update downloads docs

nim65s · Oct 23, 2023 · bdccdf8 · bdccdf8
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diff --git a/.github/workflows/conda-test.yml b/.github/workflows/conda-test.yml
@@ -67,11 +67,10 @@ jobs:
       - name: Download pre-trained models required for tests
         run: |
           mkdir local_data
-          python -m happypose.toolbox.utils.download \
-                    --cosypose_models=detector-bop-ycbv-pbr--970850 \
-                    --cosypose_models=coarse-bop-ycbv-pbr--724183 \
-                    --cosypose_models=refiner-bop-ycbv-pbr--604090 \
-                    --megapose_models
+          python -m happypose.toolbox.utils.download --megapose_models --cosypose_models
+                    detector-bop-ycbv-pbr--970850 \
+                    coarse-bop-ycbv-pbr--724183 \
+                    refiner-bop-ycbv-pbr--604090
 
           cd tests/data
           git clone https://github.com/petrikvladimir/happypose_test_data.git crackers_example

diff --git a/.github/workflows/poetry-test.yml b/.github/workflows/poetry-test.yml
@@ -30,11 +30,10 @@ jobs:
       - name: Download pre-trained models required for tests
         run: |
           mkdir local_data
-          poetry run python -m happypose.toolbox.utils.download \
-                               --cosypose_models=detector-bop-ycbv-pbr--970850 \
-                               --cosypose_models=coarse-bop-ycbv-pbr--724183 \
-                               --cosypose_models=refiner-bop-ycbv-pbr--604090 \
-                               --megapose_models
+          poetry run python -m happypose.toolbox.utils.download --megapose_models --cosypose_models \
+                               detector-bop-ycbv-pbr--970850 \
+                               coarse-bop-ycbv-pbr--724183 \
+                               refiner-bop-ycbv-pbr--604090
 
           cd tests/data
           git clone https://github.com/petrikvladimir/happypose_test_data.git crackers_example

diff --git a/docs/book/cosypose/download_data.md b/docs/book/cosypose/download_data.md
@@ -8,22 +8,19 @@ All data used (datasets, models, results, ...) are stored in a directory `$HAPPY
 For both T-LESS and YCB-Video, we use the datasets in the [BOP format](https://bop.felk.cvut.cz/datasets/). If you already have them on your disk, place them in `$HAPPYPOSE_DATA_DIR/bop_datasets`. Alternatively, you can download it using :
 
 ```sh
-python -m happypose.toolbox.utils.download --bop_dataset=ycbv
-python -m happypose.toolbox.utils.download --bop_dataset=tless
+python -m happypose.toolbox.utils.download --bop_dataset ycbv tless
 ```
 
 Additional files that contain information about the datasets used to fairly compare with prior works on both datasets.
 
 ```sh
-python -m happypose.toolbox.utils.download --bop_extra_files=ycbv
-python -m happypose.toolbox.utils.download --bop_extra_files=tless
+python -m happypose.toolbox.utils.download --bop_extra_files ycbv tless
 ```
 
 We use [pybullet](https://pybullet.org/wordpress/) for rendering images which requires object models to be provided in the URDF format. We provide converted URDF files, they can be downloaded using:
 
 ```sh
-python -m happypose.toolbox.utils.download --urdf_models=ycbv
-python -m happypose.toolbox.utils.download --urdf_models=tless.cad
+python -m happypose.toolbox.utils.download --urdf_models ycbv tless.cad
 ```
 
