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|
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
|
|
| import os |
| import pdb |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import numpy as np |
|
|
| from lib.models.tools.module_helper import ModuleHelper |
| from lib.utils.tools.logger import Logger as Log |
|
|
| if torch.__version__.startswith('1'): |
| relu_inplace = True |
| else: |
| relu_inplace = False |
|
|
|
|
| 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, bn_type=None, bn_momentum=0.1): |
| super(BasicBlock, self).__init__() |
| self.conv1 = conv3x3(inplanes, planes, stride) |
| self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes, momentum=bn_momentum) |
| self.relu = nn.ReLU(inplace=False) |
| self.relu_in = nn.ReLU(inplace=True) |
| self.conv2 = conv3x3(planes, planes) |
| self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(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 = out + residual |
| out = self.relu_in(out) |
|
|
| return out |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None, bn_type=None, bn_momentum=0.1): |
| super(Bottleneck, self).__init__() |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes, momentum=bn_momentum) |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
| padding=1, bias=False) |
| self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes, momentum=bn_momentum) |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| self.bn3 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes * 4, momentum=bn_momentum) |
| self.relu = nn.ReLU(inplace=False) |
| self.relu_in = 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 = out + residual |
| out = self.relu_in(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, bn_type=None, bn_momentum=0.1): |
| 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, bn_type=bn_type, bn_momentum=bn_momentum) |
| self.fuse_layers = self._make_fuse_layers(bn_type=bn_type, bn_momentum=bn_momentum) |
| self.relu = nn.ReLU(inplace=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)) |
| Log.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)) |
| Log.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)) |
| Log.error(error_msg) |
| raise ValueError(error_msg) |
|
|
| def _make_one_branch(self, branch_index, block, num_blocks, num_channels, |
| stride=1, bn_type=None, bn_momentum=0.1): |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)( |
| num_channels[branch_index] * block.expansion, |
| momentum=bn_momentum |
| ), |
| ) |
|
|
| layers = [] |
| layers.append( |
| block( |
| self.num_inchannels[branch_index], |
| num_channels[branch_index], |
| stride, |
| downsample, |
| bn_type=bn_type, |
| bn_momentum=bn_momentum |
| ) |
| ) |
| 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], |
| bn_type=bn_type, |
| bn_momentum=bn_momentum |
| ) |
| ) |
|
|
| return nn.Sequential(*layers) |
|
|
| def _make_branches(self, num_branches, block, num_blocks, num_channels, bn_type, bn_momentum=0.1): |
| branches = [] |
|
|
| for i in range(num_branches): |
| branches.append( |
| self._make_one_branch(i, block, num_blocks, num_channels, bn_type=bn_type, bn_momentum=bn_momentum) |
| ) |
|
|
| return nn.ModuleList(branches) |
|
|
| def _make_fuse_layers(self, bn_type, bn_momentum=0.1): |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(num_outchannels_conv3x3, |
| momentum=bn_momentum), |
| nn.ReLU(inplace=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] |
| 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 |
|
|
|
|
| blocks_dict = { |
| 'BASIC': BasicBlock, |
| 'BOTTLENECK': Bottleneck |
| } |
|
|
|
|
| class HighResolutionNet(nn.Module): |
|
|
| def __init__(self, cfg, bn_type, bn_momentum, **kwargs): |
| self.inplanes = 64 |
| super(HighResolutionNet, self).__init__() |
|
|
| if os.environ.get('full_res_stem'): |
| Log.info("using full-resolution stem with stride=1") |
| stem_stride = 1 |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=stem_stride, padding=1, |
| bias=False) |
| self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(64, momentum=bn_momentum) |
| self.relu = nn.ReLU(inplace=False) |
| self.layer1 = self._make_layer(Bottleneck, 64, 64, 4, bn_type=bn_type, bn_momentum=bn_momentum) |
| else: |
| stem_stride = 2 |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=stem_stride, padding=1, |
| bias=False) |
| self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(64, momentum=bn_momentum) |
