-08,-1.632824933039955795e-07,1.882410032294501434e-07,-1.628350219107232988e-07,2.968561507721290127e-10,-1.778498273097284255e-07,-2.405811660999290780e-10,1.392855430992767651e-08,5.112075385227399238e-08,5.776922407108031621e-08,2.871507120971728000e-08,-4.094582095603982452e-08,-1.599121617346099811e-07 --2.448340410410310142e-07,1.380423775287908938e-08,1.561118456727683679e-10,-5.812466596921694872e-08,-3.100375423059631430e-08,-1.527012405411198870e-10,-2.919994557259997237e-08,-1.069702619815871003e-07,-1.525875537033982710e-10,2.856971192954915750e-08,-3.033363005222611264e-10,-8.849530530596894096e-08,1.624740946226665983e-07,-1.728069349837824120e-07,1.750106615183710801e-09,5.574175787614876754e-08,-1.683244903460945352e-07,-1.400569082932179299e-08,2.154006040200329153e-08,-2.080925725067572785e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_acc_layer_2.csv deleted file mode 100644 index b48ef5ed5516a..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -2.044039248971785128e-08,2.853378688882912684e-08,1.634649606785387732e-07,-2.863657222462734353e-08,1.586338811421228456e-07,-3.615636856579840241e-08,4.099652528566366527e-08,-2.866763537667793571e-08,-1.085351186702609994e-07,-5.566445082649806864e-08,-1.587467721719804103e-07,1.566090617188820033e-07,-2.175595814613018320e-10,-1.384351655531190772e-08,-2.042933644474942412e-08,4.782882356835216342e-08 --1.390252002408942644e-08,1.070218473842032836e-07,1.729425065377654391e-07,-1.391764037350640137e-08,2.916138086561659293e-08,1.398903570759557624e-08,-1.225584167485749276e-08,3.631698675121697306e-08,3.613908816646471678e-08,8.851120725239525200e-08,1.629533130653726403e-07,6.196007262815328431e-08,-6.416238562678699964e-08,9.956426083590486087e-08,-1.882394116137220408e-07,5.807234160215557495e-08 -1.611729487649427028e-07,8.238533411031312426e-08,1.566412919373760815e-07,-2.847779612125123094e-08,1.286467599115326266e-09,-3.167127715641981922e-08,7.091126263958358322e-08,3.091757960760332935e-08,5.679489234466927883e-08,-1.370385227517090243e-08,1.410169647719783370e-09,2.874138971264983411e-08,-1.638500179979018867e-07,5.794003854475704429e-08,-7.060137363623653073e-08,1.622957483959908132e-07 --1.623467937861278187e-07,-2.631918505358044058e-08,-2.869449566844650690e-08,-1.628427526156883687e-07,5.188224605490177055e-08,4.915464657528900716e-08,2.867121651206616662e-08,-1.385855163960059144e-08,6.481472070163363242e-08,2.381937425077751413e-10,-1.085387779653501639e-07,2.071498172995234199e-08,-1.882856821566747385e-07,2.868025461566503509e-08,3.587895491818926530e-08,8.309746846180132707e-08 -1.505982822891382966e-07,-5.784522372209721652e-08,-5.816843540173977090e-08,1.224811096989242287e-08,3.626647426813178754e-08,1.403778693287449642e-08,-2.715608293790694461e-10,9.944508860826317687e-08,-2.869294490892571048e-08,-5.776183442662841117e-08,1.882373652506430517e-07,-2.256508082609798294e-07,-1.396278825893659814e-08,-4.146762933032732690e-08,-1.403679128486601257e-08,1.527910313825486810e-07 --2.855190217587733059e-08,3.617978805436905532e-08,-3.458159625324697117e-08,2.865908221849622350e-08,-4.992652335999991919e-08,5.798099422804625647e-08,-2.860584658037623740e-08,1.397380167134087969e-08,1.381299163938365382e-08,-1.366114865675172041e-08,1.396491278171652084e-08,3.088958422381438140e-08,1.740023520824252046e-07,-5.191849439256657206e-08,1.746996436402525887e-10,2.868677917433615221e-08 -2.865751902447755128e-08,1.385340731019368832e-08,1.376086622428829287e-08,-3.647739887924217328e-08,6.554007825343433069e-08,-2.851616542898227635e-08,2.849456492981516931e-08,5.284764270641062467e-08,-2.867079018642471056e-08,1.390746540153031674e-08,-5.188355345353556913e-08,-1.033560366181518475e-07,2.849769131785251375e-08,6.481233327804147848e-08,-2.716176727979302541e-10,2.246580805831399630e-07 -2.922092079415961052e-08,5.798915125865278242e-08,1.628198447178874630e-07,-5.790837676045157423e-08,1.587742275432901806e-07,6.101299021565864678e-08,2.829456136055341631e-08,3.612838384015049087e-08,-1.601636370196501957e-07,1.632550379326858092e-07,-3.074633880828514521e-08,8.405383056242499151e-08,-2.257046105569315841e-08,2.694796563673662604e-08,-7.445206762213274487e-08,3.401805059866092051e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_final_layer.csv deleted file mode 100644 index 9ffddc1398427..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_final_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ --2.175820981165088597e-07,-3.858937702716502827e-08,3.601965680718421936e-02,-6.376685490749878227e-08,9.113346459344029427e-05,-4.426776245236396790e-02,9.833846986293792725e-02,6.004661372571717948e-06,-2.240297192201978760e-07,1.702363405797768792e-08,-1.858604885637760162e-02,2.592217606434132904e-05,3.761655837297439575e-02,-3.518993407487869263e-02,4.833122715353965759e-03,-3.485617041587829590e-02,-2.300172781133369426e-07,-1.092737207386562659e-07,-8.071739971637725830e-02,3.225584700703620911e-02,5.831549643176003883e-08,-1.515475673841137905e-07,-3.666227310895919800e-02,2.319397651717736153e-07,-3.001941806246577471e-08,1.168520157079910859e-05,-2.192070242017507553e-04,6.062252447009086609e-02,-1.971509680151939392e-02,-9.955786168575286865e-02,7.815898209810256958e-02,3.227722644805908203e-02 -5.460777430243979325e-08,-5.533593849804674392e-08,3.333770669996738434e-03,7.166487847598546068e-08,-1.391711295582354069e-03,1.061583636328577995e-03,4.071273375302553177e-03,-1.348172547295689583e-03,4.719174384604229999e-08,7.151373893066192977e-08,9.328410029411315918e-02,-1.313446206040680408e-03,-2.303715329617261887e-03,-1.893333392217755318e-03,-7.762409746646881104e-03,3.686914220452308655e-02,-9.796949029805546161e-08,1.118454093784748693e-07,1.411804370582103729e-02,-4.080725833773612976e-02,4.410194165416214673e-08,-1.398187947643236839e-07,-6.481568561866879463e-04,-1.130788707115470970e-08,8.558367881050799042e-08,-1.345351454801857471e-03,-1.735795871354639530e-03,3.771660849452018738e-02,9.104543924331665039e-02,-2.742536924779415131e-02,5.422115698456764221e-02,-4.115459695458412170e-02 --2.242246210926168715e-07,-2.052982779332523933e-07,1.384356711059808731e-02,5.319526508174021728e-08,-1.267372499569319189e-05,-2.362287975847721100e-02,9.113749861717224121e-02,-2.884633722715079784e-04,3.039113138925131352e-08,-7.277756708390370477e-08,-2.299219369888305664e-03,-3.105224168393760920e-04,3.972678910940885544e-03,-4.982576239854097366e-03,-2.103751059621572495e-03,-1.006751414388418198e-02,1.073849773547408404e-07,1.553895287997875130e-07,-7.887374609708786011e-02,6.625558249652385712e-03,1.613452838000739575e-07,-3.843430018690696670e-09,-5.069656763225793839e-03,2.055476215900853276e-07,1.442622732383824768e-07,-3.051489184144884348e-04,-4.775732813868671656e-04,8.377529680728912354e-02,-2.330561168491840363e-03,-9.997411817312240601e-02,1.111759990453720093e-01,6.600412540137767792e-03 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_fusion_layer.csv deleted file mode 100644 index da8778e4363e8..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_fusion_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_gru_hh.csv deleted file mode 100644 index ebda9420438b0..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_gru_ih.csv deleted file mode 100644 index e27e7136113f4..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_gru_ih.csv +++ /dev/null @@ -1,96 +0,0 @@ -2.246665502525502234e-07,7.937374313371492462e-09,1.875921213922993047e-07,-5.788109191939838638e-08,-2.136287946541415295e-08,-1.567381531231148983e-07,1.882228133354146848e-07,-2.598505943751661107e-08,-3.420910132945209625e-08,-1.567461680451742723e-07,-1.786740000397912809e-07,-2.247293053869725554e-07,-3.625288869102405442e-08,1.567380394362771767e-07,-7.059875883896893356e-08,-1.627963115424790885e-07,1.566299800970227807e-07 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--4.967793287846689054e-08,2.150854783167233109e-08,4.158178512625454459e-08,-1.634528246086119907e-07,-5.678036885115034238e-08,-5.813501147144961578e-08,5.776328393380936177e-08,-1.566364602467729128e-07,-2.866544868140863400e-08,-1.388251114065042202e-08,-3.617606481043367239e-08,1.567220948572867201e-07,2.928720022055131267e-08,2.929911779858684895e-08,2.864901915700102109e-08,8.405371687558726990e-08,3.625237710025430715e-08 -8.412970231574945501e-08,-1.656615400413841144e-10,1.629579173823003657e-07,5.797545199470732769e-08,-5.806660041685063334e-08,6.418881781655727536e-08,5.807552483361178020e-08,-2.868460313720788690e-08,1.078672440257832932e-07,-1.686742412410069392e-10,-5.781333456411630323e-08,-1.638241542423202191e-07,2.925599318359672907e-08,1.597442036427310086e-07,5.808584191413501685e-08,1.628393988539755810e-07,-6.706456190386234084e-08 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--2.446447240345150931e-07,-5.797262403461900249e-08,5.784542267406322935e-08,-4.948685727867996320e-08,1.636630315715592587e-07,2.859561476498129196e-08,1.621192495804280043e-07,-1.505266027379548177e-07,-1.387028270016799070e-08,2.869823134687976562e-08,5.133692937420164526e-08,-1.388810311198085401e-08,-5.571640571133684716e-08,5.061934515993016248e-08,1.637327784465014702e-07,-5.813387460307239962e-08,-4.058297520259657176e-08 -1.414099699559301371e-07,2.215232655089494074e-07,-6.123279661096603377e-08,1.637180275793070905e-07,1.367366841975581337e-08,-2.475139240232238080e-07,1.388950288117030141e-08,-1.395921422897572484e-08,-1.566962595234144828e-07,-1.148688042462708836e-07,-1.631939312574104406e-07,5.787142853819204902e-08,1.635335706851037685e-07,4.147488397165943752e-08,8.446513533044708311e-08,-5.206110387234730297e-08,4.928049790464683610e-08 --1.479015168115438428e-07,2.861178494129035244e-08,4.100147066310455557e-08,-1.802456921495831921e-07,2.863562720278878260e-08,1.849799619035863429e-10,-2.868058679439400294e-08,1.321044107438495985e-10,-1.389591020029001811e-08,-4.108287754434059025e-08,-1.085071801298909122e-07,5.204305608685899642e-08,-1.610931974482809892e-07,-4.273213605188175279e-08,4.148085253063982236e-08,-2.508360807951248717e-08,-6.947011144120551762e-08 -2.032614787594866357e-07,-2.494784645534764422e-08,6.127978480208184919e-08,-1.614041789110842728e-09,6.153331177927157114e-10,1.507667768407827680e-08,1.382984393671904400e-08,6.191098123053961899e-08,-1.502752979831711855e-07,2.082978767248278018e-07,3.459078357082034927e-08,8.832482478737802012e-08,-1.876494195585109992e-07,-1.598167642669068300e-07,-2.500627793722287606e-08,-1.602401908940009889e-07,-1.015568855677884130e-07 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--3.626437106163393764e-08,5.786744949887179246e-08,1.566839813449405483e-07,3.633799039448604162e-08,7.937203783114910038e-09,-5.204502784295073070e-08,1.612913251847203355e-07,-4.083874927118813503e-08,6.412805220179507160e-08,6.422821030582781532e-08,5.779315515042071638e-08,-3.580516150236690009e-08,2.682239141904574353e-08,-6.242960637337091612e-08,-2.917413866043716553e-08,1.384186809616494429e-08,7.433602178252840531e-08 --1.617611199617385864e-01,-3.229899192547236453e-08,4.978855727699738054e-08,8.648074611983247451e-08,2.891559702788981667e-08,-1.747971225540823070e-07,-7.224393527849315433e-08,1.883262825685960706e-07,-1.600071328766716761e-07,7.921324396420459379e-08,-1.643652325356015353e-07,2.156182077328594460e-08,-3.010353522014952432e-09,-6.486836667818351998e-08,-2.337185094347660197e-07,-1.491870733616451616e-07,1.659868189562985208e-07 --7.365231908806890715e-07,1.553710973212218960e-07,-6.564936683162159170e-08,2.840960711125717353e-08,-5.810138858919344784e-08,4.100746764379437082e-08,-1.086001333305830485e-07,4.064239078616083134e-08,1.532125821768204332e-07,-6.708832955837351619e-08,5.783059364716791606e-08,5.159630944717719103e-08,5.793976853851745545e-08,6.176325939577509416e-08,-1.635185498116698000e-07,-1.598931476110010408e-07,1.627844170570824645e-07 --1.086785346160468180e-07,-1.567005227798290434e-07,-1.078803748555401398e-07,-3.615841492887739150e-08,-5.571111927338279202e-08,-1.635595765492325881e-07,4.101727313354786020e-08,-6.445780087460661889e-08,1.662868176488530025e-10,1.535175755407180986e-07,-2.546783339774094657e-10,-1.631506734156573657e-07,3.571023299286935071e-08,1.526687611885790830e-07,1.611768709608440986e-07,-2.080935956882967730e-07,-1.882453801727024256e-07 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--3.166725548453541705e-07,-1.633203225992474472e-07,-3.636163725673213776e-08,3.631428668882108468e-08,-1.567363057120019221e-07,-2.868525861288162560e-08,-1.881971201100895996e-07,-1.391918225124300079e-08,-1.754999914282961981e-07,1.682569319427784649e-07,4.831321476217453892e-08,-1.017154431792732794e-07,2.874035232025562436e-08,3.644488444365379110e-08,-1.488747329858597368e-07,-5.304810812845062173e-08,3.545922666603473772e-08 -6.670945396081151557e-08,-3.589379105051193619e-08,-1.631634063414821867e-07,-1.563251856850911281e-07,3.613348908970692719e-08,8.962398823086914490e-08,1.371443225650637032e-08,-1.566252405549306559e-10,-3.631039291462911933e-08,1.587823561521872762e-07,-1.749838607345566288e-10,4.090740901574463351e-08,-1.875241366633417783e-07,-6.483518433242352330e-08,-3.075546217701230489e-08,-1.145627521736969356e-07,-6.417676701175878407e-08 --8.130252361297607422e-02,1.584433135803919868e-07,-1.833516165561377420e-07,-1.830772617950060521e-07,8.387216610117320670e-08,1.954229666978335445e-08,1.269502192258187279e-08,-4.165651645848811313e-08,-2.700521761767049611e-08,-4.223102223477326334e-08,8.168512266593097593e-08,1.666401345801205025e-07,1.871812997933375300e-07,-1.687939601424659486e-07,1.593090530604968080e-07,-1.643985712007633992e-07,-2.581056790518232447e-08 --2.608125555525475647e-07,1.566414766784873791e-07,-6.233551630430156365e-08,-1.740481678780270158e-07,4.121308450066862861e-08,1.380581604593089651e-08,1.566761227422830416e-07,-1.629551036330667557e-07,-6.097637594848492881e-08,-4.088968807991477661e-08,1.029993157430908468e-07,-4.092578720360506850e-08,3.639899759377840383e-08,1.016764201722253347e-07,-1.622421734737145016e-07,3.613958909909342765e-08,5.788762891256737930e-08 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_steer_layer_1.csv deleted file mode 100644 index b3a8b6028a2ba..