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Initial HRNet classification model commit

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pull/53/head
Ross Wightman 5 years ago
parent 2393708650
commit ad93347548
2 changed files with 870 additions and 0 deletions
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1
timm/models/__init__.py
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@ -13,6 +13,7 @@ from .gluon_resnet import *
from .gluon_xception import * from .gluon_xception import *
from .res2net import * from .res2net import *
from .dla import * from .dla import *
from .hrnet import *
from .registry import * from .registry import *
from .factory import create_model from .factory import create_model

869
timm/models/hrnet.py
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@ -0,0 +1,869 @@
""" 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 os
import logging
import functools
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
from .registry import register_model
from .helpers import load_pretrained
from .helpers import load_pretrained
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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': 'fc',
**kwargs
}
default_cfgs = {
'hrnet_w18_small': _cfg(url=''),
'hrnet_w18_small_v2': _cfg(url=''),
'hrnet_w18': _cfg(url=''),
'hrnet_w30': _cfg(url=''),
'hrnet_w32': _cfg(url=''),
'hrnet_w40': _cfg(url=''),
'hrnet_w44': _cfg(url=''),
'hrnet_w48': _cfg(url=''),
}
cfg_cls_hrnet_w18_small = dict(
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',
),
)
cfg_cls_hrnet_w18_small_v2 = dict(
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',
),
)
cfg_cls_hrnet_w18 = dict(
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',
),
)
cfg_cls_hrnet_w30 = dict(
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',
),
)
cfg_cls_hrnet_w32 = dict(
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',
),
)
cfg_cls_hrnet_w40 = dict(
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',
),
)
cfg_cls_hrnet_w44 = dict(
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',
),
)
cfg_cls_hrnet_w48 = dict(
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',
),
)
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, in_chans=3, num_classes=1000, global_pool='avg'):
super(HighResolutionNet, self).__init__()
self.conv1 = nn.Conv2d(in_chans, 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['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.incre_modules, self.downsamp_modules, self.final_layer = self._make_head(pre_stage_channels)
self.classifier = nn.Linear(2048, 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=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 init_weights(self, pretrained='', ):
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 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
@register_model
def hrnet_w18_small(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w18_small']
model = HighResolutionNet(cfg_cls_hrnet_w18_small, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
@register_model
def hrnet_w18_small_v2(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w18_small_v2']
model = HighResolutionNet(cfg_cls_hrnet_w18_small_v2, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
@register_model
def hrnet_w18(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w18']
model = HighResolutionNet(cfg_cls_hrnet_w18, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
@register_model
def hrnet_w30(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w30']
model = HighResolutionNet(cfg_cls_hrnet_w30, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
@register_model
def hrnet_w32(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w32']
model = HighResolutionNet(cfg_cls_hrnet_w32, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
@register_model
def hrnet_w40(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w40']
model = HighResolutionNet(cfg_cls_hrnet_w40, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
@register_model
def hrnet_w44(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w44']
model = HighResolutionNet(cfg_cls_hrnet_w44, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
@register_model
def hrnet_w48(pretrained=True, **kwargs):
default_cfg = default_cfgs['hrnet_w48']
model = HighResolutionNet(cfg_cls_hrnet_w48, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(
model,
default_cfg,
num_classes=kwargs.get('num_classes', 0),
in_chans=kwargs.get('in_chans', 3))
return model
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