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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class SobelLayer(nn.Module): | |
| def __init__(self, device): | |
| super(SobelLayer, self).__init__() | |
| self.kernel_x = torch.tensor([[-1., 0, 1], [-2, 0, 2], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0) / 4. | |
| self.kernel_y = torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1.]]).unsqueeze(0).unsqueeze(0) / 4. | |
| self.kernel_x = self.kernel_x.to(device) | |
| self.kernel_y = self.kernel_y.to(device) | |
| self.pad = nn.ReplicationPad2d(padding=1) | |
| self.absLayer = nn.ReLU() | |
| def forward(self, images): | |
| """ | |
| Args: | |
| images: images with shape [b, c, h, w] | |
| Returns: | |
| """ | |
| images = self.pad(images) | |
| gray_images = self._convertGrey(images) | |
| edge_x = F.conv2d(gray_images, self.kernel_x, stride=1) | |
| edge_y = F.conv2d(gray_images, self.kernel_y, stride=1) | |
| edge = (self.absLayer(edge_x) + self.absLayer(edge_y)) / 2 | |
| return edge | |
| def _convertGrey(self, image): | |
| """ | |
| grey = 0.299 * r + 0.587 * g + 0.110 * b | |
| Args: | |
| image: RGB image | |
| Returns: Grey scale image | |
| """ | |
| grey_image = image[:, 0] * 0.299 + image[:, 1] * 0.587 + 0.110 * image[:, 2] | |
| output = grey_image.unsqueeze(1) | |
| return output | |
| class SeperateSobelLayer(nn.Module): | |
| def __init__(self, device): | |
| super(SeperateSobelLayer, self).__init__() | |
| self.kernel_x = torch.tensor([[-1., 0, 1], [-2, 0, 2], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0) | |
| self.kernel_y = torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1.]]).unsqueeze(0).unsqueeze(0) | |
| self.weight = torch.zeros([6, 3, 3, 3]) | |
| for c in range(3): | |
| self.weight[2 * c, c] = self.kernel_x | |
| self.weight[2 * c + 1, c] = self.kernel_y | |
| self.weight = self.weight.to(device) | |
| def forward(self, images): | |
| """ | |
| Args: | |
| images: with shape [b, c, h, w] | |
| Returns: sobel gradient image with shape [b, c, h, w] (same padding) | |
| """ | |
| gradientMap = F.conv2d(images, self.weight, stride=1, padding=1) | |
| return gradientMap | |