# The implementation is adopted from HRNet, made publicly available under the MIT License License # at https://github.com/HRNet/HRNet-Semantic-Segmentation import logging import os import numpy as np import torch import torch.nn as nn import torch.nn.functional as F BatchNorm2d = nn.BatchNorm2d 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 = BatchNorm2d(planes, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = 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 = BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d( planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM) self.conv3 = nn.Conv2d( planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = 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(inplace=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), 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 # tuple 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), BatchNorm2d( num_inchannels[i], momentum=BN_MOMENTUM))) 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), BatchNorm2d( num_outchannels_conv3x3, momentum=BN_MOMENTUM))) else: num_outchannels_conv3x3 = num_inchannels[j] conv3x3s.append( nn.Sequential( nn.Conv2d( num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False), BatchNorm2d( num_outchannels_conv3x3, momentum=BN_MOMENTUM), nn.ReLU(inplace=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] elif j > i: width_output = x[i].shape[-1] height_output = x[i].shape[-2] y = y + F.interpolate( self.fuse_layers[i][j](x[j]), size=(height_output, width_output), mode='bilinear', align_corners=True) else: y = y + self.fuse_layers[i][j](x[j]) x_fuse.append(self.relu(y)) return x_fuse model_w18v1 = { 'STAGE1': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 1, 'BLOCK': 'BOTTLENECK', 'NUM_BLOCKS': (1), 'NUM_CHANNELS': (32), 'FUSE_METHOD': 'SUM' }, 'STAGE2': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (2, 2), 'NUM_CHANNELS': (16, 32), 'FUSE_METHOD': 'SUM' }, 'STAGE3': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (2, 2, 2), 'NUM_CHANNELS': (16, 32, 64), 'FUSE_METHOD': 'SUM' }, 'STAGE4': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (2, 2, 2, 2), 'NUM_CHANNELS': (16, 32, 64, 128), 'FUSE_METHOD': 'SUM' }, 'FINAL_CONV_KERNEL': 1 } model_w18v2 = { 'STAGE1': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 1, 'BLOCK': 'BOTTLENECK', 'NUM_BLOCKS': (2), 'NUM_CHANNELS': (64), 'FUSE_METHOD': 'SUM' }, 'STAGE2': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (2, 2), 'NUM_CHANNELS': (18, 36), 'FUSE_METHOD': 'SUM' }, 'STAGE3': { 'NUM_MODULES': 3, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (2, 2, 2), 'NUM_CHANNELS': (18, 36, 72), 'FUSE_METHOD': 'SUM' }, 'STAGE4': { 'NUM_MODULES': 2, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (2, 2, 2, 2), 'NUM_CHANNELS': (18, 36, 72, 144), 'FUSE_METHOD': 'SUM' }, 'FINAL_CONV_KERNEL': 1 } model_w48 = { 'STAGE1': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 1, 'BLOCK': 'BOTTLENECK', 'NUM_BLOCKS': (4), 'NUM_CHANNELS': (64), 'FUSE_METHOD': 'SUM' }, 'STAGE2': { 'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (4, 4), 'NUM_CHANNELS': (48, 96), 'FUSE_METHOD': 'SUM' }, 'STAGE3': { 'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (4, 4, 4), 'NUM_CHANNELS': (48, 96, 192), 'FUSE_METHOD': 'SUM' }, 'STAGE4': { 'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC', 'NUM_BLOCKS': (4, 4, 4, 4), 'NUM_CHANNELS': (48, 96, 192, 384), 'FUSE_METHOD': 'SUM' }, 'FINAL_CONV_KERNEL': 1 } model_version_dict = {} model_version_dict['w48'] = model_w48 model_version_dict['w18v1'] = model_w18v1 model_version_dict['w18v2'] = model_w18v2 blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck} class HrnetBackBone(nn.Module): def __init__(self, **kwargs): super(HrnetBackBone, self).__init__() assert 'version' in kwargs, 'hrnet not exist model version' extra = model_version_dict[kwargs['version']] # stem net self.conv1 = nn.Conv2d( 3, 64, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = BatchNorm2d(64, momentum=BN_MOMENTUM) self.conv2 = nn.Conv2d( 64, 64, kernel_size=3, stride=2, padding=1, bias=False) self.bn2 = BatchNorm2d(64, momentum=BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.stage1_cfg = extra['STAGE1'] num_channels = self.stage1_cfg['NUM_CHANNELS'] block = blocks_dict[self.stage1_cfg['BLOCK']] num_blocks = self.stage1_cfg['NUM_BLOCKS'] self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) stage1_out_channel = block.expansion * num_channels 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([stage1_out_channel], 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.backbone_last_inp_channels = int(np.sum(pre_stage_channels)) 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), BatchNorm2d( num_channels_cur_layer[i], momentum=BN_MOMENTUM), 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), BatchNorm2d(outchannels, momentum=BN_MOMENTUM), nn.ReLU(inplace=True))) transition_layers.append(nn.Sequential(*conv3x3s)) return nn.ModuleList(transition_layers) def _make_layer(self, block, inplanes, planes, blocks, stride=1): downsample = None if stride != 1 or inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d( inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), ) layers = [] layers.append(block(inplanes, planes, stride, downsample)) inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(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 _backbone_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) # Upsampling 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, url, cache_file=''): pretrained_dict = load_state(url, model_dir=cache_file) model_dict = self.state_dict() model_len = len(model_dict) pretrain_len = len(pretrained_dict) common_dict = {} valid_layer_num = 0 for k, v in pretrained_dict.items(): if k in model_dict: common_dict[k] = v valid_layer_num += 1 print('*' * 50) print('Model Param Num:{} Pretrained Param Num:{} ' 'Commmon Num:{}'.format(model_len, pretrain_len, valid_layer_num)) print('-' * 50) print('Model Extra Param Names:\n\t{}'.format( set(model_dict) - set(pretrained_dict))) print('-' * 50) print('Pretrained Extra Param Names:\n\t{}'.format( set(pretrained_dict) - set(model_dict))) print('*' * 50) model_dict.update(common_dict) self.load_state_dict(model_dict)