Create app.py

app.py

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+import os
+from PIL import Image
+import torch
+from torchvision import transforms
+import gradio as gr
+
+
+# load model
+model = torch.hub.load('datvuthanh/hybridnets', 'hybridnets', pretrained=True)
+
+
+normalize = transforms.Normalize(
+        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+    )
+
+transform=transforms.Compose([
+            transforms.ToTensor(),
+            # normalize
+        ])
+
+
+def inference(img):
+
+    # print(img.size)
+    img = img.resize((640, 384))
+
+    img = torch.unsqueeze(transform(img), dim=0)
+
+    # img = transform(img)
+
+    features, regression, classification, anchors, segmentation = model(img)
+    
+    features_out = features[0][0, :, :].detach().numpy()
+    regression_out = regression[0][0, :, :].detach().numpy()
+    classification_out = classification[0][0, :, :].detach().numpy()
+    anchors_out = anchors[0][0, :, :].detach().numpy()
+    segmentation_out = segmentation[0][0, :, :].detach().numpy()
+    
+    return features_out, regression_out, classification_out, anchors_out, segmentation_out
+    
+title="HybridNets Demo"
+description="Gradio demo for HybridNets: End2End Perception Network pretrained on BDD100k Dataset. To use it, simply upload your image or click on one of the examples to load them. Read more at the links below"
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2203.09035' target='_blank'>ybridNets: End2End Perception Network</a> | <a href='https://github.com/datvuthanh/HybridNets' target='_blank'>Github Repo</a></p>"
+
+examples=[['frame_00_delay-0.13s.jpg']]
+
+gr.Interface(inference,gr.inputs.Image(type="pil"),[gr.outputs.Image(label='Features'),gr.outputs.Image(label='Regression'),gr.outputs.Image(label='Classification'),gr.outputs.Image(label='Anchors'),gr.outputs.Image(label='sSgmentation ')],article=article,description=description,title=title,examples=examples).launch()