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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import os |
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import logging |
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import torch |
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import torch.nn as nn |
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BN_MOMENTUM = 0.1 |
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logger = logging.getLogger(__name__) |
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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( |
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in_planes, out_planes, kernel_size=3, stride=stride, |
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padding=1, bias=False |
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) |
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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, momentum=BN_MOMENTUM) |
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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, momentum=BN_MOMENTUM) |
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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(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, momentum=BN_MOMENTUM) |
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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, momentum=BN_MOMENTUM) |
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self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, |
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bias=False) |
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self.bn3 = nn.BatchNorm2d(planes * self.expansion, |
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momentum=BN_MOMENTUM) |
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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 PoseResNet(nn.Module): |
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def __init__(self, block, layers, cfg, **kwargs): |
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self.inplanes = 64 |
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extra = cfg.MODEL.EXTRA |
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self.deconv_with_bias = extra.DECONV_WITH_BIAS |
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super(PoseResNet, self).__init__() |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
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bias=False) |
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self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) |
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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.deconv_layers = self._make_deconv_layer( |
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extra.NUM_DECONV_LAYERS, |
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extra.NUM_DECONV_FILTERS, |
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extra.NUM_DECONV_KERNELS, |
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) |
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self.final_layer = nn.Conv2d( |
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in_channels=extra.NUM_DECONV_FILTERS[-1], |
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out_channels=cfg.MODEL.NUM_JOINTS, |
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kernel_size=extra.FINAL_CONV_KERNEL, |
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stride=1, |
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padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 |
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) |
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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, momentum=BN_MOMENTUM), |
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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 _get_deconv_cfg(self, deconv_kernel, index): |
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if deconv_kernel == 4: |
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padding = 1 |
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output_padding = 0 |
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elif deconv_kernel == 3: |
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padding = 1 |
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output_padding = 1 |
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elif deconv_kernel == 2: |
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padding = 0 |
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output_padding = 0 |
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return deconv_kernel, padding, output_padding |
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def _make_deconv_layer(self, num_layers, num_filters, num_kernels): |
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assert num_layers == len(num_filters), \ |
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'ERROR: num_deconv_layers is different len(num_deconv_filters)' |
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assert num_layers == len(num_kernels), \ |
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'ERROR: num_deconv_layers is different len(num_deconv_filters)' |
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layers = [] |
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for i in range(num_layers): |
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kernel, padding, output_padding = \ |
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self._get_deconv_cfg(num_kernels[i], i) |
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planes = num_filters[i] |
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layers.append( |
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nn.ConvTranspose2d( |
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in_channels=self.inplanes, |
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out_channels=planes, |
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kernel_size=kernel, |
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stride=2, |
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padding=padding, |
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output_padding=output_padding, |
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bias=self.deconv_with_bias)) |
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layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) |
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layers.append(nn.ReLU(inplace=True)) |
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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.deconv_layers(x) |
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x = self.final_layer(x) |
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return x |
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def init_weights(self, pretrained=''): |
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if os.path.isfile(pretrained): |
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logger.info('=> init deconv weights from normal distribution') |
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for name, m in self.deconv_layers.named_modules(): |
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if isinstance(m, nn.ConvTranspose2d): |
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logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) |
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logger.info('=> init {}.bias as 0'.format(name)) |
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nn.init.normal_(m.weight, std=0.001) |
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if self.deconv_with_bias: |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.BatchNorm2d): |
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logger.info('=> init {}.weight as 1'.format(name)) |
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logger.info('=> init {}.bias as 0'.format(name)) |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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logger.info('=> init final conv weights from normal distribution') |
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for m in self.final_layer.modules(): |
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if isinstance(m, nn.Conv2d): |
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logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) |
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logger.info('=> init {}.bias as 0'.format(name)) |
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nn.init.normal_(m.weight, std=0.001) |
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nn.init.constant_(m.bias, 0) |
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pretrained_state_dict = torch.load(pretrained) |
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logger.info('=> loading pretrained model {}'.format(pretrained)) |
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self.load_state_dict(pretrained_state_dict, strict=False) |
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else: |
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logger.info('=> init weights from normal distribution') |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.normal_(m.weight, std=0.001) |
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elif isinstance(m, nn.BatchNorm2d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.ConvTranspose2d): |
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nn.init.normal_(m.weight, std=0.001) |
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if self.deconv_with_bias: |
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nn.init.constant_(m.bias, 0) |
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resnet_spec = { |
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18: (BasicBlock, [2, 2, 2, 2]), |
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34: (BasicBlock, [3, 4, 6, 3]), |
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50: (Bottleneck, [3, 4, 6, 3]), |
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101: (Bottleneck, [3, 4, 23, 3]), |
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152: (Bottleneck, [3, 8, 36, 3]) |
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} |
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def get_pose_net(cfg, is_train, **kwargs): |
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num_layers = cfg.MODEL.EXTRA.NUM_LAYERS |
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block_class, layers = resnet_spec[num_layers] |
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model = PoseResNet(block_class, layers, cfg, **kwargs) |
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if is_train and cfg.MODEL.INIT_WEIGHTS: |
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model.init_weights(cfg.MODEL.PRETRAINED) |
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return model |
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