Fix flake8 error

.github/contributing_templates/notebook/example_feature.ipynb

@@ -230,7 +230,6 @@
    "pygments_lexer": "ipython3",
    "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"

2d_classification/monai_101.ipynb

@@ -345,8 +345,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.10"
-  },
-  "orig_nbformat": 4
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2

3d_segmentation/swin_unetr_btcv_segmentation_3d.ipynb

@@ -523,7 +523,7 @@
     "            val_outputs_list = decollate_batch(val_outputs)\n",
     "            val_output_convert = [post_pred(val_pred_tensor) for val_pred_tensor in val_outputs_list]\n",
     "            dice_metric(y_pred=val_output_convert, y=val_labels_convert)\n",
-    "            epoch_iterator_val.set_description(\"Validate (%d / %d Steps)\" % (global_step, 10.0))\n",
+    "            epoch_iterator_val.set_description(\"Validate (%d / %d Steps)\" % (global_step, 10.0))  # noqa: B038\n",
     "        mean_dice_val = dice_metric.aggregate().item()\n",
     "        dice_metric.reset()\n",
     "    return mean_dice_val\n",
@@ -546,7 +546,9 @@
     "        scaler.step(optimizer)\n",
     "        scaler.update()\n",
     "        optimizer.zero_grad()\n",
-    "        epoch_iterator.set_description(f\"Training ({global_step} / {max_iterations} Steps) (loss={loss:2.5f})\")\n",
+    "        epoch_iterator.set_description(  # noqa: B038\n",
+    "            f\"Training ({global_step} / {max_iterations} Steps) (loss={loss:2.5f})\"\n",
+    "        )\n",
     "        if (global_step % eval_num == 0 and global_step != 0) or global_step == max_iterations:\n",
     "            epoch_iterator_val = tqdm(val_loader, desc=\"Validate (X / X Steps) (dice=X.X)\", dynamic_ncols=True)\n",
     "            dice_val = validation(epoch_iterator_val)\n",

3d_segmentation/unetr_btcv_segmentation_3d.ipynb

@@ -621,7 +621,7 @@
     "            val_outputs_list = decollate_batch(val_outputs)\n",
     "            val_output_convert = [post_pred(val_pred_tensor) for val_pred_tensor in val_outputs_list]\n",
     "            dice_metric(y_pred=val_output_convert, y=val_labels_convert)\n",
-    "            epoch_iterator_val.set_description(\"Validate (%d / %d Steps)\" % (global_step, 10.0))\n",
+    "            epoch_iterator_val.set_description(\"Validate (%d / %d Steps)\" % (global_step, 10.0))  # noqa: B038\n",
     "        mean_dice_val = dice_metric.aggregate().item()\n",
     "        dice_metric.reset()\n",
     "    return mean_dice_val\n",
@@ -641,7 +641,9 @@
     "        epoch_loss += loss.item()\n",
     "        optimizer.step()\n",
     "        optimizer.zero_grad()\n",
-    "        epoch_iterator.set_description(\"Training (%d / %d Steps) (loss=%2.5f)\" % (global_step, max_iterations, loss))\n",
+    "        epoch_iterator.set_description(  # noqa: B038\n",
+    "            \"Training (%d / %d Steps) (loss=%2.5f)\" % (global_step, max_iterations, loss)\n",
+    "        )\n",
     "        if (global_step % eval_num == 0 and global_step != 0) or global_step == max_iterations:\n",
     "            epoch_iterator_val = tqdm(val_loader, desc=\"Validate (X / X Steps) (dice=X.X)\", dynamic_ncols=True)\n",
     "            dice_val = validation(epoch_iterator_val)\n",

