Merge branch 'add-infer-accelerate-tutorial' of github.com:yiheng-wang-nv/tutorials into add-infer-accelerate-tutorial

Author: yiheng-wang-nv

acceleration/fast_inference_tutorial/fast_inference_tutorial.ipynb

@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -307,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -424,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,10 +442,10 @@
     "infer_transforms = get_transforms(device, gpu_loading_flag, gpu_transforms_flag)\n",
     "model = get_model(device, weights_path, trt_model_path, trt_flag)\n",
     "\n",
-    "total_time_dict = run_inference(train_files, infer_transforms, model, device, benchmark_type)\n",
+    "# total_time_dict = run_inference(train_files, infer_transforms, model, device, benchmark_type)\n",
     "\n",
-    "df = pd.DataFrame(list(total_time_dict.items()), columns=[\"file_name\", \"time\"])\n",
-    "df.to_csv(os.path.join(root_dir, f\"time_{benchmark_type}.csv\"), index=False)"
+    "# df = pd.DataFrame(list(total_time_dict.items()), columns=[\"file_name\", \"time\"])\n",
+    "# df.to_csv(os.path.join(root_dir, f\"time_{benchmark_type}.csv\"), index=False)"
    ]
   },
   {
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -502,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -523,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {

acceleration/fast_inference_tutorial/run_benchmark.py

@@ -20,13 +20,21 @@
 import torch_tensorrt
 from monai.inferers import sliding_window_inference
 from monai.networks.nets import SegResNet
-from monai.transforms import (Activationsd, AsDiscreted, Compose,
-                              EnsureChannelFirstd, EnsureTyped, Invertd,
-                              LoadImaged, NormalizeIntensityd, Orientationd,
-                              ScaleIntensityd, Spacingd)
-
-from utils import (prepare_model_weights, prepare_tensorrt_model,
-                   prepare_test_datalist)
+from monai.transforms import (
+    Activationsd,
+    AsDiscreted,
+    Compose,
+    EnsureChannelFirstd,
+    EnsureTyped,
+    Invertd,
+    LoadImaged,
+    NormalizeIntensityd,
+    Orientationd,
+    ScaleIntensityd,
+    Spacingd,
+)
+
+from utils import prepare_model_weights, prepare_tensorrt_model, prepare_test_datalist
 
 
 def get_transforms(device, gpu_loading_flag=False, gpu_transforms_flag=False):