Ensure ./runner.sh passess all tessts

Author: phisanti

2d_regression/image_restoration.ipynb

@@ -78,7 +78,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 69,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -94,28 +94,23 @@
         "    Compose,\n",
         "    LoadImageD,\n",
         "    ScaleIntensityd,\n",
-        "    RandGaussianNoiseD,       \n",
-        "    RandGaussianSmoothD,     \n",
-        "    RandAdjustContrastD,     \n",
+        "    RandGaussianNoiseD,\n",
+        "    RandGaussianSmoothD,\n",
         ")\n",
-        "import numpy as np\n",
         "from monai.data import DataLoader, Dataset, CacheDataset\n",
         "from monai.config import print_config\n",
         "from monai.networks.nets.restormer import Restormer\n",
         "from monai.apps import MedNISTDataset\n",
         "\n",
-        "import numpy as np\n",
         "import torch\n",
         "from monai.losses import SSIMLoss\n",
         "import matplotlib.pyplot as plt\n",
         "import os\n",
         "import tempfile\n",
         "\n",
-        "from tqdm.notebook import tqdm\n",
-        "\n",
         "\n",
-        "#print_config()\n",
-        "#set_determinism(42)"
+        "print_config()\n",
+        "set_determinism(42)"
       ]
     },
     {
@@ -361,7 +356,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 70,
+      "execution_count": null,
       "metadata": {
         "id": "zHAj8nuHXG-D",
         "outputId": "462d37f3-b59e-4d88-ca18-60224f69076d"
@@ -374,7 +369,7 @@
         "    device = torch.device(\"mps\")\n",
         "else:\n",
         "    device = torch.device(\"cpu\")\n",
-        "    \n",
+        "\n",
         "model = Restormer(\n",
         "    spatial_dims=2,\n",
         "    in_channels=1,\n",
@@ -399,7 +394,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 72,
+      "execution_count": null,
       "metadata": {
         "id": "eyiL4ccmYsjt"
       },
@@ -432,7 +427,7 @@
         }
       ],
       "source": [
-        "max_epochs = 20\n",
+        "max_epochs = 2\n",
         "epoch_loss_values = []\n",
         "\n",
         "\n",
@@ -448,7 +443,7 @@
         "        moving = batch_data[\"moving_hand\"].to(device)\n",
         "        fixed = batch_data[\"fixed_hand\"].to(device)\n",
         "        pred_image = model(moving)\n",
-        "        pred_image=torch.sigmoid(pred_image)\n",
+        "        pred_image = torch.sigmoid(pred_image)\n",
         "\n",
         "        loss = image_loss(input=pred_image, target=fixed)\n",
         "        loss.backward()\n",
@@ -514,7 +509,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 76,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -534,16 +529,16 @@
       "source": [
         "val_ds = CacheDataset(data=training_datadict[2000:2500], transform=train_transforms, cache_rate=1.0, num_workers=0)\n",
         "val_loader = DataLoader(val_ds, batch_size=16, num_workers=0)\n",
-        "model.eval() # Set model to evaluation mode\n",
+        "model.eval()  # Set model to evaluation mode\n",
         "\n",
-        "with torch.no_grad(): # Disable gradient calculation for inference\n",
+        "with torch.no_grad():  # Disable gradient calculation for inference\n",
         "    for batch_data in val_loader:\n",
         "        moving = batch_data[\"moving_hand\"].to(device)\n",
         "        fixed = batch_data[\"fixed_hand\"].to(device)\n",
         "        # Pass only the moving image, consistent with training\n",
         "        pred_image = model(moving)\n",
         "        pred_image = torch.sigmoid(pred_image)\n",
-        "        break # Process only the first batch for visualization\n",
+        "        break  # Process only the first batch for visualization\n",
         "\n",
         "fixed_image = fixed.detach().cpu().numpy()[:, 0]\n",
         "moving_image = moving.detach().cpu().numpy()[:, 0]\n",
@@ -596,13 +591,6 @@
         "plt.axis(\"off\")\n",
         "plt.show()"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {