import os import torch import torch.nn as nn from loguru import logger import torch.nn.functional as F from yacs.config import CfgNode as CN models = [ 'hrnet_w32', 'hrnet_w48', ] BN_MOMENTUM = 0.1 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 HighResolutionModule(nn.Module): def __init__(self, num_branches, blocks, num_blocks, num_inchannels, num_channels, fuse_method, multi_scale_output=True): super(HighResolutionModule, self).__init__() self._check_branches( num_branches, blocks, num_blocks, num_inchannels, num_channels) self.num_inchannels = num_inchannels self.fuse_method = fuse_method self.num_branches = num_branches self.multi_scale_output = multi_scale_output self.branches = self._make_branches( num_branches, blocks, num_blocks, num_channels) self.fuse_layers = self._make_fuse_layers() self.relu = nn.ReLU(True) def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels): if num_branches != len(num_blocks): error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( num_branches, len(num_blocks)) logger.error(error_msg) raise ValueError(error_msg) if num_branches != len(num_channels): error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( num_branches, len(num_channels)) logger.error(error_msg) raise ValueError(error_msg) if num_branches != len(num_inchannels): error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( num_branches, len(num_inchannels)) logger.error(error_msg) raise ValueError(error_msg) def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1): downsample = None if stride != 1 or \ self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: downsample = nn.Sequential( nn.Conv2d( self.num_inchannels[branch_index], num_channels[branch_index] * block.expansion, kernel_size=1, stride=stride, bias=False ), nn.BatchNorm2d( num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM ), ) layers = [] layers.append( block( self.num_inchannels[branch_index], num_channels[branch_index], stride, downsample ) ) self.num_inchannels[branch_index] = \ num_channels[branch_index] * block.expansion for i in range(1, num_blocks[branch_index]): layers.append( block( self.num_inchannels[branch_index], num_channels[branch_index] ) ) return nn.Sequential(*layers) def _make_branches(self, num_branches, block, num_blocks, num_channels): branches = [] for i in range(num_branches): branches.append( self._make_one_branch(i, block, num_blocks, num_channels) ) return nn.ModuleList(branches) def _make_fuse_layers(self): if self.num_branches == 1: return None num_branches = self.num_branches num_inchannels = self.num_inchannels fuse_layers = [] for i in range(num_branches if self.multi_scale_output else 1): fuse_layer = [] for j in range(num_branches): if j > i: fuse_layer.append( nn.Sequential( nn.Conv2d( num_inchannels[j], num_inchannels[i], 1, 1, 0, bias=False ), nn.BatchNorm2d(num_inchannels[i]), nn.Upsample(scale_factor=2**(j-i), mode='nearest') ) ) elif j == i: fuse_layer.append(None) else: conv3x3s = [] for k in range(i-j): if k == i - j - 1: num_outchannels_conv3x3 = num_inchannels[i] conv3x3s.append( nn.Sequential( nn.Conv2d( num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False ), nn.BatchNorm2d(num_outchannels_conv3x3) ) ) else: num_outchannels_conv3x3 = num_inchannels[j] conv3x3s.append( nn.Sequential( nn.Conv2d( num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False ), nn.BatchNorm2d(num_outchannels_conv3x3), nn.ReLU(True) ) ) fuse_layer.append(nn.Sequential(*conv3x3s)) fuse_layers.append(nn.ModuleList(fuse_layer)) return nn.ModuleList(fuse_layers) def get_num_inchannels(self): return self.num_inchannels def forward(self, x): if self.num_branches == 1: return [self.branches[0](x[0])] for i in range(self.num_branches): x[i] = self.branches[i](x[i]) x_fuse = [] for i in range(len(self.fuse_layers)): y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) for j in range(1, self.num_branches): if i == j: y = y + x[j] else: y = y + self.fuse_layers[i][j](x[j]) x_fuse.append(self.relu(y)) return x_fuse blocks_dict = { 'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck } class PoseHighResolutionNet(nn.Module): def __init__(self, cfg): self.inplanes = 64 extra = cfg['MODEL']['EXTRA'] super(PoseHighResolutionNet, self).__init__() self.cfg = extra # stem net self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(Bottleneck, 64, 4) self.stage2_cfg = extra['STAGE2'] num_channels = self.stage2_cfg['NUM_CHANNELS'] block = blocks_dict[self.stage2_cfg['BLOCK']] num_channels = [ num_channels[i] * block.expansion for i in range(len(num_channels)) ] self.transition1 = self._make_transition_layer([256], num_channels) self.stage2, pre_stage_channels = self._make_stage( self.stage2_cfg, num_channels) self.stage3_cfg = extra['STAGE3'] num_channels = self.stage3_cfg['NUM_CHANNELS'] block = blocks_dict[self.stage3_cfg['BLOCK']] num_channels = [ num_channels[i] * block.expansion for i in range(len(num_channels)) ] self.transition2 = self._make_transition_layer( pre_stage_channels, num_channels) self.stage3, pre_stage_channels = self._make_stage( self.stage3_cfg, num_channels) self.stage4_cfg = extra['STAGE4'] num_channels = self.stage4_cfg['NUM_CHANNELS'] block = blocks_dict[self.stage4_cfg['BLOCK']] num_channels = [ num_channels[i] * block.expansion for i in range(len(num_channels)) ] self.transition3 = self._make_transition_layer( pre_stage_channels, num_channels) self.stage4, pre_stage_channels = self._make_stage( self.stage4_cfg, num_channels, multi_scale_output=True) self.final_layer = nn.Conv2d( in_channels=pre_stage_channels[0], out_channels=cfg['MODEL']['NUM_JOINTS'], kernel_size=extra['FINAL_CONV_KERNEL'], stride=1, padding=1 if extra['FINAL_CONV_KERNEL'] == 3 else 0 ) self.pretrained_layers = extra['PRETRAINED_LAYERS'] if extra.DOWNSAMPLE and extra.USE_CONV: self.downsample_stage_1 = self._make_downsample_layer(3, num_channel=self.stage2_cfg['NUM_CHANNELS'][0]) self.downsample_stage_2 = self._make_downsample_layer(2, num_channel=self.stage2_cfg['NUM_CHANNELS'][-1]) self.downsample_stage_3 = self._make_downsample_layer(1, num_channel=self.stage3_cfg['NUM_CHANNELS'][-1]) elif not extra.DOWNSAMPLE and extra.USE_CONV: self.upsample_stage_2 = self._make_upsample_layer(1, num_channel=self.stage2_cfg['NUM_CHANNELS'][-1]) self.upsample_stage_3 = self._make_upsample_layer(2, num_channel=self.stage3_cfg['NUM_CHANNELS'][-1]) self.upsample_stage_4 = self._make_upsample_layer(3, num_channel=self.stage4_cfg['NUM_CHANNELS'][-1]) def _make_transition_layer( self, num_channels_pre_layer, num_channels_cur_layer): num_branches_cur = len(num_channels_cur_layer) num_branches_pre = len(num_channels_pre_layer) transition_layers = [] for i in range(num_branches_cur): if i < num_branches_pre: if num_channels_cur_layer[i] != num_channels_pre_layer[i]: transition_layers.append( nn.Sequential( nn.Conv2d( num_channels_pre_layer[i], num_channels_cur_layer[i], 