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136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
import torch
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import torch.nn as nn
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from torchvision.models import resnet50, densenet121, densenet201
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class FPNSegHead(nn.Module):
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def __init__(self, num_in, num_mid, num_out):
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super().__init__()
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self.block0 = nn.Conv2d(num_in, num_mid, kernel_size=3, padding=1, bias=False)
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self.block1 = nn.Conv2d(num_mid, num_out, kernel_size=3, padding=1, bias=False)
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def forward(self, x):
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x = nn.functional.relu(self.block0(x), inplace=True)
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x = nn.functional.relu(self.block1(x), inplace=True)
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return x
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class FPNDense(nn.Module):
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def __init__(self, output_ch=3, num_filters=128, num_filters_fpn=256, pretrained=True):
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super().__init__()
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# Feature Pyramid Network (FPN) with four feature maps of resolutions
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# 1/4, 1/8, 1/16, 1/32 and `num_filters` filters for all feature maps.
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self.fpn = FPN(num_filters=num_filters_fpn, pretrained=pretrained)
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# The segmentation heads on top of the FPN
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self.head1 = FPNSegHead(num_filters_fpn, num_filters, num_filters)
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self.head2 = FPNSegHead(num_filters_fpn, num_filters, num_filters)
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self.head3 = FPNSegHead(num_filters_fpn, num_filters, num_filters)
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self.head4 = FPNSegHead(num_filters_fpn, num_filters, num_filters)
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self.smooth = nn.Sequential(
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nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1),
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nn.BatchNorm2d(num_filters),
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nn.ReLU(),
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)
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self.smooth2 = nn.Sequential(
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nn.Conv2d(num_filters, num_filters // 2, kernel_size=3, padding=1),
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nn.BatchNorm2d(num_filters // 2),
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nn.ReLU(),
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)
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self.final = nn.Conv2d(num_filters // 2, output_ch, kernel_size=3, padding=1)
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def forward(self, x):
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map0, map1, map2, map3, map4 = self.fpn(x)
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map4 = nn.functional.upsample(self.head4(map4), scale_factor=8, mode="nearest")
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map3 = nn.functional.upsample(self.head3(map3), scale_factor=4, mode="nearest")
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map2 = nn.functional.upsample(self.head2(map2), scale_factor=2, mode="nearest")
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map1 = nn.functional.upsample(self.head1(map1), scale_factor=1, mode="nearest")
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smoothed = self.smooth(torch.cat([map4, map3, map2, map1], dim=1))
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smoothed = nn.functional.upsample(smoothed, scale_factor=2, mode="nearest")
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smoothed = self.smooth2(smoothed + map0)
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smoothed = nn.functional.upsample(smoothed, scale_factor=2, mode="nearest")
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final = self.final(smoothed)
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nn.Tanh(final)
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class FPN(nn.Module):
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def __init__(self, num_filters=256, pretrained=True):
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"""Creates an `FPN` instance for feature extraction.
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Args:
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num_filters: the number of filters in each output pyramid level
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pretrained: use ImageNet pre-trained backbone feature extractor
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"""
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super().__init__()
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self.features = densenet121(pretrained=pretrained).features
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self.enc0 = nn.Sequential(self.features.conv0,
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self.features.norm0,
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self.features.relu0)
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self.pool0 = self.features.pool0
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self.enc1 = self.features.denseblock1 # 256
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self.enc2 = self.features.denseblock2 # 512
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self.enc3 = self.features.denseblock3 # 1024
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self.enc4 = self.features.denseblock4 # 2048
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self.norm = self.features.norm5 # 2048
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self.tr1 = self.features.transition1 # 256
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self.tr2 = self.features.transition2 # 512
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self.tr3 = self.features.transition3 # 1024
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self.lateral4 = nn.Conv2d(1024, num_filters, kernel_size=1, bias=False)
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self.lateral3 = nn.Conv2d(1024, num_filters, kernel_size=1, bias=False)
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self.lateral2 = nn.Conv2d(512, num_filters, kernel_size=1, bias=False)
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self.lateral1 = nn.Conv2d(256, num_filters, kernel_size=1, bias=False)
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self.lateral0 = nn.Conv2d(64, num_filters // 2, kernel_size=1, bias=False)
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def forward(self, x):
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# Bottom-up pathway, from ResNet
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enc0 = self.enc0(x)
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pooled = self.pool0(enc0)
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enc1 = self.enc1(pooled) # 256
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tr1 = self.tr1(enc1)
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enc2 = self.enc2(tr1) # 512
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tr2 = self.tr2(enc2)
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enc3 = self.enc3(tr2) # 1024
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tr3 = self.tr3(enc3)
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enc4 = self.enc4(tr3) # 2048
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enc4 = self.norm(enc4)
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# Lateral connections
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lateral4 = self.lateral4(enc4)
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lateral3 = self.lateral3(enc3)
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lateral2 = self.lateral2(enc2)
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lateral1 = self.lateral1(enc1)
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lateral0 = self.lateral0(enc0)
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# Top-down pathway
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map4 = lateral4
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map3 = lateral3 + nn.functional.upsample(map4, scale_factor=2, mode="nearest")
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map2 = lateral2 + nn.functional.upsample(map3, scale_factor=2, mode="nearest")
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map1 = lateral1 + nn.functional.upsample(map2, scale_factor=2, mode="nearest")
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return lateral0, map1, map2, map3, map4
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