preprocess/humanparsing/datasets/target_generation.py

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