mmyolo.models.backbones.yolov7_backbone 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.backbones.csp_darknet import Focus
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from ..layers import MaxPoolAndStrideConvBlock
from .base_backbone import BaseBackbone
[文档]@MODELS.register_module()
class YOLOv7Backbone(BaseBackbone):
"""Backbone used in YOLOv7.
Args:
arch (str): Architecture of YOLOv7Defaults to L.
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.
plugins (list[dict]): List of plugins for stages, each dict contains:
- cfg (dict, required): Cfg dict to build plugin.
- stages (tuple[bool], optional): Stages to apply plugin, length
should be same as 'num_stages'.
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', inplace=True).
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.
"""
_tiny_stage1_cfg = dict(type='TinyDownSampleBlock', middle_ratio=0.5)
_tiny_stage2_4_cfg = dict(type='TinyDownSampleBlock', middle_ratio=1.0)
_l_expand_channel_2x = dict(
type='ELANBlock',
middle_ratio=0.5,
block_ratio=0.5,
num_blocks=2,
num_convs_in_block=2)
_l_no_change_channel = dict(
type='ELANBlock',
middle_ratio=0.25,
block_ratio=0.25,
num_blocks=2,
num_convs_in_block=2)
_x_expand_channel_2x = dict(
type='ELANBlock',
middle_ratio=0.4,
block_ratio=0.4,
num_blocks=3,
num_convs_in_block=2)
_x_no_change_channel = dict(
type='ELANBlock',
middle_ratio=0.2,
block_ratio=0.2,
num_blocks=3,
num_convs_in_block=2)
_w_no_change_channel = dict(
type='ELANBlock',
middle_ratio=0.5,
block_ratio=0.5,
num_blocks=2,
num_convs_in_block=2)
_e_no_change_channel = dict(
type='ELANBlock',
middle_ratio=0.4,
block_ratio=0.4,
num_blocks=3,
num_convs_in_block=2)
_d_no_change_channel = dict(
type='ELANBlock',
middle_ratio=1 / 3,
block_ratio=1 / 3,
num_blocks=4,
num_convs_in_block=2)
_e2e_no_change_channel = dict(
type='EELANBlock',
num_elan_block=2,
middle_ratio=0.4,
block_ratio=0.4,
num_blocks=3,
num_convs_in_block=2)
# From left to right:
# in_channels, out_channels, Block_params
arch_settings = {
'Tiny': [[64, 64, _tiny_stage1_cfg], [64, 128, _tiny_stage2_4_cfg],
[128, 256, _tiny_stage2_4_cfg],
[256, 512, _tiny_stage2_4_cfg]],
'L': [[64, 256, _l_expand_channel_2x],
[256, 512, _l_expand_channel_2x],
[512, 1024, _l_expand_channel_2x],
[1024, 1024, _l_no_change_channel]],
'X': [[80, 320, _x_expand_channel_2x],
[320, 640, _x_expand_channel_2x],
[640, 1280, _x_expand_channel_2x],
[1280, 1280, _x_no_change_channel]],
'W':
[[64, 128, _w_no_change_channel], [128, 256, _w_no_change_channel],
[256, 512, _w_no_change_channel], [512, 768, _w_no_change_channel],
[768, 1024, _w_no_change_channel]],
'E':
[[80, 160, _e_no_change_channel], [160, 320, _e_no_change_channel],
[320, 640, _e_no_change_channel], [640, 960, _e_no_change_channel],
[960, 1280, _e_no_change_channel]],
'D': [[96, 192,
_d_no_change_channel], [192, 384, _d_no_change_channel],
[384, 768, _d_no_change_channel],
[768, 1152, _d_no_change_channel],
[1152, 1536, _d_no_change_channel]],
'E2E': [[80, 160, _e2e_no_change_channel],
[160, 320, _e2e_no_change_channel],
[320, 640, _e2e_no_change_channel],
[640, 960, _e2e_no_change_channel],
[960, 1280, _e2e_no_change_channel]],
}
def __init__(self,
arch: str = 'L',
deepen_factor: float = 1.0,
widen_factor: float = 1.0,
input_channels: int = 3,
out_indices: Tuple[int] = (2, 3, 4),
frozen_stages: int = -1,
plugins: Union[dict, List[dict]] = None,
norm_cfg: ConfigType = dict(
type='BN', momentum=0.03, eps=0.001),
act_cfg: ConfigType = dict(type='SiLU', inplace=True),
norm_eval: bool = False,
init_cfg: OptMultiConfig = None):
assert arch in self.arch_settings.keys()
self.arch = arch
super().__init__(
self.arch_settings[arch],
deepen_factor,
widen_factor,
input_channels=input_channels,
out_indices=out_indices,
plugins=plugins,
frozen_stages=frozen_stages,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
norm_eval=norm_eval,
init_cfg=init_cfg)
[文档] def build_stem_layer(self) -> nn.Module:
"""Build a stem layer."""
