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Update app.py
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app.py
CHANGED
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@@ -38,6 +38,8 @@ def inference_mask1(
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for i in range(len(prompt)):
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mask = torch.from_numpy(np.stack(prompt[i]['layers'], axis=0).any(0).any(-1)).cpu()
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mask[mask > 0] = 1
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if mask.sum() == 0:
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break
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mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0).float(), query_img.size()[-2:], mode='nearest').squeeze(0).squeeze(0)
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@@ -45,7 +47,6 @@ def inference_mask1(
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support_img = Image.fromarray(prompt[i]['background'][..., :3])
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support_img = transformation(support_img)
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support_imgs.append(support_img)
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return (support_imgs[0].detach().cpu().numpy() * 255).astype(np.uint8)
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model = DCAMA('resnet50', 'resnet50_a1h-35c100f8.pth', True)
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model.eval()
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model.cpu()
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for i in range(len(prompt)):
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mask = torch.from_numpy(np.stack(prompt[i]['layers'], axis=0).any(0).any(-1)).cpu()
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mask[mask > 0] = 1
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return (mask.detach().cpu().numpy() * 255).astype(np.uint8)
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if mask.sum() == 0:
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break
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mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0).float(), query_img.size()[-2:], mode='nearest').squeeze(0).squeeze(0)
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support_img = Image.fromarray(prompt[i]['background'][..., :3])
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support_img = transformation(support_img)
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support_imgs.append(support_img)
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model = DCAMA('resnet50', 'resnet50_a1h-35c100f8.pth', True)
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model.eval()
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model.cpu()
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