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Runtime error
| import torch | |
| import torch.nn as nn | |
| import math | |
| from timm.models.layers import trunc_normal_ | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): | |
| return nn.Sequential( | |
| nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, | |
| padding=padding, dilation=dilation, bias=True), | |
| nn.PReLU(out_planes) | |
| ) | |
| def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1): | |
| return nn.Sequential( | |
| torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True), | |
| nn.PReLU(out_planes) | |
| ) | |
| class Conv2(nn.Module): | |
| def __init__(self, in_planes, out_planes, stride=2): | |
| super(Conv2, self).__init__() | |
| self.conv1 = conv(in_planes, out_planes, 3, stride, 1) | |
| self.conv2 = conv(out_planes, out_planes, 3, 1, 1) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.conv2(x) | |
| return x | |
| class Unet(nn.Module): | |
| def __init__(self, c, out=3): | |
| super(Unet, self).__init__() | |
| self.down0 = Conv2(17+c, 2*c) | |
| self.down1 = Conv2(4*c, 4*c) | |
| self.down2 = Conv2(8*c, 8*c) | |
| self.down3 = Conv2(16*c, 16*c) | |
| self.up0 = deconv(32*c, 8*c) | |
| self.up1 = deconv(16*c, 4*c) | |
| self.up2 = deconv(8*c, 2*c) | |
| self.up3 = deconv(4*c, c) | |
| self.conv = nn.Conv2d(c, out, 3, 1, 1) | |
| self.apply(self._init_weights) | |
| def _init_weights(self, m): | |
| if isinstance(m, nn.Linear): | |
| trunc_normal_(m.weight, std=.02) | |
| if isinstance(m, nn.Linear) and m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| elif isinstance(m, nn.LayerNorm): | |
| nn.init.constant_(m.bias, 0) | |
| nn.init.constant_(m.weight, 1.0) | |
| elif isinstance(m, nn.Conv2d): | |
| fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| fan_out //= m.groups | |
| m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1): | |
| s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow,c0[0], c1[0]), 1)) | |
| s1 = self.down1(torch.cat((s0, c0[1], c1[1]), 1)) | |
| s2 = self.down2(torch.cat((s1, c0[2], c1[2]), 1)) | |
| s3 = self.down3(torch.cat((s2, c0[3], c1[3]), 1)) | |
| x = self.up0(torch.cat((s3, c0[4], c1[4]), 1)) | |
| x = self.up1(torch.cat((x, s2), 1)) | |
| x = self.up2(torch.cat((x, s1), 1)) | |
| x = self.up3(torch.cat((x, s0), 1)) | |
| x = self.conv(x) | |
| return torch.sigmoid(x) | |