diff --git a/notebooks/README.md b/notebooks/frrd-60/README.md similarity index 97% rename from notebooks/README.md rename to notebooks/frrd-60/README.md index 5920abe..5190245 100644 --- a/notebooks/README.md +++ b/notebooks/frrd-60/README.md @@ -1,5 +1,3 @@ -# Jupyter Notebooks - ## FRRD-60: Consistency as a feature discriminator for Novelty Detection Hypothesis: Consistency can be used as a measure to discriminate between normal diff --git a/notebooks/frrd-80/.gitignore b/notebooks/frrd-80/.gitignore new file mode 100644 index 0000000..87f3e1f --- /dev/null +++ b/notebooks/frrd-80/.gitignore @@ -0,0 +1 @@ +!*.ckpt \ No newline at end of file diff --git a/notebooks/frrd-80/README.md b/notebooks/frrd-80/README.md new file mode 100644 index 0000000..efeb715 --- /dev/null +++ b/notebooks/frrd-80/README.md @@ -0,0 +1,5 @@ +# Jupyter Notebooks + +## FRRD-80: Visualization of FixMatch + +This simply shows the decision boundaries constructed by the FixMatch model. diff --git 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/dev/null and b/notebooks/frrd-80/epoch=4-step=750.ckpt differ diff --git a/notebooks/frrd-80/epoch=5-step=900.ckpt b/notebooks/frrd-80/epoch=5-step=900.ckpt new file mode 100644 index 0000000..7f0cea7 Binary files /dev/null and b/notebooks/frrd-80/epoch=5-step=900.ckpt differ diff --git a/notebooks/frrd-80/nb.ipynb b/notebooks/frrd-80/nb.ipynb new file mode 100644 index 0000000..750045b --- /dev/null +++ b/notebooks/frrd-80/nb.ipynb @@ -0,0 +1,54255 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "766f20d6021303d8", + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-06-04T04:53:05.673267600Z", + "start_time": "2024-06-04T04:53:05.663612600Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=0-step=150.ckpt\n", + "epoch=1-step=300.ckpt\n", + "epoch=2-step=450.ckpt\n", + "epoch=3-step=600.ckpt\n", + "epoch=4-step=750.ckpt\n", + "epoch=5-step=900.ckpt\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "import os\n", + "from pathlib import Path\n", + "\n", + "# disable gpu\n", + "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n", + "\n", + "curr_dir = Path(os.path.abspath(\"\"))\n", + "model_paths = list(curr_dir.glob(\"*.ckpt\"))\n", + "for model_path in model_paths:\n", + " print(model_path.name)" + ] + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from scipy.spatial import ConvexHull\n", + "from sklearn.preprocessing import OrdinalEncoder\n", + "from sklearn.svm import SVC\n", + "\n", + "\n", + "def get_df_lda(model_paths: list[str | Path]):\n", + " from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n", + " import torch\n", + "\n", + " ds = FRDCDatasetStaticEval(\n", + " \"chestnut_nature_park\",\n", + " \"20201218\",\n", + " None,\n", + " transform=val_preprocess(255),\n", + " )\n", + " xs_raw, ys_raw = list(zip(*ds))\n", + " xs_raw = torch.stack(xs_raw)\n", + "\n", + " def extract_model(model_path):\n", + " m = EfficientNetB1FixMatchModule.load_from_checkpoint(\n", + " model_path\n", + " ).eval()\n", + " m: EfficientNetB1FixMatchModule\n", + " m_eff = m.eff\n", + " m_fc = m.fc[0]\n", + " return m_eff, m_fc, m.x_scaler, m.y_encoder\n", + "\n", + " def extract_lda(xs_eff, xs_fc, ys, lda_eff, lda_fc, n_components=2):\n", + " if lda_eff is None:\n", + " lda_eff = LinearDiscriminantAnalysis(n_components=n_components)\n", + " lda_eff.fit(xs_eff, ys)\n", + " if lda_fc is None:\n", + " lda_fc = LinearDiscriminantAnalysis(n_components=n_components)\n", + " lda_fc.fit(xs_fc, ys)\n", + " xs_eff_lda = lda_eff.transform(xs_eff)\n", + " xs_fc_lda = lda_fc.transform(xs_fc)\n", + " return xs_eff_lda, xs_fc_lda, lda_eff, lda_fc\n", + "\n", + " dfs = []\n", + "\n", + " lda_eff = None\n", + " lda_fc = None\n", + "\n", + " for step, model_path in tqdm(\n", + " enumerate(model_paths),\n", + " total=len(model_paths),\n", + " desc=\"Extracting LDA\",\n", + " ):\n", + " # Extracts the Eff and FC layer\n", + " # and the X scaler and Y encoder\n", + " m_eff, m_fc, x_scaler, y_encoder = extract_model(model_path)\n", + "\n", + " # Preprocess the data\n", + " (xs, ys), _xs_unl = preprocess(xs_raw, ys_raw, x_scaler, y_encoder)\n", + "\n", + " # Extract the LDA\n", + " xs_eff_lda, xs_fc_lda, lda_eff, lda_fc = extract_lda(\n", + " (xs_eff := m_eff(xs)).detach().numpy(),\n", + " m_fc(xs_eff).detach().numpy(),\n", + " ys,\n", + " lda_eff=lda_eff,\n", + " lda_fc=lda_fc,\n", + " )\n", + "\n", + " # Construct the dataframe\n", + " df = pd.DataFrame(xs_fc_lda, columns=[\"x\", \"y\"])\n", + " df[\"label\"] = ys_raw\n", + " df[\"label\"] = df[\"label\"].astype(\"category\")\n", + " df[\"step\"] = step\n", + " df[[\"orient\", \"sample\"]] = [(i % 8, i // 8) for i in range(len(df))]\n", + " dfs.append(df)\n", + " df = pd.concat(dfs, ignore_index=True)\n", + " return df\n", + "\n", + "\n", + "def plot_svm_boundaries(\n", + " x,\n", + " y,\n", + " labels,\n", + " xy_range=25,\n", + " resolution=200,\n", + " contour_opacity=0.5,\n", + " width=800,\n", + " height=800,\n", + ") -> go.Figure:\n", + " \"\"\"Plot the data points and the decision boundaries.\n", + "\n", + " Args:\n", + " x: The x values of the data points.\n", + " y: The y values of the data points.\n", + " labels: The labels of the data points.\n", + " xy_range: The range of the x and y axes.\n", + " resolution: The resolution of the meshgrid.\n", + " contour_opacity: The opacity of the decision boundaries.\n", + " width: The width of the plot.\n", + " height: The height of the plot.\n", + "\n", + " \"\"\"\n", + " labels_int = (\n", + " OrdinalEncoder()\n", + " .fit_transform(np.array(labels).reshape(-1, 1))\n", + " .flatten()\n", + " )\n", + " xy = np.c_[x, y]\n", + " # Create a scatter plot\n", + " fig = go.Figure(\n", + " data=go.Scatter(\n", + " x=x,\n", + " y=y,\n", + " mode=\"markers\",\n", + " marker=dict(\n", + " color=labels_int, # Use 'Jet' color scale\n", + " size=10,\n", + " colorscale=\"jet\",\n", + " # Border\n", + " line=dict(color=\"white\", width=1),\n", + " ),\n", + " # Add names as text\n", + " text=labels,\n", + " hovertemplate=\"%{text}\",\n", + " ),\n", + " )\n", + "\n", + " # For each class in ys_p, we retrieve the xs_lda values\n", + " # then find the convex hull and draw it onto the plot\n", + " for lab in np.unique(labels):\n", + " xy_lab = xy[labels == lab]\n", + " hull = ConvexHull(xy_lab)\n", + " for simplex in hull.simplices:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=xy_lab[simplex, 0],\n", + " y=xy_lab[simplex, 1],\n", + " mode=\"lines\",\n", + " line=dict(color=\"white\"),\n", + " hoverinfo=\"skip\", # No hover info\n", + " ),\n", + " )\n", + "\n", + " # Fit the SVC model\n", + " clf = SVC()\n", + " clf.fit(xy, labels_int)\n", + "\n", + " # Create a meshgrid for contour plot\n", + " domain = np.linspace(-xy_range, xy_range, resolution)\n", + " xx, yy = np.meshgrid(domain, domain)\n", + " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + "\n", + " # Add contour plot to the figure with transparency\n", + " fig.add_trace(\n", + " go.Contour(\n", + " x=domain,\n", + " y=domain,\n", + " z=Z,\n", + " showscale=False,\n", + " opacity=contour_opacity,\n", + " colorscale=\"jet\",\n", + " hoverinfo=\"skip\", # No hover info\n", + " )\n", + " )\n", + "\n", + " # Make the aspect ratio square\n", + " fig.update_yaxes(scaleanchor=\"x\", scaleratio=1)\n", + " fig.update_layout(showlegend=False, width=width, height=height)\n", + " return fig\n", + "\n", + "\n", + "def plot_anim(\n", + " x, y, label, step, xy_range=25, width=800, height=800\n", + ") -> go.Figure:\n", + " \"\"\"Plot an animation of the data points.\n", + "\n", + " Args:\n", + " x: The x values of the data points.\n", + " y: The y values of the data points.\n", + " label: The labels of the data points.\n", + " step: The step of the animation.\n", + " xy_range: The range of the x and y axes.\n", + " width: The width of the plot.\n", + " height: The height of the plot.\n", + " \"\"\"\n", + " fig = px.scatter(\n", + " x=x,\n", + " y=y,\n", + " animation_frame=step,\n", + " color=label,\n", + " hover_name=label,\n", + " range_x=[-xy_range, xy_range],\n", + " range_y=[-xy_range, xy_range],\n", + " )\n", + " fig.update_layout(width=width, height=height)\n", + " return fig\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-06-04T04:53:08.555154Z", + "start_time": "2024-06-04T04:53:05.678486700Z" + } + }, + "id": "2c65e7d2029dab4b", + "execution_count": 2 + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d36dce5890dba29c", + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-06-04T04:53:22.493880800Z", + "start_time": "2024-06-04T04:53:08.558559200Z" + } + }, + "outputs": [], + "source": [ + "from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n", + "import torch\n", + "\n", + "ds = FRDCDatasetStaticEval(\n", + " \"chestnut_nature_park\", \"20201218\", None,\n", + " transform=val_preprocess(255),\n", + ")\n", + "xs_raw, ys_raw = list(zip(*ds))\n", + "xs_raw = torch.stack(xs_raw)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ad591487ed18579b", + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-06-04T04:53:50.135046600Z", + "start_time": "2024-06-04T04:53:22.497162200Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Extracting LDA: 100%|██████████| 6/6 [00:23<00:00, 3.95s/it]\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "from frdc.train.utils import preprocess\n", + "from frdc.models.efficientnetb1 import EfficientNetB1FixMatchModule\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "\n", + "import pandas as pd\n", + "\n", + "\n", + "def extract_model(model_path):\n", + " m = EfficientNetB1FixMatchModule.load_from_checkpoint(model_path).eval()\n", + " m: EfficientNetB1FixMatchModule\n", + " m_eff = m.eff\n", + " m_fc = m.fc[0]\n", + " return m_eff, m_fc, m.x_scaler, m.y_encoder\n", + "\n", + "\n", + "def extract_lda(xs_eff, xs_fc, ys, lda_eff, lda_fc, n_components=2):\n", + " if lda_eff is None:\n", + " lda_eff = LinearDiscriminantAnalysis(n_components=n_components)\n", + " lda_eff.fit(xs_eff, ys)\n", + " if lda_fc is None:\n", + " lda_fc = LinearDiscriminantAnalysis(n_components=n_components)\n", + " lda_fc.fit(xs_fc, ys)\n", + " xs_eff_lda = lda_eff.transform(xs_eff)\n", + " xs_fc_lda = lda_fc.transform(xs_fc)\n", + " return xs_eff_lda, xs_fc_lda, lda_eff, lda_fc\n", + "\n", + "\n", + "dfs = []\n", + "\n", + "lda_eff = None\n", + "lda_fc = None\n", + "\n", + "for step, model_path in tqdm(\n", + " enumerate(model_paths),\n", + " total=len(model_paths),\n", + " desc=\"Extracting LDA\",\n", + "):\n", + " # Extracts the Eff and FC layer\n", + " # and the X scaler and Y encoder\n", + " m_eff, m_fc, x_scaler, y_encoder = extract_model(model_path)\n", + "\n", + " # Preprocess the data\n", + " (xs, ys), _xs_unl = preprocess(xs_raw, ys_raw, x_scaler, y_encoder)\n", + "\n", + " # Extract the LDA\n", + " xs_eff_lda, xs_fc_lda, lda_eff, lda_fc = extract_lda(\n", + " (xs_eff := m_eff(xs)).detach().numpy(),\n", + " m_fc(xs_eff).detach().numpy(),\n", + " ys,\n", + " lda_eff=lda_eff,\n", + " lda_fc=lda_fc,\n", + " )\n", + "\n", + " # Construct the dataframe\n", + " df = pd.DataFrame(xs_fc_lda, columns=['x', 'y'])\n", + " df['label'] = ys_raw\n", + " df['label'] = df['label'].astype('category')\n", + " df['step'] = step\n", + " df[['orient', 'sample']] = [(i % 8, i // 8) for i in range(len(df))]\n", + " dfs.append(df)\n", + "df = pd.concat(dfs, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "478f6eb2a9eaf3c4", + "metadata": { + "collapsed": false, 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color=Campnosperma Auriculatum
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color=Leea Indica
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color=Dillenia Suffruticosa
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color=Sandoricum Koetjape
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color=Cinnamomum Iners
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color=Shorea Leprosula
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color=Alstonia Angulstiloba
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color=Terminalia Catappa
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color=Falcataria Moluccana
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color=Campnosperma Auriculatum
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color=Falcataria Moluccana
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When we loop through `x_unls` (unlabelled + batches), the gradient tape accumulates unnecessarily. + We actually just need to back propagate every batch in `x_unls`, + thus cutting the tape shorter. + + Every tape is bounded by the code: + + >>> opt.zero_grad() + >>> # Steps that requires grad + >>> loss = ... + >>> self.manual_backward(loss) + >>> opt.step() + + The losses are defined as follows: + + ℓ_lbl 1 + Labelled loss: --- Σ CE(y_true, y_weak) + n ∀ lbl + + ℓ_unl 1 if : y_weak > t, + Unlabelled loss: --- Σ { then: CE(y_strong, y_weak) } + n ∀ unl else: 0 + ℓ + Loss: ℓ_lbl + ℓ_unl + """ def training_step(self, batch, batch_idx): (x_lbl, y_lbl), x_unls = batch opt = self.optimizers() - opt.zero_grad() - - self.log("train/x_lbl_mean", x_lbl.mean()) - self.log("train/x_lbl_stdev", x_lbl.std()) - wandb.log({"train/x_lbl": wandb_hist(y_lbl, self.n_classes)}) + # Backprop for labelled data + opt.zero_grad() loss_lbl = F.cross_entropy((y_lbl_pred := self(x_lbl)), y_lbl.long()) self.manual_backward(loss_lbl) opt.step() - wandb.log( - { - "train/y_lbl_pred": wandb_hist( - torch.argmax(y_lbl_pred, dim=1), self.n_classes - ) - } - ) + # This is only for logging purposes loss_unl = 0 + # Backprop