diff --git a/docs/notebooks/UNet.ipynb b/docs/notebooks/UNet.ipynb index ee4103017e5837aa0b77d8cb553619099ca991c0..8cbc952fa4bc443596211c68c4f52325277ea837 100644 --- a/docs/notebooks/UNet.ipynb +++ b/docs/notebooks/UNet.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -114,6 +114,16 @@ "#### 1.2 Create folder structure" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Base path for the training data\n", + "base_path = os.path.expanduser(\"~/dataset\")" + ] + }, { "cell_type": "code", "execution_count": 12, @@ -132,9 +142,6 @@ } ], "source": [ - "# Base path for the training data\n", - "base_path = os.path.expanduser(\"~/dataset\")\n", - "\n", "# Create directories\n", "print(\"Creating directories:\")\n", "\n", @@ -290,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -333,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -437,14 +444,18 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "3D image shape: (128, 128, 128)\n" + "ename": "NameError", + "evalue": "name 'model' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# datasets and dataloaders\u001b[39;00m\n\u001b[0;32m 2\u001b[0m train_set, val_set, test_set \u001b[38;5;241m=\u001b[39m qim3d\u001b[38;5;241m.\u001b[39mml\u001b[38;5;241m.\u001b[39mprepare_datasets(path \u001b[38;5;241m=\u001b[39m base_path,\n\u001b[0;32m 3\u001b[0m val_fraction \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m,\n\u001b[1;32m----> 4\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m,\n\u001b[0;32m 5\u001b[0m augmentation \u001b[38;5;241m=\u001b[39m augmentation)\n\u001b[0;32m 8\u001b[0m train_loader, val_loader, test_loader \u001b[38;5;241m=\u001b[39m qim3d\u001b[38;5;241m.\u001b[39mml\u001b[38;5;241m.\u001b[39mprepare_dataloaders(train_set, \n\u001b[0;32m 9\u001b[0m val_set,\n\u001b[0;32m 10\u001b[0m test_set,\n\u001b[0;32m 11\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m 12\u001b[0m num_workers \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined" ] } ], @@ -479,9 +490,21 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'model' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# hyperparameters\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m hyperparameters \u001b[38;5;241m=\u001b[39m qim3d\u001b[38;5;241m.\u001b[39mml\u001b[38;5;241m.\u001b[39mHyperparameters(\u001b[43mmodel\u001b[49m, n_epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, \n\u001b[0;32m 3\u001b[0m learning_rate \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5e-3\u001b[39m, loss_function\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDiceCE\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 4\u001b[0m weight_decay\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-3\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined" + ] + } + ], "source": [ "# hyperparameters\n", "hyperparameters = qim3d.ml.Hyperparameters(model, n_epochs=10, \n", @@ -498,13 +521,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38e47017de7c422b81fe8a1465cedc74", + "model_id": "19121c117aba41af8d976943dba1d344", "version_major": 2, "version_minor": 0 }, @@ -519,13 +542,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0, train loss: 1.6603, val loss: 1.3486\n", - "Epoch 5, train loss: 1.4026, val loss: 0.4789\n" + "Epoch 0, train loss: 1.6856, val loss: 1.6405\n", + "Epoch 5, train loss: 1.4391, val loss: 0.5096\n" ] }, { "data": { - "image/png": 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", + "image/png": 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"text/plain": [ "<Figure size 1600x600 with 1 Axes>" ] @@ -539,6 +562,86 @@ "qim3d.ml.train_model(model, hyperparameters, train_loader, val_loader, plot=True)" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a0544269a5b6483eb38cebf99c0f4282", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0, train loss: 1.6080, val