Initial commit.

app.py

@@ -182,17 +182,18 @@ def predict(input_img, cs_mapping):
 title = "Drive&Segment"
 description = 'Gradio Demo accompanying paper "Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation"\nBecause of the CPU-only inference, it might take up to 20s for large images.\nRight now, I use the Segmenter model trained on nuScenes and with 256x256 patches (for the sake of speed).'
 # article = "<p style='text-align: center'><a href='TODO' target='_blank'>Project Page</a> | <a href='codelink' target='_blank'>Github</a></p>"
-examples = [['examples/img5.jpeg', False], ['examples/100.jpeg', False], ['examples/39076.jpeg', False],
-            ['examples/img1.jpg', False], ['examples/snow1.jpg',False]]
+examples = ['examples/img5.jpeg', 'examples/100.jpeg', 'examples/39076.jpeg', 'examples/img1.jpg', 'examples/snow1.jpg']
 
 # predict(examples[0])
 
-iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'), gr.inputs.Checkbox(label="Cityscapes mapping")],
+iface = gr.Interface(predict, gr.inputs.Image(type='filepath'),
                      "image", title=title, description=description,
+                     outputs=[gr.outputs.Image(label="Pseudo segmentation", type="pil"),
+                              gr.outputs.Image(label="Mapping to Cityscapes", type="pil")],
                      examples=examples)
 # iface = gr.Interface(predict, gr.inputs.Image(type='filepath'),
 #                      "image", title=title, description=description,
 #                      examples=examples)
 
 # iface.launch(show_error=True, share=True)
-iface.launch(show_error=True)
+iface.launch(enable_queue=True, cache_examples=True)