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import torch |
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from torch.nn import functional as F |
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def generate_edge_tensor(label, edge_width=3): |
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label = label.type(torch.cuda.FloatTensor) |
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if len(label.shape) == 2: |
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label = label.unsqueeze(0) |
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n, h, w = label.shape |
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edge = torch.zeros(label.shape, dtype=torch.float).cuda() |
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edge_right = edge[:, 1:h, :] |
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edge_right[(label[:, 1:h, :] != label[:, :h - 1, :]) & (label[:, 1:h, :] != 255) |
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& (label[:, :h - 1, :] != 255)] = 1 |
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edge_up = edge[:, :, :w - 1] |
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edge_up[(label[:, :, :w - 1] != label[:, :, 1:w]) |
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& (label[:, :, :w - 1] != 255) |
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& (label[:, :, 1:w] != 255)] = 1 |
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edge_upright = edge[:, :h - 1, :w - 1] |
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edge_upright[(label[:, :h - 1, :w - 1] != label[:, 1:h, 1:w]) |
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& (label[:, :h - 1, :w - 1] != 255) |
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& (label[:, 1:h, 1:w] != 255)] = 1 |
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edge_bottomright = edge[:, :h - 1, 1:w] |
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edge_bottomright[(label[:, :h - 1, 1:w] != label[:, 1:h, :w - 1]) |
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& (label[:, :h - 1, 1:w] != 255) |
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& (label[:, 1:h, :w - 1] != 255)] = 1 |
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kernel = torch.ones((1, 1, edge_width, edge_width), dtype=torch.float).cuda() |
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with torch.no_grad(): |
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edge = edge.unsqueeze(1) |
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edge = F.conv2d(edge, kernel, stride=1, padding=1) |
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edge[edge!=0] = 1 |
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edge = edge.squeeze() |
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return edge |
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