| | 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): |
| | |
| | up1 = inp |
| | up1 = self._modules['b1_' + str(level)](up1) |
| |
|
| | |
| | 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 |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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 |
| |
|
