update for InferenceOptimizer and new dataset

.gitattributes

@@ -26,3 +26,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -75,7 +75,7 @@ class ImageDataset(Dataset):
         return len(self.imgs)
 
 
-data_path = Path('data/webcam')
+data_path = Path('data')
 train_image_dd = prepare_data(data_path)
 dm = PatchDataModule(train_image_dd, patch_size=2**6,
                      batch_size=2**3, patch_num=2**6)
@@ -174,13 +174,13 @@ with demo:
     with gr.Row().style(equal_height=False):
         input_img = gr.Image(label="input image", value="data/COCO_image.jpg", source="upload")
         with gr.Column():
-            ori_but = gr.Button("Standard PyTorch Lightning")
+            ori_but = gr.Button("Standard PyTorch")
             nano_but = gr.Button("BigDL-Nano")
             clear_but = gr.Button("Clear Output")
     with gr.Row().style(equal_height=False):
         with gr.Column():
-            ori_time = gr.Text(label="Standard PyTorch Lightning latency")
-            ori_image = gr.Image(label="Standard PyTorch Lightning output image")
+            ori_time = gr.Text(label="Standard PyTorch latency")
+            ori_image = gr.Image(label="Standard PyTorch output image")
         with gr.Column():
             nano_time = gr.Text(label="BigDL-Nano latency")
             nano_image = gr.Image(label="BigDL-Nano output image")