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -5.784385948004455713e-08,4.115653950975683983e-08,-5.804627889460789447e-08,2.864728720908260584e-08,-3.634495371329649060e-08,-6.166982302602264099e-08,-2.448331599680386716e-07,1.567348846265304019e-07,-8.307063836809902568e-08,3.079690391594169796e-10,-1.396914051099429344e-08,-2.550808630985557102e-08,-6.109024042189048487e-08,-1.834535301448170230e-10,-5.804912106555093487e-08,-5.804991687341498618e-08 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_linear_relu.csv @@ -1,16 +1,16 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_hh_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_hh_0.csv index e72b7cca0f098..7537a9ee9c0b9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_hh_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_hh_0.csv @@ -1,72 +1,72 @@ 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--5.102316662669181824e-02,-3.507985472679138184e-01,5.010926127433776855e-01,1.573813855648040771e-01,-8.607234805822372437e-02,-3.897233903408050537e-01,3.848883807659149170e-01,3.309157192707061768e-01,-4.921467229723930359e-02,-1.468239128589630127e-01,6.316158175468444824e-02,-2.628258466720581055e-01,3.975514471530914307e-01,3.752481639385223389e-01,-6.427977979183197021e-02,1.382480859756469727e-01,-2.432262711226940155e-02,-7.466709017753601074e-01 --2.871334254741668701e-01,-1.684546619653701782e-01,1.004022881388664246e-01,1.107691153883934021e-01,-3.314368426799774170e-01,3.528238236904144287e-01,-1.947280168533325195e-01,3.410727530717849731e-02,-7.150819897651672363e-01,4.324796795845031738e-02,1.645645312964916229e-02,-6.151926144957542419e-02,2.717777192592620850e-01,2.588473558425903320e-01,-3.138980567455291748e-01,1.122600883245468140e-01,5.367688462138175964e-02,3.030427731573581696e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_ih_0.csv index a03b7b141eb63..1190c0ccb7332 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_hh.csv index 2852c6e4eff0d..99d0e5eab9e2c 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_hh.csv @@ -1,64 +1,64 @@ 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_ih.csv index 1502ee1d4acf2..1541f6535f3ad 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_lstm_ih.csv @@ -1,64 +1,64 @@ -1.354664843529462814e-02,5.303078889846801758e-02,-5.030304193496704102e-02,3.654263541102409363e-02,3.604745492339134216e-02,5.328396707773208618e-02,5.226656422019004822e-02,4.835975542664527893e-02,5.317721515893936157e-02,4.100683704018592834e-02,1.429468486458063126e-02,5.063319113105535507e-03,1.167685911059379578e-01,-1.364066172391176224e-02,1.782777607440948486e-01,-6.149024516344070435e-02,-2.606809139251708984e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_steer_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ -1.443694811314344406e-03,-2.542864158749580383e-02,4.027282074093818665e-02,-7.667397707700729370e-02,4.094406217336654663e-02,2.776812203228473663e-02,8.039279282093048096e-02,-1.509349465370178223e+00,1.310569643974304199e-01,4.136419668793678284e-02,1.308051943778991699e-01,2.373549044132232666e-01,-4.637687802314758301e-01,-2.480286872014403343e-03,2.196998000144958496e-01,1.672886610031127930e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_steer_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_2/weight_steer_layer_2.csv @@ -1,8 +1,8 @@ --5.466060340404510498e-02,-3.131796419620513916e-01,4.896617680788040161e-02,7.235971093177795410e-01,-2.694058604538440704e-02,-4.396100994199514389e-03,-3.990970551967620850e-02,1.204489588737487793e+00,1.400408148765563965e-01,-2.412422001361846924e-02,4.019365608692169189e-01,4.560724198818206787e-01,-7.040619254112243652e-01,6.191220879554748535e-01,2.303922623395919800e-01,-8.090059161186218262e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --5.687703490257263184e-01 -1.572953760623931885e-01 -5.714529380202293396e-02 -1.085763052105903625e-01 -9.483638405799865723e-02 -1.503358874469995499e-02 --6.868886295706033707e-03 --4.978453516960144043e-01 -1.244671717286109924e-01 -1.060194298624992371e-01 -1.586544699966907501e-02 -5.790882557630538940e-02 -8.724521845579147339e-02 -8.154500126838684082e-01 -3.643409162759780884e-02 -1.652323454618453979e-01 +9.447958320379257202e-02 +-3.904920443892478943e-02 +-4.699527025222778320e-01 +1.458566039800643921e-01 +-3.368199765682220459e-01 +-3.077215850353240967e-01 +-7.432874292135238647e-02 +-3.889969587326049805e-01 +7.842382043600082397e-02 +-1.985292583703994751e-01 +7.554553151130676270e-01 +-4.822994768619537354e-02 +-4.119287729263305664e-01 +2.234724313020706177e-01 +5.568625032901763916e-02 +9.671768546104431152e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_encoder_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_encoder_layer_2.csv index 2321f21b9d8a8..6bc053b95a062 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -8.771612308919429779e-03 -1.144464537501335144e-01 -6.942238658666610718e-02 -6.198814511299133301e-02 --2.246745675802230835e-02 --7.646840810775756836e-02 -1.091548949480056763e-01 -2.548992633819580078e-01 +2.911694943904876709e-01 +1.167204603552818298e-01 +1.205120310187339783e-01 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--3.642752394080162048e-02 --1.260660216212272644e-02 -9.186952561140060425e-02 -7.274972647428512573e-02 -1.017989963293075562e-01 -7.828899472951889038e-02 -1.771877259016036987e-01 -1.412725597620010376e-01 +-4.330429807305335999e-02 +-4.956674203276634216e-02 +5.305194482207298279e-02 +-9.294666349887847900e-03 +7.941994816064834595e-02 +8.546566963195800781e-02 +6.983704864978790283e-02 +5.682964250445365906e-02 +1.107634603977203369e-01 +7.904335856437683105e-02 +2.872206084430217743e-02 +8.025071769952774048e-02 +1.396385300904512405e-02 +7.349329441785812378e-02 +-1.042479351162910461e-01 +1.730972528457641602e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_layer_2.csv index 40f2a127a4f33..993c5dccd103f 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.024103090167045593e-01 -1.508852094411849976e-01 -9.780577383935451508e-03 -4.319138452410697937e-02 -9.238595515489578247e-02 -1.059526875615119934e-01 -9.110169857740402222e-02 -1.054998338222503662e-01 +5.389664322137832642e-02 +3.577540814876556396e-02 +3.646123688668012619e-03 +4.657787457108497620e-02 +4.009292274713516235e-02 +-1.234907656908035278e-01 +-9.528395719826221466e-03 +8.088576793670654297e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_complimentary_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_complimentary_layer.csv index bb1b5346932b2..a9dc3efd0edaf 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_complimentary_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_complimentary_layer.csv @@ -1,64 +1,64 @@ -1.511160749942064285e-02 -1.398833189159631729e-02 -1.167307570576667786e-01 -4.545302689075469971e-02 -1.077463570982217789e-02 -1.412830781191587448e-03 -2.155422978103160858e-02 -9.623280493542551994e-04 -4.561380296945571899e-02 -1.059380993247032166e-01 --9.806581586599349976e-02 --4.837276786565780640e-02 -3.910212963819503784e-02 --4.298238456249237061e-02 -7.735941559076309204e-02 --2.475859364494681358e-03 -2.536228299140930176e-02 --6.351507455110549927e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_final_layer.csv index 7f69ecaf304d4..e312c241bbb6e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_final_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_final_layer.csv @@ -1,4 +1,4 @@ --1.411965698935091496e-03 -9.323665290139615536e-04 --3.581988811492919922e-02 -5.641778465360403061e-03 +-2.463300246745347977e-03 +-6.471379892900586128e-04 +-6.751611828804016113e-02 +-5.644071847200393677e-03 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_acc_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 6a4f3acd10344..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.700384832936663315e-10 -5.574749906145370915e-08 --1.639053834878723137e-07 -2.375204388727070182e-07 -5.790519352899536898e-08 -5.913284439884591848e-08 --1.567347993614021107e-07 --3.626800904044102936e-08 -2.247556807333239703e-07 -2.873030524597197655e-08 -5.794816004822678224e-08 --4.090935590284061618e-08 --2.924792141811849433e-08 --2.869897031132495613e-08 --2.662483211679500528e-08 --1.567348562048209715e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_acc_layer_2.csv deleted file mode 100644 index 62f75cb43506c..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --8.786349070533105987e-08 --2.436062018773554883e-08 --7.991299355580849806e-08 --1.391345278989319922e-07 --3.481732591126274201e-08 --2.435033650272089290e-07 --6.396118124030181207e-08 --2.479723377746267943e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_final_layer.csv deleted file mode 100644 index b082a7a89c611..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_final_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.333507336676120758e-02 -6.083920970559120178e-02 --1.834504422731697559e-03 --3.571275621652603149e-02 -2.946369349956512451e-01 --1.427265582606196404e-03 --3.688247501850128174e-02 -6.005700305104255676e-02 --1.222376897931098938e-02 --3.048365712165832520e-01 --4.184846207499504089e-02 -1.948376744985580444e-02 -4.896391183137893677e-02 -3.152998397126793861e-03 --2.637754976749420166e-01 -1.586769241839647293e-03 --6.010877341032028198e-02 --2.591094970703125000e-01 -2.363688647747039795e-01 -6.578605622053146362e-03 -7.574999239295721054e-03 -2.695792615413665771e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_gru_hh.csv deleted file mode 100644 index 8057bd867e52d..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ -1.545770733635265515e-10 --8.302931320258721826e-08 --7.699894943868912378e-09 --2.921688491142049315e-08 -1.020158979514462771e-08 --1.473583746701478958e-03 --7.846460212022066116e-04 --1.084701466425030958e-07 --5.566524663436211995e-08 --1.647779015456762863e-07 -2.786956443401322758e-08 -1.140102057206604513e-07 -1.460361964689127490e-08 --1.444058312927154475e-07 -8.503373294388438808e-08 --1.640689930582084344e-07 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_steer_layer_1.csv deleted file mode 100644 index 614901c941b0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.602122381427761866e-07 -1.594512042402129737e-07 --1.383567216350911622e-08 -2.870719839620505809e-08 --4.115875640309241135e-08 --1.629571784178551752e-07 -6.326867207917530322e-08 -6.130223795253186836e-08 -2.572974278081119337e-09 --1.623540981654514326e-07 --3.618814403694159409e-08 --5.186216256447551132e-08 -1.592173859421563975e-08 -2.550376621002214961e-08 --5.809684111568458320e-08 --6.100456317881253199e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_steer_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_steer_layer_2.csv deleted file mode 100644 index fc1f85f18f28b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --1.495364330139636877e-07 --9.433398506786261350e-10 --9.447958149166879593e-08 -6.568057209221933590e-09 --2.248099519874813268e-07 --1.070967527994071133e-07 --9.932387712296986138e-08 --7.939953405866617686e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_linear_relu.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_linear_relu.csv index 6c7ba3b020e52..b40d15d15387e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_linear_relu.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_linear_relu.csv @@ -1,16 +1,16 @@ --3.609813749790191650e-02 --3.190746530890464783e-02 -4.359308257699012756e-02 -8.269068202935159206e-04 -4.143001139163970947e-02 -7.497583515942096710e-03 -7.427339255809783936e-02 --2.295951917767524719e-02 --2.274018079042434692e-01 -8.252906054258346558e-02 -7.786637544631958008e-02 -1.048227921128273010e-01 --4.350556991994380951e-04 --6.249625235795974731e-02 -1.322524547576904297e-01 --3.382597863674163818e-02 +-2.442883513867855072e-02 +5.057684332132339478e-02 +3.201630339026451111e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_encoder_ih_0.csv index bd71a79542f43..2a250786995d9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ --2.799263000488281250e-01 -8.330401778221130371e-02 -5.212230980396270752e-02 --2.048002928495407104e-02 --9.690331667661666870e-02 --1.525534093379974365e-01 --1.841630488634109497e-01 --1.662613004446029663e-01 --2.396262139081954956e-01 -6.080040335655212402e-02 -1.034230291843414307e-01 -1.198021844029426575e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_hh.csv @@ -1,64 +1,64 @@ -1.863418668508529663e-01 --1.841402947902679443e-01 -4.871492087841033936e-02 -6.630924344062805176e-01 -3.120891004800796509e-02 -5.130470544099807739e-02 -3.066616505384445190e-02 -3.965241834521293640e-02 -3.464382141828536987e-02 --1.669278144836425781e-01 --9.552434831857681274e-02 --2.999459765851497650e-02 --6.102378945797681808e-03 -1.027578711509704590e-01 -1.095576584339141846e-01 -1.344114840030670166e-01 -2.494055032730102539e-01 -1.547644734382629395e-01 -1.317952722311019897e-01 --2.324226498603820801e-01 --2.754482328891754150e-01 -1.100205332040786743e-01 --1.426737289875745773e-03 --3.385674655437469482e-01 -1.660625338554382324e-01 -5.306183919310569763e-02 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031398bdfd2a3..8511ea9e3ab32 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_lstm_ih.csv @@ -1,64 +1,64 @@ -1.862874925136566162e-01 --1.859310567378997803e-01 -4.900135472416877747e-02 -6.631961464881896973e-01 -3.110241889953613281e-02 -5.119629949331283569e-02 -2.894839085638523102e-02 -3.989761322736740112e-02 -3.549076244235038757e-02 --1.642386913299560547e-01 --9.745972603559494019e-02 --3.339832648634910583e-02 --2.647247863933444023e-03 -9.829391539096832275e-02 -1.122935935854911804e-01 -1.338122785091400146e-01 -2.497808933258056641e-01 -1.542556881904602051e-01 -1.318080276250839233e-01 --2.323063462972640991e-01 --2.763138115406036377e-01 -1.098379045724868774e-01 --1.424718881025910378e-03 --3.388048410415649414e-01 -1.662753224372863770e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_encoder_layer_1.csv index 707a956dc7533..ca1b020a82ede 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_encoder_layer_1.csv @@ -1,16 +1,16 @@ -2.288375049829483032e-02 -4.156160056591033936e-01 -1.447749207727611065e-03 --1.900681381812319160e-04 -3.408987540751695633e-03 -3.183485008776187897e-03 -4.051115512847900391e-01 --1.631803363561630249e-01 -2.039906978607177734e-01 -3.064470831304788589e-03 -2.237636968493461609e-02 -1.526999026536941528e-01 -1.990733891725540161e-01 --1.465275436639785767e-01 --4.737891629338264465e-02 -1.408819258213043213e-01 +-2.332334361199173145e-06 +1.387599296867847443e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ --4.465385898947715759e-02 --2.514035440981388092e-02 --4.148573800921440125e-02 -1.449349820613861084e-01 -9.823747910559177399e-03 --1.016079541295766830e-02 --1.387605676427483559e-03 --8.763826638460159302e-02 +2.186003774404525757e-01 +-9.245735406875610352e-02 +-2.528463006019592285e-01 +-7.972517609596252441e-02 +6.500462442636489868e-02 +2.470089346170425415e-01 +2.000899612903594971e-02 +1.971768885850906372e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_layer_1.csv index d8e5af208e79c..dc762d230ac56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/bias_steer_layer_1.csv @@ -1,16 +1,16 @@ --5.880776420235633850e-02 -1.072910651564598083e-01 -1.927932947874069214e-01 -1.416619420051574707e-01 -3.611433552578091621e-03 --2.359812110662460327e-01 -1.126473490148782730e-02 --1.914175972342491150e-02 -1.322983503341674805e-01 --9.606996551156044006e-03 --4.914920404553413391e-02 -5.091565847396850586e-02 -2.208182960748672485e-02 -4.450881108641624451e-02 -5.511307157576084137e-03 -9.871846064925193787e-03 +-1.267773564904928207e-03 +1.681745648384094238e-01 +3.152760118246078491e-02 +-3.042486310005187988e-02 +2.783328667283058167e-02 +1.784413307905197144e-01 +3.829433768987655640e-02 +-1.098260059952735901e-01 +8.191139250993728638e-02 +4.840236902236938477e-02 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-1.211080327630043030e-01 +-2.150738146156072617e-03 +4.411975294351577759e-02 +1.444365680217742920e-01 +5.844632163643836975e-02 +6.778846494853496552e-03 +5.850554630160331726e-02 +2.264000841023516841e-06 +1.272178292274475098e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/offline_features.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/offline_features.csv index 1bcdd512dea6f..fa6d9d8875c56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/offline_features.