""" HRNet Copied from https://github.com/HRNet/HRNet-Image-Classification Original header: 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) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import logging import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import load_pretrained from .layers import SelectAdaptivePool2d from .registry import register_model from .resnet import BasicBlock, Bottleneck # leveraging ResNet blocks w/ additional features like SE _BN_MOMENTUM = 0.1 logger = logging.getLogger(__name__) def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), 'crop_pct': 0.875, 'interpolation': 'bilinear', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'conv1', 'classifier': 'classifier', **kwargs } default_cfgs = { 'hrnet_w18_small': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v1-f460c6bc.pth'), 'hrnet_w18_small_v2': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v2-4c50a8cb.pth'), 'hrnet_w18': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w18-8cb57bb9.pth'), 'hrnet_w30': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w30-8d7f8dab.pth'), 'hrnet_w32': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w32-90d8c5fb.pth'), 'hrnet_w40': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w40-7cd397a4.pth'), 'hrnet_w44': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w44-c9ac8c18.pth'), 'hrnet_w48': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w48-abd2e6ab.pth'), 'hrnet_w64': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w64-b47cc881.pth'), } cfg_cls = dict( hrnet_w18_small=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(1,), NUM_CHANNELS=(32,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(2, 2), NUM_CHANNELS=(16, 32), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=1, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(2, 2, 2), NUM_CHANNELS=(16, 32, 64), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=1, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(2, 2, 2, 2), NUM_CHANNELS=(16, 32, 64, 128), FUSE_METHOD='SUM', ), ), hrnet_w18_small_v2=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(2,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(2, 2), NUM_CHANNELS=(18, 36), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=3, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(2, 2, 2), NUM_CHANNELS=(18, 36, 72), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=2, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(2, 2, 2, 2), NUM_CHANNELS=(18, 36, 72, 144), FUSE_METHOD='SUM', ), ), hrnet_w18=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(4,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(4, 4), NUM_CHANNELS=(18, 36), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=4, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4), NUM_CHANNELS=(18, 36, 72), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=3, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4, 4), NUM_CHANNELS=(18, 36, 72, 144), FUSE_METHOD='SUM', ), ), hrnet_w30=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(4,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(4, 4), NUM_CHANNELS=(30, 60), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=4, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4), NUM_CHANNELS=(30, 60, 120), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=3, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4, 4), NUM_CHANNELS=(30, 60, 120, 240), FUSE_METHOD='SUM', ), ), hrnet_w32=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(4,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(4, 4), NUM_CHANNELS=(32, 64), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=4, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4), NUM_CHANNELS=(32, 64, 128), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=3, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4, 4), NUM_CHANNELS=(32, 64, 128, 256), FUSE_METHOD='SUM', ), ), hrnet_w40=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(4,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(4, 4), NUM_CHANNELS=(40, 80), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=4, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4), NUM_CHANNELS=(40, 80, 160), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=3, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4, 4), NUM_CHANNELS=(40, 80, 160, 320), FUSE_METHOD='SUM', ), ), hrnet_w44=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(4,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(4, 4), NUM_CHANNELS=(44, 88), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=4, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4), NUM_CHANNELS=(44, 88, 176), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=3, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4, 4), NUM_CHANNELS=(44, 88, 176, 352), FUSE_METHOD='SUM', ), ), hrnet_w48=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(4,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(4, 4), NUM_CHANNELS=(48, 96), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=4, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4), NUM_CHANNELS=(48, 96, 192), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=3, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4, 4), NUM_CHANNELS=(48, 96, 192, 384), FUSE_METHOD='SUM', ), ), hrnet_w64=dict( STEM_WIDTH=64, STAGE1=dict( NUM_MODULES=1, NUM_BRANCHES=1, BLOCK='BOTTLENECK', NUM_BLOCKS=(4,), NUM_CHANNELS=(64,), FUSE_METHOD='SUM', ), STAGE2=dict( NUM_MODULES=1, NUM_BRANCHES=2, BLOCK='BASIC', NUM_BLOCKS=(4, 4), NUM_CHANNELS=(64, 128), FUSE_METHOD='SUM' ), STAGE3=dict( NUM_MODULES=4, NUM_BRANCHES=3, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4), NUM_CHANNELS=(64, 128, 256), FUSE_METHOD='SUM' ), STAGE4=dict( NUM_MODULES=3, NUM_BRANCHES=4, BLOCK='BASIC', NUM_BLOCKS=(4, 4, 4, 4), NUM_CHANNELS=(64, 128, 256, 512), FUSE_METHOD='SUM', ), ) ) 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 = [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 nn.Identity() 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(nn.Identity()) 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, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0.0): super(HighResolutionNet, self).__init__() self.num_classes = num_classes self.drop_rate = drop_rate stem_width = cfg['STEM_WIDTH'] self.conv1 = nn.Conv2d(in_chans, stem_width, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(stem_width, momentum=_BN_MOMENTUM) self.conv2 = nn.Conv2d(stem_width, 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['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['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['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['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.num_features = 2048 self.incre_modules, self.downsamp_modules, self.final_layer = self._make_head(pre_stage_channels) self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) self.init_weights() 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_modules.append( self._make_layer(head_block, channels, head_channels[i], 1, stride=1)) 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=self.num_features, kernel_size=1, stride=1, padding=0 ), nn.BatchNorm2d(self.num_features, 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(nn.Identity()) 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 = [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 init_weights(self): 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_classifier(self): return self.classifier def reset_classifier(self, num_classes, global_pool='avg'): self.num_classes = num_classes self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) num_features = self.num_features * self.global_pool.feat_mult() if num_classes: self.classifier = nn.Linear(num_features, num_classes) else: self.classifier = nn.Identity() def forward_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(len(self.transition1)): x_list.append(self.transition1[i](x)) y_list = self.stage2(x_list) x_list = [] for i in range(len(self.transition2)): if not isinstance(self.transition2[i], nn.Identity): 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(len(self.transition3)): if not isinstance(self.transition3[i], nn.Identity): 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) return y def forward(self, x): x = self.forward_features(x) x = self.global_pool(x).flatten(1) if self.drop_rate > 0.: x = F.dropout(x, p=self.drop_rate, training=self.training) x = self.classifier(x) return x def _create_model(variant, pretrained, model_kwargs): if model_kwargs.pop('features_only', False): assert False, 'Not Implemented' # TODO load_strict = False model_kwargs.pop('num_classes', 0) model_class = HighResolutionNet else: load_strict = True model_class = HighResolutionNet model = model_class(cfg_cls[variant], **model_kwargs) model.default_cfg = default_cfgs[variant] if pretrained: load_pretrained( model, num_classes=model_kwargs.get('num_classes', 0), in_chans=model_kwargs.get('in_chans', 3), strict=load_strict) return model @register_model def hrnet_w18_small(pretrained=True, **kwargs): return _create_model('hrnet_w18_small', pretrained, kwargs) @register_model def hrnet_w18_small_v2(pretrained=True, **kwargs): return _create_model('hrnet_w18_small_v2', pretrained, kwargs) @register_model def hrnet_w18(pretrained=True, **kwargs): return _create_model('hrnet_w18', pretrained, kwargs) @register_model def hrnet_w30(pretrained=True, **kwargs): return _create_model('hrnet_w30', pretrained, kwargs) @register_model def hrnet_w32(pretrained=True, **kwargs): return _create_model('hrnet_w32', pretrained, kwargs) @register_model def hrnet_w40(pretrained=True, **kwargs): return _create_model('hrnet_w40', pretrained, kwargs) @register_model def hrnet_w44(pretrained=True, **kwargs): return _create_model('hrnet_w44', pretrained, kwargs) @register_model def hrnet_w48(pretrained=True, **kwargs): return _create_model('hrnet_w48', pretrained, kwargs) @register_model def hrnet_w64(pretrained=True, **kwargs): return _create_model('hrnet_w64', pretrained, kwargs)