loss: 1.4188\n", + "Epoch 5, train loss: 1.2956, val loss: 1.2803\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1600x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load saved model \n", + "import qim3d\n", + "import glob\n", + "import torch\n", + "import os\n", + "import numpy as np\n", + "\n", + "base_path = os.path.expanduser(\"~/dataset\")\n", + "model = qim3d.ml.models.UNet(size = 'small')\n", + "augmentation = qim3d.ml.Augmentation(resize = 'crop', transform_train = 'light') # transform_train = 'None')\n", + "\n", + "# datasets and dataloaders\n", + "train_set, val_set, test_set = qim3d.ml.prepare_datasets(path = base_path,\n", + " val_fraction = 0.5,\n", + " model = model,\n", + " augmentation = augmentation)\n", + "\n", + "\n", + "train_loader, val_loader, test_loader = qim3d.ml.prepare_dataloaders(train_set, \n", + " val_set,\n", + " test_set,\n", + " batch_size = 1,\n", + " num_workers = 0)\n", + "\n", + "# for data in train_loader:\n", + "# inputs, targets = data\n", + "# inputs = inputs.squeeze().numpy()\n", + "# targets = targets.squeeze().numpy()\n", + "\n", + "# qim3d.viz.slices_grid(inputs, num_slices=5, display_figure=True)\n", + "# qim3d.viz.slices_grid(targets, num_slices=5)\n", + "\n", + "hyperparameters = qim3d.ml.Hyperparameters(model, n_epochs=10, \n", + " learning_rate = 5e-3, loss_function='DiceCE',\n", + " weight_decay=1e-3)\n", + "\n", + "qim3d.ml.train_model(model, hyperparameters, train_loader, val_loader, plot=True)\n", + "\n", + "# model.load_state_dict(torch.load(f'{base_path}/this_works.pth'))\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -548,25 +651,224 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of volumes: 2\n", + "Volume shape: (128, 128, 128)\n", + "Target shape: (128, 128, 128)\n", + "Preds shape: (128, 128, 128)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 5 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 5 Axes>" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Inference on train set\n", + "results = qim3d.ml.inference(train_set, model)\n", + "train_volume, train_target, train_preds = results[0]\n", + "\n", + "print(f\"Number of train volumes: {len(results)}\")\n", + "print(f\"Volume shape: {train_volume.shape}\")\n", + "print(f\"Target shape: {train_target.shape}\")\n", + "print(f\"Preds shape: {train_preds.shape}\")\n", + "\n", + "qim3d.viz.slices_grid(train_target, num_slices=5, display_figure=True)\n", + "qim3d.viz.slices_grid(train_preds, num_slices=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of test volumes: 1\n", + "Volume shape: (128, 128, 128)\n", + "Target shape: (128, 128, 128)\n", + "Preds shape: (128, 128, 128)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 5 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 5 Axes>" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Inference on test set\n", + "results = qim3d.ml.inference(test_set, model)\n", + "test_volume, test_target, test_preds = results[0]\n", + "\n", + "print(f\"Number of test volumes: {len(results)}\")\n", + "print(f\"Volume shape: {test_volume.shape}\")\n", + "print(f\"Target shape: {test_target.shape}\")\n", + "print(f\"Preds shape: {test_preds.shape}\")\n", + "\n", + "qim3d.viz.slices_grid(test_target, num_slices=5, display_figure=True)\n", + "qim3d.viz.slices_grid(test_preds, num_slices=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([0., 1.], dtype=float32), array([1885162, 211990], dtype=int64))\n", + "(array([0., 1.], dtype=float32), array([1911254, 185898], dtype=int64))\n" + ] + } + ], + "source": [ + "print(np.unique(target, return_counts=True))\n", + "print(np.unique(preds, return_counts=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "Input image must be (C,H,W) format", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "13c61cc3de4641b48aefe7c7cc517071", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Objects placed: 0%| | 0/5 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Inference volume\n", + "vol, label = qim3d.generate.noise_object_collection(\n", + " num_objects=5,\n", + " collection_shape=(128, 128, 128),\n", + " min_object_noise=0.03,\n", + " max_object_noise=0.08,\n", + ")\n", + "\n", + "# Convert N + 1 labels into 2 labels (background and object)\n", + "label = (label > 0).