bundle/02_mednist_classification.ipynb

@@ -265,7 +265,9 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "d11681af-3210-4b2b-b7bd-8ad8dedfe230",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -310,8 +312,7 @@
     "  - _target_: EnsureChannelFirstd\n",
     "    keys: 'image'\n",
     "  - _target_: ScaleIntensityd\n",
-    "    keys: 'image'\n",
-    "    "
+    "    keys: 'image'"
    ]
   },
   {
@@ -369,7 +370,7 @@
     "  num_workers: 4\n",
     "\n",
     "trainer:\n",
-    "    _target_: SupervisedTrainer\n",
+    "  _target_: SupervisedTrainer\n",
     "    device: '@device'\n",
     "    max_epochs: '@max_epochs'\n",
     "    train_data_loader: '@train_dl'\n",
@@ -527,8 +528,7 @@
     "            prob = result.detach().to(\"cpu\")[0]\n",
     "            pred = class_names[prob.argmax()]\n",
     "            gt = item[\"class_name\"][0]\n",
-    "            print(f\"Prediction: {pred}. Ground-truth: {gt}\")\n",
-    "            "
+    "            print(f\"Prediction: {pred}. Ground-truth: {gt}\")\n"
    ]
   },
   {
@@ -543,7 +543,9 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "b4e1f99a-a68b-4aeb-bcf2-842f26609b52",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",

bundle/03_mednist_classification_v2.ipynb

@@ -185,7 +185,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "0cb1b023-d192-4ad7-b2eb-c4a2c6b42b84",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -233,7 +235,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "d11681af-3210-4b2b-b7bd-8ad8dedfe230",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -287,8 +291,7 @@
     "- _target_: EnsureChannelFirstd\n",
     "  keys: '@image'\n",
     "- _target_: ScaleIntensityd\n",
-    "  keys: '@image'\n",
-    "    "
+    "  keys: '@image'\n"
    ]
   },
   {
@@ -306,7 +309,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "4dfd052e-abe7-473a-bbf4-25674a3b20ea",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -322,7 +327,7 @@
     "max_epochs: 25\n",
     "learning_rate: 0.00001  # learning rate, again artificially slow\n",
     "val_interval: 1  # run validation every n'th epoch\n",
-    "save_interval: 1 # save the model weights every n'th epoch\n",
+    "save_interval: 1  # save the model weights every n'th epoch\n",
     "\n",
     "# choose a unique output subdirectory every time training is started, \n",
     "output_dir: '$datetime.datetime.now().strftime(@root_dir+''/output/output_%y%m%d_%H%M%S'')'\n",
@@ -429,7 +434,7 @@
     "  output_transform: '$lambda x: None'\n",
     "- _target_: LogfileHandler\n",
     "  output_dir: '@output_dir'\n",
-    "    \n",
+    "\n",
     "# Metrics to assess validation results, you can have more than one here but may \n",
     "# need to adapt the format of pred and label.\n",
     "metrics:\n",

bundle/04_integrating_code.ipynb

@@ -206,6 +206,7 @@
    "execution_count": 3,
    "id": "dcdbe1ae-ea13-49cb-b5a3-3c2c78f91f2b",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -224,7 +225,6 @@
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
     "\n",
-    "\n",
     "class Net(nn.Module):\n",
     "    def __init__(self):\n",
     "        super().__init__()\n",
@@ -257,7 +257,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "189d71c5-6556-4891-a382-0adbc8f80d30",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -274,14 +276,15 @@
     "\n",
     "transform = transforms.Compose(\n",
     "    [transforms.ToTensor(),\n",
-    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n"
+    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "3d8f233e-495c-450c-a445-46d295ba7461",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -301,17 +304,18 @@
     "\n",
     "batch_size = 4\n",
     "\n",
+    "\n",
     "def get_dataloader(is_training, transform):\n",
-    "    \n",
+    "\n",
     "    if is_training:\n",
     "        trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
-    "                                        download=True, transform=transform)\n",
+    "                                                download=True, transform=transform)\n",
     "        trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
     "                                                  shuffle=True, num_workers=2)\n",
     "        return trainloader\n",
     "    else:\n",
     "        testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
-    "                                       download=True, transform=transform)\n",
+    "                                               download=True, transform=transform)\n",
     "        testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
     "                                                 shuffle=False, num_workers=2)\n",
     "        return testloader   "
@@ -347,8 +351,7 @@
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
     "\n",
-    "\n",
-    "def train(net,trainloader):\n",
+    "def train(net, trainloader):\n",
     "    criterion = nn.CrossEntropyLoss()\n",
     "    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
     "\n",
@@ -390,6 +393,7 @@
    "execution_count": 7,
    "id": "0b9764a8-674c-42ae-ad4b-f2dea027bdbf",
    "metadata": {
+    "lines_to_next_cell": 2,
     "tags": []
    },
    "outputs": [
@@ -542,11 +546,10 @@
     "\n",
     "import torch\n",
     "\n",
-    "\n",
     "def test(net, testloader):\n",
     "    correct = 0\n",
     "    total = 0\n",
-    "    \n",
+    "\n",
     "    with torch.no_grad():\n",
     "        for data in testloader:\n",
     "            images, labels = data\n",
@@ -555,15 +558,16 @@
     "            total += labels.size(0)\n",
     "            correct += (predicted == labels).sum().item()\n",
     "\n",
-    "    print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')\n",
-    "    "
+    "    print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "fb49aef2-9fb5-4e74-83d2-9da935e07648",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -678,13 +682,12 @@
     "import torch\n",
     "from PIL import Image\n",
     "\n",
-    "\n",
     "def inference(net, transforms, filenames):\n",
     "    for fn in filenames:\n",
     "        with Image.open(fn) as im:\n",
     "            tim=transforms(im)\n",
     "            outputs=net(tim[None])\n",
-    "            _, predictions = torch.max(outputs, 1)\n",
+    "            _, predictions=torch.max(outputs, 1)\n",
     "            print(fn, predictions[0].item())"
    ]
   },

