import torch import torch.nn as nn from torchvision import models def convrelu(in_channels, out_channels, kernel, padding): return nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel, padding=padding), nn.ReLU(inplace=True), ) class ResNetBackbone(nn.Module): def __init__(self): super().__init__() self.base_model = models.resnet50(pretrained=False) self.base_layers = list(self.base_model.children()) self.conv_original_size0 = convrelu(3, 64, 3, 1) self.conv_original_size1 = convrelu(64, 64, 3, 1) self.layer0 = nn.Sequential(*self.base_layers[:3]) # size=(N, 64, x.H/2, x.W/2) self.layer1 = nn.Sequential(*self.base_layers[3:5]) # size=(N, 64, x.H/4, x.W/4) self.layer2 = self.base_layers[5] # size=(N, 128, x.H/8, x.W/8) self.layer3 = self.base_layers[6] # size=(N, 256, x.H/16, x.W/16) self.layer4 = self.base_layers[7] # size=(N, 512, x.H/32, x.W/32) self.strides = [8, 16, 32] self.num_channels = [512, 1024, 2048] def forward(self, inputs): x_original = self.conv_original_size0(inputs) x_original = self.conv_original_size1(x_original) layer0 = self.layer0(inputs) layer1 = self.layer1(layer0) layer2 = self.layer2(layer1) layer3 = self.layer3(layer2) layer4 = self.layer4(layer3) xs = {"0": layer2, "1": layer3, "2": layer4} all_feats = {'layer0': layer0, 'layer1': layer1, 'layer2': layer2, 'layer3': layer3, 'layer4': layer4, 'x_original': x_original} mask = torch.zeros(inputs.shape)[:, 0, :, :].to(layer4.device) return xs, mask, all_feats def train(self, mode=True): # Override train so that the training mode is set as we want nn.Module.train(self, mode) if mode: # fix all bn layers def set_bn_eval(m): classname = m.__class__.__name__ if classname.find('BatchNorm') != -1: m.eval() self.apply(set_bn_eval) class ResNetUNet(nn.Module): def __init__(self, n_class, out_dim=None, ms_feat=False): super().__init__() self.return_ms_feat = ms_feat self.out_dim = out_dim self.base_model = models.resnet50(pretrained=True) self.base_layers = list(self.base_model.children()) self.layer0 = nn.Sequential(*self.base_layers[:3]) # size=(N, 64, x.H/2, x.W/2) # self.layer0_1x1 = convrelu(64, 64, 1, 0) self.layer1 = nn.Sequential(*self.base_layers[3:5]) # size=(N, 64, x.H/4, x.W/4) # self.layer1_1x1 = convrelu(256, 256, 1, 0) self.layer2 = self.base_layers[5] # size=(N, 128, x.H/8, x.W/8) # self.layer2_1x1 = convrelu(512, 512, 1, 0) self.layer3 = self.base_layers[6] # size=(N, 256, x.H/16, x.W/16) # self.layer3_1x1 = convrelu(1024, 1024, 1, 0) self.layer4 = self.base_layers[7] # size=(N, 512, x.H/32, x.W/32) # self.layer4_1x1 = convrelu(2048, 2048, 1, 0) self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) self.conv_up3 = convrelu(1024 + 2048, 1024, 3, 1) self.conv_up2 = convrelu(512 + 1024, 512, 3, 1) self.conv_up1 = convrelu(256 + 512, 256, 3, 1) self.conv_up0 = convrelu(64 + 256, 128, 3, 1) # self.conv_up1 = convrelu(512, 256, 3, 1) # self.conv_up0 = convrelu(256, 128, 3, 1) self.conv_original_size0 = convrelu(3, 64, 3, 1) self.conv_original_size1 = convrelu(64, 64, 3, 1) self.conv_original_size2 = convrelu(64 + 128, 64, 3, 1) # self.conv_last = nn.Conv2d(128, n_class, 1) self.conv_last = nn.Conv2d(64, n_class, 1) if out_dim: self.conv_out = nn.Conv2d(64, out_dim, 1) # self.conv_out = nn.Conv2d(128, out_dim, 1) # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} self.strides = [8, 16, 32] self.num_channels = [512, 1024, 2048] def forward(self, inputs): x_original = self.conv_original_size0(inputs) x_original = self.conv_original_size1(x_original) layer0 = self.layer0(inputs) layer1 = self.layer1(layer0) layer2 = self.layer2(layer1) layer3 = self.layer3(layer2) layer4 = self.layer4(layer3) # layer4 = self.layer4_1x1(layer4) x = self.upsample(layer4) # layer3 = self.layer3_1x1(layer3) x = torch.cat([x, layer3], dim=1) x = self.conv_up3(x) layer3_up = x x = self.upsample(x) # layer2 = self.layer2_1x1(layer2) x = torch.cat([x, layer2], dim=1) x = self.conv_up2(x) layer2_up = x x = self.upsample(x) # layer1 = self.layer1_1x1(layer1) x = torch.cat([x, layer1], dim=1) x = self.conv_up1(x) x = self.upsample(x) # layer0 = self.layer0_1x1(layer0) x = torch.cat([x, layer0], dim=1) x = self.conv_up0(x) x = self.upsample(x) x = torch.cat([x, x_original], dim=1) x = self.conv_original_size2(x) out = self.conv_last(x) out = out.sigmoid().squeeze(1) # xs = {"0": layer2, "1": layer3, "2": layer4} xs = {"0": layer2_up, "1": layer3_up, "2": layer4} mask = torch.zeros(inputs.shape)[:, 0, :, :].to(layer4.device) # ms_feats = self.ms_feat(xs, mask) if self.return_ms_feat: if self.out_dim: out_feat = self.conv_out(x) out_feat = out_feat.permute(0, 2, 3, 1) return xs, mask, out, out_feat else: return xs, mask, out else: return out def train(self, mode=True): # Override train so that the training mode is set as we want nn.Module.train(self, mode) if mode: # fix all bn layers def set_bn_eval(m): classname = m.__class__.__name__ if classname.find('BatchNorm') != -1: m.eval() self.apply(set_bn_eval)