186 lines
6.1 KiB
Python
186 lines
6.1 KiB
Python
import math
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import torch.nn as nn
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import torch.utils.model_zoo as model_zoo
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
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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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def conv1x1(in_planes, out_planes, stride=1):
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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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, downsample=None, groups=1,
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base_width=64, dilation=1, norm_layer=None):
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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if groups != 1 or base_width != 64:
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raise ValueError('BasicBlock only supports groups=1 and base_width=64')
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if dilation > 1:
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = norm_layer(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = norm_layer(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 Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
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base_width=64, dilation=1, norm_layer=None):
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super(Bottleneck, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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width = int(planes * (base_width / 64.)) * groups
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# 利用1x1卷积下降通道数
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self.conv1 = conv1x1(inplanes, width)
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self.bn1 = norm_layer(width)
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# 利用3x3卷积进行特征提取
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self.conv2 = conv3x3(width, width, stride, groups, dilation)
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self.bn2 = norm_layer(width)
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# 利用1x1卷积上升通道数
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self.conv3 = conv1x1(width, planes * self.expansion)
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self.bn3 = norm_layer(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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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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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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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 ResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1000):
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#-----------------------------------------------------------#
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# 假设输入图像为600,600,3
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# 当我们使用resnet50的时候
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#-----------------------------------------------------------#
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self.inplanes = 64
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super(ResNet, self).__init__()
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# 600,600,3 -> 300,300,64
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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# 300,300,64 -> 150,150,64
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True) # change
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# 150,150,64 -> 150,150,256
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self.layer1 = self._make_layer(block, 64, layers[0])
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# 150,150,256 -> 75,75,512
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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# 75,75,512 -> 38,38,1024
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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# 38,38,1024 -> 19,19,2048
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AvgPool2d(7)
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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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 _make_layer(self, block, planes, blocks, stride=1):
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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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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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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))
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return nn.Sequential(*layers)
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def forward(self, x):
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# x = self.conv1(x)
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# x = self.bn1(x)
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# x = self.relu(x)
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# x = self.maxpool(x)
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# x = self.layer1(x)
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# x = self.layer2(x)
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# x = self.layer3(x)
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# x = self.layer4(x)
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# x = self.avgpool(x)
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# x = x.view(x.size(0), -1)
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# x = self.fc(x)
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x = self.conv1(x)
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x = self.bn1(x)
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feat1 = self.relu(x)
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x = self.maxpool(feat1)
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feat2 = self.layer1(x)
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feat3 = self.layer2(feat2)
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feat4 = self.layer3(feat3)
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feat5 = self.layer4(feat4)
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return [feat1, feat2, feat3, feat4, feat5]
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def resnet50(pretrained=False, **kwargs):
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url('https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth', model_dir='model_data'), strict=False)
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del model.avgpool
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del model.fc
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return model
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