 In the BOP format, the YCB objects `002_master_chef_can` and `040_large_marker` are considered symmetric, but not by previous works such as PoseCNN, PVNet and DeepIM. To ensure a fair comparison (using ADD instead of ADD-S for ADD-(S) for these objects), these objects must *not* be considered symmetric in the evaluation. To keep the uniformity of the models format, we generate a set of YCB objects `models_bop-compat_eval` that can be used to fairly compare our approach against previous works. You can download them directly:
@@ -51,14 +48,16 @@ Notes:
 
 ```sh
  #ycbv
-  python -m happypose.toolbox.utils.download --cosypose_models=detector-bop-ycbv-pbr--970850
-  python -m happypose.toolbox.utils.download --cosypose_models=coarse-bop-ycbv-pbr--724183
-  python -m happypose.toolbox.utils.download --cosypose_models=refiner-bop-ycbv-pbr--604090
+  python -m happypose.toolbox.utils.download --cosypose_models \
+            detector-bop-ycbv-pbr--970850 \
+            coarse-bop-ycbv-pbr--724183 \
+            refiner-bop-ycbv-pbr--604090
 
  #tless
-  python -m happypose.toolbox.utils.download --cosypose_models=detector-bop-tless-pbr--873074
-  python -m happypose.toolbox.utils.download --cosypose_models=coarse-bop-tless-pbr--506801
-  python -m happypose.toolbox.utils.download --cosypose_models=refiner-bop-tless-pbr--233420
+  python -m happypose.toolbox.utils.download --cosypose_models \
+            detector-bop-tless-pbr--873074 \
+            coarse-bop-tless-pbr--506801 \
+            refiner-bop-tless-pbr--233420
 ```
 
 ## Pre-trained models for single-view estimator
@@ -68,32 +67,31 @@ The pre-trained models of the single-view pose estimator can be downloaded using
 
 ```sh
 # YCB-V Single-view refiner
-python -m happypose.toolbox.utils.download --cosypose_models=ycbv-refiner-finetune--251020
+python -m happypose.toolbox.utils.download --cosypose_models ycbv-refiner-finetune--251020
 
 # YCB-V Single-view refiner trained on synthetic data only
 # Only download this if you are interested in retraining the above model
-python -m happypose.toolbox.utils.download --cosypose_models=ycbv-refiner-syntonly--596719
+python -m happypose.toolbox.utils.download --cosypose_models ycbv-refiner-syntonly--596719
 
 # T-LESS coarse and refiner models
-python -m happypose.toolbox.utils.download --cosypose_models=tless-coarse--10219
-python -m happypose.toolbox.utils.download --cosypose_models=tless-refiner--585928
+python -m happypose.toolbox.utils.download --cosypose_models tless-coarse--10219 tless-refiner--585928
 ```
 
 ## 2D detections
 
 To ensure a fair comparison with prior works on both datasets, we use the same detections as DeepIM (from PoseCNN) on YCB-Video and the same as Pix2pose (from a RetinaNet model) on T-LESS. Download the saved 2D detections for both datasets using
 
 ```sh
-python -m happypose.toolbox.utils.download --detections=ycbv_posecnn
+python -m happypose.toolbox.utils.download --detections ycbv_posecnn
 
 # SiSo detections: 1 detection with highest per score per class per image on all images
 # Available for each image of the T-LESS dataset (primesense sensor)
 # These are the same detections as used in Pix2pose's experiments
-python -m happypose.toolbox.utils.download --detections=tless_pix2pose_retinanet_siso_top1
+python -m happypose.toolbox.utils.download --detections tless_pix2pose_retinanet_siso_top1
 
 # ViVo detections: All detections for a subset of 1000 images of T-LESS.
 # Used in our multi-view experiments.
-python -m happypose.toolbox.utils.download --detections=tless_pix2pose_retinanet_vivo_all
+python -m happypose.toolbox.utils.download --detections tless_pix2pose_retinanet_vivo_all
 ```
 
 If you are interested in re-training a detector, please see the BOP 2020 section.

diff --git a/docs/book/cosypose/inference.md b/docs/book/cosypose/inference.md
@@ -6,9 +6,10 @@ Here are provided the minimal commands you have to run in order to run the infer
 
 ```sh
  #ycbv
-python -m happypose.toolbox.utils.download --cosypose_models=detector-bop-ycbv-pbr--970850
-python -m happypose.toolbox.utils.download --cosypose_models=coarse-bop-ycbv-pbr--724183
-python -m happypose.toolbox.utils.download --cosypose_models=refiner-bop-ycbv-pbr--604090
+python -m happypose.toolbox.utils.download --cosypose_models \
+            detector-bop-ycbv-pbr--970850 \
+            coarse-bop-ycbv-pbr--724183 \
+            refiner-bop-ycbv-pbr--604090
 ```
 
 ## 2. Download YCB-V Dataset