Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'freeze_norm_revisit'
${ noResults }
pytorch-image-models/timm/models/hrnet.py

861 lines
30 KiB
Raw Permalink Blame History

""" 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)
"""
import logging
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import create_classifier
from ._builder import build_model_with_cfg, pretrained_cfg_for_features
from ._features import FeatureInfo
from ._registry import register_model
from .resnet import BasicBlock, Bottleneck # leveraging ResNet blocks w/ additional features like SE
__all__ = ['HighResolutionNet', 'HighResolutionNetFeatures'] # model_registry will add each entrypoint fn to this
_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_in_chs,
num_channels, fuse_method, multi_scale_output=True):
super(HighResolutionModule, self).__init__()
self._check_branches(
num_branches, blocks, num_blocks, num_in_chs, num_channels)
self.num_in_chs = num_in_chs
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.fuse_act = nn.ReLU(False)
def _check_branches(self, num_branches, blocks, num_blocks, num_in_chs, num_channels):
error_msg = ''
if num_branches != len(num_blocks):
error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(num_branches, len(num_blocks))
elif num_branches != len(num_channels):
error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(num_branches, len(num_channels))
elif num_branches != len(num_in_chs):
error_msg = 'NUM_BRANCHES({}) <> num_in_chs({})'.format(num_branches, len(num_in_chs))
if error_msg:
_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_in_chs[branch_index] != num_channels[branch_index] * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.num_in_chs[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_in_chs[branch_index], num_channels[branch_index], stride, downsample)]
self.num_in_chs[branch_index] = num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(block(self.num_in_chs[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_in_chs = self.num_in_chs
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_in_chs[j], num_in_chs[i], 1, 1, 0, bias=False),
nn.BatchNorm2d(num_in_chs[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_in_chs[i]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_in_chs[j], num_outchannels_conv3x3, 3, 2, 1, bias=False),
nn.BatchNorm2d(num_outchannels_conv3x3, momentum=_BN_MOMENTUM)))
else:
num_outchannels_conv3x3 = num_in_chs[j]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_in_chs[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_in_chs(self):
return self.num_in_chs
def forward(self, x: List[torch.Tensor]):
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i, branch in enumerate(self.branches):
x[i] = branch(x[i])
x_fuse = []
for i, fuse_outer in enumerate(self.fuse_layers):
y = x[0] if i == 0 else fuse_outer[0](x[0])
for j in range(1, self.num_branches):
if i == j:
y = y + x[j]
else:
y = y + fuse_outer[j](x[j])
x_fuse.append(self.fuse_act(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, head='classification'):
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.act1 = nn.ReLU(inplace=True)
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.act2 = 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)
self.head = head
self.head_channels = None # set if _make_head called
if head == 'classification':
# Classification Head
self.num_features = 2048
self.incre_modules, self.downsamp_modules, self.final_layer = self._make_head(pre_stage_channels)
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
elif head == 'incre':
self.num_features = 2048
self.incre_modules, _, _ = self._make_head(pre_stage_channels, True)
else:
self.incre_modules = None
self.num_features = 256
curr_stride = 2
# module names aren't actually valid here, hook or FeatureNet based extraction would not work
self.feature_info = [dict(num_chs=64, reduction=curr_stride, module='stem')]
for i, c in enumerate(self.head_channels if self.head_channels else num_channels):
curr_stride *= 2
c = c * 4 if self.head_channels else c # head block expansion factor of 4
self.feature_info += [dict(num_chs=c, reduction=curr_stride, module=f'stage{i + 1}')]
self.init_weights()
def _make_head(self, pre_stage_channels, incre_only=False):
head_block = Bottleneck
self.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, self.head_channels[i], 1, stride=1))
incre_modules = nn.ModuleList(incre_modules)
if incre_only:
return incre_modules, None, None
# downsampling modules
downsamp_modules = []
for i in range(len(pre_stage_channels) - 1):
in_channels = self.head_channels[i] * head_block.expansion
out_channels = self.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=self.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_in_chs, 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
