fix #1649

Signed-off-by: YunLiu <[email protected]>
Project-MONAI · Feb 22, 2024 · b6a83da · b6a83da
1 parent 1783005
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diff --git a/2d_classification/mednist_tutorial.ipynb b/2d_classification/mednist_tutorial.ipynb
diff --git a/2d_classification/monai_101.ipynb b/2d_classification/monai_101.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,45 +57,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MONAI version: 1.2.0rc4+21.g7f067564\n",
+      "MONAI version: 1.3.0\n",
       "Numpy version: 1.22.2\n",
-      "Pytorch version: 2.0.0a0+1767026\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 7f06756472fd5514c3c2f2a6710e3fa4d1748e90\n",
-      "MONAI __file__: /workspace/monai/monai-in-dev/monai/__init__.py\n",
+      "Pytorch version: 2.1.0a0+29c30b1\n",
+      "MONAI flags: HAS_EXT = True, USE_COMPILED = False, USE_META_DICT = False\n",
+      "MONAI rev id: 865972f7a791bf7b42efbcd87c8402bd865b329e\n",
+      "MONAI __file__: /opt/monai/monai/__init__.py\n",
       "\n",
       "Optional dependencies:\n",
       "Pytorch Ignite version: 0.4.11\n",
       "ITK version: 5.3.0\n",
       "Nibabel version: 5.1.0\n",
-      "scikit-image version: 0.20.0\n",
+      "scikit-image version: 0.22.0\n",
+      "scipy version: 1.11.1\n",
       "Pillow version: 9.2.0\n",
       "Tensorboard version: 2.9.0\n",
       "gdown version: 4.7.1\n",
-      "TorchVision version: 0.15.0a0\n",
+      "TorchVision version: 0.16.0a0\n",
       "tqdm version: 4.65.0\n",
       "lmdb version: 1.4.1\n",
       "psutil version: 5.9.4\n",
       "pandas version: 1.5.2\n",
       "einops version: 0.6.1\n",
       "transformers version: 4.21.3\n",
-      "mlflow version: 2.3.0\n",
+      "mlflow version: 2.7.1\n",
       "pynrrd version: 1.0.0\n",
+      "clearml version: 1.13.1\n",
       "\n",
       "For details about installing the optional dependencies, please visit:\n",
       "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
@@ -111,12 +113,14 @@
     "import sys\n",
     "import tempfile\n",
     "import torch\n",
+    "import ignite\n",
     "\n",
     "from monai.apps import MedNISTDataset\n",
     "from monai.config import print_config\n",
     "from monai.data import DataLoader\n",
-    "from monai.engines import SupervisedTrainer\n",
-    "from monai.handlers import StatsHandler\n",
+    "from monai.engines import SupervisedTrainer, SupervisedEvaluator\n",
+    "from monai.handlers import StatsHandler, TensorBoardStatsHandler, ValidationHandler\n",
+    "from monai.handlers.utils import from_engine\n",
     "from monai.inferers import SimpleInferer\n",
     "from monai.networks import eval_mode\n",
     "from monai.networks.nets import densenet121\n",
@@ -139,14 +143,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/workspace/data\n"
+      "/workspace/Data\n"
      ]
     }
    ],
@@ -171,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,28 +209,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-21 15:37:46,567 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-21 15:37:46,567 - INFO - File exists: /workspace/data/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-04-21 15:37:46,568 - INFO - Non-empty folder exists in /workspace/data/MedNIST, skipped extracting.\n"
+      "2024-02-22 05:34:20,240 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2024-02-22 05:34:20,241 - INFO - File exists: /workspace/Data/MedNIST.tar.gz, skipped downloading.\n",
+      "2024-02-22 05:34:20,241 - INFO - Non-empty folder exists in /workspace/Data/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|██████████| 47164/47164 [00:18<00:00, 2525.58it/s]\n"
+      "Loading dataset: 100%|██████████| 47164/47164 [00:19<00:00, 2430.78it/s]\n",
+      "Loading dataset: 100%|██████████| 5895/5895 [00:02<00:00, 2508.13it/s]\n"
      ]
     }
    ],
    "source": [
-    "dataset = MedNISTDataset(root_dir=root_dir, transform=transform, section=\"training\", download=True)"
+    "dataset = MedNISTDataset(root_dir=root_dir, transform=transform, section=\"training\", download=True)\n",
+    "valdata = MedNISTDataset(root_dir=root_dir, transform=transform, section=\"validation\", download=False)"
    ]
   },
   {
@@ -238,19 +244,31 @@
     "\n",
     "To train a model that can perform the classification task, we will use the DenseNet-121 which is known for its performance on the ImageNet dataset.\n",
     "\n",
-    "For a typical supervised training workflow, MONAI provides `SupervisedTrainer` to define the hyper-parameters."
+    "For a typical supervised training workflow, MONAI provides `SupervisedTrainer` to define the hyper-parameters and `SupervisedEvaluator` to track model progress with handlers to save metrics. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "max_epochs = 5\n",
     "model = densenet121(spatial_dims=2, in_channels=1, out_channels=6).to(\"cuda:0\")\n",
     "\n",
     "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+    "\n",
+    "evaluator = SupervisedEvaluator(\n",
+    "    device=torch.device(\"cuda:0\"),\n",
+    "    val_data_loader=DataLoader(valdata, batch_size=512, shuffle=False, num_workers=4),\n",
+    "    network=model,\n",
+    "    inferer=SimpleInferer(),\n",
+    "    key_val_metric={\"val_acc\": ignite.metrics.Accuracy(from_engine([\"pred\", \"label\"]))},\n",
+    "    val_handlers=[\n",
+    "        StatsHandler(iteration_log=False),\n",
+    "        TensorBoardStatsHandler(iteration_log=False)\n",
+    "    ],\n",
+    ")\n",
     "trainer = SupervisedTrainer(\n",
     "    device=torch.device(\"cuda:0\"),\n",
     "    max_epochs=max_epochs,\n",
@@ -259,7 +277,11 @@
     "    optimizer=torch.optim.Adam(model.parameters(), lr=1e-5),\n",
     "    loss_function=torch.nn.CrossEntropyLoss(),\n",
     "    inferer=SimpleInferer(),\n",
-    "    train_handlers=StatsHandler(),\n",
+    "    train_handlers=[\n",
+    "        ValidationHandler(validator=evaluator, epoch_level=True, interval=1),\n",
+    "        StatsHandler(),\n",
+    "        TensorBoardStatsHandler(tag_name=\"train_loss\", output_transform=from_engine([\"loss\"], first=True))\n",
+    "    ],\n",
     ")"
    ]
   },
@@ -290,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -344,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,