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/offline_features.csv @@ -1,36 +1,36 @@ --1.365479675996539299e-07 -4.500528660855707130e-08 --2.888707228976272745e-07 --3.052138808357085509e-08 --3.403145854008471360e-08 --1.366562401017290540e-07 -9.226326369571324904e-09 --1.461385323864305974e-07 -2.401009169261669740e-07 -3.847664586942300957e-08 --4.707507983425784914e-08 -4.440217082901654067e-09 --3.469495624131013756e-08 -3.053523656149081944e-08 -4.850487655971846834e-08 --4.678990350726053293e-08 --1.465268173461709011e-08 --3.929526570800589980e-08 --1.382004199967923341e-07 --3.059484043888005544e-08 -1.119208832278673071e-07 -4.225704230975679820e-09 --1.164682288390395115e-07 --1.629832553362575709e-07 -3.149804683744150680e-08 --1.490812984172862343e-08 -5.521310342260221660e-08 --6.760313198128642398e-08 -1.837763718981477723e-08 --1.734968861910601845e-07 -1.474870856554844067e-07 --1.383132399723763228e-07 -3.487329980345066360e-09 -8.957044173030226375e-08 -2.936226373151384905e-08 --1.337812438606533760e-08 +5.170850083231925964e-02 +-2.384004145860671997e-01 +2.417143583297729492e-01 +3.888960182666778564e-01 +-2.718242108821868896e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_acc_encoder_layer_1.csv index 5eb1c7c33213a..09a6b18c285fa 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_acc_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --6.779996305704116821e-02,3.508124351501464844e-01,2.439208775758743286e-01,2.227861583232879639e-01,2.015045583248138428e-01,1.802337467670440674e-01,1.594138443470001221e-01,1.380567848682403564e-01,1.166481673717498779e-01,9.721208363771438599e-02,7.622497528791427612e-02,5.588675662875175476e-02,3.574046865105628967e-02,1.916410401463508606e-02,1.854662410914897919e-02,1.806927099823951721e-02,1.663219928741455078e-02,1.390712894499301910e-02,8.530278690159320831e-03,-9.777169907465577126e-04 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--1.034822897054255009e-03,-6.608315557241439819e-02,-9.176946431398391724e-02,-5.862812697887420654e-02,-2.625368721783161163e-02,-5.165014881640672684e-04,1.223031710833311081e-02,2.518984861671924591e-02,3.586468473076820374e-02,5.349204316735267639e-02,7.512801885604858398e-02,1.046478971838951111e-01,1.383297592401504517e-01,1.735281050205230713e-01,2.093181908130645752e-01,2.467417269945144653e-01,2.847021520137786865e-01,3.234089016914367676e-01,3.612569272518157959e-01,4.009398818016052246e-01 --1.854499280452728271e-01,1.914535760879516602e-01,4.152707383036613464e-02,3.073596581816673279e-02,2.163004875183105469e-02,1.425455417484045029e-02,7.673011161386966705e-03,1.589223393239080906e-03,-2.790175611153244972e-03,-6.258539855480194092e-03,-9.682977572083473206e-03,-1.227880362421274185e-02,-1.485804840922355652e-02,-1.617628522217273712e-02,-1.666615158319473267e-02,-1.672841608524322510e-02,-1.409822050482034683e-02,-1.107515487819910049e-02,-7.739217020571231842e-03,-2.378774574026465416e-03 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -3.336199373006820679e-02,2.459247857332229614e-01,-6.924922019243240356e-02,-3.334385901689529419e-02,2.794704735279083252e-01,-1.098308246582746506e-02,-3.318902477622032166e-02,-2.375750094652175903e-01,-6.978246849030256271e-03,-7.694390369579195976e-04,3.750725463032722473e-02,-1.894451864063739777e-02,-1.075058132410049438e-01,1.155056133866310120e-01,-3.716661781072616577e-02,4.225961863994598389e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.827372312545776367e-01,1.442782729864120483e-01,1.350615918636322021e-01,1.406146585941314697e-01,7.798003405332565308e-02,1.429848670959472656e-01,1.125954836606979370e-01,5.638980120420455933e-02,8.144410699605941772e-02,-5.328862462192773819e-03,-2.268474362790584564e-02,4.206001386046409607e-02,1.457983851432800293e-01,-5.146942660212516785e-02,-6.503234058618545532e-02,-3.310494497418403625e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_final_layer.csv @@ -1,4 +1,4 @@ --4.076786339282989502e-03,-1.227277144789695740e-02,3.244567289948463440e-02,1.793841016478836536e-03,4.501522984355688095e-03,-2.283791452646255493e-02,-1.217357907444238663e-02,-2.098338445648550987e-03,-2.394610270857810974e-02,3.298125788569450378e-02,3.658420871943235397e-03,3.390965238213539124e-02,-1.559238694608211517e-02,-1.272494345903396606e-02,1.682596048340201378e-03,-1.867336407303810120e-02 --1.042943261563777924e-02,-4.991219844669103622e-03,1.183264539577066898e-03,5.007501225918531418e-03,-6.344680878100916743e-05,2.041139872744679451e-03,7.769418880343437195e-03,-3.926411271095275879e-03,-1.374095008941367269e-04,-7.521932013332843781e-03,2.816032152622938156e-03,5.137700587511062622e-03,-1.068992633372545242e-02,-1.806749030947685242e-03,1.107970019802451134e-03,2.244758186861872673e-03 --2.750689350068569183e-02,-3.015980422496795654e-01,8.099721074104309082e-01,5.499200895428657532e-02,1.074275374412536621e-01,-5.590542554855346680e-01,-3.127891719341278076e-01,-1.049866527318954468e-01,-5.508310794830322266e-01,8.068168163299560547e-01,1.146488413214683533e-01,8.553577661514282227e-01,-3.928754031658172607e-01,-3.463063836097717285e-01,3.211103007197380066e-02,-4.625632762908935547e-01 --1.714976727962493896e-01,-3.647217899560928345e-02,1.288588275201618671e-03,-1.639245077967643738e-02,-3.204738348722457886e-02,4.737513139843940735e-03,4.795992001891136169e-02,-2.080518007278442383e-01,9.203262743540108204e-04,-4.152239300310611725e-03,4.169252887368202209e-02,-4.800185561180114746e-03,1.465110201388597488e-02,-2.583358809351921082e-02,5.423993244767189026e-02,-1.011096313595771790e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 09b35a7fc4e0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --5.795665813934647304e-08,2.448358884521439904e-07,-3.380812785280795651e-08,-2.136942289787668869e-10,4.084210303290092270e-08,5.576080042146713822e-08,1.567351972653341363e-07,3.467380693678023817e-08,6.475600144995041774e-08,7.276236857478579623e-08,-1.776304969780539977e-07,3.134217507749781362e-08,1.528735111833157134e-07,1.598162242544276523e-07,4.906196338083645969e-08,5.687583737312706944e-08,2.663517761902767234e-08,-1.778388565298882895e-07,9.540057988033368019e-08,-1.728522325272763283e-10 --2.865231785165178735e-08,1.085090843844227493e-07,4.934348041274461139e-08,6.418773779159892001e-08,1.228051171864308344e-08,1.398261240126430494e-08,-5.777479472612867539e-08,-1.366190183205162612e-08,-7.060171469674969558e-08,-2.162425261076350580e-08,5.675544301197987807e-08,-3.080576860270411998e-08,1.389905257553891715e-08,5.775637745841777360e-08,5.794838742190222547e-08,1.389194714818131615e-08,5.799671143336126988e-08,1.682496844068737119e-07,-5.566382554889059975e-08,1.608615036730043357e-07 --2.546920541135477833e-08,-1.084940137729972776e-07,-2.864933357216159493e-08,-1.399994964401685138e-08,-1.849883024540588394e-10,5.802163727253173420e-08,6.589188217276387149e-08,5.794140278680970368e-08,-1.919061715094372289e-10,-1.567351404219152755e-07,-1.567356520126850228e-07,-1.874498991583095631e-07,3.460730013671309280e-08,-2.833207801700154960e-08,-5.565841121324410778e-08,2.246823527229935280e-07,7.060046414153475780e-08,-4.112459350835706573e-08,1.538483758167785709e-07,4.987273527490287961e-08 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--2.572746904405676105e-09,1.086709744413383305e-07,1.400614557667267945e-08,7.936692192345162766e-09,4.102665940308725112e-08,-8.246642124731806689e-08,5.573721040263990290e-08,6.818912368089513620e-08,4.132279229906998808e-08,-1.598831147475721082e-07,2.749714345107179270e-10,-4.084756000111156027e-08,2.869085591328257578e-08,1.387898684868105192e-08,-6.484240344661884592e-08,-6.196152213533423492e-08,-5.634582800340126596e-10,-1.612109485904511530e-07,2.844504010113269032e-08,2.246597858857057872e-07 --2.837610324490924540e-08,-1.612319806554296520e-07,6.102120408968403353e-08,2.872587145930083352e-08,-2.865953518949027057e-08,2.850439884127808909e-08,1.778415992248483235e-07,2.926793030155749875e-08,-1.032509047149687831e-07,1.884557510045681283e-10,2.864214287967570272e-08,-4.828538990864217340e-08,1.726883596120387665e-07,-6.240391314804583089e-08,5.785573975458646601e-08,1.387097192662167799e-08,-1.379480174534819525e-08,2.865901116422264749e-08,-2.918067565360615845e-08,-2.918391572848122451e-08 --2.864954673498232296e-08,-1.368725399686354649e-08,1.085663257072155830e-07,-2.883944461018472794e-08,1.085741132555995136e-07,-5.121204438296445005e-08,6.429602450452875928e-08,5.809104308696078078e-08,-4.156912325470329961e-08,-3.439327400656111422e-08,-5.183908413641802326e-08,1.530865603172060219e-07,8.827194619698275346e-08,-5.001684755256974313e-08,1.873391681783687091e-07,1.567354246390095796e-07,5.798773017318126222e-08,1.020795323825041123e-07,1.085405756384716369e-07,1.529302977587576606e-07 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--2.852652158935597981e-08,-9.824449875850405078e-08,-1.754762308792123804e-07,5.789251744658940879e-08,4.112624196750402916e-08,5.801046754072558542e-08,6.086136750127479900e-08,-2.864963200011061417e-08,-1.392582582582235773e-08,1.740867645594335045e-07,-1.605755812761344714e-07,-3.050213948085911397e-08,-2.212133978218844277e-07,1.637957893763086759e-07,1.566371992112181033e-07,1.566785812201487715e-07,1.622258025690825889e-07,5.108397616027104959e-08,1.567347993614021107e-07,2.550069666540366597e-08 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--3.479204124801071885e-08,-6.744752312215496204e-08,-6.184050960200693225e-08,2.946307043824591965e-12,-6.112707495731228846e-08,1.314774333849300092e-08,-9.540066514546197141e-08,-5.567803640360580175e-08,1.567590146578368149e-07,-6.107648431452616933e-08,1.636276465433184057e-07,2.845211288793336735e-10,-2.862939751935300592e-08,2.852742042591671634e-08,-4.092018457413360011e-08,-4.101761419406102505e-08,1.497509884984538076e-07,1.882291797983270953e-07,1.623722880594868911e-07,6.816661368702625623e-08 --1.567573093552709906e-07,5.190680241184963961e-08,-4.090967564707170823e-08,-2.676181409810851619e-08,1.796450210811428860e-10,-2.870267934440562385e-08,1.629214239073917270e-07,1.625622587653197115e-07,5.780725231829819677e-08,-6.437543476067730808e-08,1.078759979122878576e-07,-5.816283632498198131e-08,-2.863949966069867514e-08,1.598080103804022656e-07,1.085153371604974382e-07,-3.626300681958127825e-08,2.851320957120151434e-08,-7.937203783114910038e-09,-1.778377480832205038e-07,1.665710347431570426e-10 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--2.448340410410310142e-07,1.380423775287908938e-08,1.561118456727683679e-10,-5.812466596921694872e-08,-3.100375423059631430e-08,-1.527012405411198870e-10,-2.919994557259997237e-08,-1.069702619815871003e-07,-1.525875537033982710e-10,2.856971192954915750e-08,-3.033363005222611264e-10,-8.849530530596894096e-08,1.624740946226665983e-07,-1.728069349837824120e-07,1.750106615183710801e-09,5.574175787614876754e-08,-1.683244903460945352e-07,-1.400569082932179299e-08,2.154006040200329153e-08,-2.080925725067572785e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_acc_layer_2.csv deleted file mode 100644 index b48ef5ed5516a..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -2.044039248971785128e-08,2.853378688882912684e-08,1.634649606785387732e-07,-2.863657222462734353e-08,1.586338811421228456e-07,-3.615636856579840241e-08,4.099652528566366527e-08,-2.866763537667793571e-08,-1.085351186702609994e-07,-5.566445082649806864e-08,-1.587467721719804103e-07,1.566090617188820033e-07,-2.175595814613018320e-10,-1.384351655531190772e-08,-2.042933644474942412e-08,4.782882356835216342e-08 --1.390252002408942644e-08,1.070218473842032836e-07,1.729425065377654391e-07,-1.391764037350640137e-08,2.916138086561659293e-08,1.398903570759557624e-08,-1.225584167485749276e-08,3.631698675121697306e-08,3.613908816646471678e-08,8.851120725239525200e-08,1.629533130653726403e-07,6.196007262815328431e-08,-6.416238562678699964e-08,9.956426083590486087e-08,-1.882394116137220408e-07,5.807234160215557495e-08 -1.611729487649427028e-07,8.238533411031312426e-08,1.566412919373760815e-07,-2.847779612125123094e-08,1.286467599115326266e-09,-3.167127715641981922e-08,7.091126263958358322e-08,3.091757960760332935e-08,5.679489234466927883e-08,-1.370385227517090243e-08,1.410169647719783370e-09,2.874138971264983411e-08,-1.638500179979018867e-07,5.794003854475704429e-08,-7.060137363623653073e-08,1.622957483959908132e-07 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_fusion_layer.csv deleted file mode 100644 index da8778e4363e8..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_fusion_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_gru_hh.csv deleted file mode 100644 index ebda9420438b0..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ 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--1.575083672378241317e-07,-2.456893177793517680e-08,1.299299183301627636e-03,4.583574853000982330e-09,7.148290137592994142e-07,-1.196986180730164051e-03,-3.440467698965221643e-04,9.724246774567291141e-05,1.484971789977862500e-07,1.561929110494020279e-07,1.268110796809196472e-02,1.301198644796386361e-04,1.105297007597982883e-03,-9.569925023242831230e-04,-3.483079746365547180e-02,4.560764878988265991e-02,-2.130093790242426621e-08,-2.800469189878640464e-08,2.677923534065485001e-03,-1.244230009615421295e-02,-1.687294997054777923e-07,1.886606071366259130e-08,-9.817488025873899460e-04,5.614008102838852210e-08,1.669344698029817664e-07,1.194582000607624650e-04,2.625588967930525541e-04,2.482417039573192596e-03,1.514875143766403198e-02,-7.238288526423275471e-04,2.977170515805482864e-03,-3.903895150870084763e-03 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--1.053751930157886818e-07,-2.511040122499252902e-07,2.483378048054873943e-04,3.056874575690926576e-08,-3.617716356529854238e-05,-5.083134965389035642e-05,-3.177255392074584961e-02,-8.462891855742782354e-05,1.506569304865479353e-07,1.389682040553452680e-07,4.094154108315706253e-03,-9.500762098468840122e-05,-1.690746139502152801e-04,-1.181037150672636926e-04,1.810444295406341553e-01,-5.167514551430940628e-03,-5.528867674797766085e-08,-1.995898770701387548e-07,1.678826212882995605e-01,-7.984144613146781921e-04,5.774685618575858825e-08,-1.090582202323275851e-07,-1.362478506052866578e-04,-2.869202875288578980e-09,9.864854177976667415e-08,-9.314462658949196339e-05,-7.239124533953145146e-05,3.248244465794414282e-04,2.916221274062991142e-03,4.665430262684822083e-02,-6.928963214159011841e-02,-1.553085516206920147e-03 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_gru_ih.csv deleted file mode 100644 index e27e7136113f4..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_gru_ih.csv +++ /dev/null @@ -1,96 +0,0 @@ -2.246665502525502234e-07,7.937374313371492462e-09,1.875921213922993047e-07,-5.788109191939838638e-08,-2.136287946541415295e-08,-1.567381531231148983e-07,1.882228133354146848e-07,-2.598505943751661107e-08,-3.420910132945209625e-08,-1.567461680451742723e-07,-1.786740000397912809e-07,-2.247293053869725554e-07,-3.625288869102405442e-08,1.567380394362771767e-07,-7.059875883896893356e-08,-1.627963115424790885e-07,1.566299800970227807e-07 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--1.215396798215806484e-03,5.976485084602245479e-08,1.600115666633428191e-07,1.141837469020856588e-08,1.607542259307592758e-07,9.066708273053336598e-09,5.679105896660985309e-08,1.958212081376586866e-08,-5.138260661396998330e-08,1.638554607552578091e-07,1.604297921176112141e-07,1.590367446624441072e-07,5.910016653842831147e-08,-2.827723299958506686e-08,-3.570564288679634046e-08,-1.010003884971411026e-07,-1.589997964401845820e-07 --6.777899805456399918e-03,-2.999029291572696820e-08,-4.067278780439664843e-08,-4.132837361225938366e-08,1.150064079524781846e-07,-5.908596989456782467e-08,5.002346453730766029e-11,8.926747341320151463e-08,1.633947164236815297e-07,3.595106434772787907e-08,-1.779992153672083077e-09,-4.192965263882797444e-08,-7.076450003751233453e-08,1.377628588983270674e-08,2.150428635161460988e-08,-6.205691249761002837e-08,1.360416046480850127e-08 --4.967793287846689054e-08,2.150854783167233109e-08,4.158178512625454459e-08,-1.634528246086119907e-07,-5.678036885115034238e-08,-5.813501147144961578e-08,5.776328393380936177e-08,-1.566364602467729128e-07,-2.866544868140863400e-08,-1.388251114065042202e-08,-3.617606481043367239e-08,1.567220948572867201e-07,2.928720022055131267e-08,2.929911779858684895e-08,2.864901915700102109e-08,8.405371687558726990e-08,3.625237710025430715e-08 -8.412970231574945501e-08,-1.656615400413841144e-10,1.629579173823003657e-07,5.797545199470732769e-08,-5.806660041685063334e-08,6.418881781655727536e-08,5.807552483361178020e-08,-2.868460313720788690e-08,1.078672440257832932e-07,-1.686742412410069392e-10,-5.781333456411630323e-08,-1.638241542423202191e-07,2.925599318359672907e-08,1.597442036427310086e-07,5.808584191413501685e-08,1.628393988539755810e-07,-6.706456190386234084e-08 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--3.725880980491638184e-01,2.458690744333580369e-07,5.973591044039494591e-08,1.018822288756382477e-07,-1.559959343921946129e-07,-1.629242376566253370e-07,-2.616832794899437431e-08,1.366417734516289784e-07,2.215908807556843385e-07,1.500245332408667309e-07,5.155551718871720368e-08,-1.559374709358962718e-07,-9.040774351376512641e-09,-1.607899235978038632e-07,5.084858401005476480e-08,-1.145307777505877311e-07,-2.697387557759611809e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_only_online.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_only_online.csv deleted file mode 100644 index d1cd6de226089..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_only_online.csv +++ /dev/null @@ -1,36 +0,0 @@ -8.279625326395034790e-03,3.381546586751937866e-03,2.164445631206035614e-02,8.621551096439361572e-03,-3.941265493631362915e-02,3.542314842343330383e-02,-4.984164610505104065e-03,2.074131276458501816e-03,1.995079033076763153e-02,3.991886973381042480e-02,-2.352407574653625488e-02,-2.274756785482168198e-03,1.613918878138065338e-02,-1.945486105978488922e-02,2.679401077330112457e-02,-1.906441152095794678e-02,-1.385917142033576965e-02,2.443172410130500793e-02,-2.863631211221218109e-02,-1.984162628650665283e-02,-2.116794139146804810e-02,-4.520586878061294556e-02,8.199180010706186295e-04,-1.936784014105796814e-02,-7.391568273305892944e-02,7.122860755771398544e-03,2.108660340309143066e-02,2.120924182236194611e-02,-3.055499168112874031e-03,1.603046059608459473e-02,-1.856614463031291962e-02,1.623449660837650299e-02,2.594424784183502197e-02,2.137184143066406250e-02,1.489730551838874817e-02,2.175739407539367676e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_steer_layer_1.csv deleted file mode 100644 index b3a8b6028a2ba..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -5.784385948004455713e-08,4.115653950975683983e-08,-5.804627889460789447e-08,2.864728720908260584e-08,-3.634495371329649060e-08,-6.166982302602264099e-08,-2.448331599680386716e-07,1.567348846265304019e-07,-8.307063836809902568e-08,3.079690391594169796e-10,-1.396914051099429344e-08,-2.550808630985557102e-08,-6.109024042189048487e-08,-1.834535301448170230e-10,-5.804912106555093487e-08,-5.804991687341498618e-08 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_linear_relu.csv @@ -1,16 +1,16 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_hh_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_hh_0.csv index e72b7cca0f098..7537a9ee9c0b9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_hh_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_hh_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_ih_0.csv index a03b7b141eb63..1190c0ccb7332 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_hh.csv index 2852c6e4eff0d..99d0e5eab9e2c 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_hh.csv @@ -1,64 +1,64 @@ 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_ih.csv index 1502ee1d4acf2..1541f6535f3ad 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_lstm_ih.csv @@ -1,64 +1,64 @@ -1.354664843529462814e-02,5.303078889846801758e-02,-5.030304193496704102e-02,3.654263541102409363e-02,3.604745492339134216e-02,5.328396707773208618e-02,5.226656422019004822e-02,4.835975542664527893e-02,5.317721515893936157e-02,4.100683704018592834e-02,1.429468486458063126e-02,5.063319113105535507e-03,1.167685911059379578e-01,-1.364066172391176224e-02,1.782777607440948486e-01,-6.149024516344070435e-02,-2.606809139251708984e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_steer_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ -1.443694811314344406e-03,-2.542864158749580383e-02,4.027282074093818665e-02,-7.667397707700729370e-02,4.094406217336654663e-02,2.776812203228473663e-02,8.039279282093048096e-02,-1.509349465370178223e+00,1.310569643974304199e-01,4.136419668793678284e-02,1.308051943778991699e-01,2.373549044132232666e-01,-4.637687802314758301e-01,-2.480286872014403343e-03,2.196998000144958496e-01,1.672886610031127930e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_steer_layer_1.csv @@ -1,16 +1,16 @@ -1.337991125183179975e-04,1.429468512535095215e+00,-2.060595005750656128e-01,-1.311620473861694336e-01,-9.247932583093643188e-02,-5.962440371513366699e-02,-1.770549826323986053e-02,8.904102258384227753e-03,1.928523369133472443e-02,2.661564759910106659e-02,5.267998669296503067e-03,-3.306114301085472107e-02,-7.490258663892745972e-02,-1.124373152852058411e-01,-1.150781363248825073e-01,-1.032200455665588379e-01,-8.375178277492523193e-02,-6.815285235643386841e-02,-8.122835308313369751e-02,-1.426265239715576172e-01,-2.786263525485992432e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_steer_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_3/weight_steer_layer_2.csv @@ -1,8 +1,8 @@ --5.466060340404510498e-02,-3.131796419620513916e-01,4.896617680788040161e-02,7.235971093177795410e-01,-2.694058604538440704e-02,-4.396100994199514389e-03,-3.990970551967620850e-02,1.204489588737487793e+00,1.400408148765563965e-01,-2.412422001361846924e-02,4.019365608692169189e-01,4.560724198818206787e-01,-7.040619254112243652e-01,6.191220879554748535e-01,2.303922623395919800e-01,-8.090059161186218262e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --5.687703490257263184e-01 -1.572953760623931885e-01 -5.714529380202293396e-02 -1.085763052105903625e-01 -9.483638405799865723e-02 -1.503358874469995499e-02 --6.868886295706033707e-03 --4.978453516960144043e-01 -1.244671717286109924e-01 -1.060194298624992371e-01 -1.586544699966907501e-02 -5.790882557630538940e-02 -8.724521845579147339e-02 -8.154500126838684082e-01 -3.643409162759780884e-02 -1.652323454618453979e-01 +9.447958320379257202e-02 +-3.904920443892478943e-02 +-4.699527025222778320e-01 +1.458566039800643921e-01 +-3.368199765682220459e-01 +-3.077215850353240967e-01 +-7.432874292135238647e-02 +-3.889969587326049805e-01 +7.842382043600082397e-02 +-1.985292583703994751e-01 +7.554553151130676270e-01 +-4.822994768619537354e-02 +-4.119287729263305664e-01 +2.234724313020706177e-01 +5.568625032901763916e-02 +9.671768546104431152e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_acc_encoder_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_acc_encoder_layer_2.csv index 2321f21b9d8a8..6bc053b95a062 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -8.771612308919429779e-03 -1.144464537501335144e-01 -6.942238658666610718e-02 -6.198814511299133301e-02 --2.246745675802230835e-02 --7.646840810775756836e-02 -1.091548949480056763e-01 -2.548992633819580078e-01 +2.911694943904876709e-01 +1.167204603552818298e-01 +1.205120310187339783e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.024103090167045593e-01 -1.508852094411849976e-01 -9.780577383935451508e-03 -4.319138452410697937e-02 -9.238595515489578247e-02 -1.059526875615119934e-01 -9.110169857740402222e-02 -1.054998338222503662e-01 +5.389664322137832642e-02 +3.577540814876556396e-02 +3.646123688668012619e-03 +4.657787457108497620e-02 +4.009292274713516235e-02 +-1.234907656908035278e-01 +-9.528395719826221466e-03 +8.088576793670654297e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_complimentary_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_complimentary_layer.csv index bb1b5346932b2..a9dc3efd0edaf 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_complimentary_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_complimentary_layer.csv @@ -1,64 +1,64 @@ -1.511160749942064285e-02 -1.398833189159631729e-02 -1.167307570576667786e-01 -4.545302689075469971e-02 -1.077463570982217789e-02 -1.412830781191587448e-03 -2.155422978103160858e-02 -9.623280493542551994e-04 -4.561380296945571899e-02 -1.059380993247032166e-01 --9.806581586599349976e-02 --4.837276786565780640e-02 -3.910212963819503784e-02 --4.298238456249237061e-02 -7.735941559076309204e-02 --2.475859364494681358e-03 -2.536228299140930176e-02 --6.351507455110549927e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_final_layer.csv index 7f69ecaf304d4..e312c241bbb6e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_final_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_final_layer.csv @@ -1,4 +1,4 @@ --1.411965698935091496e-03 -9.323665290139615536e-04 --3.581988811492919922e-02 -5.641778465360403061e-03 +-2.463300246745347977e-03 +-6.471379892900586128e-04 +-6.751611828804016113e-02 +-5.644071847200393677e-03 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_acc_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 6a4f3acd10344..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.700384832936663315e-10 -5.574749906145370915e-08 --1.639053834878723137e-07 -2.375204388727070182e-07 -5.790519352899536898e-08 -5.913284439884591848e-08 --1.567347993614021107e-07 --3.626800904044102936e-08 -2.247556807333239703e-07 -2.873030524597197655e-08 -5.794816004822678224e-08 --4.090935590284061618e-08 --2.924792141811849433e-08 --2.869897031132495613e-08 --2.662483211679500528e-08 --1.567348562048209715e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_acc_layer_2.csv deleted file mode 100644 index 62f75cb43506c..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --8.786349070533105987e-08 --2.436062018773554883e-08 --7.991299355580849806e-08 --1.391345278989319922e-07 --3.481732591126274201e-08 --2.435033650272089290e-07 --6.396118124030181207e-08 --2.479723377746267943e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_final_layer.csv deleted file mode 100644 index b082a7a89c611..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_final_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.333507336676120758e-02 -6.083920970559120178e-02 --1.834504422731697559e-03 --3.571275621652603149e-02 -2.946369349956512451e-01 --1.427265582606196404e-03 --3.688247501850128174e-02 -6.005700305104255676e-02 --1.222376897931098938e-02 --3.048365712165832520e-01 --4.184846207499504089e-02 -1.948376744985580444e-02 -4.896391183137893677e-02 -3.152998397126793861e-03 --2.637754976749420166e-01 -1.586769241839647293e-03 --6.010877341032028198e-02 --2.591094970703125000e-01 -2.363688647747039795e-01 -6.578605622053146362e-03 -7.574999239295721054e-03 -2.695792615413665771e-01 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--7.104340195655822754e-02 --2.075286209583282471e-02 -1.169870570302009583e-01 --2.211910635232925415e-01 --8.285447955131530762e-02 --6.559275090694427490e-03 -2.289427518844604492e-01 --5.724086612462997437e-02 --2.474612742662429810e-01 -6.690966337919235229e-02 --3.198300674557685852e-02 -2.926952540874481201e-01 --3.303932026028633118e-02 --1.297835707664489746e-01 --1.295106951147317886e-02 -5.919522419571876526e-02 -5.894464626908302307e-02 -3.509227558970451355e-02 --2.393457889556884766e-01 --2.499583642929792404e-03 --3.512866795063018799e-03 --1.666042804718017578e-01 --1.854570060968399048e-01 -8.786503970623016357e-02 --9.355223178863525391e-02 -1.098060086369514465e-01 --1.124663949012756348e-01 --2.270619757473468781e-02 --4.248482640832662582e-03 -4.918081313371658325e-02 -2.424361556768417358e-02 --1.200262010097503662e-01 -2.896825373172760010e-01 --4.990265145897865295e-02 --5.451077967882156372e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_gru_hh.csv deleted file mode 100644 index 8057bd867e52d..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ -1.545770733635265515e-10 --8.302931320258721826e-08 --7.699894943868912378e-09 --2.921688491142049315e-08 -1.020158979514462771e-08 --1.473583746701478958e-03 --7.846460212022066116e-04 --1.084701466425030958e-07 --5.566524663436211995e-08 --1.647779015456762863e-07 -2.786956443401322758e-08 -1.140102057206604513e-07 -1.460361964689127490e-08 --1.444058312927154475e-07 -8.503373294388438808e-08 --1.640689930582084344e-07 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-2.848630538210272789e-04 -1.822299510240554810e-01 -4.630837123841047287e-03 --1.818964928388595581e-01 -2.066866159439086914e-01 --2.953495271503925323e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_gru_ih.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_gru_ih.csv deleted file mode 100644 index 9416f9f158008..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_gru_ih.csv +++ /dev/null @@ -1,96 +0,0 @@ --5.804929159580751730e-08 --8.302931320258721826e-08 --1.416650974306321586e-07 --3.625823197239697038e-08 --7.970431425974311423e-08 --1.473613083362579346e-03 --7.847624947316944599e-04 -5.631300581399045768e-08 --5.566524663436211995e-08 -5.795373780870249902e-08 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--1.957485125103630708e-07 --1.044421349405411092e-08 --4.099469631910324097e-02 --5.203025565947427822e-09 -4.812124032582687505e-08 --2.186568221077322960e-03 -6.986832013353705406e-04 -2.696507871150970459e-01 -5.738455429673194885e-02 --3.137570619583129883e-01 -2.364166826009750366e-01 --2.259131222963333130e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_only_online.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_only_online.csv deleted file mode 100644 index 0418c7e532bb9..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_only_online.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.383439749479293823e-01 --2.788069546222686768e-01 -1.486504375934600830e-01 -2.100807130336761475e-01 --3.237645328044891357e-01 --2.319215536117553711e-01 -1.512393504381179810e-01 -2.785775065422058105e-01 --1.205068901181221008e-01 --5.426710471510887146e-02 -1.322433911263942719e-02 --3.126586675643920898e-01 -2.621484696865081787e-01 -1.820376664400100708e-01 --1.614455431699752808e-01 --2.637141570448875427e-02 -6.637267023324966431e-02 -1.449829190969467163e-01 -5.794738978147506714e-02 --2.220921069383621216e-01 --3.295454382896423340e-02 --1.984102465212345123e-02 --1.371687352657318115e-01 --2.934356629848480225e-01 --5.533078685402870178e-02 --1.244134381413459778e-01 -7.511977106332778931e-02 -7.495564967393875122e-02 --9.701989591121673584e-02 --9.739152342081069946e-02 -7.872800924815237522e-04 -1.701286584138870239e-01 --8.328914642333984375e-03 -1.629772633314132690e-01 --1.352069899439811707e-02 -7.753793150186538696e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_steer_layer_1.csv deleted file mode 100644 index 614901c941b0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.602122381427761866e-07 -1.594512042402129737e-07 --1.383567216350911622e-08 -2.870719839620505809e-08 --4.115875640309241135e-08 --1.629571784178551752e-07 -6.326867207917530322e-08 -6.130223795253186836e-08 -2.572974278081119337e-09 --1.623540981654514326e-07 --3.618814403694159409e-08 --5.186216256447551132e-08 -1.592173859421563975e-08 -2.550376621002214961e-08 --5.809684111568458320e-08 --6.100456317881253199e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_steer_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_steer_layer_2.csv deleted file mode 100644 index fc1f85f18f28b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --1.495364330139636877e-07 --9.433398506786261350e-10 --9.447958149166879593e-08 -6.568057209221933590e-09 --2.248099519874813268e-07 --1.070967527994071133e-07 --9.932387712296986138e-08 --7.939953405866617686e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_linear_relu.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_linear_relu.csv index 6c7ba3b020e52..b40d15d15387e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_linear_relu.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_linear_relu.csv @@ -1,16 +1,16 @@ --3.609813749790191650e-02 --3.190746530890464783e-02 -4.359308257699012756e-02 -8.269068202935159206e-04 -4.143001139163970947e-02 -7.497583515942096710e-03 -7.427339255809783936e-02 --2.295951917767524719e-02 --2.274018079042434692e-01 -8.252906054258346558e-02 -7.786637544631958008e-02 -1.048227921128273010e-01 --4.350556991994380951e-04 --6.249625235795974731e-02 -1.322524547576904297e-01 --3.382597863674163818e-02 +-2.442883513867855072e-02 +5.057684332132339478e-02 +3.201630339026451111e-02 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-1,72 +1,72 @@ --2.799303233623504639e-01 -8.258733898401260376e-02 -5.214651674032211304e-02 --2.117088623344898224e-02 --9.740439057350158691e-02 --1.514682471752166748e-01 --1.841652244329452515e-01 --1.662612110376358032e-01 --2.394026368856430054e-01 -6.085600331425666809e-02 -1.038094758987426758e-01 -1.195812672376632690e-01 --1.226203665137290955e-01 --1.429048180580139160e-01 -6.019178405404090881e-02 --1.727182716131210327e-01 --2.290469259023666382e-01 --3.691007494926452637e-01 -4.015429019927978516e-01 -1.827716976404190063e-01 -1.926851719617843628e-01 -1.571784615516662598e-01 -1.860545575618743896e-01 -2.430029809474945068e-01 -4.373685643076896667e-02 -1.225811541080474854e-01 --1.930130422115325928e-01 --9.093154966831207275e-02 -2.350523173809051514e-01 -5.819558352231979370e-02 -1.559833139181137085e-01 -2.092577219009399414e-01 -2.149106264114379883e-01 -2.697693407535552979e-01 -2.954715490341186523e-01 -6.605675816535949707e-01 --9.321784228086471558e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_encoder_ih_0.csv index bd71a79542f43..2a250786995d9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ --2.799263000488281250e-01 -8.330401778221130371e-02 -5.212230980396270752e-02 --2.048002928495407104e-02 --9.690331667661666870e-02 --1.525534093379974365e-01 --1.841630488634109497e-01 --1.662613004446029663e-01 --2.396262139081954956e-01 -6.080040335655212402e-02 -1.034230291843414307e-01 -1.198021844029426575e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_hh.csv @@ -1,64 +1,64 @@ -1.863418668508529663e-01 --1.841402947902679443e-01 -4.871492087841033936e-02 -6.630924344062805176e-01 -3.120891004800796509e-02 -5.130470544099807739e-02 -3.066616505384445190e-02 -3.965241834521293640e-02 -3.464382141828536987e-02 --1.669278144836425781e-01 --9.552434831857681274e-02 --2.999459765851497650e-02 --6.102378945797681808e-03 -1.027578711509704590e-01 -1.095576584339141846e-01 -1.344114840030670166e-01 -2.494055032730102539e-01 -1.547644734382629395e-01 -1.317952722311019897e-01 --2.324226498603820801e-01 --2.754482328891754150e-01 -1.100205332040786743e-01 --1.426737289875745773e-03 --3.385674655437469482e-01 -1.660625338554382324e-01 -5.306183919310569763e-02 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031398bdfd2a3..8511ea9e3ab32 