| '''
|
| # author: Zhiyuan Yan
|
| # email: zhiyuanyan@link.cuhk.edu.cn
|
| # date: 2023-0706
|
|
|
| # ------------------------------------------------------------------------------
|
| # Copyright (c) Microsoft
|
| # Licensed under the MIT License.
|
| # Written by Bin Xiao (Bin.Xiao@microsoft.com)
|
| # Modified by Ke Sun (sunk@mail.ustc.edu.cn)
|
| # ------------------------------------------------------------------------------
|
|
|
| The code is mainly modified from the below link:
|
| https://github.com/HRNet/HRNet-Image-Classification/tree/master
|
| '''
|
|
|
| from __future__ import absolute_import
|
| from __future__ import division
|
| from __future__ import print_function
|
|
|
| import os
|
| import logging
|
| import functools
|
|
|
| import numpy as np
|
| from typing import Union
|
|
|
| import torch
|
| import torch.nn as nn
|
| import torch._utils
|
| import torch.nn.functional as F
|
|
|
| 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 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(False)
|
|
|
| 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],
|
| momentum=BN_MOMENTUM),
|
| 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,
|
| 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),
|
| nn.BatchNorm2d(num_outchannels_conv3x3,
|
| momentum=BN_MOMENTUM),
|
| nn.ReLU(False)))
|
| 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 HighResolutionNet(nn.Module):
|
|
|
| def __init__(self, cfg):
|
| super(HighResolutionNet, self).__init__()
|
|
|
| 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.stage1_cfg = cfg['MODEL']['EXTRA']['STAGE1']
|
| num_channels = self.stage1_cfg['NUM_CHANNELS'][0]
|
| block = blocks_dict[self.stage1_cfg['BLOCK']]
|
| num_blocks = self.stage1_cfg['NUM_BLOCKS'][0]
|
| self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
|
| stage1_out_channel = block.expansion*num_channels
|
|
|
| self.stage2_cfg = cfg['MODEL']['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 = cfg['MODEL']['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 = cfg['MODEL']['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.incre_modules, self.downsamp_modules, \
|
| self.final_layer = self._make_head(pre_stage_channels)
|
|
|
| self.fc = nn.Linear(2048, 1000)
|
|
|
|
|
| def _make_head(self, pre_stage_channels):
|
| head_block = Bottleneck
|
| head_channels = [32, 64, 128, 256]
|
|
|
|
|
|
|
| incre_modules = []
|
| for i, channels in enumerate(pre_stage_channels):
|
| incre_module = self._make_layer(head_block,
|
| channels,
|
| head_channels[i],
|
| 1,
|
| stride=1)
|
| incre_modules.append(incre_module)
|
| incre_modules = nn.ModuleList(incre_modules)
|
|
|
|
|
| downsamp_modules = []
|
| for i in range(len(pre_stage_channels)-1):
|
| in_channels = head_channels[i] * head_block.expansion
|
| out_channels = head_channels[i+1] * head_block.expansion
|
|
|
| downsamp_module = nn.Sequential(
|
| nn.Conv2d(in_channels=in_channels,
|
| out_channels=out_channels,
|
| kernel_size=3,
|
| stride=2,
|
| padding=1),
|
| nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM),
|
| nn.ReLU(inplace=True)
|
| )
|
|
|
| downsamp_modules.append(downsamp_module)
|
| downsamp_modules = nn.ModuleList(downsamp_modules)
|
|
|
| final_layer = nn.Sequential(
|
| nn.Conv2d(
|
| in_channels=head_channels[3] * head_block.expansion,
|
| out_channels=2048,
|
| kernel_size=1,
|
| stride=1,
|
| padding=0
|
| ),
|
| nn.BatchNorm2d(2048, momentum=BN_MOMENTUM),
|
| nn.ReLU(inplace=True)
|
| )
|
|
|
| return incre_modules, downsamp_modules, final_layer
|
|
|
| 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], 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),
|
| nn.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),
|
| nn.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):
|
|
|
| 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)
|
|
|
| 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)
|
|
|
|
|
| y = self.incre_modules[0](y_list[0])
|
| for i in range(len(self.downsamp_modules)):
|
| y = self.incre_modules[i+1](y_list[i+1]) + \
|
| self.downsamp_modules[i](y)
|
|
|
| y = self.final_layer(y)
|
|
|
| if torch._C._get_tracing_state():
|
| y = y.flatten(start_dim=2).mean(dim=2)
|
| else:
|
| y = F.avg_pool2d(y, kernel_size=y.size()
|
| [2:]).view(y.size(0), -1)
|
|
|
| y = self.fc(y)
|
|
|
| return y
|
|
|
| def features(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])
|
| y_list = self.stage4(x_list)
|
|
|
|
|
| x0, x1, x2, x3 = y_list
|
| x0_h, x0_w = x0.size(2), x0.size(3)
|
| x1 = F.upsample(x1, size=(x0_h, x0_w), mode='bilinear')
|
| x2 = F.upsample(x2, size=(x0_h, x0_w), mode='bilinear')
|
| x3 = F.upsample(x3, size=(x0_h, x0_w), mode='bilinear')
|
|
|
| x_out = torch.cat([x0, x1, x2, x3], 1)
|
|
|
|
|
|
|
| return x_out
|
|
|
| def classifier(self, x):
|
|
|
| y = self.incre_modules[0](x[0])
|
| for i in range(len(self.downsamp_modules)):
|
| y = self.incre_modules[i+1](x[i+1]) + \
|
| self.downsamp_modules[i](y)
|
|
|
| y = self.final_layer(y)
|
|
|
| if torch._C._get_tracing_state():
|
| y = y.flatten(start_dim=2).mean(dim=2)
|
| else:
|
| y = F.avg_pool2d(y, kernel_size=y.size()
|
| [2:]).view(y.size(0), -1)
|
|
|
| y = self.fc(y)
|
|
|
| def get_cls_net(config, **kwargs):
|
| model = HighResolutionNet(config, **kwargs)
|
| return model
|
|
|