if self.arch in ['L', 'X']:
stem = nn.Sequential(
ConvModule(
3,
int(self.arch_setting[0][0] * self.widen_factor // 2),
3,
padding=1,
stride=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg),
ConvModule(
int(self.arch_setting[0][0] * self.widen_factor // 2),
int(self.arch_setting[0][0] * self.widen_factor),
3,
padding=1,
stride=2,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg),
ConvModule(
int(self.arch_setting[0][0] * self.widen_factor),
int(self.arch_setting[0][0] * self.widen_factor),
3,
padding=1,
stride=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
elif self.arch == 'Tiny':
stem = nn.Sequential(
ConvModule(
3,
int(self.arch_setting[0][0] * self.widen_factor // 2),
3,
padding=1,
stride=2,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg),
ConvModule(
int(self.arch_setting[0][0] * self.widen_factor // 2),
int(self.arch_setting[0][0] * self.widen_factor),
3,
padding=1,
stride=2,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
elif self.arch in ['W', 'E', 'D', 'E2E']:
stem = Focus(
3,
int(self.arch_setting[0][0] * self.widen_factor),
kernel_size=3,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
return stem
[文档] def build_stage_layer(self, stage_idx: int, setting: list) -> list:
"""Build a stage layer.
Args:
stage_idx (int): The index of a stage layer.
setting (list): The architecture setting of a stage layer.
"""
in_channels, out_channels, stage_block_cfg = setting
in_channels = int(in_channels * self.widen_factor)
out_channels = int(out_channels * self.widen_factor)
stage_block_cfg = stage_block_cfg.copy()
stage_block_cfg.setdefault('norm_cfg', self.norm_cfg)
stage_block_cfg.setdefault('act_cfg', self.act_cfg)
stage_block_cfg['in_channels'] = in_channels
stage_block_cfg['out_channels'] = out_channels
stage = []
if self.arch in ['W', 'E', 'D', 'E2E']:
stage_block_cfg['in_channels'] = out_channels
elif self.arch in ['L', 'X']:
if stage_idx == 0:
stage_block_cfg['in_channels'] = out_channels // 2
downsample_layer = self._build_downsample_layer(
stage_idx, in_channels, out_channels)
stage.append(MODELS.build(stage_block_cfg))
if downsample_layer is not None:
stage.insert(0, downsample_layer)
return stage
def _build_downsample_layer(self, stage_idx: int, in_channels: int,
out_channels: int) -> Optional[nn.Module]:
"""Build a downsample layer pre stage."""
if self.arch in ['E', 'D', 'E2E']:
downsample_layer = MaxPoolAndStrideConvBlock(
in_channels,
out_channels,
use_in_channels_of_middle=True,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
elif self.arch == 'W':
downsample_layer = ConvModule(
in_channels,
out_channels,
3,
stride=2,
padding=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
elif self.arch == 'Tiny':
if stage_idx != 0:
downsample_layer = nn.MaxPool2d(2, 2)
else:
downsample_layer = None
elif self.arch in ['L', 'X']:
if stage_idx == 0:
downsample_layer = ConvModule(
in_channels,
out_channels // 2,
3,
stride=2,
padding=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
else:
downsample_layer = MaxPoolAndStrideConvBlock(
in_channels,
in_channels,
use_in_channels_of_middle=False,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
return downsample_layer