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_lstm_ih.csv @@ -1,64 +1,64 @@ -1.862874925136566162e-01 --1.859310567378997803e-01 -4.900135472416877747e-02 -6.631961464881896973e-01 -3.110241889953613281e-02 -5.119629949331283569e-02 -2.894839085638523102e-02 -3.989761322736740112e-02 -3.549076244235038757e-02 --1.642386913299560547e-01 --9.745972603559494019e-02 --3.339832648634910583e-02 --2.647247863933444023e-03 -9.829391539096832275e-02 -1.122935935854911804e-01 -1.338122785091400146e-01 -2.497808933258056641e-01 -1.542556881904602051e-01 -1.318080276250839233e-01 --2.323063462972640991e-01 --2.763138115406036377e-01 -1.098379045724868774e-01 --1.424718881025910378e-03 --3.388048410415649414e-01 -1.662753224372863770e-01 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-1.202719435095787048e-01 +-9.739692509174346924e-02 +2.492910809814929962e-02 +-1.838845312595367432e-01 +-2.135178744792938232e-01 +-2.200213447213172913e-02 +-1.861061155796051025e-01 +1.671033054590225220e-01 +9.593650698661804199e-02 +3.913679718971252441e-02 +3.075380437076091766e-02 +8.047340437769889832e-03 +-2.656531631946563721e-01 +1.129456683993339539e-01 +-3.222428858280181885e-01 +1.091994494199752808e-01 +9.096560627222061157e-02 +2.707954943180084229e-01 +2.616540491580963135e-01 +1.402111947536468506e-01 +7.889152318239212036e-02 +-3.627977669239044189e-01 +1.870117783546447754e-01 +-6.229979172348976135e-02 +3.609412349760532379e-03 +1.729766875505447388e-01 +-8.216486126184463501e-02 +4.784847795963287354e-02 +2.171274870634078979e-01 +-2.092107012867927551e-02 +2.288868129253387451e-01 +1.596487313508987427e-01 +1.025586947798728943e-01 +-4.004389420151710510e-02 +-1.315923184156417847e-01 +9.082223474979400635e-02 +-5.278126150369644165e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_encoder_layer_1.csv index 707a956dc7533..ca1b020a82ede 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_encoder_layer_1.csv @@ -1,16 +1,16 @@ -2.288375049829483032e-02 -4.156160056591033936e-01 -1.447749207727611065e-03 --1.900681381812319160e-04 -3.408987540751695633e-03 -3.183485008776187897e-03 -4.051115512847900391e-01 --1.631803363561630249e-01 -2.039906978607177734e-01 -3.064470831304788589e-03 -2.237636968493461609e-02 -1.526999026536941528e-01 -1.990733891725540161e-01 --1.465275436639785767e-01 --4.737891629338264465e-02 -1.408819258213043213e-01 +-2.332334361199173145e-06 +1.387599296867847443e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ --4.465385898947715759e-02 --2.514035440981388092e-02 --4.148573800921440125e-02 -1.449349820613861084e-01 -9.823747910559177399e-03 --1.016079541295766830e-02 --1.387605676427483559e-03 --8.763826638460159302e-02 +2.186003774404525757e-01 +-9.245735406875610352e-02 +-2.528463006019592285e-01 +-7.972517609596252441e-02 +6.500462442636489868e-02 +2.470089346170425415e-01 +2.000899612903594971e-02 +1.971768885850906372e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_1.csv index d8e5af208e79c..dc762d230ac56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_1.csv @@ -1,16 +1,16 @@ --5.880776420235633850e-02 -1.072910651564598083e-01 -1.927932947874069214e-01 -1.416619420051574707e-01 -3.611433552578091621e-03 --2.359812110662460327e-01 -1.126473490148782730e-02 --1.914175972342491150e-02 -1.322983503341674805e-01 --9.606996551156044006e-03 --4.914920404553413391e-02 -5.091565847396850586e-02 -2.208182960748672485e-02 -4.450881108641624451e-02 -5.511307157576084137e-03 -9.871846064925193787e-03 +-1.267773564904928207e-03 +1.681745648384094238e-01 +3.152760118246078491e-02 +-3.042486310005187988e-02 +2.783328667283058167e-02 +1.784413307905197144e-01 +3.829433768987655640e-02 +-1.098260059952735901e-01 +8.191139250993728638e-02 +4.840236902236938477e-02 +7.292035222053527832e-02 +1.257531065493822098e-02 +1.193764209747314453e-01 +1.460036914795637131e-02 +1.528179645538330078e-01 +4.296629875898361206e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_2.csv index 987b36427e772..7c8e42e5dc1dc 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/bias_steer_layer_2.csv @@ -1,8 +1,8 @@ -1.204485148191452026e-01 -5.225432664155960083e-02 --3.464022651314735413e-03 -1.128473356366157532e-01 -1.898873150348663330e-01 -1.061795502901077271e-01 -6.685238331556320190e-02 -1.211080327630043030e-01 +-2.150738146156072617e-03 +4.411975294351577759e-02 +1.444365680217742920e-01 +5.844632163643836975e-02 +6.778846494853496552e-03 +5.850554630160331726e-02 +2.264000841023516841e-06 +1.272178292274475098e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/offline_features.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/offline_features.csv index 1bcdd512dea6f..fa6d9d8875c56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/offline_features.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/offline_features.csv @@ -1,36 +1,36 @@ --1.365479675996539299e-07 -4.500528660855707130e-08 --2.888707228976272745e-07 --3.052138808357085509e-08 --3.403145854008471360e-08 --1.366562401017290540e-07 -9.226326369571324904e-09 --1.461385323864305974e-07 -2.401009169261669740e-07 -3.847664586942300957e-08 --4.707507983425784914e-08 -4.440217082901654067e-09 --3.469495624131013756e-08 -3.053523656149081944e-08 -4.850487655971846834e-08 --4.678990350726053293e-08 --1.465268173461709011e-08 --3.929526570800589980e-08 --1.382004199967923341e-07 --3.059484043888005544e-08 -1.119208832278673071e-07 -4.225704230975679820e-09 --1.164682288390395115e-07 --1.629832553362575709e-07 -3.149804683744150680e-08 --1.490812984172862343e-08 -5.521310342260221660e-08 --6.760313198128642398e-08 -1.837763718981477723e-08 --1.734968861910601845e-07 -1.474870856554844067e-07 --1.383132399723763228e-07 -3.487329980345066360e-09 -8.957044173030226375e-08 -2.936226373151384905e-08 --1.337812438606533760e-08 +5.170850083231925964e-02 +-2.384004145860671997e-01 +2.417143583297729492e-01 +3.888960182666778564e-01 +-2.718242108821868896e-01 +-5.412524193525314331e-02 +1.712931394577026367e-01 +1.944515109062194824e-01 +-1.523365378379821777e-01 +1.768556088209152222e-01 +-3.439860641956329346e-01 +4.308066964149475098e-01 +2.342039048671722412e-01 +2.263623923063278198e-01 +8.685183525085449219e-02 +-3.498302400112152100e-02 +7.183667272329330444e-02 +-1.743947416543960571e-01 +6.060919165611267090e-02 +-7.453544437885284424e-02 +3.309508860111236572e-01 +1.058077290654182434e-01 +-9.985291212797164917e-02 +2.645366787910461426e-01 +6.488673090934753418e-01 +1.053337156772613525e-01 +-1.421345621347427368e-01 +-4.722569137811660767e-02 +-3.759335875511169434e-01 +-1.368334740400314331e-01 +2.373346388339996338e-01 +2.330867946147918701e-02 +-7.088837027549743652e-02 +-4.330652952194213867e-01 +5.272904038429260254e-01 +-8.399436622858047485e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_encoder_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_encoder_layer_1.csv index 5eb1c7c33213a..09a6b18c285fa 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --6.779996305704116821e-02,3.508124351501464844e-01,2.439208775758743286e-01,2.227861583232879639e-01,2.015045583248138428e-01,1.802337467670440674e-01,1.594138443470001221e-01,1.380567848682403564e-01,1.166481673717498779e-01,9.721208363771438599e-02,7.622497528791427612e-02,5.588675662875175476e-02,3.574046865105628967e-02,1.916410401463508606e-02,1.854662410914897919e-02,1.806927099823951721e-02,1.663219928741455078e-02,1.390712894499301910e-02,8.530278690159320831e-03,-9.777169907465577126e-04 --1.337423324584960938e-01,-8.315050601959228516e-02,-2.004925161600112915e-01,-1.865487545728683472e-01,-1.720552295446395874e-01,-1.579438298940658569e-01,-1.453569382429122925e-01,-1.358291804790496826e-01,-1.300119906663894653e-01,-1.207905784249305725e-01,-1.113225594162940979e-01,-9.820590913295745850e-02,-8.600991219282150269e-02,-7.682002335786819458e-02,-6.521806120872497559e-02,-5.824101716279983521e-02,-4.736216366291046143e-02,-3.631968051195144653e-02,-2.911020442843437195e-02,-2.347799576818943024e-02 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--1.621379517018795013e-02,6.511135725304484367e-04,-3.536446020007133484e-02,-3.627477958798408508e-02,-3.721936792135238647e-02,-3.808093816041946411e-02,-3.901234269142150879e-02,-3.985518589615821838e-02,-4.090359061956405640e-02,-4.165715724229812622e-02,-4.242743179202079773e-02,-4.329784959554672241e-02,-4.404501989483833313e-02,-4.490786790847778320e-02,-4.554320126771926880e-02,-4.622422531247138977e-02,-4.683774337172508240e-02,-4.749411344528198242e-02,-4.818574711680412292e-02,-4.867927357554435730e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -3.336199373006820679e-02,2.459247857332229614e-01,-6.924922019243240356e-02,-3.334385901689529419e-02,2.794704735279083252e-01,-1.098308246582746506e-02,-3.318902477622032166e-02,-2.375750094652175903e-01,-6.978246849030256271e-03,-7.694390369579195976e-04,3.750725463032722473e-02,-1.894451864063739777e-02,-1.075058132410049438e-01,1.155056133866310120e-01,-3.716661781072616577e-02,4.225961863994598389e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.827372312545776367e-01,1.442782729864120483e-01,1.350615918636322021e-01,1.406146585941314697e-01,7.798003405332565308e-02,1.429848670959472656e-01,1.125954836606979370e-01,5.638980120420455933e-02,8.144410699605941772e-02,-5.328862462192773819e-03,-2.268474362790584564e-02,4.206001386046409607e-02,1.457983851432800293e-01,-5.146942660212516785e-02,-6.503234058618545532e-02,-3.310494497418403625e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_final_layer.csv @@ -1,4 +1,4 @@ --4.076786339282989502e-03,-1.227277144789695740e-02,3.244567289948463440e-02,1.793841016478836536e-03,4.501522984355688095e-03,-2.283791452646255493e-02,-1.217357907444238663e-02,-2.098338445648550987e-03,-2.394610270857810974e-02,3.298125788569450378e-02,3.658420871943235397e-03,3.390965238213539124e-02,-1.559238694608211517e-02,-1.272494345903396606e-02,1.682596048340201378e-03,-1.867336407303810120e-02 --1.042943261563777924e-02,-4.991219844669103622e-03,1.183264539577066898e-03,5.007501225918531418e-03,-6.344680878100916743e-05,2.041139872744679451e-03,7.769418880343437195e-03,-3.926411271095275879e-03,-1.374095008941367269e-04,-7.521932013332843781e-03,2.816032152622938156e-03,5.137700587511062622e-03,-1.068992633372545242e-02,-1.806749030947685242e-03,1.107970019802451134e-03,2.244758186861872673e-03 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 09b35a7fc4e0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --5.795665813934647304e-08,2.448358884521439904e-07,-3.380812785280795651e-08,-2.136942289787668869e-10,4.084210303290092270e-08,5.576080042146713822e-08,1.567351972653341363e-07,3.467380693678023817e-08,6.475600144995041774e-08,7.276236857478579623e-08,-1.776304969780539977e-07,3.134217507749781362e-08,1.528735111833157134e-07,1.598162242544276523e-07,4.906196338083645969e-08,5.687583737312706944e-08,2.663517761902767234e-08,-1.778388565298882895e-07,9.540057988033368019e-08,-1.728522325272763283e-10 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--2.572746904405676105e-09,1.086709744413383305e-07,1.400614557667267945e-08,7.936692192345162766e-09,4.102665940308725112e-08,-8.246642124731806689e-08,5.573721040263990290e-08,6.818912368089513620e-08,4.132279229906998808e-08,-1.598831147475721082e-07,2.749714345107179270e-10,-4.084756000111156027e-08,2.869085591328257578e-08,1.387898684868105192e-08,-6.484240344661884592e-08,-6.196152213533423492e-08,-5.634582800340126596e-10,-1.612109485904511530e-07,2.844504010113269032e-08,2.246597858857057872e-07 --2.837610324490924540e-08,-1.612319806554296520e-07,6.102120408968403353e-08,2.872587145930083352e-08,-2.865953518949027057e-08,2.850439884127808909e-08,1.778415992248483235e-07,2.926793030155749875e-08,-1.032509047149687831e-07,1.884557510045681283e-10,2.864214287967570272e-08,-4.828538990864217340e-08,1.726883596120387665e-07,-6.240391314804583089e-08,5.785573975458646601e-08,1.387097192662167799e-08,-1.379480174534819525e-08,2.865901116422264749e-08,-2.918067565360615845e-08,-2.918391572848122451e-08 --2.864954673498232296e-08,-1.368725399686354649e-08,1.085663257072155830e-07,-2.883944461018472794e-08,1.085741132555995136e-07,-5.121204438296445005e-08,6.429602450452875928e-08,5.809104308696078078e-08,-4.156912325470329961e-08,-3.439327400656111422e-08,-5.183908413641802326e-08,1.530865603172060219e-07,8.827194619698275346e-08,-5.001684755256974313e-08,1.873391681783687091e-07,1.567354246390095796e-07,5.798773017318126222e-08,1.020795323825041123e-07,1.085405756384716369e-07,1.529302977587576606e-07 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-2.866891612995914329e-08,-2.193380339576833649e-07,-1.533985454216235667e-07,-1.635322632864699699e-07,2.861279924104564998e-08,2.814341470980252780e-08,-4.094303562851564493e-08,1.627933556846983265e-07,7.935441637130224990e-09,-2.871069426646499778e-08,1.594537053506428492e-07,-5.806688463394493738e-08,-1.085669509848230518e-07,-1.718574726972121880e-10,-1.398409033015468594e-08,-3.620758448619199044e-08,-1.398062288160417665e-08,6.922468998027397902e-08,-3.616870358769119775e-08,1.628658310437458567e-07 --2.852652158935597981e-08,-9.824449875850405078e-08,-1.754762308792123804e-07,5.789251744658940879e-08,4.112624196750402916e-08,5.801046754072558542e-08,6.086136750127479900e-08,-2.864963200011061417e-08,-1.392582582582235773e-08,1.740867645594335045e-07,-1.605755812761344714e-07,-3.050213948085911397e-08,-2.212133978218844277e-07,1.637957893763086759e-07,1.566371992112181033e-07,1.566785812201487715e-07,1.622258025690825889e-07,5.108397616027104959e-08,1.567347993614021107e-07,2.550069666540366597e-08 -1.628367272132891230e-07,1.778454077339119976e-07,2.857147407553384255e-08,-5.112677925467323803e-08,6.183482526012085145e-08,1.639484707993688062e-07,-1.148689463548180356e-07,-1.397482485288037424e-08,-4.146149024109035963e-08,-5.801484448397786764e-08,-1.394867688020440255e-08,-1.758933554363295571e-10,4.105030626533334726e-08,-1.391860671162703511e-08,3.104235091200280294e-08,-1.567348562048209715e-07,1.909966768076643007e-10,5.804411884469118377e-08,-4.906690875827734999e-08,6.098650118246951024e-08 --3.479204124801071885e-08,-6.744752312215496204e-08,-6.184050960200693225e-08,2.946307043824591965e-12,-6.112707495731228846e-08,1.314774333849300092e-08,-9.540066514546197141e-08,-5.567803640360580175e-08,1.567590146578368149e-07,-6.107648431452616933e-08,1.636276465433184057e-07,2.845211288793336735e-10,-2.862939751935300592e-08,2.852742042591671634e-08,-4.092018457413360011e-08,-4.101761419406102505e-08,1.497509884984538076e-07,1.882291797983270953e-07,1.623722880594868911e-07,6.816661368702625623e-08 --1.567573093552709906e-07,5.190680241184963961e-08,-4.090967564707170823e-08,-2.676181409810851619e-08,1.796450210811428860e-10,-2.870267934440562385e-08,1.629214239073917270e-07,1.625622587653197115e-07,5.780725231829819677e-08,-6.437543476067730808e-08,1.078759979122878576e-07,-5.816283632498198131e-08,-2.863949966069867514e-08,1.598080103804022656e-07,1.085153371604974382e-07,-3.626300681958127825e-08,2.851320957120151434e-08,-7.937203783114910038e-09,-1.778377480832205038e-07,1.665710347431570426e-10 -1.085244889509340283e-07,-5.769888034024006629e-08,2.921932917843150790e-08,6.484848569243695238e-08,-3.524516856145964994e-08,-8.309299204256603844e-08,-2.873326110375273856e-08,-2.872246085416918504e-08,-1.632824933039955795e-07,1.882410032294501434e-07,-1.628350219107232988e-07,2.968561507721290127e-10,-1.778498273097284255e-07,-2.405811660999290780e-10,1.392855430992767651e-08,5.112075385227399238e-08,5.776922407108031621e-08,2.871507120971728000e-08,-4.094582095603982452e-08,-1.599121617346099811e-07 --2.448340410410310142e-07,1.380423775287908938e-08,1.561118456727683679e-10,-5.812466596921694872e-08,-3.100375423059631430e-08,-1.527012405411198870e-10,-2.919994557259997237e-08,-1.069702619815871003e-07,-1.525875537033982710e-10,2.856971192954915750e-08,-3.033363005222611264e-10,-8.849530530596894096e-08,1.624740946226665983e-07,-1.728069349837824120e-07,1.750106615183710801e-09,5.574175787614876754e-08,-1.683244903460945352e-07,-1.400569082932179299e-08,2.154006040200329153e-08,-2.080925725067572785e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_acc_layer_2.csv deleted file mode 100644 index b48ef5ed5516a..