# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao (Bin.Xiao@microsoft.com) # Modified by Sen Yang (yangsenius@seu.edu.cn) # ------------------------------------------------------------------------------ import copy import logging import os import torch import torch.nn.functional as F from torch import 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 def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) def _get_activation_fn(activation): """Return an activation function given a string""" if activation == "relu": return F.relu if activation == "gelu": return F.gelu if activation == "glu": return F.glu raise RuntimeError(f"activation should be relu/gelu, not {activation}.") 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 HRNET_base(nn.Module): def __init__(self, cfg, **kwargs): self.inplanes = 64 extra = cfg["MODEL"]["EXTRA"] super(HRNET_base, self).__init__() # 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, multi_scale_output=False ) self.pretrained_layers = extra["PRETRAINED_LAYERS"] 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 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) return y_list[0] def init_weights(self, pretrained="", print_load_info=False): logger.info("=> init weights from normal distribution") for m in self.modules(): if isinstance(m, nn.Conv2d): 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, map_location=torch.device("cpu") ) logger.info("=> loading pretrained model {}".format(pretrained)) existing_state_dict = {} for name, m in pretrained_state_dict.items(): if ( name.split(".")[0] in self.pretrained_layers and name in self.state_dict() or self.pretrained_layers[0] is "*" ): existing_state_dict[name] = m if print_load_info: print(":: {} is loaded from {}".format(name, pretrained)) self.load_state_dict(existing_state_dict, strict=False) elif pretrained: logger.error("=> please download pre-trained models first!") raise ValueError("{} is not exist!".format(pretrained)) class HRNET4_base(nn.Module): def __init__(self, cfg, **kwargs): self.inplanes = 64 extra = cfg["MODEL"]["EXTRA"] super(HRNET4_base, self).__init__() # 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) # stage 2 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 ) # stage 3 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 ) # stage 4 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=False ) self.pretrained_layers = extra["PRETRAINED_LAYERS"] 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 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) # stage 2 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) # stage 3 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) # stage 4 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]) y_list = self.stage4(x_list) return y_list[0] def init_weights(self, pretrained="", print_load_info=False): logger.info("=> init weights from normal distribution") for m in self.modules(): if isinstance(m, nn.Conv2d): 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)) existing_state_dict = {} for name, m in pretrained_state_dict.items(): if ( name.split(".")[0] in self.pretrained_layers and name in self.state_dict() or self.pretrained_layers[0] is "*" ): existing_state_dict[name] = m if print_load_info: print(":: {} is loaded from {}".format(name, pretrained)) self.load_state_dict(existing_state_dict, strict=False) elif pretrained: logger.error("=> please download pre-trained models first!") raise ValueError("{} is not exist!".format(pretrained))