# Copyright (c) OpenMMLab. All rights reserved.
# Adapt from https://github.com/open-mmlab/mmpose/blob/master/mmpose/models/backbones/litehrnet.py
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule,
build_conv_layer, build_norm_layer, constant_init,
normal_init)
from torch.nn.modules.batchnorm import _BatchNorm
from easycv.framework.errors import ValueError
from easycv.models.registry import BACKBONES
[docs]def channel_shuffle(x, groups):
"""Channel Shuffle operation.
This function enables cross-group information flow for multiple groups
convolution layers.
Args:
x (Tensor): The input tensor.
groups (int): The number of groups to divide the input tensor
in the channel dimension.
Returns:
Tensor: The output tensor after channel shuffle operation.
"""
batch_size, num_channels, height, width = x.size()
assert (num_channels % groups == 0), ('num_channels should be '
'divisible by groups')
channels_per_group = num_channels // groups
x = x.view(batch_size, groups, channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
x = x.view(batch_size, -1, height, width)
return x
[docs]class SpatialWeighting(nn.Module):
"""Spatial weighting module.
Args:
channels (int): The channels of the module.
ratio (int): channel reduction ratio.
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: None.
act_cfg (dict): Config dict for activation layer.
Default: (dict(type='ReLU'), dict(type='Sigmoid')).
The last ConvModule uses Sigmoid by default.
"""
[docs] def __init__(self,
channels,
ratio=16,
conv_cfg=None,
norm_cfg=None,
act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = ConvModule(
in_channels=channels,
out_channels=int(channels / ratio),
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[0])
self.conv2 = ConvModule(
in_channels=int(channels / ratio),
out_channels=channels,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[1])
[docs] def forward(self, x):
out = self.global_avgpool(x)
out = self.conv1(out)
out = self.conv2(out)
return x * out
[docs]class CrossResolutionWeighting(nn.Module):
"""Cross-resolution channel weighting module.
Args:
channels (int): The channels of the module.
ratio (int): channel reduction ratio.
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: None.
act_cfg (dict): Config dict for activation layer.
Default: (dict(type='ReLU'), dict(type='Sigmoid')).
The last ConvModule uses Sigmoid by default.
"""
[docs] def __init__(self,
channels,
ratio=16,
conv_cfg=None,
norm_cfg=None,
act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.channels = channels
total_channel = sum(channels)
self.conv1 = ConvModule(
in_channels=total_channel,
out_channels=int(total_channel / ratio),
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[0])
self.conv2 = ConvModule(
in_channels=int(total_channel / ratio),
out_channels=total_channel,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[1])
[docs] def forward(self, x):
mini_size = x[-1].size()[-2:]
out = [F.adaptive_avg_pool2d(s, mini_size) for s in x[:-1]] + [x[-1]]
out = torch.cat(out, dim=1)
out = self.conv1(out)
out = self.conv2(out)
out = torch.split(out, self.channels, dim=1)
out = [
s * F.interpolate(a, size=s.size()[-2:], mode='nearest')
for s, a in zip(x, out)
]
return out
[docs]class ConditionalChannelWeighting(nn.Module):
"""Conditional channel weighting block.
Args:
in_channels (int): The input channels of the block.
stride (int): Stride of the 3x3 convolution layer.
reduce_ratio (int): channel reduction ratio.