3, 1, 1, bias=False ), nn.BatchNorm2d(num_channels_cur_layer[i]), nn.ReLU(inplace=True) ) ) else: transition_layers.append(None) else: conv3x3s = [] for j in range(i+1-num_branches_pre): inchannels = num_channels_pre_layer[-1] outchannels = num_channels_cur_layer[i] \ if j == i-num_branches_pre else inchannels conv3x3s.append( nn.Sequential( nn.Conv2d( inchannels, outchannels, 3, 2, 1, bias=False ), nn.BatchNorm2d(outchannels), nn.ReLU(inplace=True) ) ) transition_layers.append(nn.Sequential(*conv3x3s)) return nn.ModuleList(transition_layers) 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 _make_stage(self, layer_config, num_inchannels, multi_scale_output=True): num_modules = layer_config['NUM_MODULES'] num_branches = layer_config['NUM_BRANCHES'] num_blocks = layer_config['NUM_BLOCKS'] num_channels = layer_config['NUM_CHANNELS'] block = blocks_dict[layer_config['BLOCK']] fuse_method = layer_config['FUSE_METHOD'] modules = [] for i in range(num_modules): # multi_scale_output is only used last module if not multi_scale_output and i == num_modules - 1: reset_multi_scale_output = False else: reset_multi_scale_output = True modules.append( HighResolutionModule( num_branches, block, num_blocks, num_inchannels, num_channels, fuse_method, reset_multi_scale_output ) ) num_inchannels = modules[-1].get_num_inchannels() return nn.Sequential(*modules), num_inchannels def _make_upsample_layer(self, num_layers, num_channel, kernel_size=3): layers = [] for i in range(num_layers): layers.append(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)) layers.append( nn.Conv2d( in_channels=num_channel, out_channels=num_channel, kernel_size=kernel_size, stride=1, padding=1, bias=False, ) ) layers.append(nn.BatchNorm2d(num_channel, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) return nn.Sequential(*layers) def _make_downsample_layer(self, num_layers, num_channel, kernel_size=3): layers = [] for i in range(num_layers): layers.append( nn.Conv2d( in_channels=num_channel, out_channels=num_channel, kernel_size=kernel_size, stride=2, padding=1, bias=False, ) ) layers.append(nn.BatchNorm2d(num_channel, momentum=BN_MOMENTUM)) layers.append(nn.ReLU(inplace=True)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.layer1(x) x_list = [] for i in range(self.stage2_cfg['NUM_BRANCHES']): if self.transition1[i] is not None: x_list.append(self.transition1[i](x)) else: x_list.append(x) y_list = self.stage2(x_list) x_list = [] for i in range(self.stage3_cfg['NUM_BRANCHES']): if self.transition2[i] is not None: x_list.append(self.transition2[i](y_list[-1])) else: x_list.append(y_list[i]) y_list = self.stage3(x_list) x_list = [] for i in range(self.stage4_cfg['NUM_BRANCHES']): if self.transition3[i] is not None: x_list.append(self.transition3[i](y_list[-1])) else: x_list.append(y_list[i]) x = self.stage4(x_list) if self.cfg.DOWNSAMPLE: if self.cfg.USE_CONV: # Downsampling with strided convolutions x1 = self.downsample_stage_1(x[0]) x2 = self.downsample_stage_2(x[1]) x3 = self.downsample_stage_3(x[2]) x = torch.cat([x1, x2, x3, x[3]], 1) else: # Downsampling with interpolation x0_h, x0_w = x[3].size(2), x[3].size(3) x1 = F.interpolate(x[0], size=(x0_h, x0_w), mode='bilinear', align_corners=True) x2 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear', align_corners=True) x3 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=True) x = torch.cat([x1, x2, x3, x[3]], 