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