Update app.py

app.py

@@ -3,6 +3,7 @@ import cv2
 import numpy as np
 import os
 from PIL import Image
+import spaces
 import torch
 import torch.nn.functional as F
 from torchvision.transforms import Compose
@@ -24,11 +25,11 @@ css = """
     }
 """
 DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
-model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(DEVICE).eval()
+encoder = 'vitl' # can also be 'vitb' or 'vitl'
+model = DepthAnything.from_pretrained(f"LiheYoung/depth_anything_{encoder}14").to(DEVICE).eval()
 
 title = "# Depth Anything"
 description = """Official demo for **Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data**.
-
 Please refer to our [paper](https://arxiv.org/abs/2401.10891), [project page](https://depth-anything.github.io), or [github](https://github.com/LiheYoung/Depth-Anything) for more details."""
 
 transform = Compose([
@@ -45,54 +46,53 @@ transform = Compose([
         PrepareForNet(),
 ])
 
-margin_width = 50
-
+@spaces.GPU
 @torch.no_grad()
 def predict_depth(model, image):
     return model(image)
 
+
 with gr.Blocks(css=css) as demo:
     gr.Markdown(title)
     gr.Markdown(description)
     gr.Markdown("### Depth Prediction demo")
     gr.Markdown("You can slide the output to compare the depth prediction with input image")
 
-    with gr.Tab("Image Depth Prediction"):
-        with gr.Row():
-            input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
-            depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
-        raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)")
-        submit = gr.Button("Submit")
+    with gr.Row():
+        input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
+        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5,)
+    raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)")
+    submit = gr.Button("Submit")
 
-        def on_submit(image):
-            original_image = image.copy()
+    def on_submit(image):
+        original_image = image.copy()
 
-            h, w = image.shape[:2]
+        h, w = image.shape[:2]
 
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
-            image = transform({'image': image})['image']
-            image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
+        image = transform({'image': image})['image']
+        image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)
 
-            depth = predict_depth(model, image)
-            depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
+        depth = predict_depth(model, image)
+        depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
 
-            raw_depth = Image.fromarray(depth.cpu().numpy().astype('uint16'))
-            tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
-            raw_depth.save(tmp.name)
+        raw_depth = Image.fromarray(depth.cpu().numpy().astype('uint16'))
+        tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        raw_depth.save(tmp.name)
 
-            depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-            depth = depth.cpu().numpy().astype(np.uint8)
-            colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.cpu().numpy().astype(np.uint8)
+        colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
 
-            return [(original_image, colored_depth), tmp.name]
+        return [(original_image, colored_depth), tmp.name]
 
-        submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, raw_file])
+    submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, raw_file])
 
-        example_files = os.listdir('examples')
-        example_files.sort()
-        example_files = [os.path.join('examples', filename) for filename in example_files]
-        examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, raw_file], fn=on_submit, cache_examples=True)
+    example_files = os.listdir('examples')
+    example_files.sort()
+    example_files = [os.path.join('examples', filename) for filename in example_files]
+    examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, raw_file], fn=on_submit, cache_examples=True)
 
 
 if __name__ == '__main__':
-    demo.queue().launch()
+    demo.queue().launch()