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
"""
[docs] def __init__(self,
in_channels,
stride,
reduce_ratio,
conv_cfg=None,
norm_cfg=dict(type='BN'),
with_cp=False):
super().__init__()
self.with_cp = with_cp
self.stride = stride
assert stride in [1, 2]
branch_channels = [channel // 2 for channel in in_channels]
self.cross_resolution_weighting = CrossResolutionWeighting(
branch_channels,
ratio=reduce_ratio,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg)
self.depthwise_convs = nn.ModuleList([
ConvModule(
channel,
channel,
kernel_size=3,
stride=self.stride,
padding=1,
groups=channel,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None) for channel in branch_channels
])
self.spatial_weighting = nn.ModuleList([
SpatialWeighting(channels=channel, ratio=4)
for channel in branch_channels
])
[docs] def forward(self, x):
def _inner_forward(x):
x = [s.chunk(2, dim=1) for s in x]
x1 = [s[0] for s in x]
x2 = [s[1] for s in x]
x2 = self.cross_resolution_weighting(x2)
x2 = [dw(s) for s, dw in zip(x2, self.depthwise_convs)]
x2 = [sw(s) for s, sw in zip(x2, self.spatial_weighting)]
out = [torch.cat([s1, s2], dim=1) for s1, s2 in zip(x1, x2)]
out = [channel_shuffle(s, 2) for s in out]
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
return out
[docs]class Stem(nn.Module):
"""Stem network block.
Args:
in_channels (int): The input channels of the block.
stem_channels (int): Output channels of the stem layer.
out_channels (int): The output channels of the block.
expand_ratio (int): adjusts number of channels of the hidden layer
in InvertedResidual by this amount.
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
"""
[docs] def __init__(self,
in_channels,
stem_channels,
out_channels,
expand_ratio,
conv_cfg=None,
norm_cfg=dict(type='BN'),
with_cp=False):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.with_cp = with_cp
self.conv1 = ConvModule(
in_channels=in_channels,
out_channels=stem_channels,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type='ReLU'))
mid_channels = int(round(stem_channels * expand_ratio))
branch_channels = stem_channels // 2
if stem_channels == self.out_channels:
inc_channels = self.out_channels - branch_channels
else:
inc_channels = self.out_channels - stem_channels
self.branch1 = nn.Sequential(
ConvModule(
branch_channels,
branch_channels,
kernel_size=3,
stride=2,
padding=1,
groups=branch_channels,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None),
ConvModule(
branch_channels,
inc_channels,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU')),
)
self.expand_conv = ConvModule(
branch_channels,
mid_channels,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU'))
self.depthwise_conv = ConvModule(
mid_channels,
mid_channels,
kernel_size=3,
stride=2,
padding=1,
groups=mid_channels,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
self.linear_conv = ConvModule(
mid_channels,
branch_channels
if stem_channels == self.out_channels else stem_channels,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU'))
[docs] def forward(self, x):
def _inner_forward(x):
x = self.conv1(x)
x1, x2 = x.chunk(2, dim=1)
x2 = self.expand_conv(x2)
x2 = self.depthwise_conv(x2)
x2 = self.linear_conv(x2)
out = torch.cat((self.branch1(x1), x2), dim=1)
out = channel_shuffle(out, 2)
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
return out
[docs]class IterativeHead(nn.Module):
"""Extra iterative head for feature learning.
Args:
in_channels (int): The input channels of the block.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
"""
[docs] def __init__(self, in_channels, norm_cfg=dict(type='BN')):
super().__init__()
projects = []
num_branchs = len(in_channels)
self.in_channels = in_channels[::-1]
for i in range(num_branchs):
if i != num_branchs - 1:
projects.append(
DepthwiseSeparableConvModule(
in_channels=self.in_channels[i],
out_channels=self.in_channels[i + 1],
kernel_size=3,
stride=1,
padding=1,
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU'),
dw_act_cfg=None,
pw_act_cfg=dict(type='ReLU')))
else:
projects.append(
DepthwiseSeparableConvModule(
in_channels=self.in_channels[i],
out_channels=self.in_channels[i],
kernel_size=3,
stride=1,
padding=1,
norm_cfg=norm_cfg,
act_cfg=dict(type='ReLU'),
dw_act_cfg=None,
pw_act_cfg=dict(type='ReLU')))
self.projects = nn.ModuleList(projects)
[docs] def forward(self, x):
x = x[::-1]
y = []
last_x = None
for i, s in enumerate(x):
if last_x is not None:
last_x = F.interpolate(
last_x,
size=s.size()[-2:],
mode='bilinear',
align_corners=True)
s = s + last_x
s = self.projects[i](s)
y.append(s)
last_x = s
return y[::-1]
[docs]class ShuffleUnit(nn.Module):
"""InvertedResidual block for ShuffleNetV2 backbone.
