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from collections import OrderedDict |
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from torch import nn |
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from torch.nn import functional as F |
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import torch |
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import torchvision |
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def conv3x3(in_, out): |
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return nn.Conv2d(in_, out, 3, padding=1) |
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class ConvRelu(nn.Module): |
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def __init__(self, in_, out): |
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super().__init__() |
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self.conv = conv3x3(in_, out) |
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self.activation = nn.ReLU(inplace=True) |
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def forward(self, x): |
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x = self.conv(x) |
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x = self.activation(x) |
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return x |
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class DecoderBlockV2(nn.Module): |
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def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True): |
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super(DecoderBlockV2, self).__init__() |
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self.in_channels = in_channels |
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if is_deconv: |
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""" |
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Parameters for Deconvolution were chosen to avoid artifacts, following |
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link https://distill.pub/2016/deconv-checkerboard/ |
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""" |
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self.block = nn.Sequential( |
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ConvRelu(in_channels, middle_channels), |
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nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=4, stride=2, |
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padding=1), |
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nn.ReLU(inplace=True) |
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) |
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else: |
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self.block = nn.Sequential( |
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nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), |
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nn.Conv2d(in_channels, middle_channels, 3, padding=1, bias=True), |
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nn.BatchNorm2d(middle_channels), |
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nn.ELU(), |
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nn.Conv2d(middle_channels, out_channels, 3, padding=1, bias=True), |
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nn.BatchNorm2d(out_channels), |
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nn.ELU() |
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) |
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def forward(self, x): |
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return self.block(x) |
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def cat_non_matching(x1, x2): |
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diffY = x1.size()[2] - x2.size()[2] |
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diffX = x1.size()[3] - x2.size()[3] |
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x2 = F.pad(x2, (diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2)) |
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x = torch.cat([x1, x2], dim=1) |
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return x |
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class UNetResNetBackbone(nn.Module): |
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"""PyTorch U-Net model using ResNet(34, 101 or 152) encoder. |
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UNet: https://arxiv.org/abs/1505.04597 |
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ResNet: https://arxiv.org/abs/1512.03385 |
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Proposed by Alexander Buslaev: https://www.linkedin.com/in/al-buslaev/ |
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Args: |
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encoder_depth (int): Depth of a ResNet encoder (34, 101 or 152). |
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num_filters (int, optional): Number of filters in the last layer of decoder. Defaults to 32. |
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dropout_2d (float, optional): Probability factor of dropout layer before output layer. Defaults to 0.2. |
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pretrained (bool, optional): |
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False - no pre-trained weights are being used. |
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True - ResNet encoder is pre-trained on ImageNet. |
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Defaults to False. |
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is_deconv (bool, optional): |
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False: bilinear interpolation is used in decoder. |
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True: deconvolution is used in decoder. |
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Defaults to False. |
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""" |
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def __init__(self, encoder_depth, num_filters=32, dropout_2d=0.2, |
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pretrained=False, is_deconv=False): |
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super().__init__() |
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self.dropout_2d = dropout_2d |
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if encoder_depth == 34: |
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self.encoder = torchvision.models.resnet34(pretrained=pretrained) |
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bottom_channel_nr = 512 |
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elif encoder_depth == 101: |
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self.encoder = torchvision.models.resnet101(pretrained=pretrained) |
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bottom_channel_nr = 2048 |
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elif encoder_depth == 152: |
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self.encoder = torchvision.models.resnet152(pretrained=pretrained) |
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bottom_channel_nr = 2048 |
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else: |
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raise NotImplementedError('only 34, 101, 152 version of ResNet are implemented') |
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self.pool = nn.MaxPool2d(2, 2) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv1 = nn.Sequential(self.encoder.conv1, |
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self.encoder.bn1, |
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self.encoder.relu, |
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self.pool) |
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self.conv2 = self.encoder.layer1 |
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self.conv3 = self.encoder.layer2 |
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self.conv4 = self.encoder.layer3 |
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self.conv5 = self.encoder.layer4 |
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self.center = DecoderBlockV2(bottom_channel_nr, num_filters * 8 * 2, num_filters * 8, is_deconv) |
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self.dec5 = DecoderBlockV2(bottom_channel_nr + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv) |
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self.dec4 = DecoderBlockV2(bottom_channel_nr // 2 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, |
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is_deconv) |
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self.dec3 = DecoderBlockV2(bottom_channel_nr // 4 + num_filters * 8, num_filters * 4 * 2, num_filters * 2, |
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is_deconv) |
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self.dec2 = DecoderBlockV2(bottom_channel_nr // 8 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2, |
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is_deconv) |
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self.dec1 = DecoderBlockV2(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv) |
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def forward(self, x): |
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conv1 = self.conv1(x) |
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conv2 = self.conv2(conv1) |
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conv3 = self.conv3(conv2) |
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conv4 = self.conv4(conv3) |
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conv5 = self.conv5(conv4) |
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pool = self.pool(conv5) |
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center = self.center(pool) |
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dec5 = self.dec5(cat_non_matching(conv5, center)) |
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dec4 = self.dec4(cat_non_matching(conv4, dec5)) |
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dec3 = self.dec3(cat_non_matching(conv3, dec4)) |
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dec2 = self.dec2(cat_non_matching(conv2, dec3)) |
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dec1 = self.dec1(dec2) |
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y = F.dropout2d(dec1, p=self.dropout_2d) |
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result = OrderedDict() |
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result["out"] = y |
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return result |
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