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -2.044039248971785128e-08,2.853378688882912684e-08,1.634649606785387732e-07,-2.863657222462734353e-08,1.586338811421228456e-07,-3.615636856579840241e-08,4.099652528566366527e-08,-2.866763537667793571e-08,-1.085351186702609994e-07,-5.566445082649806864e-08,-1.587467721719804103e-07,1.566090617188820033e-07,-2.175595814613018320e-10,-1.384351655531190772e-08,-2.042933644474942412e-08,4.782882356835216342e-08 --1.390252002408942644e-08,1.070218473842032836e-07,1.729425065377654391e-07,-1.391764037350640137e-08,2.916138086561659293e-08,1.398903570759557624e-08,-1.225584167485749276e-08,3.631698675121697306e-08,3.613908816646471678e-08,8.851120725239525200e-08,1.629533130653726403e-07,6.196007262815328431e-08,-6.416238562678699964e-08,9.956426083590486087e-08,-1.882394116137220408e-07,5.807234160215557495e-08 -1.611729487649427028e-07,8.238533411031312426e-08,1.566412919373760815e-07,-2.847779612125123094e-08,1.286467599115326266e-09,-3.167127715641981922e-08,7.091126263958358322e-08,3.091757960760332935e-08,5.679489234466927883e-08,-1.370385227517090243e-08,1.410169647719783370e-09,2.874138971264983411e-08,-1.638500179979018867e-07,5.794003854475704429e-08,-7.060137363623653073e-08,1.622957483959908132e-07 --1.623467937861278187e-07,-2.631918505358044058e-08,-2.869449566844650690e-08,-1.628427526156883687e-07,5.188224605490177055e-08,4.915464657528900716e-08,2.867121651206616662e-08,-1.385855163960059144e-08,6.481472070163363242e-08,2.381937425077751413e-10,-1.085387779653501639e-07,2.071498172995234199e-08,-1.882856821566747385e-07,2.868025461566503509e-08,3.587895491818926530e-08,8.309746846180132707e-08 -1.505982822891382966e-07,-5.784522372209721652e-08,-5.816843540173977090e-08,1.224811096989242287e-08,3.626647426813178754e-08,1.403778693287449642e-08,-2.715608293790694461e-10,9.944508860826317687e-08,-2.869294490892571048e-08,-5.776183442662841117e-08,1.882373652506430517e-07,-2.256508082609798294e-07,-1.396278825893659814e-08,-4.146762933032732690e-08,-1.403679128486601257e-08,1.527910313825486810e-07 --2.855190217587733059e-08,3.617978805436905532e-08,-3.458159625324697117e-08,2.865908221849622350e-08,-4.992652335999991919e-08,5.798099422804625647e-08,-2.860584658037623740e-08,1.397380167134087969e-08,1.381299163938365382e-08,-1.366114865675172041e-08,1.396491278171652084e-08,3.088958422381438140e-08,1.740023520824252046e-07,-5.191849439256657206e-08,1.746996436402525887e-10,2.868677917433615221e-08 -2.865751902447755128e-08,1.385340731019368832e-08,1.376086622428829287e-08,-3.647739887924217328e-08,6.554007825343433069e-08,-2.851616542898227635e-08,2.849456492981516931e-08,5.284764270641062467e-08,-2.867079018642471056e-08,1.390746540153031674e-08,-5.188355345353556913e-08,-1.033560366181518475e-07,2.849769131785251375e-08,6.481233327804147848e-08,-2.716176727979302541e-10,2.246580805831399630e-07 -2.922092079415961052e-08,5.798915125865278242e-08,1.628198447178874630e-07,-5.790837676045157423e-08,1.587742275432901806e-07,6.101299021565864678e-08,2.829456136055341631e-08,3.612838384015049087e-08,-1.601636370196501957e-07,1.632550379326858092e-07,-3.074633880828514521e-08,8.405383056242499151e-08,-2.257046105569315841e-08,2.694796563673662604e-08,-7.445206762213274487e-08,3.401805059866092051e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_final_layer.csv deleted file mode 100644 index 9ffddc1398427..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_final_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ --2.175820981165088597e-07,-3.858937702716502827e-08,3.601965680718421936e-02,-6.376685490749878227e-08,9.113346459344029427e-05,-4.426776245236396790e-02,9.833846986293792725e-02,6.004661372571717948e-06,-2.240297192201978760e-07,1.702363405797768792e-08,-1.858604885637760162e-02,2.592217606434132904e-05,3.761655837297439575e-02,-3.518993407487869263e-02,4.833122715353965759e-03,-3.485617041587829590e-02,-2.300172781133369426e-07,-1.092737207386562659e-07,-8.071739971637725830e-02,3.225584700703620911e-02,5.831549643176003883e-08,-1.515475673841137905e-07,-3.666227310895919800e-02,2.319397651717736153e-07,-3.001941806246577471e-08,1.168520157079910859e-05,-2.192070242017507553e-04,6.062252447009086609e-02,-1.971509680151939392e-02,-9.955786168575286865e-02,7.815898209810256958e-02,3.227722644805908203e-02 -5.460777430243979325e-08,-5.533593849804674392e-08,3.333770669996738434e-03,7.166487847598546068e-08,-1.391711295582354069e-03,1.061583636328577995e-03,4.071273375302553177e-03,-1.348172547295689583e-03,4.719174384604229999e-08,7.151373893066192977e-08,9.328410029411315918e-02,-1.313446206040680408e-03,-2.303715329617261887e-03,-1.893333392217755318e-03,-7.762409746646881104e-03,3.686914220452308655e-02,-9.796949029805546161e-08,1.118454093784748693e-07,1.411804370582103729e-02,-4.080725833773612976e-02,4.410194165416214673e-08,-1.398187947643236839e-07,-6.481568561866879463e-04,-1.130788707115470970e-08,8.558367881050799042e-08,-1.345351454801857471e-03,-1.735795871354639530e-03,3.771660849452018738e-02,9.104543924331665039e-02,-2.742536924779415131e-02,5.422115698456764221e-02,-4.115459695458412170e-02 --2.242246210926168715e-07,-2.052982779332523933e-07,1.384356711059808731e-02,5.319526508174021728e-08,-1.267372499569319189e-05,-2.362287975847721100e-02,9.113749861717224121e-02,-2.884633722715079784e-04,3.039113138925131352e-08,-7.277756708390370477e-08,-2.299219369888305664e-03,-3.105224168393760920e-04,3.972678910940885544e-03,-4.982576239854097366e-03,-2.103751059621572495e-03,-1.006751414388418198e-02,1.073849773547408404e-07,1.553895287997875130e-07,-7.887374609708786011e-02,6.625558249652385712e-03,1.613452838000739575e-07,-3.843430018690696670e-09,-5.069656763225793839e-03,2.055476215900853276e-07,1.442622732383824768e-07,-3.051489184144884348e-04,-4.775732813868671656e-04,8.377529680728912354e-02,-2.330561168491840363e-03,-9.997411817312240601e-02,1.111759990453720093e-01,6.600412540137767792e-03 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_fusion_layer.csv deleted file mode 100644 index da8778e4363e8..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_fusion_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_gru_hh.csv deleted file mode 100644 index ebda9420438b0..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_gru_ih.csv deleted file mode 100644 index e27e7136113f4..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_gru_ih.csv +++ /dev/null @@ -1,96 +0,0 @@ -2.246665502525502234e-07,7.937374313371492462e-09,1.875921213922993047e-07,-5.788109191939838638e-08,-2.136287946541415295e-08,-1.567381531231148983e-07,1.882228133354146848e-07,-2.598505943751661107e-08,-3.420910132945209625e-08,-1.567461680451742723e-07,-1.786740000397912809e-07,-2.247293053869725554e-07,-3.625288869102405442e-08,1.567380394362771767e-07,-7.059875883896893356e-08,-1.627963115424790885e-07,1.566299800970227807e-07 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--4.967793287846689054e-08,2.150854783167233109e-08,4.158178512625454459e-08,-1.634528246086119907e-07,-5.678036885115034238e-08,-5.813501147144961578e-08,5.776328393380936177e-08,-1.566364602467729128e-07,-2.866544868140863400e-08,-1.388251114065042202e-08,-3.617606481043367239e-08,1.567220948572867201e-07,2.928720022055131267e-08,2.929911779858684895e-08,2.864901915700102109e-08,8.405371687558726990e-08,3.625237710025430715e-08 -8.412970231574945501e-08,-1.656615400413841144e-10,1.629579173823003657e-07,5.797545199470732769e-08,-5.806660041685063334e-08,6.418881781655727536e-08,5.807552483361178020e-08,-2.868460313720788690e-08,1.078672440257832932e-07,-1.686742412410069392e-10,-5.781333456411630323e-08,-1.638241542423202191e-07,2.925599318359672907e-08,1.597442036427310086e-07,5.808584191413501685e-08,1.628393988539755810e-07,-6.706456190386234084e-08 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--2.446447240345150931e-07,-5.797262403461900249e-08,5.784542267406322935e-08,-4.948685727867996320e-08,1.636630315715592587e-07,2.859561476498129196e-08,1.621192495804280043e-07,-1.505266027379548177e-07,-1.387028270016799070e-08,2.869823134687976562e-08,5.133692937420164526e-08,-1.388810311198085401e-08,-5.571640571133684716e-08,5.061934515993016248e-08,1.637327784465014702e-07,-5.813387460307239962e-08,-4.058297520259657176e-08 -1.414099699559301371e-07,2.215232655089494074e-07,-6.123279661096603377e-08,1.637180275793070905e-07,1.367366841975581337e-08,-2.475139240232238080e-07,1.388950288117030141e-08,-1.395921422897572484e-08,-1.566962595234144828e-07,-1.148688042462708836e-07,-1.631939312574104406e-07,5.787142853819204902e-08,1.635335706851037685e-07,4.147488397165943752e-08,8.446513533044708311e-08,-5.206110387234730297e-08,4.928049790464683610e-08 --1.479015168115438428e-07,2.861178494129035244e-08,4.100147066310455557e-08,-1.802456921495831921e-07,2.863562720278878260e-08,1.849799619035863429e-10,-2.868058679439400294e-08,1.321044107438495985e-10,-1.389591020029001811e-08,-4.108287754434059025e-08,-1.085071801298909122e-07,5.204305608685899642e-08,-1.610931974482809892e-07,-4.273213605188175279e-08,4.148085253063982236e-08,-2.508360807951248717e-08,-6.947011144120551762e-08 -2.032614787594866357e-07,-2.494784645534764422e-08,6.127978480208184919e-08,-1.614041789110842728e-09,6.153331177927157114e-10,1.507667768407827680e-08,1.382984393671904400e-08,6.191098123053961899e-08,-1.502752979831711855e-07,2.082978767248278018e-07,3.459078357082034927e-08,8.832482478737802012e-08,-1.876494195585109992e-07,-1.598167642669068300e-07,-2.500627793722287606e-08,-1.602401908940009889e-07,-1.015568855677884130e-07 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--3.626437106163393764e-08,5.786744949887179246e-08,1.566839813449405483e-07,3.633799039448604162e-08,7.937203783114910038e-09,-5.204502784295073070e-08,1.612913251847203355e-07,-4.083874927118813503e-08,6.412805220179507160e-08,6.422821030582781532e-08,5.779315515042071638e-08,-3.580516150236690009e-08,2.682239141904574353e-08,-6.242960637337091612e-08,-2.917413866043716553e-08,1.384186809616494429e-08,7.433602178252840531e-08 --1.617611199617385864e-01,-3.229899192547236453e-08,4.978855727699738054e-08,8.648074611983247451e-08,2.891559702788981667e-08,-1.747971225540823070e-07,-7.224393527849315433e-08,1.883262825685960706e-07,-1.600071328766716761e-07,7.921324396420459379e-08,-1.643652325356015353e-07,2.156182077328594460e-08,-3.010353522014952432e-09,-6.486836667818351998e-08,-2.337185094347660197e-07,-1.491870733616451616e-07,1.659868189562985208e-07 --7.365231908806890715e-07,1.553710973212218960e-07,-6.564936683162159170e-08,2.840960711125717353e-08,-5.810138858919344784e-08,4.100746764379437082e-08,-1.086001333305830485e-07,4.064239078616083134e-08,1.532125821768204332e-07,-6.708832955837351619e-08,5.783059364716791606e-08,5.159630944717719103e-08,5.793976853851745545e-08,6.176325939577509416e-08,-1.635185498116698000e-07,-1.598931476110010408e-07,1.627844170570824645e-07 --1.086785346160468180e-07,-1.567005227798290434e-07,-1.078803748555401398e-07,-3.615841492887739150e-08,-5.571111927338279202e-08,-1.635595765492325881e-07,4.101727313354786020e-08,-6.445780087460661889e-08,1.662868176488530025e-10,1.535175755407180986e-07,-2.546783339774094657e-10,-1.631506734156573657e-07,3.571023299286935071e-08,1.526687611885790830e-07,1.611768709608440986e-07,-2.080935956882967730e-07,-1.882453801727024256e-07 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--3.166725548453541705e-07,-1.633203225992474472e-07,-3.636163725673213776e-08,3.631428668882108468e-08,-1.567363057120019221e-07,-2.868525861288162560e-08,-1.881971201100895996e-07,-1.391918225124300079e-08,-1.754999914282961981e-07,1.682569319427784649e-07,4.831321476217453892e-08,-1.017154431792732794e-07,2.874035232025562436e-08,3.644488444365379110e-08,-1.488747329858597368e-07,-5.304810812845062173e-08,3.545922666603473772e-08 -6.670945396081151557e-08,-3.589379105051193619e-08,-1.631634063414821867e-07,-1.563251856850911281e-07,3.613348908970692719e-08,8.962398823086914490e-08,1.371443225650637032e-08,-1.566252405549306559e-10,-3.631039291462911933e-08,1.587823561521872762e-07,-1.749838607345566288e-10,4.090740901574463351e-08,-1.875241366633417783e-07,-6.483518433242352330e-08,-3.075546217701230489e-08,-1.145627521736969356e-07,-6.417676701175878407e-08 --8.130252361297607422e-02,1.584433135803919868e-07,-1.833516165561377420e-07,-1.830772617950060521e-07,8.387216610117320670e-08,1.954229666978335445e-08,1.269502192258187279e-08,-4.165651645848811313e-08,-2.700521761767049611e-08,-4.223102223477326334e-08,8.168512266593097593e-08,1.666401345801205025e-07,1.871812997933375300e-07,-1.687939601424659486e-07,1.593090530604968080e-07,-1.643985712007633992e-07,-2.581056790518232447e-08 --2.608125555525475647e-07,1.566414766784873791e-07,-6.233551630430156365e-08,-1.740481678780270158e-07,4.121308450066862861e-08,1.380581604593089651e-08,1.566761227422830416e-07,-1.629551036330667557e-07,-6.097637594848492881e-08,-4.088968807991477661e-08,1.029993157430908468e-07,-4.092578720360506850e-08,3.639899759377840383e-08,1.016764201722253347e-07,-1.622421734737145016e-07,3.613958909909342765e-08,5.788762891256737930e-08 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_steer_layer_1.csv deleted file mode 100644 index b3a8b6028a2ba..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -5.784385948004455713e-08,4.115653950975683983e-08,-5.804627889460789447e-08,2.864728720908260584e-08,-3.634495371329649060e-08,-6.166982302602264099e-08,-2.448331599680386716e-07,1.567348846265304019e-07,-8.307063836809902568e-08,3.079690391594169796e-10,-1.396914051099429344e-08,-2.550808630985557102e-08,-6.109024042189048487e-08,-1.834535301448170230e-10,-5.804912106555093487e-08,-5.804991687341498618e-08 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_linear_relu.csv @@ -1,16 +1,16 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_hh_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_hh_0.csv index e72b7cca0f098..7537a9ee9c0b9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_hh_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_hh_0.csv @@ -1,72 +1,72 @@ 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--5.102316662669181824e-02,-3.507985472679138184e-01,5.010926127433776855e-01,1.573813855648040771e-01,-8.607234805822372437e-02,-3.897233903408050537e-01,3.848883807659149170e-01,3.309157192707061768e-01,-4.921467229723930359e-02,-1.468239128589630127e-01,6.316158175468444824e-02,-2.628258466720581055e-01,3.975514471530914307e-01,3.752481639385223389e-01,-6.427977979183197021e-02,1.382480859756469727e-01,-2.432262711226940155e-02,-7.466709017753601074e-01 --2.871334254741668701e-01,-1.684546619653701782e-01,1.004022881388664246e-01,1.107691153883934021e-01,-3.314368426799774170e-01,3.528238236904144287e-01,-1.947280168533325195e-01,3.410727530717849731e-02,-7.150819897651672363e-01,4.324796795845031738e-02,1.645645312964916229e-02,-6.151926144957542419e-02,2.717777192592620850e-01,2.588473558425903320e-01,-3.138980567455291748e-01,1.122600883245468140e-01,5.367688462138175964e-02,3.030427731573581696e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_ih_0.csv index a03b7b141eb63..1190c0ccb7332 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_hh.csv index 2852c6e4eff0d..99d0e5eab9e2c 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_hh.csv @@ -1,64 +1,64 @@ 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_ih.csv index 1502ee1d4acf2..1541f6535f3ad 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_lstm_ih.csv @@ -1,64 +1,64 @@ -1.354664843529462814e-02,5.303078889846801758e-02,-5.030304193496704102e-02,3.654263541102409363e-02,3.604745492339134216e-02,5.328396707773208618e-02,5.226656422019004822e-02,4.835975542664527893e-02,5.317721515893936157e-02,4.100683704018592834e-02,1.429468486458063126e-02,5.063319113105535507e-03,1.167685911059379578e-01,-1.364066172391176224e-02,1.782777607440948486e-01,-6.149024516344070435e-02,-2.606809139251708984e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_steer_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ -1.443694811314344406e-03,-2.542864158749580383e-02,4.027282074093818665e-02,-7.667397707700729370e-02,4.094406217336654663e-02,2.776812203228473663e-02,8.039279282093048096e-02,-1.509349465370178223e+00,1.310569643974304199e-01,4.136419668793678284e-02,1.308051943778991699e-01,2.373549044132232666e-01,-4.637687802314758301e-01,-2.480286872014403343e-03,2.196998000144958496e-01,1.672886610031127930e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_steer_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_4/weight_steer_layer_2.csv @@ -1,8 +1,8 @@ --5.466060340404510498e-02,-3.131796419620513916e-01,4.896617680788040161e-02,7.235971093177795410e-01,-2.694058604538440704e-02,-4.396100994199514389e-03,-3.990970551967620850e-02,1.204489588737487793e+00,1.400408148765563965e-01,-2.412422001361846924e-02,4.019365608692169189e-01,4.560724198818206787e-01,-7.040619254112243652e-01,6.191220879554748535e-01,2.303922623395919800e-01,-8.090059161186218262e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --5.687703490257263184e-01 -1.572953760623931885e-01 -5.714529380202293396e-02 -1.085763052105903625e-01 -9.483638405799865723e-02 -1.503358874469995499e-02 --6.868886295706033707e-03 --4.978453516960144043e-01 -1.244671717286109924e-01 -1.060194298624992371e-01 -1.586544699966907501e-02 -5.790882557630538940e-02 -8.724521845579147339e-02 -8.154500126838684082e-01 -3.643409162759780884e-02 -1.652323454618453979e-01 +9.447958320379257202e-02 +-3.904920443892478943e-02 +-4.699527025222778320e-01 +1.458566039800643921e-01 +-3.368199765682220459e-01 +-3.077215850353240967e-01 +-7.432874292135238647e-02 +-3.889969587326049805e-01 +7.842382043600082397e-02 +-1.985292583703994751e-01 +7.554553151130676270e-01 +-4.822994768619537354e-02 +-4.119287729263305664e-01 +2.234724313020706177e-01 +5.568625032901763916e-02 +9.671768546104431152e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_encoder_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_encoder_layer_2.csv index 2321f21b9d8a8..6bc053b95a062 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -8.771612308919429779e-03 -1.144464537501335144e-01 -6.942238658666610718e-02 -6.198814511299133301e-02 --2.246745675802230835e-02 --7.646840810775756836e-02 -1.091548949480056763e-01 -2.548992633819580078e-01 +2.911694943904876709e-01 +1.167204603552818298e-01 +1.205120310187339783e-01 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--3.642752394080162048e-02 --1.260660216212272644e-02 -9.186952561140060425e-02 -7.274972647428512573e-02 -1.017989963293075562e-01 -7.828899472951889038e-02 -1.771877259016036987e-01 -1.412725597620010376e-01 +-4.330429807305335999e-02 +-4.956674203276634216e-02 +5.305194482207298279e-02 +-9.294666349887847900e-03 +7.941994816064834595e-02 +8.546566963195800781e-02 +6.983704864978790283e-02 +5.682964250445365906e-02 +1.107634603977203369e-01 +7.904335856437683105e-02 +2.872206084430217743e-02 +8.025071769952774048e-02 +1.396385300904512405e-02 +7.349329441785812378e-02 +-1.042479351162910461e-01 +1.730972528457641602e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_layer_2.csv index 40f2a127a4f33..993c5dccd103f 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.024103090167045593e-01 -1.508852094411849976e-01 -9.780577383935451508e-03 -4.319138452410697937e-02 -9.238595515489578247e-02 -1.059526875615119934e-01 -9.110169857740402222e-02 -1.054998338222503662e-01 +5.389664322137832642e-02 +3.577540814876556396e-02 +3.646123688668012619e-03 +4.657787457108497620e-02 +4.009292274713516235e-02 +-1.234907656908035278e-01 +-9.528395719826221466e-03 +8.088576793670654297e-02 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_complimentary_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_complimentary_layer.csv index bb1b5346932b2..a9dc3efd0edaf 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_complimentary_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_complimentary_layer.csv @@ -1,64 +1,64 @@ -1.511160749942064285e-02 -1.398833189159631729e-02 -1.167307570576667786e-01 -4.545302689075469971e-02 -1.077463570982217789e-02 -1.412830781191587448e-03 -2.155422978103160858e-02 -9.623280493542551994e-04 -4.561380296945571899e-02 -1.059380993247032166e-01 --9.806581586599349976e-02 --4.837276786565780640e-02 -3.910212963819503784e-02 --4.298238456249237061e-02 -7.735941559076309204e-02 --2.475859364494681358e-03 -2.536228299140930176e-02 --6.351507455110549927e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_final_layer.csv index 7f69ecaf304d4..e312c241bbb6e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_final_layer.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_final_layer.csv @@ -1,4 +1,4 @@ --1.411965698935091496e-03 -9.323665290139615536e-04 --3.581988811492919922e-02 -5.641778465360403061e-03 +-2.463300246745347977e-03 +-6.471379892900586128e-04 +-6.751611828804016113e-02 +-5.644071847200393677e-03 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_acc_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 6a4f3acd10344..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.700384832936663315e-10 -5.574749906145370915e-08 --1.639053834878723137e-07 -2.375204388727070182e-07 -5.790519352899536898e-08 -5.913284439884591848e-08 --1.567347993614021107e-07 --3.626800904044102936e-08 -2.247556807333239703e-07 -2.873030524597197655e-08 -5.794816004822678224e-08 --4.090935590284061618e-08 --2.924792141811849433e-08 --2.869897031132495613e-08 --2.662483211679500528e-08 --1.567348562048209715e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_acc_layer_2.csv deleted file mode 100644 index 62f75cb43506c..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --8.786349070533105987e-08 --2.436062018773554883e-08 --7.991299355580849806e-08 --1.391345278989319922e-07 --3.481732591126274201e-08 --2.435033650272089290e-07 --6.396118124030181207e-08 --2.479723377746267943e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_final_layer.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_final_layer.csv deleted file mode 100644 index b082a7a89c611..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_final_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ --1.333507336676120758e-02 -6.083920970559120178e-02 --1.834504422731697559e-03 --3.571275621652603149e-02 -2.946369349956512451e-01 --1.427265582606196404e-03 --3.688247501850128174e-02 -6.005700305104255676e-02 --1.222376897931098938e-02 --3.048365712165832520e-01 --4.184846207499504089e-02 -1.948376744985580444e-02 -4.896391183137893677e-02 -3.152998397126793861e-03 --2.637754976749420166e-01 -1.586769241839647293e-03 --6.010877341032028198e-02 --2.591094970703125000e-01 -2.363688647747039795e-01 -6.578605622053146362e-03 -7.574999239295721054e-03 -2.695792615413665771e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_gru_hh.csv deleted file mode 100644 index 8057bd867e52d..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ -1.545770733635265515e-10 --8.302931320258721826e-08 --7.699894943868912378e-09 --2.921688491142049315e-08 -1.020158979514462771e-08 --1.473583746701478958e-03 --7.846460212022066116e-04 --1.084701466425030958e-07 --5.566524663436211995e-08 --1.647779015456762863e-07 -2.786956443401322758e-08 -1.140102057206604513e-07 -1.460361964689127490e-08 --1.444058312927154475e-07 -8.503373294388438808e-08 --1.640689930582084344e-07 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_steer_layer_1.csv deleted file mode 100644 index 614901c941b0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --1.602122381427761866e-07 -1.594512042402129737e-07 --1.383567216350911622e-08 -2.870719839620505809e-08 --4.115875640309241135e-08 --1.629571784178551752e-07 -6.326867207917530322e-08 -6.130223795253186836e-08 -2.572974278081119337e-09 --1.623540981654514326e-07 --3.618814403694159409e-08 --5.186216256447551132e-08 -1.592173859421563975e-08 -2.550376621002214961e-08 --5.809684111568458320e-08 --6.100456317881253199e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_steer_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_steer_layer_2.csv deleted file mode 100644 index fc1f85f18f28b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ --1.495364330139636877e-07 --9.433398506786261350e-10 --9.447958149166879593e-08 -6.568057209221933590e-09 --2.248099519874813268e-07 --1.070967527994071133e-07 --9.932387712296986138e-08 --7.939953405866617686e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_linear_relu.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_linear_relu.csv index 6c7ba3b020e52..b40d15d15387e 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_linear_relu.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_linear_relu.csv @@ -1,16 +1,16 @@ --3.609813749790191650e-02 --3.190746530890464783e-02 -4.359308257699012756e-02 -8.269068202935159206e-04 -4.143001139163970947e-02 -7.497583515942096710e-03 -7.427339255809783936e-02 --2.295951917767524719e-02 --2.274018079042434692e-01 -8.252906054258346558e-02 -7.786637544631958008e-02 -1.048227921128273010e-01 --4.350556991994380951e-04 --6.249625235795974731e-02 -1.322524547576904297e-01 --3.382597863674163818e-02 +-2.442883513867855072e-02 +5.057684332132339478e-02 +3.201630339026451111e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_encoder_ih_0.csv index bd71a79542f43..2a250786995d9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ --2.799263000488281250e-01 -8.330401778221130371e-02 -5.212230980396270752e-02 --2.048002928495407104e-02 --9.690331667661666870e-02 --1.525534093379974365e-01 --1.841630488634109497e-01 --1.662613004446029663e-01 --2.396262139081954956e-01 -6.080040335655212402e-02 -1.034230291843414307e-01 -1.198021844029426575e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_hh.csv @@ -1,64 +1,64 @@ -1.863418668508529663e-01 --1.841402947902679443e-01 -4.871492087841033936e-02 -6.630924344062805176e-01 -3.120891004800796509e-02 -5.130470544099807739e-02 -3.066616505384445190e-02 -3.965241834521293640e-02 -3.464382141828536987e-02 --1.669278144836425781e-01 --9.552434831857681274e-02 --2.999459765851497650e-02 --6.102378945797681808e-03 -1.027578711509704590e-01 -1.095576584339141846e-01 -1.344114840030670166e-01 -2.494055032730102539e-01 -1.547644734382629395e-01 -1.317952722311019897e-01 --2.324226498603820801e-01 --2.754482328891754150e-01 -1.100205332040786743e-01 --1.426737289875745773e-03 --3.385674655437469482e-01 -1.660625338554382324e-01 -5.306183919310569763e-02 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031398bdfd2a3..8511ea9e3ab32 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_lstm_ih.csv @@ -1,64 +1,64 @@ -1.862874925136566162e-01 --1.859310567378997803e-01 -4.900135472416877747e-02 -6.631961464881896973e-01 -3.110241889953613281e-02 -5.119629949331283569e-02 -2.894839085638523102e-02 -3.989761322736740112e-02 -3.549076244235038757e-02 --1.642386913299560547e-01 --9.745972603559494019e-02 --3.339832648634910583e-02 --2.647247863933444023e-03 -9.829391539096832275e-02 -1.122935935854911804e-01 -1.338122785091400146e-01 -2.497808933258056641e-01 -1.542556881904602051e-01 -1.318080276250839233e-01 --2.323063462972640991e-01 --2.763138115406036377e-01 -1.098379045724868774e-01 --1.424718881025910378e-03 --3.388048410415649414e-01 -1.662753224372863770e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_encoder_layer_1.csv index 707a956dc7533..ca1b020a82ede 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_encoder_layer_1.csv @@ -1,16 +1,16 @@ -2.288375049829483032e-02 -4.156160056591033936e-01 -1.447749207727611065e-03 --1.900681381812319160e-04 -3.408987540751695633e-03 -3.183485008776187897e-03 -4.051115512847900391e-01 --1.631803363561630249e-01 -2.039906978607177734e-01 -3.064470831304788589e-03 -2.237636968493461609e-02 -1.526999026536941528e-01 -1.990733891725540161e-01 --1.465275436639785767e-01 --4.737891629338264465e-02 -1.408819258213043213e-01 +-2.332334361199173145e-06 +1.387599296867847443e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ --4.465385898947715759e-02 --2.514035440981388092e-02 --4.148573800921440125e-02 -1.449349820613861084e-01 -9.823747910559177399e-03 --1.016079541295766830e-02 --1.387605676427483559e-03 --8.763826638460159302e-02 +2.186003774404525757e-01 +-9.245735406875610352e-02 +-2.528463006019592285e-01 +-7.972517609596252441e-02 +6.500462442636489868e-02 +2.470089346170425415e-01 +2.000899612903594971e-02 +1.971768885850906372e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_layer_1.csv index d8e5af208e79c..dc762d230ac56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/bias_steer_layer_1.csv @@ -1,16 +1,16 @@ --5.880776420235633850e-02 -1.072910651564598083e-01 -1.927932947874069214e-01 -1.416619420051574707e-01 -3.611433552578091621e-03 --2.359812110662460327e-01 -1.126473490148782730e-02 --1.914175972342491150e-02 -1.322983503341674805e-01 --9.606996551156044006e-03 --4.914920404553413391e-02 -5.091565847396850586e-02 -2.208182960748672485e-02 -4.450881108641624451e-02 -5.511307157576084137e-03 -9.871846064925193787e-03 +-1.267773564904928207e-03 +1.681745648384094238e-01 +3.152760118246078491e-02 +-3.042486310005187988e-02 +2.783328667283058167e-02 +1.784413307905197144e-01 +3.829433768987655640e-02 +-1.098260059952735901e-01 +8.191139250993728638e-02 +4.840236902236938477e-02 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-1.211080327630043030e-01 +-2.150738146156072617e-03 +4.411975294351577759e-02 +1.444365680217742920e-01 +5.844632163643836975e-02 +6.778846494853496552e-03 +5.850554630160331726e-02 +2.264000841023516841e-06 +1.272178292274475098e-01 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/offline_features.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/offline_features.csv index 1bcdd512dea6f..fa6d9d8875c56 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/offline_features.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/offline_features.csv @@ -1,36 +1,36 @@ --1.365479675996539299e-07 -4.500528660855707130e-08 --2.888707228976272745e-07 --3.052138808357085509e-08 --3.403145854008471360e-08 --1.366562401017290540e-07 -9.226326369571324904e-09 --1.461385323864305974e-07 -2.401009169261669740e-07 -3.847664586942300957e-08 --4.707507983425784914e-08 -4.440217082901654067e-09 --3.469495624131013756e-08 -3.053523656149081944e-08 -4.850487655971846834e-08 --4.678990350726053293e-08 --1.465268173461709011e-08 --3.929526570800589980e-08 --1.382004199967923341e-07 --3.059484043888005544e-08 -1.119208832278673071e-07 -4.225704230975679820e-09 --1.164682288390395115e-07 --1.629832553362575709e-07 -3.149804683744150680e-08 --1.490812984172862343e-08 -5.521310342260221660e-08 --6.760313198128642398e-08 -1.837763718981477723e-08 --1.734968861910601845e-07 -1.474870856554844067e-07 --1.383132399723763228e-07 -3.487329980345066360e-09 -8.957044173030226375e-08 -2.936226373151384905e-08 --1.337812438606533760e-08 +5.170850083231925964e-02 +-2.384004145860671997e-01 +2.417143583297729492e-01 +3.888960182666778564e-01 +-2.718242108821868896e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_acc_encoder_layer_1.csv index 5eb1c7c33213a..09a6b18c285fa 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_acc_encoder_layer_1.