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume shape: (128, 128, 128)\n" + ] + } + ], + "source": [ + "print(f\"Volume shape: {vol.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Sizes of tensors must match except in dimension 1. Expected size 128 but got size 256 for tensor number 1 in the list.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[24], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m in_targ_preds_test \u001b[38;5;241m=\u001b[39m \u001b[43mqim3d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mml\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_set\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\s193396\\qim3d\\qim3d\\ml\\_ml_utils.py:214\u001b[0m, in \u001b[0;36minference\u001b[1;34m(data, model)\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 213\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 214\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput image must be (C,H,W) format\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 216\u001b[0m model\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 217\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n", - "\u001b[1;31mValueError\u001b[0m: Input image must be (C,H,W) format" + "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m inference_vol \u001b[38;5;241m=\u001b[39m \u001b[43mqim3d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mml\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvolume_inference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m qim3d\u001b[38;5;241m.\u001b[39mviz\u001b[38;5;241m.\u001b[39mslicer(inference_vol)\n", + "File \u001b[1;32mc:\\s193396\\qim3d\\qim3d\\ml\\_ml_utils.py:278\u001b[0m, in \u001b[0;36mvolume_inference\u001b[1;34m(volume, model, threshold)\u001b[0m\n\u001b[0;32m 276\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(input_with_channel, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 277\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m input_tensor\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m) \u001b[38;5;66;03m# TODO: Not sure if unsqueeze (add extra dimension) is necessary\u001b[39;00m\n\u001b[1;32m--> 278\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m>\u001b[39m threshold\n\u001b[0;32m 279\u001b[0m output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mcpu() \u001b[38;5;28;01mif\u001b[39;00m device \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m output\n\u001b[0;32m 280\u001b[0m output_detached \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mdetach()\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\s193396\\qim3d\\qim3d\\ml\\models\\_unet.py:83\u001b[0m, in \u001b[0;36mUNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m---> 83\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\monai\\networks\\nets\\unet.py:300\u001b[0m, in \u001b[0;36mUNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 299\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m--> 300\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m 216\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 217\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\monai\\networks\\layers\\simplelayers.py:129\u001b[0m, in \u001b[0;36mSkipConnection.