[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

acceleration/fast_inference_tutorial/fast_inference_tutorial.ipynb

@@ -105,9 +105,9 @@
     "    Orientationd,\n",
     "    Spacingd,\n",
     "    ScaleIntensityRanged,\n",
-    "    Compose\n",
+    "    Compose,\n",
     ")\n",
-    "from monai.data import Dataset,ThreadDataLoader\n",
+    "from monai.data import Dataset, ThreadDataLoader\n",
     "import torch\n",
     "import numpy as np\n",
     "import copy\n",
@@ -273,14 +273,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "transforms = Compose([\n",
-    "    LoadImaged(keys=\"image\", reader=\"NibabelReader\", to_gpu=False),\n",
-    "    EnsureTyped(keys=\"image\", device=torch.device(\"cuda:0\")),\n",
-    "    EnsureChannelFirstd(keys=\"image\"),\n",
-    "    Orientationd(keys=\"image\", axcodes=\"RAS\"),\n",
-    "    Spacingd(keys=\"image\", pixdim=[1.5, 1.5, 2.0], mode=\"bilinear\"),\n",
-    "    ScaleIntensityRanged(keys=\"image\", a_min=-57, a_max=164, b_min=0, b_max=1, clip=True),\n",
-    "])\n",
+    "transforms = Compose(\n",
+    "    [\n",
+    "        LoadImaged(keys=\"image\", reader=\"NibabelReader\", to_gpu=False),\n",
+    "        EnsureTyped(keys=\"image\", device=torch.device(\"cuda:0\")),\n",
+    "        EnsureChannelFirstd(keys=\"image\"),\n",
+    "        Orientationd(keys=\"image\", axcodes=\"RAS\"),\n",
+    "        Spacingd(keys=\"image\", pixdim=[1.5, 1.5, 2.0], mode=\"bilinear\"),\n",
+    "        ScaleIntensityRanged(keys=\"image\", a_min=-57, a_max=164, b_min=0, b_max=1, clip=True),\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "dataset = Dataset(data=[{\"image\": i} for i in train_files], transform=transforms)\n",
     "dataloader = ThreadDataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)"

acceleration/fast_inference_tutorial/utils.py

@@ -71,7 +71,7 @@ def prepare_tensorrt_model(bundle_path, trt_model_name="model_trt.ts"):
             precision="fp16",
             dynamic_batchsize=[1, 4, 8],
             use_onnx=True,
-            use_trace=True
+            use_trace=True,
         )
     else:
         print(f"TensorRT model already exists at {output_path}")
@@ -182,6 +182,7 @@ def prepare_workflow(inference_config, meta_config, bundle_path, override):
 
     return workflow
 
+
 def benchmark_workflow(workflow, timer, benchmark_type):
     workflow.initialize()
     timer.attach(workflow.evaluator)