# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao (Bin.Xiao@microsoft.com) # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import logging import torch import torch.nn as nn BN_MOMENTUM = 0.1 logger = logging.getLogger(__name__) def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d( in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False ) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) 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) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out 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, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) 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 PoseResNet(nn.Module): def __init__(self, block, layers, cfg, **kwargs): self.inplanes = 64 extra = cfg.MODEL.EXTRA self.deconv_with_bias = extra.DECONV_WITH_BIAS super(PoseResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) 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) # used for deconv layers self.deconv_layers = self._make_deconv_layer( extra.NUM_DECONV_LAYERS, extra.NUM_DECONV_FILTERS, extra.NUM_DECONV_KERNELS, ) self.final_layer = nn.Conv2d( in_channels=extra.NUM_DECONV_FILTERS[-1], out_channels=cfg.MODEL.NUM_JOINTS, kernel_size=extra.FINAL_CONV_KERNEL, stride=1, padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 ) 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, momentum=BN_MOMENTUM), ) 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 _get_deconv_cfg(self, deconv_kernel, index): if deconv_kernel == 4: padding = 1 output_padding = 0 elif deconv_kernel == 3: padding = 1 output_padding = 1 elif deconv_kernel == 2: padding = 0 output_padding = 0 return deconv_kernel, padding, output_padding def _make_deconv_layer(self, num_layers, num_filters, num_kernels): assert num_layers == len(num_filters), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' assert num_layers == len(num_kernels), \ 'ERROR: num_deconv_layers is different len(num_deconv_filters)' layers = [] for i in range(num_layers): kernel, padding, output_padding = \ self._get_deconv_cfg(num_kernels[i], i) planes = num_filters[i] layers.append( nn.ConvTranspose2d( in_channels=self.inplanes, out_channels=planes, kernel_size=kernel, stride=2, padding=padding, output_padding=output_padding, bias=self.deconv_with_bias)) layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) 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.deconv_layers(x) x = self.final_layer(x) return x def init_weights(self, pretrained=''): if os.path.isfile(pretrained): logger.info('=> init deconv weights from normal distribution') for name, m in self.deconv_layers.named_modules(): if isinstance(m, nn.ConvTranspose2d): logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) if self.deconv_with_bias: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): logger.info('=> init {}.weight as 1'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) logger.info('=> init final conv weights from normal distribution') for m in self.final_layer.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) logger.info('=> init {}.bias as 0'.format(name)) nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) pretrained_state_dict = torch.load(pretrained) logger.info('=> loading pretrained model {}'.format(pretrained)) self.load_state_dict(pretrained_state_dict, strict=False) else: logger.info('=> init weights from normal distribution') for m in self.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') nn.init.normal_(m.weight, std=0.001) # nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.ConvTranspose2d): nn.init.normal_(m.weight, std=0.001) if self.deconv_with_bias: nn.init.constant_(m.bias, 0) resnet_spec = { 18: (BasicBlock, [2, 2, 2, 2]), 34: (BasicBlock, [3, 4, 6, 3]), 50: (Bottleneck, [3, 4, 6, 3]), 101: (Bottleneck, [3, 4, 23, 3]), 152: (Bottleneck, [3, 8, 36, 3]) } def get_pose_net(cfg, is_train, **kwargs): num_layers = cfg.MODEL.EXTRA.NUM_LAYERS block_class, layers = resnet_spec[num_layers] model = PoseResNet(block_class, layers, cfg, **kwargs) if is_train and cfg.MODEL.INIT_WEIGHTS: model.init_weights(cfg.MODEL.PRETRAINED) return model