262 lines
8.4 KiB
Python
262 lines
8.4 KiB
Python
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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# Upper branch
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up1 = inp
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up1 = self._modules['b1_' + str(level)](up1)
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# Lower branch
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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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# Base part
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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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# Stacking part
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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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# Predict heatmaps
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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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