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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import math | |
| def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): | |
| "3x3 convolution with padding" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, | |
| stride=strd, padding=padding, bias=bias) | |
| class ConvBlock(nn.Module): | |
| def __init__(self, in_planes, out_planes): | |
| super(ConvBlock, self).__init__() | |
| self.bn1 = nn.BatchNorm2d(in_planes) | |
| self.conv1 = conv3x3(in_planes, int(out_planes / 2)) | |
| self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) | |
| self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) | |
| self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) | |
| self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) | |
| if in_planes != out_planes: | |
| self.downsample = nn.Sequential( | |
| nn.BatchNorm2d(in_planes), | |
| nn.ReLU(True), | |
| nn.Conv2d(in_planes, out_planes, | |
| kernel_size=1, stride=1, bias=False), | |
| ) | |
| else: | |
| self.downsample = None | |
| def forward(self, x): | |
| residual = x | |
| out1 = self.bn1(x) | |
| out1 = F.relu(out1, True) | |
| out1 = self.conv1(out1) | |
| out2 = self.bn2(out1) | |
| out2 = F.relu(out2, True) | |
| out2 = self.conv2(out2) | |
| out3 = self.bn3(out2) | |
| out3 = F.relu(out3, True) | |
| out3 = self.conv3(out3) | |
| out3 = torch.cat((out1, out2, out3), 1) | |
| if self.downsample is not None: | |
| residual = self.downsample(residual) | |
| out3 += residual | |
| return out3 | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, | |
| padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = nn.BatchNorm2d(planes * 4) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class HourGlass(nn.Module): | |
| def __init__(self, num_modules, depth, num_features): | |
| super(HourGlass, self).__init__() | |
| self.num_modules = num_modules | |
| self.depth = depth | |
| self.features = num_features | |
| self._generate_network(self.depth) | |
| def _generate_network(self, level): | |
| self.add_module('b1_' + str(level), ConvBlock(self.features, self.features)) | |
| self.add_module('b2_' + str(level), ConvBlock(self.features, self.features)) | |
| if level > 1: | |
| self._generate_network(level - 1) | |
| else: | |
| self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features)) | |
| self.add_module('b3_' + str(level), ConvBlock(self.features, self.features)) | |
| def _forward(self, level, inp): | |
| # Upper branch | |
| up1 = inp | |
| up1 = self._modules['b1_' + str(level)](up1) | |
| # Lower branch | |
| low1 = F.avg_pool2d(inp, 2, stride=2) | |
| low1 = self._modules['b2_' + str(level)](low1) | |
| if level > 1: | |
| low2 = self._forward(level - 1, low1) | |
| else: | |
| low2 = low1 | |
| low2 = self._modules['b2_plus_' + str(level)](low2) | |
| low3 = low2 | |
| low3 = self._modules['b3_' + str(level)](low3) | |
| up2 = F.interpolate(low3, scale_factor=2, mode='nearest') | |
| return up1 + up2 | |
| def forward(self, x): | |
| return self._forward(self.depth, x) | |
| class FAN(nn.Module): | |
| def __init__(self, num_modules=1): | |
| super(FAN, self).__init__() | |
| self.num_modules = num_modules | |
| # Base part | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.conv2 = ConvBlock(64, 128) | |
| self.conv3 = ConvBlock(128, 128) | |
| self.conv4 = ConvBlock(128, 256) | |
| # Stacking part | |
| for hg_module in range(self.num_modules): | |
| self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) | |
| self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) | |
| self.add_module('conv_last' + str(hg_module), | |
| nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) | |
| self.add_module('l' + str(hg_module), nn.Conv2d(256, | |
| 68, kernel_size=1, stride=1, padding=0)) | |
| if hg_module < self.num_modules - 1: | |
| self.add_module( | |
| 'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| self.add_module('al' + str(hg_module), nn.Conv2d(68, | |
| 256, kernel_size=1, stride=1, padding=0)) | |
| def forward(self, x): | |
| x = F.relu(self.bn1(self.conv1(x)), True) | |
| x = F.avg_pool2d(self.conv2(x), 2, stride=2) | |
| x = self.conv3(x) | |
| x = self.conv4(x) | |
| previous = x | |
| outputs = [] | |
| for i in range(self.num_modules): | |
| hg = self._modules['m' + str(i)](previous) | |
| ll = hg | |
| ll = self._modules['top_m_' + str(i)](ll) | |
| ll = F.relu(self._modules['bn_end' + str(i)] | |
| (self._modules['conv_last' + str(i)](ll)), True) | |
| # Predict heatmaps | |
| tmp_out = self._modules['l' + str(i)](ll) | |
| outputs.append(tmp_out) | |
| if i < self.num_modules - 1: | |
| ll = self._modules['bl' + str(i)](ll) | |
| tmp_out_ = self._modules['al' + str(i)](tmp_out) | |
| previous = previous + ll + tmp_out_ | |
| return outputs | |
| class ResNetDepth(nn.Module): | |
| def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68): | |
| self.inplanes = 64 | |
| super(ResNetDepth, self).__init__() | |
| self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
| self.avgpool = nn.AvgPool2d(7) | |
| self.fc = nn.Linear(512 * block.expansion, num_classes) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| x = self.fc(x) | |
| return x | |