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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 math |
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def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): |
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"3x3 convolution with padding" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, |
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stride=strd, padding=padding, bias=bias) |
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class ConvBlock(nn.Module): |
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def __init__(self, in_planes, out_planes): |
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super(ConvBlock, self).__init__() |
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self.bn1 = nn.BatchNorm2d(in_planes) |
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self.conv1 = conv3x3(in_planes, int(out_planes / 2)) |
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self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) |
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self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) |
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self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) |
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self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) |
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if in_planes != out_planes: |
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self.downsample = nn.Sequential( |
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nn.BatchNorm2d(in_planes), |
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nn.ReLU(True), |
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nn.Conv2d(in_planes, out_planes, |
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kernel_size=1, stride=1, bias=False), |
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) |
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else: |
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self.downsample = None |
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def forward(self, x): |
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residual = x |
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out1 = self.bn1(x) |
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out1 = F.relu(out1, True) |
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out1 = self.conv1(out1) |
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out2 = self.bn2(out1) |
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out2 = F.relu(out2, True) |
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out2 = self.conv2(out2) |
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out3 = self.bn3(out2) |
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out3 = F.relu(out3, True) |
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out3 = self.conv3(out3) |
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out3 = torch.cat((out1, out2, out3), 1) |
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if self.downsample is not None: |
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residual = self.downsample(residual) |
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out3 += residual |
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return out3 |
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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): |
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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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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
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padding=1, 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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out += residual |
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out = self.relu(out) |
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return out |
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class HourGlass(nn.Module): |
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def __init__(self, num_modules, depth, num_features): |
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super(HourGlass, self).__init__() |
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self.num_modules = num_modules |
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self.depth = depth |
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self.features = num_features |
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self._generate_network(self.depth) |
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def _generate_network(self, level): |
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self.add_module('b1_' + str(level), ConvBlock(self.features, self.features)) |
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self.add_module('b2_' + str(level), ConvBlock(self.features, self.features)) |
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if level > 1: |
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self._generate_network(level - 1) |
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else: |
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self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features)) |
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self.add_module('b3_' + str(level), ConvBlock(self.features, self.features)) |
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def _forward(self, level, inp): |
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up1 = inp |
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up1 = self._modules['b1_' + str(level)](up1) |
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low1 = F.avg_pool2d(inp, 2, stride=2) |
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low1 = self._modules['b2_' + str(level)](low1) |
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if level > 1: |
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low2 = self._forward(level - 1, low1) |
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else: |
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low2 = low1 |
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low2 = self._modules['b2_plus_' + str(level)](low2) |
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low3 = low2 |
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low3 = self._modules['b3_' + str(level)](low3) |
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up2 = F.interpolate(low3, scale_factor=2, mode='nearest') |
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return up1 + up2 |
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def forward(self, x): |
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return self._forward(self.depth, x) |
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class FAN(nn.Module): |
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def __init__(self, num_modules=1): |
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super(FAN, self).__init__() |
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self.num_modules = num_modules |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) |
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self.bn1 = nn.BatchNorm2d(64) |
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self.conv2 = ConvBlock(64, 128) |
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self.conv3 = ConvBlock(128, 128) |
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self.conv4 = ConvBlock(128, 256) |
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for hg_module in range(self.num_modules): |
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self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) |
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self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) |
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self.add_module('conv_last' + str(hg_module), |
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nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) |
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self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) |
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self.add_module('l' + str(hg_module), nn.Conv2d(256, |
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68, kernel_size=1, stride=1, padding=0)) |
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if hg_module < self.num_modules - 1: |
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self.add_module( |
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'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) |
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self.add_module('al' + str(hg_module), nn.Conv2d(68, |
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256, kernel_size=1, stride=1, padding=0)) |
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def forward(self, x): |
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x = F.relu(self.bn1(self.conv1(x)), True) |
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x = F.avg_pool2d(self.conv2(x), 2, stride=2) |
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x = self.conv3(x) |
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x = self.conv4(x) |
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previous = x |
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outputs = [] |
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for i in range(self.num_modules): |
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hg = self._modules['m' + str(i)](previous) |
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ll = hg |
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ll = self._modules['top_m_' + str(i)](ll) |
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ll = F.relu(self._modules['bn_end' + str(i)] |
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(self._modules['conv_last' + str(i)](ll)), True) |
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tmp_out = self._modules['l' + str(i)](ll) |
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outputs.append(tmp_out) |
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if i < self.num_modules - 1: |
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ll = self._modules['bl' + str(i)](ll) |
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tmp_out_ = self._modules['al' + str(i)](tmp_out) |
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previous = previous + ll + tmp_out_ |
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return outputs |
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class ResNetDepth(nn.Module): |
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def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68): |
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self.inplanes = 64 |
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super(ResNetDepth, self).__init__() |
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self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3, |
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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.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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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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return x |
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