Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix flake8 error #1618

Merged
merged 11 commits into from
Jan 19, 2024
Merged

Fix flake8 error #1618

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
81fd5ad
fix #1617
KumoLiu Jan 17, 2024
d9badbb
fix flake8
KumoLiu Jan 17, 2024
01673c4
fix flake8
KumoLiu Jan 17, 2024
98447af
revert bundle tutorial
KumoLiu Jan 18, 2024
68ad467
minor fix
KumoLiu Jan 18, 2024
7092001
minor fix
KumoLiu Jan 18, 2024
3ec08dd
Update mednist_classifier_bentoml.ipynb
KumoLiu Jan 18, 2024
cec5bda
Merge remote-tracking branch 'yliu/flake8' into flake8
KumoLiu Jan 18, 2024
525072b
DCO Remediation Commit for YunLiu <[email protected]…
KumoLiu Jan 18, 2024
279e317
minor fix
KumoLiu Jan 18, 2024
c7ac206
address comments
KumoLiu Jan 18, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion .github/contributing_templates/notebook/example_feature.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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"
Expand Down
3 changes: 1 addition & 2 deletions 2d_classification/monai_101.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -345,8 +345,7 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"orig_nbformat": 4
}
},
"nbformat": 4,
"nbformat_minor": 2
Expand Down
6 changes: 4 additions & 2 deletions 3d_segmentation/swin_unetr_btcv_segmentation_3d.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand All @@ -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",
Expand Down
6 changes: 4 additions & 2 deletions 3d_segmentation/unetr_btcv_segmentation_3d.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand All @@ -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",
Expand Down
40 changes: 21 additions & 19 deletions bundle/02_mednist_classification.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -265,7 +265,9 @@
"cell_type": "code",
KumoLiu marked this conversation as resolved.
Show resolved Hide resolved
"execution_count": 3,
"id": "d11681af-3210-4b2b-b7bd-8ad8dedfe230",
"metadata": {},
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
Expand Down Expand Up @@ -310,8 +312,7 @@
" - _target_: EnsureChannelFirstd\n",
" keys: 'image'\n",
" - _target_: ScaleIntensityd\n",
" keys: 'image'\n",
" "
" keys: 'image'"
]
},
{
Expand Down Expand Up @@ -369,19 +370,19 @@
" num_workers: 4\n",
"\n",
"trainer:\n",
" _target_: SupervisedTrainer\n",
" device: '@device'\n",
" max_epochs: '@max_epochs'\n",
" train_data_loader: '@train_dl'\n",
" network: '@net'\n",
" optimizer: \n",
" _target_: torch.optim.Adam\n",
" params: '[email protected]()'\n",
" lr: 0.00001 # learning rate set slow so that you can see network improvement over epochs\n",
" loss_function: \n",
" _target_: torch.nn.CrossEntropyLoss\n",
" inferer: \n",
" _target_: SimpleInferer\n",
" _target_: SupervisedTrainer\n",
" device: '@device'\n",
" max_epochs: '@max_epochs'\n",
" train_data_loader: '@train_dl'\n",
" network: '@net'\n",
" optimizer: \n",
" _target_: torch.optim.Adam\n",
" params: '[email protected]()'\n",
" lr: 0.00001 # learning rate set slow so that you can see network improvement over epochs\n",
" loss_function: \n",
" _target_: torch.nn.CrossEntropyLoss\n",
" inferer: \n",
" _target_: SimpleInferer\n",
"\n",
"train:\n",
"- '[email protected]()'\n",
Expand Down Expand Up @@ -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"
]
},
{
Expand All @@ -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",
Expand Down
19 changes: 12 additions & 7 deletions bundle/03_mednist_classification_v2.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -287,8 +291,7 @@
"- _target_: EnsureChannelFirstd\n",
" keys: '@image'\n",
"- _target_: ScaleIntensityd\n",
" keys: '@image'\n",
" "
" keys: '@image'\n"
]
},
{
Expand All @@ -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",
Expand All @@ -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: '$datetime.datetime.now().strftime(@root_dir+''/output/output_%y%m%d_%H%M%S'')'\n",
Expand Down Expand Up @@ -429,7 +434,7 @@
" output_transform: '$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",
Expand Down
33 changes: 18 additions & 15 deletions bundle/04_integrating_code.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -206,6 +206,7 @@
"execution_count": 3,
"id": "dcdbe1ae-ea13-49cb-b5a3-3c2c78f91f2b",
"metadata": {
"lines_to_next_cell": 2,
"tags": []
},
"outputs": [
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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": [
Expand All @@ -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 "
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -390,6 +393,7 @@
"execution_count": 7,
"id": "0b9764a8-674c-42ae-ad4b-f2dea027bdbf",
"metadata": {
"lines_to_next_cell": 2,
"tags": []
},
"outputs": [
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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())"
]
},
Expand Down
2 changes: 1 addition & 1 deletion bundle/05_spleen_segmentation_lightning.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1187,7 +1187,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
"version": "3.10.12"
}
},
"nbformat": 4,
Expand Down
2 changes: 1 addition & 1 deletion computer_assisted_intervention/video_seg.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand Down
1 change: 1 addition & 0 deletions deployment/ray/mednist_classifier_ray.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -368,6 +368,7 @@
"\n",
"ray.init(address=\"auto\", namespace=\"serve\")\n",
"\n",
"\n",
"@serve.deployment\n",
"class MedNISTClassifier:\n",
" def __init__(self):\n",
Expand Down
Loading
Loading