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 128\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m--> 129\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcat\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 132\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcat([x, y], dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdim)\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m 216\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 217\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\s193396\\AppData\\Local\\miniconda3\\envs\\qim3d\\lib\\site-packages\\monai\\networks\\layers\\simplelayers.py:132\u001b[0m, in \u001b[0;36mSkipConnection.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 129\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubmodule(x)\n\u001b[0;32m 131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcat\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 132\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124madd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39madd(x, y)\n", + "\u001b[1;31mRuntimeError\u001b[0m: Sizes of tensors must match except in dimension 1. Expected size 128 but got size 256 for tensor number 1 in the list." ] } ], "source": [ - "# Needs to be updated to handle 3D as well \n", - "in_targ_preds_test = qim3d.ml.inference(test_set, model)" + "inference_vol = qim3d.ml.volume_inference(vol, model)\n", + "qim3d.viz.slicer(inference_vol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol_masked = qim3d.viz.vol_masked(vol, inference_vol, viz_delta=128)\n", + "qim3d.viz.slicer(vol_masked, color_map=\"PiYG\")" ] } ], diff --git a/qim3d/ml/_augmentations.py b/qim3d/ml/_augmentations.py index 30a2a7f0df34ccea9917e51d6abd0d0f8aeff7b5..219c208c16c1b720c92e6db6f98722043d1be590 100644 --- a/qim3d/ml/_augmentations.py +++ b/qim3d/ml/_augmentations.py @@ -53,8 +53,8 @@ class Augmentation: ValueError: If `level` is neither None, light, moderate nor heavy. """ from monai.transforms import ( - Compose, RandRotate90, RandFlip, RandAffine, ToTensor, \ - RandGaussianSmooth, NormalizeIntensity, Resize, CenterSpatialCrop, SpatialPad + Compose, RandRotate90d, RandFlipd, RandAffined, ToTensor, \ + RandGaussianSmoothd, NormalizeIntensityd, Resized, CenterSpatialCropd, SpatialPadd ) # Check if 2D or 3D @@ -74,41 +74,64 @@ class Augmentation: # For 2D, add normalization to the baseline augmentations # TODO: Figure out how to properly do this in 3D (normalization should be done channel-wise) if not self.is_3d: - baseline_aug.append(NormalizeIntensity(subtrahend=self.mean, divisor=self.std)) + # baseline_aug.append(NormalizeIntensity(subtrahend=self.mean, divisor=self.std)) + baseline_aug.append(NormalizeIntensityd(keys=["image"], subtrahend=self.mean, divisor=self.std)) # Resize augmentations if self.resize == 'crop': - resize_aug = [CenterSpatialCrop((im_d, im_h, im_w))] if self.is_3d else [CenterSpatialCrop((im_h, im_w))] + # resize_aug = [CenterSpatialCrop((im_d, im_h, im_w))] + resize_aug = [CenterSpatialCropd(keys=["image", "label"], roi_size=(im_d, im_h, im_w))] elif self.resize == 'reshape': - resize_aug = [Resize((im_d, im_h, im_w))] if self.is_3d else [Resize((im_h, im_w))] + # resize_aug = [Resize((im_d, im_h, im_w))] + resize_aug = [Resized(keys=["image", "label"], spatial_size=(im_d, im_h, im_w))] elif self.resize == 'padding': - resize_aug = [SpatialPad((im_d, im_h, im_w))] if self.is_3d else [SpatialPad((im_h, im_w))] + # resize_aug = [SpatialPad((im_d, im_h, im_w))] + resize_aug = [SpatialPadd(keys=["image", "label"], spatial_size=(im_d, im_h, im_w))] # Level of augmentation if level == None: + + # No augmentation for the validation and test sets level_aug = [] + resize_aug = [] elif level == 'light': - level_aug = [RandRotate90(prob=1, spatial_axes=(0, 1))] if self.is_3d else [RandRotate90(prob=1)] + # level_aug = [RandRotate90(prob=1, spatial_axes=(0, 1))] + level_aug = [RandRotate90d(keys=["image", "label"], prob=1, spatial_axes=(0, 