Args:
in_channels (int): The input channels of the block.
out_channels (int): The output channels of the block.
stride (int): Stride of the 3x3 convolution layer. Default: 1
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU').
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
"""
[docs] def __init__(self,
in_channels,
out_channels,
stride=1,
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
with_cp=False):
super().__init__()
self.stride = stride
self.with_cp = with_cp
branch_features = out_channels // 2
if self.stride == 1:
assert in_channels == branch_features * 2, (
f'in_channels ({in_channels}) should equal to '
f'branch_features * 2 ({branch_features * 2}) '
'when stride is 1')
if in_channels != branch_features * 2:
assert self.stride != 1, (
f'stride ({self.stride}) should not equal 1 when '
f'in_channels != branch_features * 2')
if self.stride > 1:
self.branch1 = nn.Sequential(
ConvModule(
in_channels,
in_channels,
kernel_size=3,
stride=self.stride,
padding=1,
groups=in_channels,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None),
ConvModule(
in_channels,
branch_features,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg),
)
self.branch2 = nn.Sequential(
ConvModule(
in_channels if (self.stride > 1) else branch_features,
branch_features,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg),
ConvModule(
branch_features,
branch_features,
kernel_size=3,
stride=self.stride,
padding=1,
groups=branch_features,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None),
ConvModule(
branch_features,
branch_features,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
[docs] def forward(self, x):
def _inner_forward(x):
if self.stride > 1:
out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
else:
x1, x2 = x.chunk(2, dim=1)
out = torch.cat((x1, self.branch2(x2)), dim=1)
out = channel_shuffle(out, 2)
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
return out
[docs]class LiteHRModule(nn.Module):
"""High-Resolution Module for LiteHRNet.
It contains conditional channel weighting blocks and
shuffle blocks.
Args:
num_branches (int): Number of branches in the module.
num_blocks (int): Number of blocks in the module.
in_channels (list(int)): Number of input image channels.
reduce_ratio (int): Channel reduction ratio.
module_type (str): 'LITE' or 'NAIVE'
multiscale_output (bool): Whether to output multi-scale features.
with_fuse (bool): Whether to use fuse layers.
conv_cfg (dict): dictionary to construct and config conv layer.
norm_cfg (dict): dictionary to construct and config norm layer.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
"""
[docs] def __init__(
self,
num_branches,
num_blocks,
in_channels,
reduce_ratio,
module_type,
multiscale_output=False,
with_fuse=True,
conv_cfg=None,
norm_cfg=dict(type='BN'),
with_cp=False,
):
super().__init__()
self._check_branches(num_branches, in_channels)
self.in_channels = in_channels
self.num_branches = num_branches
self.module_type = module_type
self.multiscale_output = multiscale_output
self.with_fuse = with_fuse
self.norm_cfg = norm_cfg
self.conv_cfg = conv_cfg
self.with_cp = with_cp
if self.module_type.upper() == 'LITE':
self.layers = self._make_weighting_blocks(num_blocks, reduce_ratio)
elif self.module_type.upper() == 'NAIVE':
self.layers = self._make_naive_branches(num_branches, num_blocks)
else:
raise ValueError("module_type should be either 'LITE' or 'NAIVE'.")
if self.with_fuse:
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU()
def _check_branches(self, num_branches, in_channels):
"""Check input to avoid ValueError."""
if num_branches != len(in_channels):
error_msg = f'NUM_BRANCHES({num_branches}) ' \
f'!= NUM_INCHANNELS({len(in_channels)})'
raise ValueError(error_msg)
def _make_weighting_blocks(self, num_blocks, reduce_ratio, stride=1):
"""Make channel weighting blocks."""
