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| import logging | |
| import torch.nn as nn | |
| import torch | |
| import torch.nn.functional as F | |
| from networks import ops | |
| def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1): | |
| """5x5 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride, | |
| padding=2, groups=groups, bias=False, dilation=dilation) | |
| def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=dilation, groups=groups, bias=False, dilation=dilation) | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| """1x1 convolution""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, upsample=None, norm_layer=None, large_kernel=False): | |
| super(BasicBlock, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| self.stride = stride | |
| conv = conv5x5 if large_kernel else conv3x3 | |
| # Both self.conv1 and self.downsample layers downsample the input when stride != 1 | |
| if self.stride > 1: | |
| self.conv1 = ops.SpectralNorm(nn.ConvTranspose2d(inplanes, inplanes, kernel_size=4, stride=2, padding=1, bias=False)) | |
| else: | |
| self.conv1 = ops.SpectralNorm(conv(inplanes, inplanes)) | |
| self.bn1 = norm_layer(inplanes) | |
| self.activation = nn.LeakyReLU(0.2, inplace=True) | |
| self.conv2 = ops.SpectralNorm(conv(inplanes, planes)) | |
| self.bn2 = norm_layer(planes) | |
| self.upsample = upsample | |
| def forward(self, x): | |
| identity = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.activation(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.upsample is not None: | |
| identity = self.upsample(x) | |
| out += identity | |
| out = self.activation(out) | |
| return out | |
| class SAM_Decoder_Deep(nn.Module): | |
| def __init__(self, nc, layers, block=BasicBlock, norm_layer=None, large_kernel=False, late_downsample=False): | |
| super(SAM_Decoder_Deep, self).__init__() | |
| self.logger = logging.getLogger("Logger") | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| self._norm_layer = norm_layer | |
| self.large_kernel = large_kernel | |
| self.kernel_size = 5 if self.large_kernel else 3 | |
| #self.inplanes = 512 if layers[0] > 0 else 256 | |
| self.inplanes = 256 | |
| self.late_downsample = late_downsample | |
| self.midplanes = 64 if late_downsample else 32 | |
| self.conv1 = ops.SpectralNorm(nn.ConvTranspose2d(self.midplanes, 32, kernel_size=4, stride=2, padding=1, bias=False)) | |
| self.bn1 = norm_layer(32) | |
| self.leaky_relu = nn.LeakyReLU(0.2, inplace=True) | |
| self.upsample = nn.UpsamplingNearest2d(scale_factor=2) | |
| self.tanh = nn.Tanh() | |
| #self.layer1 = self._make_layer(block, 256, layers[0], stride=2) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 64, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, self.midplanes, layers[3], stride=2) | |
| self.refine_OS1 = nn.Sequential( | |
| nn.Conv2d(32, 32, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size//2, bias=False), | |
| norm_layer(32), | |
| self.leaky_relu, | |
| nn.Conv2d(32, 1, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size//2),) | |
| self.refine_OS4 = nn.Sequential( | |
| nn.Conv2d(64, 32, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size//2, bias=False), | |
| norm_layer(32), | |
| self.leaky_relu, | |
| nn.Conv2d(32, 1, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size//2),) | |
| self.refine_OS8 = nn.Sequential( | |
| nn.Conv2d(128, 32, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size//2, bias=False), | |
| norm_layer(32), | |
| self.leaky_relu, | |
| nn.Conv2d(32, 1, kernel_size=self.kernel_size, stride=1, padding=self.kernel_size//2),) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| if hasattr(m, "weight_bar"): | |
| nn.init.xavier_uniform_(m.weight_bar) | |
| else: | |
| nn.init.xavier_uniform_(m.weight) | |
| elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| # Zero-initialize the last BN in each residual branch, | |
| # so that the residual branch starts with zeros, and each residual block behaves like an identity. | |
| # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 | |
| for m in self.modules(): | |
| if isinstance(m, BasicBlock): | |
| nn.init.constant_(m.bn2.weight, 0) | |
| self.logger.debug(self) | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| if blocks == 0: | |
| return nn.Sequential(nn.Identity()) | |
| norm_layer = self._norm_layer | |
| upsample = None | |
| if stride != 1: | |
| upsample = nn.Sequential( | |
| nn.UpsamplingNearest2d(scale_factor=2), | |
| ops.SpectralNorm(conv1x1(self.inplanes + 4, planes * block.expansion)), | |
| norm_layer(planes * block.expansion), | |
| ) | |
| elif self.inplanes != planes * block.expansion: | |
| upsample = nn.Sequential( | |
| ops.SpectralNorm(conv1x1(self.inplanes + 4, planes * block.expansion)), | |
| norm_layer(planes * block.expansion), | |
| ) | |
| layers = [block(self.inplanes + 4, planes, stride, upsample, norm_layer, self.large_kernel)] | |
| self.inplanes = planes * block.expansion | |
| for _ in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, norm_layer=norm_layer, large_kernel=self.large_kernel)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x_os16, img, mask): | |
| ret = {} | |
| mask_os16 = F.interpolate(mask, x_os16.shape[2:], mode='bilinear', align_corners=False) | |
| img_os16 = F.interpolate(img, x_os16.shape[2:], mode='bilinear', align_corners=False) | |
| x = self.layer2(torch.cat((x_os16, img_os16, mask_os16), dim=1)) # N x 128 x 128 x 128 | |
| x_os8 = self.refine_OS8(x) | |
| mask_os8 = F.interpolate(mask, x.shape[2:], mode='bilinear', align_corners=False) | |
| img_os8 = F.interpolate(img, x.shape[2:], mode='bilinear', align_corners=False) | |
| x = self.layer3(torch.cat((x, img_os8, mask_os8), dim=1)) # N x 64 x 256 x 256 | |
| x_os4 = self.refine_OS4(x) | |
| mask_os4 = F.interpolate(mask, x.shape[2:], mode='bilinear', align_corners=False) | |
| img_os4 = F.interpolate(img, x.shape[2:], mode='bilinear', align_corners=False) | |
| x = self.layer4(torch.cat((x, img_os4, mask_os4), dim=1)) # N x 32 x 512 x 512 | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.leaky_relu(x) # N x 32 x 1024 x 1024 | |
| x_os1 = self.refine_OS1(x) # N | |
| x_os4 = F.interpolate(x_os4, scale_factor=4.0, mode='bilinear', align_corners=False) | |
| x_os8 = F.interpolate(x_os8, scale_factor=8.0, mode='bilinear', align_corners=False) | |
| x_os1 = (torch.tanh(x_os1) + 1.0) / 2.0 | |
| x_os4 = (torch.tanh(x_os4) + 1.0) / 2.0 | |
| x_os8 = (torch.tanh(x_os8) + 1.0) / 2.0 | |
| mask_os1 = F.interpolate(mask, x_os1.shape[2:], mode='bilinear', align_corners=False) | |
| ret['alpha_os1'] = x_os1 | |
| ret['alpha_os4'] = x_os4 | |
| ret['alpha_os8'] = x_os8 | |
| ret['mask'] = mask_os1 | |
| return ret |