1))] elif level == 'moderate': + # level_aug = [ + # RandRotate90(prob=1, spatial_axes=(0, 1)), + # RandFlip(prob=0.3, spatial_axis=0), + # RandFlip(prob=0.3, spatial_axis=1), + # RandGaussianSmooth(sigma_x=(0.7, 0.7), prob=0.1), + # RandAffine(prob=0.5, translate_range=(0.1, 0.1), scale_range=(0.9, 1.1)), + # ] level_aug = [ - RandRotate90(prob=1, spatial_axes=(0, 1)) if self.is_3d else RandRotate90(prob=1), - RandFlip(prob=0.3, spatial_axis=0), - RandFlip(prob=0.3, spatial_axis=1), - RandGaussianSmooth(sigma_x=(0.7, 0.7), prob=0.1), - RandAffine(prob=0.5, translate_range=(0.1, 0.1), scale_range=(0.9, 1.1)), - ] - + RandRotate90d(keys=["image", "label"], prob=1, spatial_axes=(0, 1)), + RandFlipd(keys=["image", "label"], prob=0.3, spatial_axis=0), + RandFlipd(keys=["image", "label"], prob=0.3, spatial_axis=1), + RandGaussianSmoothd(keys=["image"], sigma_x=(0.7, 0.7), prob=0.1), + RandAffined(keys=["image", "label"], prob=0.5, translate_range=(0.1, 0.1), scale_range=(0.9, 1.1)), + ] + elif level == 'heavy': - level_aug = [ - RandRotate90(prob=1, spatial_axes=(0, 1)) if self.is_3d else RandRotate90(prob=1), - RandFlip(prob=0.7, spatial_axis=0), - RandFlip(prob=0.7, spatial_axis=1), - RandGaussianSmooth(sigma_x=(1.2, 1.2), prob=0.3), - RandAffine(prob=0.5, translate_range=(0.2, 0.2), scale_range=(0.8, 1.4), shear_range=(-15, 15)) - ] + # level_aug = [ + # RandRotate90(prob=1, spatial_axes=(0, 1)), + # RandFlip(prob=0.7, spatial_axis=0), + # RandFlip(prob=0.7, spatial_axis=1), + # RandGaussianSmooth(sigma_x=(1.2, 1.2), prob=0.3), + # RandAffine(prob=0.5, translate_range=(0.2, 0.2), scale_range=(0.8, 1.4), shear_range=(-15, 15)) + # ] + level_aug = [ + RandRotate90d(keys=["image", "label"], prob=1, spatial_axes=(0, 1)), + RandFlipd(keys=["image", "label"], prob=0.7, spatial_axis=0), + RandFlipd(keys=["image", "label"], prob=0.7, spatial_axis=1), + RandGaussianSmoothd(keys=["image"], sigma_x=(1.2, 1.2), prob=0.3), + RandAffined(keys=["image", "label"], prob=0.5, translate_range=(0.2, 0.2), scale_range=(0.8, 1.4), shear_range=(-15, 15)) + ] + return Compose(baseline_aug + resize_aug + level_aug) \ No newline at end of file diff --git a/qim3d/ml/_data.py b/qim3d/ml/_data.py index 0a8fca48a613dee02c361e987c54b9a7d678279b..d2bd93a148e3b728bd97b5487fa595d5d6764d21 100644 --- a/qim3d/ml/_data.py +++ b/qim3d/ml/_data.py @@ -104,8 +104,12 @@ class Dataset(torch.utils.data.Dataset): target = target.transpose((2, 0, 1)) if self.transform: - image = self.transform(image) # uint8 - target = self.transform(target) # int32 + transformed = self.transform({"image": image, "label": target}) + image = transformed["image"] + target = transformed["label"] + + # image = self.transform(image) # uint8 + # target = self.transform(target) # int32 # TODO: Which dtype? image = image.clone().detach().to(dtype=torch.float32) @@ -160,7 +164,7 @@ def check_resize( orig_shape (tuple): Original shape of the image. resize (tuple): Desired resize dimensions. n_channels (int): Number of channels in the model. - is_3d (bool): Whether the data is 3D or not. + is_3d (bool): If True, the input data is 3D. Otherwise the input data is 2D. Defaults to True. Returns: tuple: Final resize dimensions. @@ -230,7 +234,12 @@ def check_resize( return final_h, final_w -def prepare_datasets(path: str, val_fraction: float, model: nn.Module, augmentation: Augmentation) -> tuple[torch.utils.data.Subset, torch.utils.data.Subset, torch.utils.data.Subset]: +def prepare_datasets( + path: str, + val_fraction: float, + model: nn.Module, + augmentation: Augmentation, + ) -> tuple[torch.utils.data.Subset, torch.utils.data.Subset, torch.utils.data.Subset]: """ Splits and augments the train/validation/test datasets. diff --git a/qim3d/ml/_ml_utils.py b/qim3d/ml/_ml_utils.py index