From 24923e92763297d93b70be7f6280c8b1c6d032ff Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Anna=20B=C3=B8gevang=20Ekner?= <s193396@dtu.dk>
Date: Tue, 11 Feb 2025 14:58:24 +0100
Subject: [PATCH] testing things in notebook

---
 docs/notebooks/UNet.ipynb | 347 +++++++++++++++++++++++++++++++-------
 1 file changed, 287 insertions(+), 60 deletions(-)

diff --git a/docs/notebooks/UNet.ipynb b/docs/notebooks/UNet.ipynb
index 1c39366d..56094eb2 100644
--- a/docs/notebooks/UNet.ipynb
+++ b/docs/notebooks/UNet.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,32 +46,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d9b9bb97fe7d4828b5b7f359cb8ac50a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Objects placed:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "num_objects = 5\n",
-    "vol, labels = qim3d.generate.noise_object_collection(\n",
-    "    num_objects = num_objects,\n",
-    "    collection_shape = (128, 128, 128),\n",
-    "    min_object_noise = 0.03, \n",
-    "    max_object_noise = 0.08,\n",
-    "    )"
+    "# num_objects = 5\n",
+    "# vol, labels = qim3d.generate.noise_object_collection(\n",
+    "#     num_objects = num_objects,\n",
+    "#     collection_shape = (128, 128, 128),\n",
+    "#     min_object_noise = 0.03, \n",
+    "#     max_object_noise = 0.08,\n",
+    "#     )"
    ]
   },
   {
@@ -108,8 +93,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Convert N + 1 labels into 2 labels (background and object)\n",
-    "labels = (labels > 0).astype(int)"
+    "# # Convert N + 1 labels into 2 labels (background and object)\n",
+    "# labels = (labels > 0).astype(int)"
    ]
   },
   {
@@ -152,8 +137,10 @@
     "\n",
     "# Create directories\n",
     "print(\"Creating directories:\")\n",
+    "\n",
     "for folder_split in [\"train\", \"test\"]:\n",
     "    for folder_type in [\"images\", \"labels\"]:\n",
+    "        \n",
     "        path = os.path.join(base_path, folder_split, folder_type)\n",
     "        os.makedirs(path, exist_ok=True)\n",
     "        print(path)\n",
@@ -183,40 +170,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_samples = 5\n",
+    "# Specify the number of training and testing samples\n",
+    "num_train = 4\n",
+    "num_test = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68e0874197f643a396e201261a73569b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Objects placed:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6902ade22e8b4336ac72814060f9a1ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Objects placed:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31f9905cd13940d48ff27b03142640ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Objects placed:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4402a10f2b454b359a5b91735a546725",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Objects placed:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74e74cfafb6e4cccb3b98879374e1285",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Objects placed:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Specify the number of objects in each volume\n",
+    "num_objects = 5\n",
+    "\n",
+    "# Generate training and testing samples\n",
+    "for idx in range(num_train + num_test):\n",
+    "\n",
+    "    # Determine the folder split (train or test)\n",
+    "    if idx < num_train:\n",
+    "        folder_split = \"train\"\n",
+    "\n",
+    "    else:\n",
+    "        folder_split = \"test\"\n",
     "\n",
-    "for idx in range(num_samples):\n",
     "    # TODO: Figure out whether or not the seed makes it such that all volumes are identical?\n",
     "\n",
     "    vol, label = qim3d.generate.noise_object_collection(\n",
-    "        num_objects = num_objects,\n",
-    "        collection_shape = (128, 128, 128),\n",
-    "        min_object_noise = 0.03, \n",
-    "        max_object_noise = 0.08,\n",
-    "        )\n",
+    "        num_objects=num_objects,\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 = (labels > 0).astype(int)\n",
+    "    label = (label > 0).astype(int)\n",
     "\n",
     "    # Save volume\n",
-    "    qim3d.io.save(os.path.join(base_path, folder_split, \"images\", f\"{idx}.nii.gz\"), vol, replace = True)\n",
+    "    qim3d.io.save(os.path.join(base_path, folder_split, \"images\", f\"{idx + 1}.nii.gz\"), vol, compression = True, replace=True)\n",
     "\n",
     "    # Save label\n",
-    "    qim3d.io.save(os.path.join(base_path, folder_split, \"labels\", f\"{idx}.nii.gz\"), label, replace = True)"
+    "    qim3d.io.save(os.path.join(base_path, folder_split, \"labels\", f\"{idx + 1}.nii.gz\"), label, compression = True, replace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image indices for training data.....: [1, 2, 3, 4]\n",