1) else: if self.cfg.USE_CONV: # Upsampling with interpolations + convolutions x1 = self.upsample_stage_2(x[1]) x2 = self.upsample_stage_3(x[2]) x3 = self.upsample_stage_4(x[3]) x = torch.cat([x[0], x1, x2, x3], 1) else: # Upsampling with interpolation x0_h, x0_w = x[0].size(2), x[0].size(3) x1 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear', align_corners=True) x2 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=True) x3 = F.interpolate(x[3], size=(x0_h, x0_w), mode='bilinear', align_corners=True) x = torch.cat([x[0], x1, x2, x3], 1) return x def init_weights(self, pretrained=''): 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) for name, _ in m.named_parameters(): if name in ['bias']: 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) for name, _ in m.named_parameters(): if name in ['bias']: nn.init.constant_(m.bias, 0) if os.path.isfile(pretrained): pretrained_state_dict = torch.load(pretrained) logger.info('=> loading pretrained model {}'.format(pretrained)) need_init_state_dict = {} for name, m in pretrained_state_dict.items(): if name.split('.')[0] in self.pretrained_layers \ or self.pretrained_layers[0] == '*': need_init_state_dict[name] = m self.load_state_dict(need_init_state_dict, strict=False) elif pretrained: logger.warning('IMPORTANT WARNING!! Please download pre-trained models if you are in TRAINING mode!') # raise ValueError('{} is not exist!'.format(pretrained)) def get_pose_net(cfg, is_train): model = PoseHighResolutionNet(cfg) if is_train and cfg['MODEL']['INIT_WEIGHTS']: model.init_weights(cfg['MODEL']['PRETRAINED']) return model def get_cfg_defaults(pretrained, width=32, downsample=False, use_conv=False): # pose_multi_resoluton_net related params HRNET = CN() HRNET.PRETRAINED_LAYERS = [ 'conv1', 'bn1', 'conv2', 'bn2', 'layer1', 'transition1', 'stage2', 'transition2', 'stage3', 'transition3', 'stage4', ] HRNET.STEM_INPLANES = 64 HRNET.FINAL_CONV_KERNEL = 1 HRNET.STAGE2 = CN() HRNET.STAGE2.NUM_MODULES = 1 HRNET.STAGE2.NUM_BRANCHES = 2 HRNET.STAGE2.NUM_BLOCKS = [4, 4] HRNET.STAGE2.NUM_CHANNELS = [width, width*2] HRNET.STAGE2.BLOCK = 'BASIC' HRNET.STAGE2.FUSE_METHOD = 'SUM' HRNET.STAGE3 = CN() HRNET.STAGE3.NUM_MODULES = 4 HRNET.STAGE3.NUM_BRANCHES = 3 HRNET.STAGE3.NUM_BLOCKS = [4, 4, 4] HRNET.STAGE3.NUM_CHANNELS = [width, width*2, width*4] HRNET.STAGE3.BLOCK = 'BASIC' HRNET.STAGE3.FUSE_METHOD = 'SUM' HRNET.STAGE4 = CN() HRNET.STAGE4.NUM_MODULES = 3 HRNET.STAGE4.NUM_BRANCHES = 4 HRNET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] HRNET.STAGE4.NUM_CHANNELS = [width, width*2, width*4, width*8] HRNET.STAGE4.BLOCK = 'BASIC' HRNET.STAGE4.FUSE_METHOD = 'SUM' HRNET.DOWNSAMPLE = downsample HRNET.USE_CONV = use_conv cfg = CN() cfg.MODEL = CN() cfg.MODEL.INIT_WEIGHTS = True cfg.MODEL.PRETRAINED = pretrained # 'data/pretrained_models/hrnet_w32-36af842e.pth' cfg.MODEL.EXTRA = HRNET cfg.MODEL.NUM_JOINTS = 24 return cfg def hrnet_w32( pretrained=True, pretrained_ckpt='data/weights/pose_hrnet_w32_256x192.pth', downsample=False, use_conv=False, ): cfg = get_cfg_defaults(pretrained_ckpt, width=32, downsample=downsample, use_conv=use_conv) return get_pose_net(cfg, is_train=True) def hrnet_w48( pretrained=True, pretrained_ckpt='data/weights/pose_hrnet_w48_256x192.pth', downsample=False, use_conv=False, ): cfg = get_cfg_defaults(pretrained_ckpt, width=48, downsample=downsample, use_conv=use_conv) return get_pose_net(cfg, is_train=True)