| self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=stem_stride, padding=1, |
| bias=False) |
| self.bn2 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(64, momentum=bn_momentum) |
| self.relu = nn.ReLU(inplace=False) |
| self.layer1 = self._make_layer(Bottleneck, 64, 64, 4, bn_type=bn_type, bn_momentum=bn_momentum) |
|
|
| self.stage2_cfg = cfg['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, bn_type=bn_type, bn_momentum=bn_momentum) |
|
|
| self.stage2, pre_stage_channels = self._make_stage( |
| self.stage2_cfg, num_channels, bn_type=bn_type, bn_momentum=bn_momentum) |
|
|
| self.stage3_cfg = cfg['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, bn_type=bn_type, bn_momentum=bn_momentum) |
| self.stage3, pre_stage_channels = self._make_stage( |
| self.stage3_cfg, num_channels, bn_type=bn_type, bn_momentum=bn_momentum) |
|
|
| self.stage4_cfg = cfg['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, bn_type=bn_type, bn_momentum=bn_momentum) |
|
|
| self.stage4, pre_stage_channels = self._make_stage( |
| self.stage4_cfg, num_channels, multi_scale_output=True, bn_type=bn_type, bn_momentum=bn_momentum) |
|
|
| if os.environ.get('keep_imagenet_head'): |
| self.incre_modules, self.downsamp_modules, \ |
| self.final_layer = self._make_head(pre_stage_channels, bn_type=bn_type, bn_momentum=bn_momentum) |
|
|
| def _make_head(self, pre_stage_channels, bn_type, bn_momentum): |
| head_block = Bottleneck |
| head_channels = [32, 64, 128, 256] |
|
|
| Log.info("pre_stage_channels: {}".format(pre_stage_channels)) |
| Log.info("head_channels: {}".format(head_channels)) |
|
|
| |
| |
| incre_modules = [] |
| for i, channels in enumerate(pre_stage_channels): |
| incre_module = self._make_layer(head_block, |
| channels, |
| head_channels[i], |
| 1, |
| bn_type=bn_type, |
| bn_momentum=bn_momentum |
| ) |
| 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), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(out_channels, momentum=bn_momentum), |
| nn.ReLU(inplace=False) |
| ) |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(2048, momentum=bn_momentum), |
| nn.ReLU(inplace=False) |
| ) |
| return incre_modules, downsamp_modules, final_layer |
|
|
| def _make_transition_layer( |
| self, num_channels_pre_layer, num_channels_cur_layer, bn_type, bn_momentum): |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(num_channels_cur_layer[i], momentum=bn_momentum), |
| nn.ReLU(inplace=False) |
| ) |
| ) |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(outchannels, momentum=bn_momentum), |
| nn.ReLU(inplace=False) |
| ) |
| ) |
| transition_layers.append(nn.Sequential(*conv3x3s)) |
|
|
| return nn.ModuleList(transition_layers) |
|
|
| def _make_layer(self, block, inplanes, planes, blocks, stride=1, bn_type=None, bn_momentum=0.1): |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(planes * block.expansion, momentum=bn_momentum) |
| ) |
|
|
| layers = [] |
| layers.append(block(inplanes, planes, stride, downsample, bn_type=bn_type, bn_momentum=bn_momentum)) |
|
|
| inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(inplanes, planes, bn_type=bn_type, bn_momentum=bn_momentum)) |
|
|
| return nn.Sequential(*layers) |
|
|
| def _make_stage(self, layer_config, num_inchannels, |
| multi_scale_output=True, bn_type=None, bn_momentum=0.1): |
| 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, |
| bn_type, |
| bn_momentum |
| ) |
| ) |
| num_inchannels = modules[-1].get_num_inchannels() |
|
|
| return nn.Sequential(*modules), num_inchannels |
|
|
| def forward(self, x): |
|
|
| if os.environ.get('full_res_stem'): |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu(x) |
| else: |
| 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) |
|
|
| if os.environ.get('drop_stage4'): |
| return y_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) |
|
|
| if os.environ.get('keep_imagenet_head'): |
| |
| x_list = [] |
| y = self.incre_modules[0](y_list[0]) |
| x_list.append(y) |
| for i in range(len(self.downsamp_modules)): |
| y = self.incre_modules[i + 1](y_list[i + 1]) + \ |
| self.downsamp_modules[i](y) |
| x_list.append(y) |
|
|
| y = self.final_layer(y) |
| del x_list[-1] |
| x_list.append(y) |
|
|
| return x_list |
|
|
| return y_list |
|
|
|
|
| class HighResolutionNext(nn.Module): |
|
|
| def __init__(self, cfg, bn_type, **kwargs): |
| super(HighResolutionNext, self).__init__() |
| |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, |