bundle/05_spleen_segmentation_lightning.ipynb

@@ -1187,7 +1187,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

computer_assisted_intervention/video_seg.ipynb

@@ -364,7 +364,7 @@
     "    iou_list.append(epoch_iou)\n",
     "    epoch_loss /= len(dl)\n",
     "    losses.append(epoch_loss)\n",
-    "    tr.set_description(f\"Loss: {epoch_loss:.4f}\")\n",
+    "    tr.set_description(f\"Loss: {epoch_loss:.4f}\")  # noqa: B038\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(6, 6), facecolor=\"white\")\n",
     "ax.set_xlabel(\"Epoch\")\n",

deployment/bentoml/mednist_classifier_bentoml.ipynb

@@ -94,6 +94,12 @@
     }
    ],
    "source": [
+    "from bentoml.utils import cached_property  # noqa: F401\n",
+    "from bentoml.adapters import FileInput, JsonOutput  # noqa: F401\n",
+    "from bentoml.frameworks.pytorch import PytorchModelArtifact  # noqa: F401\n",
+    "import bentoml  # noqa: F401\n",
+    "from PIL import Image  # noqa: F401\n",
+    "from typing import BinaryIO, List  # noqa: F401\n",
     "import os\n",
     "import shutil\n",
     "import tempfile\n",
@@ -373,23 +379,6 @@
    "source": [
     "%%writefile mednist_classifier_bentoml.py\n",
     "\n",
-    "from typing import BinaryIO, List\n",
-    "import numpy as np\n",
-    "from PIL import Image\n",
-    "import torch\n",
-    "\n",
-    "from monai.transforms import (\n",
-    "    EnsureChannelFirst,\n",
-    "    Compose,\n",
-    "    Transform,\n",
-    "    ScaleIntensity,\n",
-    "    EnsureType,\n",
-    ")\n",
-    "\n",
-    "import bentoml\n",
-    "from bentoml.frameworks.pytorch import PytorchModelArtifact\n",
-    "from bentoml.adapters import FileInput, JsonOutput\n",
-    "from bentoml.utils import cached_property\n",
     "\n",
     "MEDNIST_CLASSES = [\"AbdomenCT\", \"BreastMRI\", \"CXR\", \"ChestCT\", \"Hand\", \"HeadCT\"]\n",
     "\n",

deployment/ray/mednist_classifier_ray.ipynb

@@ -348,26 +348,12 @@
    "source": [
     "%%writefile mednist_classifier_start.py\n",
     "\n",
-    "import io\n",
-    "from PIL import Image\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "\n",
-    "import ray\n",
-    "from ray import serve\n",
-    "\n",
-    "from monai.config import print_config\n",
-    "from monai.transforms import (\n",
-    "    EnsureChannelFirst,\n",
-    "    Compose,\n",
-    "    ScaleIntensity,\n",
-    "    EnsureType,\n",
-    ")\n",
     "\n",
     "MEDNIST_CLASSES = [\"AbdomenCT\", \"BreastMRI\", \"CXR\", \"ChestCT\", \"Hand\", \"HeadCT\"]\n",
     "\n",
     "ray.init(address=\"auto\", namespace=\"serve\")\n",
     "\n",
+    "\n",
     "@serve.deployment\n",
     "class MedNISTClassifier:\n",
     "    def __init__(self):\n",