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_acc_encoder_layer_1.csv @@ -1,16 +1,16 @@ --6.779996305704116821e-02,3.508124351501464844e-01,2.439208775758743286e-01,2.227861583232879639e-01,2.015045583248138428e-01,1.802337467670440674e-01,1.594138443470001221e-01,1.380567848682403564e-01,1.166481673717498779e-01,9.721208363771438599e-02,7.622497528791427612e-02,5.588675662875175476e-02,3.574046865105628967e-02,1.916410401463508606e-02,1.854662410914897919e-02,1.806927099823951721e-02,1.663219928741455078e-02,1.390712894499301910e-02,8.530278690159320831e-03,-9.777169907465577126e-04 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--1.854499280452728271e-01,1.914535760879516602e-01,4.152707383036613464e-02,3.073596581816673279e-02,2.163004875183105469e-02,1.425455417484045029e-02,7.673011161386966705e-03,1.589223393239080906e-03,-2.790175611153244972e-03,-6.258539855480194092e-03,-9.682977572083473206e-03,-1.227880362421274185e-02,-1.485804840922355652e-02,-1.617628522217273712e-02,-1.666615158319473267e-02,-1.672841608524322510e-02,-1.409822050482034683e-02,-1.107515487819910049e-02,-7.739217020571231842e-03,-2.378774574026465416e-03 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_acc_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_acc_encoder_layer_2.csv @@ -1,8 +1,8 @@ -3.336199373006820679e-02,2.459247857332229614e-01,-6.924922019243240356e-02,-3.334385901689529419e-02,2.794704735279083252e-01,-1.098308246582746506e-02,-3.318902477622032166e-02,-2.375750094652175903e-01,-6.978246849030256271e-03,-7.694390369579195976e-04,3.750725463032722473e-02,-1.894451864063739777e-02,-1.075058132410049438e-01,1.155056133866310120e-01,-3.716661781072616577e-02,4.225961863994598389e-02 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_acc_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_acc_layer_2.csv @@ -1,8 +1,8 @@ -1.827372312545776367e-01,1.442782729864120483e-01,1.350615918636322021e-01,1.406146585941314697e-01,7.798003405332565308e-02,1.429848670959472656e-01,1.125954836606979370e-01,5.638980120420455933e-02,8.144410699605941772e-02,-5.328862462192773819e-03,-2.268474362790584564e-02,4.206001386046409607e-02,1.457983851432800293e-01,-5.146942660212516785e-02,-6.503234058618545532e-02,-3.310494497418403625e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_final_layer.csv @@ -1,4 +1,4 @@ --4.076786339282989502e-03,-1.227277144789695740e-02,3.244567289948463440e-02,1.793841016478836536e-03,4.501522984355688095e-03,-2.283791452646255493e-02,-1.217357907444238663e-02,-2.098338445648550987e-03,-2.394610270857810974e-02,3.298125788569450378e-02,3.658420871943235397e-03,3.390965238213539124e-02,-1.559238694608211517e-02,-1.272494345903396606e-02,1.682596048340201378e-03,-1.867336407303810120e-02 --1.042943261563777924e-02,-4.991219844669103622e-03,1.183264539577066898e-03,5.007501225918531418e-03,-6.344680878100916743e-05,2.041139872744679451e-03,7.769418880343437195e-03,-3.926411271095275879e-03,-1.374095008941367269e-04,-7.521932013332843781e-03,2.816032152622938156e-03,5.137700587511062622e-03,-1.068992633372545242e-02,-1.806749030947685242e-03,1.107970019802451134e-03,2.244758186861872673e-03 --2.750689350068569183e-02,-3.015980422496795654e-01,8.099721074104309082e-01,5.499200895428657532e-02,1.074275374412536621e-01,-5.590542554855346680e-01,-3.127891719341278076e-01,-1.049866527318954468e-01,-5.508310794830322266e-01,8.068168163299560547e-01,1.146488413214683533e-01,8.553577661514282227e-01,-3.928754031658172607e-01,-3.463063836097717285e-01,3.211103007197380066e-02,-4.625632762908935547e-01 --1.714976727962493896e-01,-3.647217899560928345e-02,1.288588275201618671e-03,-1.639245077967643738e-02,-3.204738348722457886e-02,4.737513139843940735e-03,4.795992001891136169e-02,-2.080518007278442383e-01,9.203262743540108204e-04,-4.152239300310611725e-03,4.169252887368202209e-02,-4.800185561180114746e-03,1.465110201388597488e-02,-2.583358809351921082e-02,5.423993244767189026e-02,-1.011096313595771790e-02 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_acc_layer_1.csv deleted file mode 100644 index 09b35a7fc4e0b..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_acc_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ --5.795665813934647304e-08,2.448358884521439904e-07,-3.380812785280795651e-08,-2.136942289787668869e-10,4.084210303290092270e-08,5.576080042146713822e-08,1.567351972653341363e-07,3.467380693678023817e-08,6.475600144995041774e-08,7.276236857478579623e-08,-1.776304969780539977e-07,3.134217507749781362e-08,1.528735111833157134e-07,1.598162242544276523e-07,4.906196338083645969e-08,5.687583737312706944e-08,2.663517761902767234e-08,-1.778388565298882895e-07,9.540057988033368019e-08,-1.728522325272763283e-10 --2.865231785165178735e-08,1.085090843844227493e-07,4.934348041274461139e-08,6.418773779159892001e-08,1.228051171864308344e-08,1.398261240126430494e-08,-5.777479472612867539e-08,-1.366190183205162612e-08,-7.060171469674969558e-08,-2.162425261076350580e-08,5.675544301197987807e-08,-3.080576860270411998e-08,1.389905257553891715e-08,5.775637745841777360e-08,5.794838742190222547e-08,1.389194714818131615e-08,5.799671143336126988e-08,1.682496844068737119e-07,-5.566382554889059975e-08,1.608615036730043357e-07 --2.546920541135477833e-08,-1.084940137729972776e-07,-2.864933357216159493e-08,-1.399994964401685138e-08,-1.849883024540588394e-10,5.802163727253173420e-08,6.589188217276387149e-08,5.794140278680970368e-08,-1.919061715094372289e-10,-1.567351404219152755e-07,-1.567356520126850228e-07,-1.874498991583095631e-07,3.460730013671309280e-08,-2.833207801700154960e-08,-5.565841121324410778e-08,2.246823527229935280e-07,7.060046414153475780e-08,-4.112459350835706573e-08,1.538483758167785709e-07,4.987273527490287961e-08 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--2.572746904405676105e-09,1.086709744413383305e-07,1.400614557667267945e-08,7.936692192345162766e-09,4.102665940308725112e-08,-8.246642124731806689e-08,5.573721040263990290e-08,6.818912368089513620e-08,4.132279229906998808e-08,-1.598831147475721082e-07,2.749714345107179270e-10,-4.084756000111156027e-08,2.869085591328257578e-08,1.387898684868105192e-08,-6.484240344661884592e-08,-6.196152213533423492e-08,-5.634582800340126596e-10,-1.612109485904511530e-07,2.844504010113269032e-08,2.246597858857057872e-07 --2.837610324490924540e-08,-1.612319806554296520e-07,6.102120408968403353e-08,2.872587145930083352e-08,-2.865953518949027057e-08,2.850439884127808909e-08,1.778415992248483235e-07,2.926793030155749875e-08,-1.032509047149687831e-07,1.884557510045681283e-10,2.864214287967570272e-08,-4.828538990864217340e-08,1.726883596120387665e-07,-6.240391314804583089e-08,5.785573975458646601e-08,1.387097192662167799e-08,-1.379480174534819525e-08,2.865901116422264749e-08,-2.918067565360615845e-08,-2.918391572848122451e-08 --2.864954673498232296e-08,-1.368725399686354649e-08,1.085663257072155830e-07,-2.883944461018472794e-08,1.085741132555995136e-07,-5.121204438296445005e-08,6.429602450452875928e-08,5.809104308696078078e-08,-4.156912325470329961e-08,-3.439327400656111422e-08,-5.183908413641802326e-08,1.530865603172060219e-07,8.827194619698275346e-08,-5.001684755256974313e-08,1.873391681783687091e-07,1.567354246390095796e-07,5.798773017318126222e-08,1.020795323825041123e-07,1.085405756384716369e-07,1.529302977587576606e-07 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--2.852652158935597981e-08,-9.824449875850405078e-08,-1.754762308792123804e-07,5.789251744658940879e-08,4.112624196750402916e-08,5.801046754072558542e-08,6.086136750127479900e-08,-2.864963200011061417e-08,-1.392582582582235773e-08,1.740867645594335045e-07,-1.605755812761344714e-07,-3.050213948085911397e-08,-2.212133978218844277e-07,1.637957893763086759e-07,1.566371992112181033e-07,1.566785812201487715e-07,1.622258025690825889e-07,5.108397616027104959e-08,1.567347993614021107e-07,2.550069666540366597e-08 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--3.479204124801071885e-08,-6.744752312215496204e-08,-6.184050960200693225e-08,2.946307043824591965e-12,-6.112707495731228846e-08,1.314774333849300092e-08,-9.540066514546197141e-08,-5.567803640360580175e-08,1.567590146578368149e-07,-6.107648431452616933e-08,1.636276465433184057e-07,2.845211288793336735e-10,-2.862939751935300592e-08,2.852742042591671634e-08,-4.092018457413360011e-08,-4.101761419406102505e-08,1.497509884984538076e-07,1.882291797983270953e-07,1.623722880594868911e-07,6.816661368702625623e-08 --1.567573093552709906e-07,5.190680241184963961e-08,-4.090967564707170823e-08,-2.676181409810851619e-08,1.796450210811428860e-10,-2.870267934440562385e-08,1.629214239073917270e-07,1.625622587653197115e-07,5.780725231829819677e-08,-6.437543476067730808e-08,1.078759979122878576e-07,-5.816283632498198131e-08,-2.863949966069867514e-08,1.598080103804022656e-07,1.085153371604974382e-07,-3.626300681958127825e-08,2.851320957120151434e-08,-7.937203783114910038e-09,-1.778377480832205038e-07,1.665710347431570426e-10 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--2.448340410410310142e-07,1.380423775287908938e-08,1.561118456727683679e-10,-5.812466596921694872e-08,-3.100375423059631430e-08,-1.527012405411198870e-10,-2.919994557259997237e-08,-1.069702619815871003e-07,-1.525875537033982710e-10,2.856971192954915750e-08,-3.033363005222611264e-10,-8.849530530596894096e-08,1.624740946226665983e-07,-1.728069349837824120e-07,1.750106615183710801e-09,5.574175787614876754e-08,-1.683244903460945352e-07,-1.400569082932179299e-08,2.154006040200329153e-08,-2.080925725067572785e-07 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_acc_layer_2.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_acc_layer_2.csv deleted file mode 100644 index b48ef5ed5516a..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_acc_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -2.044039248971785128e-08,2.853378688882912684e-08,1.634649606785387732e-07,-2.863657222462734353e-08,1.586338811421228456e-07,-3.615636856579840241e-08,4.099652528566366527e-08,-2.866763537667793571e-08,-1.085351186702609994e-07,-5.566445082649806864e-08,-1.587467721719804103e-07,1.566090617188820033e-07,-2.175595814613018320e-10,-1.384351655531190772e-08,-2.042933644474942412e-08,4.782882356835216342e-08 --1.390252002408942644e-08,1.070218473842032836e-07,1.729425065377654391e-07,-1.391764037350640137e-08,2.916138086561659293e-08,1.398903570759557624e-08,-1.225584167485749276e-08,3.631698675121697306e-08,3.613908816646471678e-08,8.851120725239525200e-08,1.629533130653726403e-07,6.196007262815328431e-08,-6.416238562678699964e-08,9.956426083590486087e-08,-1.882394116137220408e-07,5.807234160215557495e-08 -1.611729487649427028e-07,8.238533411031312426e-08,1.566412919373760815e-07,-2.847779612125123094e-08,1.286467599115326266e-09,-3.167127715641981922e-08,7.091126263958358322e-08,3.091757960760332935e-08,5.679489234466927883e-08,-1.370385227517090243e-08,1.410169647719783370e-09,2.874138971264983411e-08,-1.638500179979018867e-07,5.794003854475704429e-08,-7.060137363623653073e-08,1.622957483959908132e-07 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_fusion_layer.csv deleted file mode 100644 index da8778e4363e8..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_fusion_layer.csv +++ /dev/null @@ -1,36 +0,0 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_gru_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_gru_hh.csv deleted file mode 100644 index ebda9420438b0..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_gru_hh.csv +++ /dev/null @@ -1,96 +0,0 @@ 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--1.053751930157886818e-07,-2.511040122499252902e-07,2.483378048054873943e-04,3.056874575690926576e-08,-3.617716356529854238e-05,-5.083134965389035642e-05,-3.177255392074584961e-02,-8.462891855742782354e-05,1.506569304865479353e-07,1.389682040553452680e-07,4.094154108315706253e-03,-9.500762098468840122e-05,-1.690746139502152801e-04,-1.181037150672636926e-04,1.810444295406341553e-01,-5.167514551430940628e-03,-5.528867674797766085e-08,-1.995898770701387548e-07,1.678826212882995605e-01,-7.984144613146781921e-04,5.774685618575858825e-08,-1.090582202323275851e-07,-1.362478506052866578e-04,-2.869202875288578980e-09,9.864854177976667415e-08,-9.314462658949196339e-05,-7.239124533953145146e-05,3.248244465794414282e-04,2.916221274062991142e-03,4.665430262684822083e-02,-6.928963214159011841e-02,-1.553085516206920147e-03 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_gru_ih.csv deleted file mode 100644 index e27e7136113f4..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_gru_ih.csv +++ /dev/null @@ -1,96 +0,0 @@ -2.246665502525502234e-07,7.937374313371492462e-09,1.875921213922993047e-07,-5.788109191939838638e-08,-2.136287946541415295e-08,-1.567381531231148983e-07,1.882228133354146848e-07,-2.598505943751661107e-08,-3.420910132945209625e-08,-1.567461680451742723e-07,-1.786740000397912809e-07,-2.247293053869725554e-07,-3.625288869102405442e-08,1.567380394362771767e-07,-7.059875883896893356e-08,-1.627963115424790885e-07,1.566299800970227807e-07 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--3.725880980491638184e-01,2.458690744333580369e-07,5.973591044039494591e-08,1.018822288756382477e-07,-1.559959343921946129e-07,-1.629242376566253370e-07,-2.616832794899437431e-08,1.366417734516289784e-07,2.215908807556843385e-07,1.500245332408667309e-07,5.155551718871720368e-08,-1.559374709358962718e-07,-9.040774351376512641e-09,-1.607899235978038632e-07,5.084858401005476480e-08,-1.145307777505877311e-07,-2.697387557759611809e-08 diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_only_online.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_only_online.csv deleted file mode 100644 index d1cd6de226089..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_only_online.csv +++ /dev/null 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_steer_layer_1.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_steer_layer_1.csv deleted file mode 100644 index b3a8b6028a2ba..0000000000000 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_steer_layer_1.csv +++ /dev/null @@ -1,16 +0,0 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_initial_hidden_steer_layer_2.csv +++ /dev/null @@ -1,8 +0,0 @@ -5.784385948004455713e-08,4.115653950975683983e-08,-5.804627889460789447e-08,2.864728720908260584e-08,-3.634495371329649060e-08,-6.166982302602264099e-08,-2.448331599680386716e-07,1.567348846265304019e-07,-8.307063836809902568e-08,3.079690391594169796e-10,-1.396914051099429344e-08,-2.550808630985557102e-08,-6.109024042189048487e-08,-1.834535301448170230e-10,-5.804912106555093487e-08,-5.804991687341498618e-08 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_linear_relu.csv @@ -1,16 +1,16 @@ 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diff --git a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_hh_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_hh_0.csv index e72b7cca0f098..7537a9ee9c0b9 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_hh_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_hh_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_ih_0.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_ih_0.csv index a03b7b141eb63..1190c0ccb7332 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_ih_0.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_encoder_ih_0.csv @@ -1,72 +1,72 @@ 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_hh.csv b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_hh.csv index 2852c6e4eff0d..99d0e5eab9e2c 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_hh.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_hh.csv @@ -1,64 +1,64 @@ 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_ih.csv index 1502ee1d4acf2..1541f6535f3ad 100644 --- a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_ih.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_lstm_ih.csv @@ -1,64 +1,64 @@ -1.354664843529462814e-02,5.303078889846801758e-02,-5.030304193496704102e-02,3.654263541102409363e-02,3.604745492339134216e-02,5.328396707773208618e-02,5.226656422019004822e-02,4.835975542664527893e-02,5.317721515893936157e-02,4.100683704018592834e-02,1.429468486458063126e-02,5.063319113105535507e-03,1.167685911059379578e-01,-1.364066172391176224e-02,1.782777607440948486e-01,-6.149024516344070435e-02,-2.606809139251708984e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_steer_encoder_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_steer_encoder_layer_2.csv @@ -1,8 +1,8 @@ -1.443694811314344406e-03,-2.542864158749580383e-02,4.027282074093818665e-02,-7.667397707700729370e-02,4.094406217336654663e-02,2.776812203228473663e-02,8.039279282093048096e-02,-1.509349465370178223e+00,1.310569643974304199e-01,4.136419668793678284e-02,1.308051943778991699e-01,2.373549044132232666e-01,-4.637687802314758301e-01,-2.480286872014403343e-03,2.196998000144958496e-01,1.672886610031127930e-01 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b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_steer_layer_1.csv @@ -1,16 +1,16 @@ -1.337991125183179975e-04,1.429468512535095215e+00,-2.060595005750656128e-01,-1.311620473861694336e-01,-9.247932583093643188e-02,-5.962440371513366699e-02,-1.770549826323986053e-02,8.904102258384227753e-03,1.928523369133472443e-02,2.661564759910106659e-02,5.267998669296503067e-03,-3.306114301085472107e-02,-7.490258663892745972e-02,-1.124373152852058411e-01,-1.150781363248825073e-01,-1.032200455665588379e-01,-8.375178277492523193e-02,-6.815285235643386841e-02,-8.122835308313369751e-02,-1.426265239715576172e-01,-2.786263525485992432e-01 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a/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_steer_layer_2.csv +++ b/vehicle/autoware_raw_vehicle_cmd_converter/src/vehicle_adaptor/autoware_vehicle_adaptor/vehicle_models/vehicle_model_5/weight_steer_layer_2.csv @@ -1,8 +1,8 @@ --5.466060340404510498e-02,-3.131796419620513916e-01,4.896617680788040161e-02,7.235971093177795410e-01,-2.694058604538440704e-02,-4.396100994199514389e-03,-3.990970551967620850e-02,1.204489588737487793e+00,1.400408148765563965e-01,-2.412422001361846924e-02,4.019365608692169189e-01,4.560724198818206787e-01,-7.040619254112243652e-01,6.191220879554748535e-01,2.303922623395919800e-01,-8.090059161186218262e-01 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