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Inference.py modified
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@ -28,7 +28,7 @@ if __name__ == "__main__":
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net=net.cuda()
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else:
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net.load_state_dict(torch.load(model_path,map_location="cpu"))
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net.eval()
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im_list = glob(dataset_path+"/*.jpg")
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for i, im_path in tqdm(enumerate(im_list), total=len(im_list)):
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print("im_path: ", im_path)
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@ -37,11 +37,12 @@ if __name__ == "__main__":
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im = im[:, :, np.newaxis]
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im_shp=im.shape[0:2]
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im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
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im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear")
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image = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0]).type(torch.uint8)
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im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), input_size, mode="bilinear").type(torch.uint8)
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image = torch.divide(im_tensor,255.0)
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image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
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if torch.cuda.is_available():
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image=image.cuda()
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image = torch.divide(image,255.0)
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result=net(image)
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result=torch.squeeze(F.upsample(result[0][0],im_shp,mode='bilinear'),0)
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ma = torch.max(result)
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