reset_multi_scale_output = multi_scale_output or i < num_modules - 1
modules.append(HighResolutionModule(
num_branches, block, num_blocks, num_in_chs, num_channels, fuse_method, reset_multi_scale_output)
)
num_in_chs = modules[-1].get_num_in_chs()
return nn.Sequential(*modules), num_in_chs
@torch.jit.ignore
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)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^conv[12]|bn[12]',
blocks=r'^(?:layer|stage|transition)(\d+)' if coarse else [
(r'^layer(\d+)\.(\d+)', None),
(r'^stage(\d+)\.(\d+)', None),
(r'^transition(\d+)', (99999,)),
],
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, "gradient checkpointing not supported"
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def stages(self, x) -> List[torch.Tensor]:
x = self.layer1(x)
xl = [t(x) for i, t in enumerate(self.transition1)]
yl = self.stage2(xl)
xl = [t(yl[-1]) if not isinstance(t, nn.Identity) else yl[i] for i, t in enumerate(self.transition2)]
yl = self.stage3(xl)
xl = [t(yl[-1]) if not isinstance(t, nn.Identity) else yl[i] for i, t in enumerate(self.transition3)]
yl = self.stage4(xl)
return yl
def forward_features(self, x):
# Stem
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
# Stages
yl = self.stages(x)
if self.incre_modules is None or self.downsamp_modules is None:
return yl
y = self.incre_modules[0](yl[0])
for i, down in enumerate(self.downsamp_modules):
y = self.incre_modules[i + 1](yl[i + 1]) + down(y)
y = self.final_layer(y)
return y
def forward_head(self, x, pre_logits: bool = False):
# Classification Head
x = self.global_pool(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return x if pre_logits else self.classifier(x)
def forward(self, x):
y = self.forward_features(x)
x = self.forward_head(y)
return x
class HighResolutionNetFeatures(HighResolutionNet):
"""HighResolutionNet feature extraction
The design of HRNet makes it easy to grab feature maps, this class provides a simple wrapper to do so.
It would be more complicated to use the FeatureNet helpers.
The `feature_location=incre` allows grabbing increased channel count features using part of the
classification head. If `feature_location=''` the default HRNet features are returned. First stem
conv is used for stride 2 features.
"""
def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0.0,
feature_location='incre', out_indices=(0, 1, 2, 3, 4)):
assert feature_location in ('incre', '')
super(HighResolutionNetFeatures, self).__init__(
cfg, in_chans=in_chans, num_classes=num_classes, global_pool=global_pool,
drop_rate=drop_rate, head=feature_location)
self.feature_info = FeatureInfo(self.feature_info, out_indices)
self._out_idx = {i for i in out_indices}
def forward_features(self, x):
assert False, 'Not supported'
def forward(self, x) -> List[torch.tensor]:
out = []
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
if 0 in self._out_idx:
out.append(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
x = self.stages(x)
if self.incre_modules is not None:
x = [incre(f) for f, incre in zip(x, self.incre_modules)]
for i, f in enumerate(x):
if i + 1 in self._out_idx:
out.append(f)
return out
def _create_hrnet(variant, pretrained, **model_kwargs):
model_cls = HighResolutionNet
features_only = False
kwargs_filter = None
if model_kwargs.pop('features_only', False):
model_cls = HighResolutionNetFeatures
kwargs_filter = ('num_classes', 'global_pool')
features_only = True
model = build_model_with_cfg(
model_cls, variant, pretrained,
model_cfg=cfg_cls[variant],
pretrained_strict=not features_only,
kwargs_filter=kwargs_filter,
**model_kwargs)
if features_only:
model.pretrained_cfg = pretrained_cfg_for_features(model.default_cfg)
model.default_cfg = model.pretrained_cfg # backwards compat
return model
@register_model
def hrnet_w18_small(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w18_small', pretrained, **kwargs)
@register_model
def hrnet_w18_small_v2(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w18_small_v2', pretrained, **kwargs)
@register_model
def hrnet_w18(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w18', pretrained, **kwargs)
@register_model
def hrnet_w30(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w30', pretrained, **kwargs)
@register_model
def hrnet_w32(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w32', pretrained, **kwargs)
@register_model
def hrnet_w40(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w40', pretrained, **kwargs)
@register_model
def hrnet_w44(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w44', pretrained, **kwargs)
@register_model
def hrnet_w48(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w48', pretrained, **kwargs)
@register_model
def hrnet_w64(pretrained=False, **kwargs):
return _create_hrnet('hrnet_w64', pretrained, **kwargs)
Reference in new issue View Git Blame Copy Permalink