+      "Image indices for testing data......: [5]\n"
+     ]
+    }
+   ],
    "source": [
-    "# volumes = sorted(glob.glob(os.path.join(base_path, \"im*.nii.gz\")))\n",
-    "# labels = sorted(glob.glob(os.path.join(base_path, \"seg*.nii.gz\")))"
+    "# Check the image indices for training and testing data\n",
+    "for folder_split in [\"train\", \"test\"]:\n",
+    "\n",
+    "    path = os.path.join(base_path, folder_split, \"images\")\n",
+    "    files = os.listdir(path)\n",
+    "    files = [int(os.path.basename(f)[0]) for f in files]\n",
+    "\n",
+    "    if folder_split == \"train\":\n",
+    "        print(f\"Image indices for training data.....: {files}\")\n",
+    "    \n",
+    "    elif folder_split == \"test\":\n",
+    "        print(f\"Image indices for testing data......: {files}\")\n"
    ]
   },
   {
@@ -235,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,21 +365,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae55eb4ba14f44118283f31acbea6ad6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Objects placed:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before augmentation: (128, 128, 128) and uint8\n",
+      "Augmentation: <qim3d.ml._augmentations.Augmentation object at 0x0000028B367D1870>\n",
+      "\n",
+      "After channel expansion and augmentation: torch.Size([1, 128, 128, 128])\n",
+      "Dtype after augmentation: torch.uint8\n",
+      "\n",
+      "After reducing channel dimension: (128, 128, 128)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Debug stuff\n",
+    "# import qim3d\n",
+    "# import numpy as np\n",
+    "\n",
+    "# vol, labels = qim3d.generate.noise_object_collection(\n",
+    "#     num_objects = 1,\n",
+    "#     collection_shape = (128, 128, 128),\n",
+    "#     min_object_noise = 0.03, \n",
+    "#     max_object_noise = 0.04,\n",
+    "#     )\n",
+    "\n",
+    "# augmentation =  qim3d.ml.Augmentation(resize = 'crop', transform_train = 'light')\n",
+    "\n",
+    "# print(f\"Before augmentation: {vol.shape} and {vol.dtype}\")\n",
+    "# print(f\"Augmentation: {augmentation}\\n\")\n",
+    "\n",
+    "# compose_augmentation = augmentation.augment(*vol.shape, level = augmentation.transform_train)\n",
+    "# vol_expanded = np.expand_dims(vol, axis = 0)\n",
+    "# transformed_vol = compose_augmentation(vol_expanded)\n",
+    "\n",
+    "# print(f\"After channel expansion and augmentation: {transformed_vol.shape}\")\n",
+    "# print(f\"Dtype after augmentation: {transformed_vol.dtype}\\n\")\n",
+    "\n",
+    "# # Remove the channel dimension\n",
+    "# vol_reduced = np.squeeze(transformed_vol)\n",
+    "\n",
+    "# print(f\"After reducing channel dimension: {vol_reduced.shape}\")\n",
+    "\n",
+    "# # qim3d.viz.volumetric(vol_reduced)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3D image shape:  (128, 128, 128)\n"
+     ]
+    }
+   ],
    "source": [
     "# 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",
+    "                                                         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)"
+    "                                                                     val_set,\n",
+    "                                                                     test_set,\n",
+    "                                                                     batch_size = 1)"
    ]
   },
   {
@@ -300,14 +478,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
     "# hyperparameters\n",
     "hyperparameters = qim3d.ml.Hyperparameters(model, n_epochs=10, \n",
-    "                                               learning_rate = 5e-3, loss_function='DiceCE',\n",
-    "                                               weight_decay=1e-3)"
+    "                                           learning_rate = 5e-3, loss_function='DiceCE',\n",
+    "                                           weight_decay=1e-3)"
    ]
   },
   {
@@ -319,9 +497,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38e47017de7c422b81fe8a1465cedc74",
+       "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: 1.6603, val loss: 1.3486\n",
+      "Epoch   5, train loss: 1.4026, val loss: 0.4789\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# training model\n",
     "qim3d.ml.train_model(model, hyperparameters, train_loader, val_loader, plot=True)"
@@ -336,10 +547,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Input image must be (C,H,W) format",
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "# Needs to be updated to handle 3D as well \n",
+    "in_targ_preds_test = qim3d.ml.inference(test_set, model)"
+   ]
   }
  ],
  "metadata": {
-- 
GitLab