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