297 lines
9.9 KiB
Python
297 lines
9.9 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.model_zoo as model_zoo
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import math
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=dilation, groups=groups, bias=False, dilation=dilation)
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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}
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def ResNet34(output_stride, BatchNorm=nn.BatchNorm2d, pretrained=True, in_c=3):
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"""
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output, low_level_feat:
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512, 64
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"""
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print(in_c)
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model = ResNet(BasicBlock, [3, 4, 6, 3], output_stride, BatchNorm, in_c=in_c)
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if in_c != 3:
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pretrained = False
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if pretrained:
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model._load_pretrained_model(model_urls['resnet34'])
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return model
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def ResNet18(output_stride, BatchNorm=nn.BatchNorm2d, pretrained=True, in_c=3):
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"""
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output, low_level_feat:
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512, 256, 128, 64, 64
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"""
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model = ResNet(BasicBlock, [2, 2, 2, 2], output_stride, BatchNorm, in_c=in_c)
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if in_c !=3:
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pretrained=False
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if pretrained:
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model._load_pretrained_model(model_urls['resnet18'])
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return model
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def ResNet50(output_stride, BatchNorm=nn.BatchNorm2d, pretrained=True, in_c=3):
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"""
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output, low_level_feat:
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2048, 256
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"""
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model = ResNet(Bottleneck, [3, 4, 6, 3], output_stride, BatchNorm, in_c=in_c)
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if in_c !=3:
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pretrained=False
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if pretrained:
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model._load_pretrained_model(model_urls['resnet50'])
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return model
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None):
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
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dilation=dilation, padding=dilation, bias=False)
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self.bn1 = BatchNorm(planes)
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self.relu = nn.ReLU(inplace=True)
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# self.do1 = nn.Dropout2d(p=0.2)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = BatchNorm(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=16):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction, bias=False),
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nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel, bias=False),
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nn.Sigmoid()
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y.expand_as(x)
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = BatchNorm(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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dilation=dilation, padding=dilation, bias=False)
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self.bn2 = BatchNorm(planes)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = BatchNorm(planes * 4)
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self.relu = nn.ReLU()
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self.downsample = downsample
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self.stride = stride
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self.dilation = dilation
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class PA(nn.Module):
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def __init__(self, inchan = 512, out_chan = 32):
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super().__init__()
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self.conv = nn.Conv2d(inchan, out_chan, kernel_size=3, padding=1, bias=False)
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self.bn = nn.BatchNorm2d(out_chan)
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self.re = nn.ReLU()
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self.do = nn.Dropout2d(0.2)
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self.pa_conv = nn.Conv2d(out_chan, out_chan, kernel_size=1, padding=0, groups=out_chan)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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x0 = self.conv(x)
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x = self.do(self.re(self.bn(x0)))
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return x0 *self.sigmoid(self.pa_conv(x))
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class ResNet(nn.Module):
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def __init__(self, block, layers, output_stride, BatchNorm, pretrained=True, in_c=3):
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self.inplanes = 64
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self.in_c = in_c
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print('in_c: ',self.in_c)
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super(ResNet, self).__init__()
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blocks = [1, 2, 4]
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if output_stride == 32:
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strides = [1, 2, 2, 2]
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dilations = [1, 1, 1, 1]
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elif output_stride == 16:
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strides = [1, 2, 2, 1]
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dilations = [1, 1, 1, 2]
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elif output_stride == 8:
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strides = [1, 2, 1, 1]
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dilations = [1, 1, 2, 4]
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elif output_stride == 4:
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strides = [1, 1, 1, 1]
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dilations = [1, 2, 4, 8]
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else:
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raise NotImplementedError
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# Modules
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self.conv1 = nn.Conv2d(self.in_c, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = BatchNorm(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0], BatchNorm=BatchNorm)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1], BatchNorm=BatchNorm)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2], BatchNorm=BatchNorm)
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self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
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self._init_weight()
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self.pos_s16 = PA(512, 32)
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self.pos_s8 = PA(128, 32)
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self.pos_s4 = PA(64, 32)
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def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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BatchNorm(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, dilation, downsample, BatchNorm))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, dilation=dilation, BatchNorm=BatchNorm))
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return nn.Sequential(*layers)
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def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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BatchNorm(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
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downsample=downsample, BatchNorm=BatchNorm))
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self.inplanes = planes * block.expansion
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for i in range(1, len(blocks)):
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layers.append(block(self.inplanes, planes, stride=1,
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dilation=blocks[i]*dilation, BatchNorm=BatchNorm))
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return nn.Sequential(*layers)
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def forward(self, input):
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x = self.conv1(input)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x) # | 4
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x = self.layer1(x) # | 4
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low_level_feat2 = x
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x = self.layer2(x) # | 8
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low_level_feat3 = x
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x = self.layer3(x) # | 16
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x = self.layer4(x) # | 16
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out_s16, out_s8, out_s4 = self.pos_s16(x), self.pos_s8(low_level_feat3), self.pos_s4(low_level_feat2)
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return out_s16, out_s8, out_s4
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def _init_weight(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _load_pretrained_model(self, model_path):
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pretrain_dict = model_zoo.load_url(model_path)
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model_dict = {}
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state_dict = self.state_dict()
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for k, v in pretrain_dict.items():
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if k in state_dict:
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model_dict[k] = v
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state_dict.update(model_dict)
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self.load_state_dict(state_dict)
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def build_backbone(backbone, output_stride, BatchNorm, in_c=3):
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if backbone == 'resnet50':
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return ResNet50(output_stride, BatchNorm, in_c=in_c)
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elif backbone == 'resnet34':
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return ResNet34(output_stride, BatchNorm, in_c=in_c)
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elif backbone == 'resnet18':
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return ResNet18(output_stride, BatchNorm, in_c=in_c)
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else:
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raise NotImplementedError
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