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import torch.nn as nn |
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import torch |
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from torch.autograd import Variable |
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import math |
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import torch.utils.model_zoo as model_zoo |
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from models.features import Features |
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from utils.log_helper import log_once |
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', |
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'resnet152'] |
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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 conv3x3(in_planes, out_planes, stride=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=1, 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): |
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super(BasicBlock, self).__init__() |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = nn.BatchNorm2d(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 = nn.BatchNorm2d(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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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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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 Bottleneck(Features): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): |
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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 = nn.BatchNorm2d(planes) |
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padding = 2 - stride |
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assert stride==1 or dilation==1, "stride and dilation must have one equals to zero at least" |
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if dilation > 1: |
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padding = dilation |
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
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padding=padding, bias=False, dilation=dilation) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
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self.bn3 = nn.BatchNorm2d(planes * 4) |
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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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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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if out.size() != residual.size(): |
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print(out.size(), residual.size()) |
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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 Bottleneck_nop(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super(Bottleneck_nop, self).__init__() |
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
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padding=0, bias=False) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
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self.bn3 = nn.BatchNorm2d(planes * 4) |
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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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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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s = residual.size(3) |
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residual = residual[:, :, 1:s-1, 1:s-1] |
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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 ResNet(nn.Module): |
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def __init__(self, block, layers, layer4=False, layer3=False): |
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self.inplanes = 64 |
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super(ResNet, self).__init__() |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, |
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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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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.feature_size = 128 * block.expansion |
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if layer3: |
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self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2) |
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self.feature_size = (256 + 128) * block.expansion |
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else: |
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self.layer3 = lambda x:x |
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if layer4: |
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self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4) |
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self.feature_size = 512 * block.expansion |
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else: |
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self.layer4 = lambda x:x |
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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, dilation=1): |
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downsample = None |
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dd = dilation |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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if stride == 1 and dilation == 1: |
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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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else: |
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if dilation > 1: |
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dd = dilation // 2 |
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padding = dd |
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else: |
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dd = 1 |
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padding = 0 |
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downsample = nn.Sequential( |
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nn.Conv2d(self.inplanes, planes * block.expansion, |
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kernel_size=3, stride=stride, bias=False, |
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padding=padding, dilation=dd), |
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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, dilation=dd)) |
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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)) |
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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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p1 = self.layer1(x) |
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p2 = self.layer2(p1) |
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p3 = self.layer3(p2) |
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log_once("p3 {}".format(p3.size())) |
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p4 = self.layer4(p3) |
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return p2, p3, p4 |
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class ResAdjust(nn.Module): |
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def __init__(self, |
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block=Bottleneck, |
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out_channels=256, |
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adjust_number=1, |
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fuse_layers=[2,3,4]): |
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super(ResAdjust, self).__init__() |
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self.fuse_layers = set(fuse_layers) |
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if 2 in self.fuse_layers: |
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self.layer2 = self._make_layer(block, 128, 1, out_channels, adjust_number) |
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if 3 in self.fuse_layers: |
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self.layer3 = self._make_layer(block, 256, 2, out_channels, adjust_number) |
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if 4 in self.fuse_layers: |
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self.layer4 = self._make_layer(block, 512, 4, out_channels, adjust_number) |
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self.feature_size = out_channels * len(self.fuse_layers) |
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def _make_layer(self, block, plances, dilation, out, number=1): |
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layers = [] |
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for _ in range(number): |
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layer = block(plances * block.expansion, plances, dilation=dilation) |
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layers.append(layer) |
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downsample = nn.Sequential( |
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nn.Conv2d(plances * block.expansion, out, kernel_size=3, padding=1, bias=False), |
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nn.BatchNorm2d(out) |
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) |
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layers.append(downsample) |
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return nn.Sequential(*layers) |
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def forward(self, p2, p3, p4): |
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outputs = [] |
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if 2 in self.fuse_layers: |
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outputs.append(self.layer2(p2)) |
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if 3 in self.fuse_layers: |
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outputs.append(self.layer3(p3)) |
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if 4 in self.fuse_layers: |
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outputs.append(self.layer4(p4)) |
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return outputs |
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def resnet18(pretrained=False, **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) |
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return model |
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def resnet34(pretrained=False, **kwargs): |
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"""Constructs a ResNet-34 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) |
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return model |
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def resnet50(pretrained=False, **kwargs): |
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"""Constructs a ResNet-50 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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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(model_urls['resnet50'])) |
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return model |
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def resnet101(pretrained=False, **kwargs): |
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"""Constructs a ResNet-101 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) |
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return model |
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def resnet152(pretrained=False, **kwargs): |
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"""Constructs a ResNet-152 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) |
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return model |
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if __name__ == '__main__': |
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net = resnet50() |
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print(net) |
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net = net.cuda() |
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var = torch.FloatTensor(1,3,127,127).cuda() |
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var = Variable(var) |
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template = net(var) |
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print('Examplar Size: {}'.format(template.shape)) |
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var = torch.FloatTensor(1,3,255,255).cuda() |
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var = Variable(var) |
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net(var) |
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