d926024935635123f4b1a186ffb6dd1a1c528840..b90c00148b5414950bfe9e15dc96548c97f67e60 100644 --- a/qim3d/ml/_ml_utils.py +++ b/qim3d/ml/_ml_utils.py @@ -132,6 +132,10 @@ def train_model( f"Epoch {epoch: 3}, train loss: {train_loss['loss'][epoch]:.4f}, " f"val loss: {val_loss['loss'][epoch]:.4f}" ) + + # NOTE: Delete this again + # Save model checkpoint to .pth file + torch.save(model.state_dict(), "C:/Users\s193396/dataset/model.pth") if plot: plot_metrics(train_loss, val_loss, labels=["Train", "Valid."], show=True) @@ -163,7 +167,12 @@ def model_summary(dataloader: torch.utils.data.DataLoader, model: torch.nn.Modul return model_s -def inference(data: torch.utils.data.Dataset, model: torch.nn.Module) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: +def inference( + data: torch.utils.data.Dataset, + model: torch.nn.Module, + threshold: float = 0.5, + is_3d: bool = True, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Performs inference on input data using the specified model. Performs inference on the input data using the provided model. The input data should be in the form of a list, @@ -177,6 +186,8 @@ def inference(data: torch.utils.data.Dataset, model: torch.nn.Module) -> tuple[t data (torch.utils.data.Dataset): A Torch dataset containing input image and ground truth label data. model (torch.nn.Module): The trained network model used for predicting segmentations. + threshold (float): The threshold value used to binarize the model predictions. + is_3d (bool): If True, the input data is 3D. Otherwise the input data is 2D. Defaults to True. Returns: tuple: A tuple containing the input images, target labels, and predicted labels. @@ -194,59 +205,130 @@ def inference(data: torch.utils.data.Dataset, model: torch.nn.Module) -> tuple[t model = MySegmentationModel() qim3d.ml.inference(data,model) """ - - # Get device + # Set model to evaluation mode device = "cuda" if torch.cuda.is_available() else "cpu" + model.to(device) + model.eval() - # Check if data have the right format - if not isinstance(data[0], tuple): - raise ValueError("Data items must be tuples") + results = [] - # Check if data is torch tensors - for element in data[0]: - if not isinstance(element, torch.Tensor): - raise ValueError("Data items must consist of tensors") + # 3D data + if is_3d: + for volume, target in data: + if not isinstance(volume, torch.Tensor) or not isinstance(target, torch.Tensor): + raise ValueError("Data items must consist of tensors") - # Check if input image is (C,H,W) format - if data[0][0].dim() == 3 and (data[0][0].shape[0] in [1, 3]): - pass - else: - raise ValueError("Input image must be (C,H,W) format") + # Add batch and channel dimensions + volume = volume.unsqueeze(0).to(device) # Shape: [1, 1, D, H, W] + target = target.unsqueeze(0).to(device) # Shape: [1, 1, D, H, W] - model.to(device) - model.eval() + with torch.no_grad(): + + # Get model predictions (logits) + output = model(volume) - # Make new list such that possible augmentations remain identical for all three rows - plot_data = [data[idx] for idx in range(len(data))] + # Convert logits to probabilities [0, 1] + preds = torch.sigmoid(output) - # Create input and target batch - inputs = torch.stack([item[0] for item in plot_data], dim=0).to(device) - targets = torch.stack([item[1] for item in plot_data], dim=0) + # Convert to binary mask by thresholding the probabilities + preds = (preds > threshold).float() - # Get output predictions - with torch.no_grad(): - outputs = model(inputs) + # Remove batch and channel dimensions + volume = volume.squeeze().cpu().numpy() + target = target.squeeze().cpu().numpy() + preds = preds.squeeze().cpu().numpy() - # Prepare data for plotting - inputs = inputs.cpu().squeeze() - targets = targets.squeeze() - if outputs.shape[1] == 1: - preds = ( - outputs.cpu().squeeze() > 0.5 - ) # TODO: