""" 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) https://github.com/HRNet/HRNet-Image-Classification/blob/master/lib/models/cls_hrnet.py """ import functools import logging import os import numpy as np import torch import torch._utils import torch.nn as nn import torch.nn.functional as F from modelscope.utils.logger import get_logger BN_MOMENTUM = 0.01 # 0.01 for seg logger = get_logger() 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.info(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.info(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.info(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, leaky_relu=False, attn_weight=1, fix_domain=1, domain_center_model='', **kwargs): super(HighResolutionNet, self).__init__() self.criterion_attn = torch.nn.MSELoss(reduction='sum') self.domain_center_model = domain_center_model self.attn_weight = attn_weight self.fix_domain = fix_domain self.cosine = 1 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) num_channels = 64 block = blocks_dict['BOTTLENECK'] num_blocks = 4 self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) stage1_out_channel = block.expansion * num_channels # -- stage 2 self.stage2_cfg = {} self.stage2_cfg['NUM_MODULES'] = 1 self.stage2_cfg['NUM_BRANCHES'] = 2 self.stage2_cfg['BLOCK'] = 'BASIC' self.stage2_cfg['NUM_BLOCKS'] = [4, 4] self.stage2_cfg['NUM_CHANNELS'] = [40, 80] self.stage2_cfg['FUSE_METHOD'] = 'SUM' 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) # -- stage 3 self.stage3_cfg = {} self.stage3_cfg['NUM_MODULES'] = 4 self.stage3_cfg['NUM_BRANCHES'] = 3 self.stage3_cfg['BLOCK'] = 'BASIC' self.stage3_cfg['NUM_BLOCKS'] = [4, 4, 4] self.stage3_cfg['NUM_CHANNELS'] = [40, 80, 160] self.stage3_cfg['FUSE_METHOD'] = 'SUM' 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) last_inp_channels = int(np.sum(pre_stage_channels)) + 256 self.redc_layer = nn.Sequential( nn.Conv2d( in_channels=last_inp_channels, out_channels=128, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(128, momentum=BN_MOMENTUM), nn.ReLU(True), ) self.aspp = nn.ModuleList(aspp(in_channel=128)) # additional layers specific for Phase 3 self.pred_conv = nn.Conv2d(128, 512, 3, padding=1) self.pred_bn = nn.BatchNorm2d(512) self.GAP = nn.AdaptiveAvgPool2d(1) # Specially for hidden domain # Set the domain for learnable parameters domain_center_src = np.load(self.domain_center_model) G_SHA = torch.from_numpy(domain_center_src['G_SHA']).view(1, -1, 1, 1) G_SHB = torch.from_numpy(domain_center_src['G_SHB']).view(1, -1, 1, 1) G_QNRF = torch.from_numpy(domain_center_src['G_QNRF']).view( 1, -1, 1, 1) self.n_domain = 3 self.G_all = torch.cat( [G_SHA.clone(), G_SHB.clone(), G_QNRF.clone()], dim=0) self.G_all = nn.Parameter(self.G_all) self.last_layer = nn.Sequential( nn.Conv2d( in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(64, momentum=BN_MOMENTUM), nn.ReLU(True), nn.Conv2d( in_channels=64, out_channels=32, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(32, momentum=BN_MOMENTUM), nn.ReLU(True), nn.Conv2d( in_channels=32, out_channels=1, kernel_size=1, stride=1, padding=0), ) 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): # 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_head_1 = 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]) x = self.stage3(x_list) # Replace the classification heaeder with custom setting # 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=False) x2 = F.interpolate( x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=False) x = torch.cat([x[0], x1, x2, x_head_1], 1) # first, reduce the channel down x = self.redc_layer(x) pred_attn = self.GAP(F.relu_(self.pred_bn(self.pred_conv(x)))) pred_attn = F.softmax(pred_attn, dim=1) pred_attn_list = torch.chunk(pred_attn, 4, dim=1) aspp_out = [] for k, v in enumerate(self.aspp): if k % 2 == 0: aspp_out.append(self.aspp[k + 1](v(x))) else: continue # Using Aspp add, and relu inside for i in range(4): x = x + F.relu_(aspp_out[i] * 0.25) * pred_attn_list[i] bz = x.size(0) # -- Besides, we also need to let the prediction attention be close to visible domain # -- Calculate the domain distance and get the weights # - First, detach domains G_all_d = self.G_all.detach() # use detached G_all for calculating pred_attn_d = pred_attn.detach().view(bz, 512, 1, 1) if self.cosine == 1: G_A, G_B, G_Q = torch.chunk(G_all_d, self.n_domain, dim=0) cos_dis_A = F.cosine_similarity(pred_attn_d, G_A, dim=1).view(-1) cos_dis_B = F.cosine_similarity(pred_attn_d, G_B, dim=1).view(-1) cos_dis_Q = F.cosine_similarity(pred_attn_d, G_Q, dim=1).view(-1) cos_dis_all = torch.stack([cos_dis_A, cos_dis_B, cos_dis_Q]).view(bz, -1) # bz*3 cos_dis_all = F.softmax(cos_dis_all, dim=1) target_attn = cos_dis_all.view(bz, self.n_domain, 1, 1, 1).expand( bz, self.n_domain, 512, 1, 1) * self.G_all.view( 1, self.n_domain, 512, 1, 1).expand( bz, self.n_domain, 512, 1, 1) target_attn = torch.sum( target_attn, dim=1, keepdim=False) # bz * 512 * 1 * 1 if self.fix_domain: target_attn = target_attn.detach() else: raise ValueError('Have not implemented not cosine distance yet') x = self.last_layer(x) x = F.relu_(x) x = F.interpolate( x, size=(x0_h * 2, x0_w * 2), mode='bilinear', align_corners=False) return x, pred_attn, target_attn def init_weights( self, pretrained='', ): logger.info('=> init weights from normal distribution') for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight, std=0.01) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) if os.path.isfile(pretrained): pretrained_dict = torch.load(pretrained, weights_only=True) logger.info(f'=> loading pretrained model {pretrained}') model_dict = self.state_dict() pretrained_dict = { k: v for k, v in pretrained_dict.items() if k in model_dict.keys() } for k, _ in pretrained_dict.items(): logger.info(f'=> loading {k} pretrained model {pretrained}') model_dict.update(pretrained_dict) self.load_state_dict(model_dict) else: assert 1 == 2 def aspp(aspp_num=4, aspp_stride=2, in_channel=512, use_bn=True): aspp_list = [] for i in range(aspp_num): pad = (i + 1) * aspp_stride dilate = pad conv_aspp = nn.Conv2d( in_channel, in_channel, 3, padding=pad, dilation=dilate) aspp_list.append(conv_aspp) if use_bn: aspp_list.append(nn.BatchNorm2d(in_channel)) return aspp_list