Spaces:
Sleeping
Sleeping
| # ------------------------------------------------------------------------------ | |
| # Copyright (c) Microsoft | |
| # Licensed under the MIT License. | |
| # Written by Bin Xiao ([email protected]) | |
| # Modified by Ke Sun ([email protected]) | |
| # ------------------------------------------------------------------------------ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os | |
| import logging | |
| import torch | |
| import torch.nn as nn | |
| import torch._utils | |
| import torch.nn.functional as F | |
| BN_MOMENTUM = 0.1 | |
| logger = logging.getLogger(__name__) | |
| logger.setLevel(logging.INFO) | |
| ch = logging.StreamHandler() | |
| ch.setLevel(logging.INFO) | |
| logger.addHandler(ch) | |
| 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, **kwargs): | |
| 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) | |
| # Classification Head | |
| self.incre_modules, self.downsamp_modules, \ | |
| self.final_layer = self._make_head(pre_stage_channels) | |
| # self.classifier = nn.Linear(2048, 1000) | |
| def _make_head(self, pre_stage_channels): | |
| head_block = Bottleneck | |
| head_channels = [32, 64, 128, 256] | |
| # Increasing the #channels on each resolution | |
| # from C, 2C, 4C, 8C to 128, 256, 512, 1024 | |
| 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) | |
| # downsampling 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): | |
| # 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) | |
| 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) | |
| # Classification Head | |
| 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.classifier(y) | |
| return y | |
| def init_weights(self, pretrained='', ): | |
| logger.info('=> pretrained: ' + pretrained) | |
| if os.path.isfile(pretrained): | |
| pretrained_dict = torch.load(pretrained) | |
| # logger.info('=> loading pretrained model {}'.format(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('=> loading {} pretrained model {}'.format(k, pretrained)) | |
| # model_dict.update(pretrained_dict) | |
| # self.load_state_dict(model_dict) | |
| self.load_state_dict(pretrained_dict, strict=True) | |
| else: | |
| 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') | |
| elif isinstance(m, nn.BatchNorm2d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def get_cls_net(config, **kwargs): | |
| model = HighResolutionNet(config, **kwargs) | |
| model.init_weights(**kwargs) | |
| return model | |