layers = []
for i in range(num_blocks):
layers.append(
ConditionalChannelWeighting(
self.in_channels,
stride=stride,
reduce_ratio=reduce_ratio,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
with_cp=self.with_cp))
return nn.Sequential(*layers)
def _make_one_branch(self, branch_index, num_blocks, stride=1):
"""Make one branch."""
layers = []
layers.append(
ShuffleUnit(
self.in_channels[branch_index],
self.in_channels[branch_index],
stride=stride,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type='ReLU'),
with_cp=self.with_cp))
for i in range(1, num_blocks):
layers.append(
ShuffleUnit(
self.in_channels[branch_index],
self.in_channels[branch_index],
stride=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type='ReLU'),
with_cp=self.with_cp))
return nn.Sequential(*layers)
def _make_naive_branches(self, num_branches, num_blocks):
"""Make branches."""
branches = []
for i in range(num_branches):
branches.append(self._make_one_branch(i, num_blocks))
return nn.ModuleList(branches)
def _make_fuse_layers(self):
"""Make fuse layer."""
if self.num_branches == 1:
return None
num_branches = self.num_branches
in_channels = self.in_channels
fuse_layers = []
num_out_branches = num_branches if self.multiscale_output else 1
for i in range(num_out_branches):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg, in_channels[i])[1],
nn.Upsample(
scale_factor=2**(j - i), mode='nearest')))
elif j == i:
fuse_layer.append(None)
else:
conv_downsamples = []
for k in range(i - j):
if k == i - j - 1:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=3,
stride=2,
padding=1,
groups=in_channels[j],
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[i])[1]))
else:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=3,
stride=2,
padding=1,
groups=in_channels[j],
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
nn.ReLU(inplace=True)))
fuse_layer.append(nn.Sequential(*conv_downsamples))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
[docs] def forward(self, x):
"""Forward function."""
if self.num_branches == 1:
return [self.layers[0](x[0])]
if self.module_type.upper() == 'LITE':
out = self.layers(x)
elif self.module_type.upper() == 'NAIVE':
for i in range(self.num_branches):
x[i] = self.layers[i](x[i])
out = x
if self.with_fuse:
out_fuse = []
for i in range(len(self.fuse_layers)):
# `y = 0` will lead to decreased accuracy (0.5~1 mAP)
y = out[0] if i == 0 else self.fuse_layers[i][0](out[0])
for j in range(self.num_branches):
if i == j:
y += out[j]
else:
y += self.fuse_layers[i][j](out[j])
out_fuse.append(self.relu(y))
out = out_fuse
if not self.multiscale_output:
out = [out[0]]
return out
[docs]@BACKBONES.register_module()
class LiteHRNet(nn.Module):
"""Lite-HRNet backbone.
`Lite-HRNet: A Lightweight High-Resolution Network
<https://arxiv.org/abs/2104.06403>`__
Code adapted from 'https://github.com/HRNet/Lite-HRNet/'
'blob/hrnet/models/backbones/litehrnet.py'
Args:
extra (dict): detailed configuration for each stage of HRNet.
in_channels (int): Number of input image channels. Default: 3.
conv_cfg (dict): dictionary to construct and config conv layer.
norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. Default: False
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
Example:
>>> from mmpose.models import LiteHRNet
>>> import torch
>>> extra=dict(
>>> stem=dict(stem_channels=32, out_channels=32, expand_ratio=1),
>>> num_stages=3,
>>> stages_spec=dict(
>>> num_modules=(2, 4, 2),
>>> num_branches=(2, 3, 4),
>>> num_blocks=(2, 2, 2),
>>> module_type=('LITE', 'LITE', 'LITE'),
>>> with_fuse=(True, True, True),
>>> reduce_ratios=(8, 8, 8),
>>> num_channels=(
>>> (40, 80),
>>> (40, 80, 160),
>>> (40, 80, 160, 320),
>>> )),
>>> with_head=False)
>>> self = LiteHRNet(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 40, 8, 8)
"""
[docs] def __init__(self,
extra,
in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN'),
norm_eval=False,
with_cp=False):
super().__init__()
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.stem = Stem(
in_channels,
stem_channels=self.extra['stem']['stem_channels'],
out_channels=self.extra['stem']['out_channels'],
expand_ratio=self.extra['stem']['expand_ratio'],
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg)
self.num_stages = self.extra['num_stages']
self.stages_spec = self.extra['stages_spec']
num_channels_last = [
self.stem.out_channels,
]
for i in range(self.num_stages):
num_channels = self.stages_spec['num_channels'][i]
num_channels = [num_channels[i] for i in range(len(num_channels))]
setattr(
self, f'transition{i}',
self._make_transition_layer(num_channels_last, num_channels))
stage, num_channels_last = self._make_stage(
self.stages_spec, i, num_channels, multiscale_output=True)
setattr(self, f'stage{i}', stage)
self.with_head = self.extra['with_head']
if self.with_head:
self.head_layer = IterativeHead(
in_channels=num_channels_last,
norm_cfg=self.norm_cfg,
)
def _make_transition_layer(self, num_channels_pre_layer,
num_channels_cur_layer):
"""Make transition 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(
build_conv_layer(
self.conv_cfg,
num_channels_pre_layer[i],
num_channels_pre_layer[i],
kernel_size=3,
stride=1,
padding=1,
groups=num_channels_pre_layer[i],
bias=False),
build_norm_layer(self.norm_cfg,
num_channels_pre_layer[i])[1],
build_conv_layer(
self.conv_cfg,
num_channels_pre_layer[i],
num_channels_cur_layer[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg,
num_channels_cur_layer[i])[1],
nn.ReLU()))
else:
transition_layers.append(None)
else:
conv_downsamples = []
for j in range(i + 1 - num_branches_pre):
in_channels = num_channels_pre_layer[-1]
out_channels = num_channels_cur_layer[i] \
if j == i - num_branches_pre else in_channels
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels,
in_channels,
kernel_size=3,
stride=2,
padding=1,
groups=in_channels,
bias=False),
build_norm_layer(self.norm_cfg, in_channels)[1],
build_conv_layer(
self.conv_cfg,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg, out_channels)[1],
nn.ReLU()))
transition_layers.append(nn.Sequential(*conv_downsamples))
return nn.ModuleList(transition_layers)
def _make_stage(self,
stages_spec,
stage_index,
in_channels,
multiscale_output=True):
num_modules = stages_spec['num_modules'][stage_index]
num_branches = stages_spec['num_branches'][stage_index]
num_blocks = stages_spec['num_blocks'][stage_index]
reduce_ratio = stages_spec['reduce_ratios'][stage_index]
with_fuse = stages_spec['with_fuse'][stage_index]
module_type = stages_spec['module_type'][stage_index]
modules = []
for i in range(num_modules):
# multi_scale_output is only used last module
if not multiscale_output and i == num_modules - 1:
reset_multiscale_output = False
else:
reset_multiscale_output = True
modules.append(
LiteHRModule(
num_branches,
num_blocks,
in_channels,
reduce_ratio,
module_type,
multiscale_output=reset_multiscale_output,
with_fuse=with_fuse,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
with_cp=self.with_cp))
in_channels = modules[-1].in_channels
return nn.Sequential(*modules), in_channels
[docs] def init_weights(self):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
for m in self.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
[docs] def forward(self, x):
"""Forward function."""
x = self.stem(x)
y_list = [x]
for i in range(self.num_stages):
x_list = []
transition = getattr(self, f'transition{i}')
for j in range(self.stages_spec['num_branches'][i]):
if transition[j]:
if j >= len(y_list):
x_list.append(transition[j](y_list[-1]))
else:
x_list.append(transition[j](y_list[j]))
else:
x_list.append(y_list[j])
y_list = getattr(self, f'stage{i}')(x_list)
x = y_list
if self.with_head:
x = self.head_layer(x)
return [x[0]]
[docs] def train(self, mode=True):
"""Convert the model into training mode."""
super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()