outputs from model are not between [0,1] yet, need to implement that + # Append results to list + results.append((volume, target, preds)) + + # 2D data else: - preds = outputs.cpu().argmax(axis=1) + # Check if data have the right format + if not isinstance(data[0], tuple): + raise ValueError("Data items must be tuples") - # if there is only one image - if inputs.dim() == 2: - inputs = inputs.unsqueeze(0) # TODO: Not sure if unsqueeze (add extra dimension) is necessary - targets = targets.unsqueeze(0) - preds = preds.unsqueeze(0) + # Check if data is torch tensors + for element in data[0]: + if not isinstance(element, torch.Tensor): + raise ValueError("Data items must consist of tensors") - return inputs, targets, preds + for inputs, targets in data: + inputs = inputs.to(device) + targets = targets.to(device) + with torch.no_grad(): + outputs = model(inputs) -def volume_inference(volume: np.ndarray, model: torch.nn.Module, threshold:float = 0.5) -> np.ndarray: + # Prepare data for plotting + inputs_cpu = inputs.cpu().squeeze() + targets_cpu = targets.cpu().squeeze() + if outputs.shape[1] == 1: + preds = outputs.cpu().squeeze() > threshold + else: + preds = outputs.cpu().argmax(axis=1) + + # If there is only one image + if inputs_cpu.dim() == 2: + inputs_cpu = inputs_cpu.unsqueeze(0).numpy() + targets_cpu = targets_cpu.unsqueeze(0).numpy() + preds = preds.unsqueeze(0).numpy() + + # Append results to list + results.append((inputs_cpu, targets_cpu, preds)) + + return results + + # Old implementation: + # else: + # # Check if data have the right format + # if not isinstance(data[0], tuple): + # raise ValueError("Data items must be tuples") + + # # Check if data is torch tensors + # for element in data[0]: + # if not isinstance(element, torch.Tensor): + # raise ValueError("Data items must consist of tensors") + + # # Check if input image is (C,H,W) format + # if data[0][0].dim() == 3 and (data[0][0].shape[0] in [1, 3]): + # pass + # else: + # raise ValueError("Input image must be (C,H,W) format") + + # # Make new list such that possible augmentations remain identical for all three rows + # plot_data = [data[idx] for idx in range(len(data))] + + # # Create input and target batch + # inputs = torch.stack([item[0] for item in plot_data], dim=0).to(device) + # targets = torch.stack([item[1] for item in plot_data], dim=0) + + # # Get output predictions + # with torch.no_grad(): + # outputs = model(inputs) + + # # Prepare data for plotting + # inputs = inputs.cpu().squeeze() + # targets = targets.squeeze() + # if outputs.shape[1] == 1: + # preds = ( + # outputs.cpu().squeeze() > threshold + # ) # TODO: outputs from model are not between [0,1] yet, need to implement that + # else: + # preds = outputs.cpu().argmax(axis=1) + + # # if there is only one image + # if inputs.dim() == 2: + # inputs = inputs.unsqueeze(0) # TODO: Not sure if unsqueeze (add extra dimension) is necessary + # targets = targets.unsqueeze(0) + # preds = preds.unsqueeze(0) + + # return inputs, targets, preds + +def volume_inference( + volume: np.ndarray, + model: torch.nn.Module, + threshold:float = 0.5, + ) -> np.ndarray: """ Compute on the entire volume Args: diff --git a/qim3d/ml/models/_unet.py b/qim3d/ml/models/_unet.py index 22e7794b3d5f58133956842067a5b77a68f9d1fa..5d47b61c26d163137f8674fa942b0c04c6c0857b 100644 --- a/qim3d/ml/models/_unet.py +++ b/qim3d/ml/models/_unet.py @@ -69,7 +69,8 @@ class UNet(nn.Module): in_channels=1, # TODO: check if image has 1 or multiple input channels out_channels=1, channels=self.channels, - strides=(2,) * (len(self.channels) - 1), + strides=(2,) * (len(self.channels) - 1), # TODO: Check if the strides are correct? + num_res_units=2, # TODO: This was not here before kernel_size=self.kernel_size, up_kernel_size=self.up_kernel_size, act=self.activation,