| bias=False) |
| self.bn1 = ModuleHelper.BatchNorm2d(bn_type=bn_type)(64) |
| self.relu = nn.ReLU(relu_inplace) |
|
|
| self.stage1_cfg = cfg['STAGE1'] |
| num_channels = self.stage1_cfg['NUM_CHANNELS'] |
| block = blocks_dict[self.stage1_cfg['BLOCK']] |
| num_channels = [ |
| num_channels[i] * block.expansion for i in range(len(num_channels))] |
| self.transition0 = self._make_transition_layer([64], num_channels, bn_type=bn_type) |
| self.stage1, pre_stage_channels = self._make_stage( |
| self.stage1_cfg, num_channels, bn_type=bn_type) |
|
|
| self.stage2_cfg = cfg['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( |
| pre_stage_channels, num_channels, bn_type=bn_type) |
| self.stage2, pre_stage_channels = self._make_stage( |
| self.stage2_cfg, num_channels, bn_type=bn_type) |
|
|
| self.stage3_cfg = cfg['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, bn_type=bn_type) |
| self.stage3, pre_stage_channels = self._make_stage( |
| self.stage3_cfg, num_channels, bn_type=bn_type) |
|
|
| self.stage4_cfg = cfg['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, bn_type=bn_type) |
| self.stage4, pre_stage_channels = self._make_stage( |
| self.stage4_cfg, num_channels, multi_scale_output=True, bn_type=bn_type) |
|
|
| def _make_transition_layer( |
| self, num_channels_pre_layer, num_channels_cur_layer, bn_type): |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(num_channels_cur_layer[i]), |
| nn.ReLU(relu_inplace) |
| ) |
| ) |
| 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 |
| ), |
| ModuleHelper.BatchNorm2d(bn_type=bn_type)(outchannels), |
| nn.ReLU(relu_inplace) |
| ) |
| ) |
| transition_layers.append(nn.Sequential(*conv3x3s)) |
|
|
| return nn.ModuleList(transition_layers) |
|
|
| def _make_stage(self, layer_config, num_inchannels, |
| multi_scale_output=True, bn_type=None): |
| 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, |
| bn_type |
| ) |
| ) |
| 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_list = [] |
| for i in range(self.stage1_cfg['NUM_BRANCHES']): |
| if self.transition0[i] is not None: |
| x_list.append(self.transition0[i](x)) |
| else: |
| x_list.append(x) |
| y_list = self.stage1(x_list) |
|
|
| x_list = [] |
| for i in range(self.stage2_cfg['NUM_BRANCHES']): |
| if self.transition1[i] is not None: |
| if i == 0: |
| x_list.append(self.transition1[i](y_list[0])) |
| else: |
| x_list.append(self.transition1[i](y_list[-1])) |
| else: |
| x_list.append(y_list[i]) |
| 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) |
| return x |
|
|
|
|
| class HRNetBackbone(object): |
| def __init__(self, configer): |
| self.configer = configer |
|
|
| def __call__(self): |
| arch = self.configer.get('network', 'backbone') |
| resume = self.configer.get('network', 'resume') |
| from lib.models.backbones.hrnet.hrnet_config import MODEL_CONFIGS |
|
|
| if arch == 'hrnet18': |
| arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet18'], |
| bn_type='torchsyncbn', |
| bn_momentum=0.1) |
| if resume is None: |
| arch_net = ModuleHelper.load_model(arch_net, |
| pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, |
| network='hrnet') |
|
|
| elif arch == 'hrnet32': |
| arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet32'], |
| bn_type='torchsyncbn', |
| bn_momentum=0.1) |
| if resume is None: |
| arch_net = ModuleHelper.load_model(arch_net, |
| pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, |
| network='hrnet') |
|
|
| elif arch == 'hrnet48': |
| arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet48'], |
| bn_type='torchsyncbn', |
| bn_momentum=0.1) |
| if resume is None: |
| arch_net = ModuleHelper.load_model(arch_net, |
| pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, |
| network='hrnet') |
|
|
| elif arch == 'hrnet64': |
| arch_net = HighResolutionNet(MODEL_CONFIGS['hrnet64'], |
| bn_type='torchsyncbn', |
| bn_momentum=0.1) |
| if resume is None: |
| arch_net = ModuleHelper.load_model(arch_net, |
| pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, |
| network='hrnet') |
|
|
| elif arch == 'hrnet2x20': |
| arch_net = HighResolutionNext(MODEL_CONFIGS['hrnet2x20'], |
| bn_type=self.configer.get('network', 'bn_type')) |
| if resume is None: |
| arch_net = ModuleHelper.load_model(arch_net, |
| pretrained=self.configer.get('network', 'pretrained'), |
| all_match=False, |
| network='hrnet') |
|
|
| else: |
| raise Exception('Architecture undefined!') |
|
|
| return arch_net |
|
|