for unlabelled data for x_weak, x_strong in x_unls: opt.zero_grad() - self.log("train/x0_unl_mean", x_weak[0].mean()) - self.log("train/x0_unl_stdev", x_weak[0].std()) - with torch.no_grad(): - y_pred_weak = self(x_weak) - y_pred_weak_max, y_pred_weak_max_ix = torch.max( - y_pred_weak, dim=1 - ) - is_confident = y_pred_weak_max >= self.unl_conf_threshold - y_pred_strong = self(x_strong[is_confident]) + # Test if y_weak is over the threshold + # if so, include into the loss + # else, we simply mask it out + with torch.no_grad(): + y_weak = self(x_weak) + y_weak_max, y_weak_max_ix = torch.max(y_weak, dim=1) + is_confident = y_weak_max >= self.unl_conf_threshold + + y_strong = self(x_strong[is_confident]) + + # CE only on the masked out samples + # We perform `reduction="sum"` so that we "include" the masked out + # samples by fixing the denominator. + # E.g. + # y_weak > t = [T, F, T, F] + # Losses = [1, 2, 3, 4] + # Masked Losses = [1, 3, ] + # Incorrect CE Mean = (1 + 3) / 2 + # Correct CE Mean = (1 + 3) / 4 + batch_size = x_lbl.shape[0] loss_unl_i = F.cross_entropy( - y_pred_strong, - y_pred_weak_max_ix[is_confident], + y_strong, + y_weak_max_ix[is_confident], reduction="sum", - ) / (len(x_unls) * x_lbl.shape[0]) + ) / (len(x_unls) * batch_size) self.manual_backward(loss_unl_i) opt.step() loss_unl += loss_unl_i.detach().item() + self.log("train/x0_unl_mean", x_weak[0].mean()) + self.log("train/x0_unl_stdev", x_weak[0].std()) wandb.log( { "train/y_unl_pred": wandb_hist( - torch.argmax(y_pred_strong, dim=1), self.n_classes + torch.argmax(y_strong, dim=1), self.n_classes ) } ) - self.log("train/ce_loss_lbl", loss_lbl) - self.log("train/ce_loss_unl", loss_unl) - self.log("train/loss", loss_lbl + loss_unl) - # Evaluate train accuracy with torch.no_grad(): y_pred = self(x_lbl) acc = accuracy( y_pred, y_lbl, task="multiclass", num_classes=y_pred.shape[1] ) - self.log("train/acc", acc, prog_bar=True) + + self.log("train/x_lbl_mean", x_lbl.mean()) + self.log("train/x_lbl_stdev", x_lbl.std()) + wandb.log({"train/x_lbl": wandb_hist(y_lbl, self.n_classes)}) + self.log("train/ce_loss_lbl", loss_lbl) + self.log("train/ce_loss_unl", loss_unl) + self.log("train/loss", loss_lbl + loss_unl) + self.log("train/acc", acc, prog_bar=True) + + wandb.log( + { + "train/y_lbl_pred": wandb_hist( + torch.argmax(y_lbl_pred, dim=1), self.n_classes + ) + } + ) def validation_step(self, batch, batch_idx): # The batch outputs x_unls due to our on_before_batch_transfer (x, y), _x_unls = batch wandb.log({"val/y_lbl": wandb_hist(y, self.n_classes)}) y_pred = self(x) + loss = F.cross_entropy(y_pred, y.long()) + acc = accuracy( + y_pred, y, task="multiclass", num_classes=self.n_classes + ) + + wandb.log({"val/y_lbl": wandb_hist(y, self.n_classes)}) wandb.log( { "val/y_lbl_pred": wandb_hist( @@ -144,12 +191,6 @@ def validation_step(self, batch, batch_idx): ) } ) - loss = F.cross_entropy(y_pred, y.long()) - - acc = accuracy( - y_pred, y, task="multiclass", num_classes=y_pred.shape[1] - ) - self.log("val/ce_loss", loss) self.log("val/acc", acc, prog_bar=True) return loss @@ -161,7 +202,7 @@ def test_step(self, batch, batch_idx): loss = F.cross_entropy(y_pred, y.long()) acc = accuracy( - y_pred, y, task="multiclass", num_classes=y_pred.shape[1] + y_pred, y, task="multiclass", num_classes=self.n_classes ) self.log("test/ce_loss", loss) self.log("test/acc", acc, prog_bar=True)