Source code for mmpose.models.backbones.cspnext
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional, Sequence, Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmengine.model import BaseModule
from torch import Tensor
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.registry import MODELS
from mmpose.utils.typing import ConfigType
from ..utils import CSPLayer
from .csp_darknet import SPPBottleneck
[docs]@MODELS.register_module()
class CSPNeXt(BaseModule):
"""CSPNeXt backbone used in RTMDet.
Args:
arch (str): Architecture of CSPNeXt, from {P5, P6}.
Defaults to P5.
expand_ratio (float): Ratio to adjust the number of channels of the
hidden layer. Defaults to 0.5.
deepen_factor (float): Depth multiplier, multiply number of
blocks in CSP layer by this amount. Defaults to 1.0.
widen_factor (float): Width multiplier, multiply number of
channels in each layer by this amount. Defaults to 1.0.
out_indices (Sequence[int]): Output from which stages.
Defaults to (2, 3, 4).
frozen_stages (int): Stages to be frozen (stop grad and set eval
mode). -1 means not freezing any parameters. Defaults to -1.
use_depthwise (bool): Whether to use depthwise separable convolution.
Defaults to False.
arch_ovewrite (list): Overwrite default arch settings.
Defaults to None.
spp_kernel_sizes: (tuple[int]): Sequential of kernel sizes of SPP
layers. Defaults to (5, 9, 13).
channel_attention (bool): Whether to add channel attention in each
stage. Defaults to True.
conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
convolution layer. Defaults to None.
norm_cfg (:obj:`ConfigDict` or dict): Dictionary to construct and
config norm layer. Defaults to dict(type='BN', requires_grad=True).
act_cfg (:obj:`ConfigDict` or dict): Config dict for activation layer.
Defaults to dict(type='SiLU').
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.
init_cfg (:obj:`ConfigDict` or dict or list[dict] or
list[:obj:`ConfigDict`]): Initialization config dict.
"""
# From left to right:
# in_channels, out_channels, num_blocks, add_identity, use_spp
arch_settings = {
'P5': [[64, 128, 3, True, False], [128, 256, 6, True, False],
[256, 512, 6, True, False], [512, 1024, 3, False, True]],
'P6': [[64, 128, 3, True, False], [128, 256, 6, True, False],
[256, 512, 6, True, False], [512, 768, 3, True, False],
[768, 1024, 3, False, True]]
}
def __init__(
self,
arch: str = 'P5',
deepen_factor: float = 1.0,
widen_factor: float = 1.0,
out_indices: Sequence[int] = (2, 3, 4),
frozen_stages: int = -1,
use_depthwise: bool = False,
expand_ratio: float = 0.5,
arch_ovewrite: dict = None,
spp_kernel_sizes: Sequence[int] = (5, 9, 13),
channel_attention: bool = True,
conv_cfg: Optional[ConfigType] = None,
norm_cfg: ConfigType = dict(type='BN', momentum=0.03, eps=0.001),
act_cfg: ConfigType = dict(type='SiLU'),
norm_eval: bool = False,
init_cfg: Optional[ConfigType] = dict(
type='Kaiming',
layer='Conv2d',
a=math.sqrt(5),
distribution='uniform',
mode='fan_in',
nonlinearity='leaky_relu')
) -> None:
super().__init__(init_cfg=init_cfg)
arch_setting = self.arch_settings[arch]
if arch_ovewrite:
arch_setting = arch_ovewrite
assert set(out_indices).issubset(
i for i in range(len(arch_setting) + 1))
if frozen_stages not in range(-1, len(arch_setting) + 1):
raise ValueError('frozen_stages must be in range(-1, '
'len(arch_setting) + 1). But received '
f'{frozen_stages}')
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.use_depthwise = use_depthwise
self.norm_eval = norm_eval
conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
self.stem = nn.Sequential(
ConvModule(
3,
int(arch_setting[0][0] * widen_factor // 2),
3,
padding=1,
stride=2,
norm_cfg=norm_cfg,
act_cfg=act_cfg),
ConvModule(
int(arch_setting[0][0] * widen_factor // 2),
int(arch_setting[0][0] * widen_factor // 2),
3,
padding=1,
stride=1,
norm_cfg=norm_cfg,
act_cfg=act_cfg),
ConvModule(
int(arch_setting[0][0] * widen_factor // 2),
int(arch_setting[0][0] * widen_factor),
3,
padding=1,
stride=1,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.layers = ['stem']
for i, (in_channels, out_channels, num_blocks, add_identity,
use_spp) in enumerate(arch_setting):
in_channels = int(in_channels * widen_factor)
out_channels = int(out_channels * widen_factor)
num_blocks = max(round(num_blocks * deepen_factor), 1)
stage = []
conv_layer = conv(
in_channels,
out_channels,
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
stage.append(conv_layer)
if use_spp:
spp = SPPBottleneck(
out_channels,
out_channels,
kernel_sizes=spp_kernel_sizes,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
stage.append(spp)
csp_layer = CSPLayer(
out_channels,
out_channels,
num_blocks=num_blocks,
add_identity=add_identity,
use_depthwise=use_depthwise,
use_cspnext_block=True,
expand_ratio=expand_ratio,
channel_attention=channel_attention,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
stage.append(csp_layer)
self.add_module(f'stage{i + 1}', nn.Sequential(*stage))
self.layers.append(f'stage{i + 1}')
def _freeze_stages(self) -> None:
if self.frozen_stages >= 0:
for i in range(self.frozen_stages + 1):
m = getattr(self, self.layers[i])
m.eval()
for param in m.parameters():
param.requires_grad = False
[docs] def train(self, mode=True) -> None:
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
[docs] def forward(self, x: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]:
outs = []
for i, layer_name in enumerate(self.layers):
layer = getattr(self, layer_name)
x = layer(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)