| 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()
|
|
|
| edge_right = edge[:, 1:h, :]
|
| edge_right[(label[:, 1:h, :] != label[:, :h - 1, :]) & (label[:, 1:h, :] != 255)
|
| & (label[:, :h - 1, :] != 255)] = 1
|
|
|
|
|
| edge_up = edge[:, :, :w - 1]
|
| edge_up[(label[:, :, :w - 1] != label[:, :, 1:w])
|
| & (label[:, :, :w - 1] != 255)
|
| & (label[:, :, 1:w] != 255)] = 1
|
|
|
|
|
| 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
|
|
|
|
|
| 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
|
|
|
