mmyolo.models.backbones.efficient_rep 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import torch
import torch.nn as nn
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.models.layers.yolo_bricks import CSPSPPFBottleneck, SPPFBottleneck
from mmyolo.registry import MODELS
from ..layers import BepC3StageBlock, RepStageBlock
from ..utils import make_round
from .base_backbone import BaseBackbone
[文档]@MODELS.register_module()
class YOLOv6EfficientRep(BaseBackbone):
"""EfficientRep backbone used in YOLOv6.
Args:
arch (str): Architecture of BaseDarknet, from {P5, P6}.
Defaults to P5.
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'.
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.
input_channels (int): Number of input image channels. Defaults to 3.
out_indices (Tuple[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.
norm_cfg (dict): Dictionary to construct and config norm layer.
Defaults to dict(type='BN', requires_grad=True).
act_cfg (dict): Config dict for activation layer.
Defaults to dict(type='LeakyReLU', negative_slope=0.1).
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. Defaults to False.
block_cfg (dict): Config dict for the block used to build each
layer. Defaults to dict(type='RepVGGBlock').
init_cfg (Union[dict, list[dict]], optional): Initialization config
dict. Defaults to None.
Example:
>>> from mmyolo.models import YOLOv6EfficientRep
>>> import torch
>>> model = YOLOv6EfficientRep()
>>> model.eval()
>>> inputs = torch.rand(1, 3, 416, 416)
>>> level_outputs = model(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
...
(1, 256, 52, 52)
(1, 512, 26, 26)
(1, 1024, 13, 13)
"""
# From left to right:
# in_channels, out_channels, num_blocks, use_spp
arch_settings = {
'P5': [[64, 128, 6, False], [128, 256, 12, False],
[256, 512, 18, False], [512, 1024, 6, True]]
}
def __init__(self,
arch: str = 'P5',
plugins: Union[dict, List[dict]] = None,
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,
use_cspsppf: bool = False,
norm_cfg: ConfigType = dict(
type='BN', momentum=0.03, eps=0.001),
act_cfg: ConfigType = dict(type='ReLU', inplace=True),
norm_eval: bool = False,
block_cfg: ConfigType = dict(type='RepVGGBlock'),
init_cfg: OptMultiConfig = None):
self.block_cfg = block_cfg
self.use_cspsppf = use_cspsppf
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."""
block_cfg = self.block_cfg.copy()
block_cfg.update(
dict(
in_channels=self.input_channels,
out_channels=int(self.arch_setting[0][0] * self.widen_factor),
kernel_size=3,
stride=2,
))
return MODELS.build(block_cfg)
[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, num_blocks, use_spp = setting
in_channels = int(in_channels * self.widen_factor)
out_channels = int(out_channels * self.widen_factor)
num_blocks = make_round(num_blocks, self.deepen_factor)
rep_stage_block = RepStageBlock(
in_channels=out_channels,
out_channels=out_channels,
num_blocks=num_blocks,
block_cfg=self.block_cfg,
)
block_cfg = self.block_cfg.copy()
block_cfg.update(
dict(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=2))
stage = []
ef_block = nn.Sequential(MODELS.build(block_cfg), rep_stage_block)
stage.append(ef_block)
if use_spp:
spp = SPPFBottleneck(
in_channels=out_channels,
out_channels=out_channels,
kernel_sizes=5,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
if self.use_cspsppf:
spp = CSPSPPFBottleneck(
in_channels=out_channels,
out_channels=out_channels,
kernel_sizes=5,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
stage.append(spp)
return stage
[文档] def init_weights(self):
if self.init_cfg is None:
"""Initialize the parameters."""
for m in self.modules():
if isinstance(m, torch.nn.Conv2d):
# In order to be consistent with the source code,
# reset the Conv2d initialization parameters
m.reset_parameters()
else:
super().init_weights()
[文档]@MODELS.register_module()
class YOLOv6CSPBep(YOLOv6EfficientRep):
"""CSPBep backbone used in YOLOv6.
Args:
arch (str): Architecture of BaseDarknet, from {P5, P6}.
Defaults to P5.
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'.
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.
input_channels (int): Number of input image channels. Defaults to 3.
out_indices (Tuple[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.
norm_cfg (dict): Dictionary to construct and config norm layer.
Defaults to dict(type='BN', requires_grad=True).
act_cfg (dict): Config dict for activation layer.
Defaults to dict(type='LeakyReLU', negative_slope=0.1).
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. Defaults to False.
block_cfg (dict): Config dict for the block used to build each
layer. Defaults to dict(type='RepVGGBlock').
block_act_cfg (dict): Config dict for activation layer used in each
stage. Defaults to dict(type='SiLU', inplace=True).
init_cfg (Union[dict, list[dict]], optional): Initialization config
dict. Defaults to None.
Example:
>>> from mmyolo.models import YOLOv6CSPBep
>>> import torch
>>> model = YOLOv6CSPBep()
>>> model.eval()
>>> inputs = torch.rand(1, 3, 416, 416)
>>> level_outputs = model(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
...
(1, 256, 52, 52)
(1, 512, 26, 26)
(1, 1024, 13, 13)
"""
# From left to right:
# in_channels, out_channels, num_blocks, use_spp
arch_settings = {
'P5': [[64, 128, 6, False], [128, 256, 12, False],
[256, 512, 18, False], [512, 1024, 6, True]]
}
def __init__(self,
arch: str = 'P5',
plugins: Union[dict, List[dict]] = None,
deepen_factor: float = 1.0,
widen_factor: float = 1.0,
input_channels: int = 3,
hidden_ratio: float = 0.5,
out_indices: Tuple[int] = (2, 3, 4),
frozen_stages: int = -1,
use_cspsppf: bool = False,
norm_cfg: ConfigType = dict(
type='BN', momentum=0.03, eps=0.001),
act_cfg: ConfigType = dict(type='SiLU', inplace=True),
norm_eval: bool = False,
block_cfg: ConfigType = dict(type='ConvWrapper'),
init_cfg: OptMultiConfig = None):
self.hidden_ratio = hidden_ratio
self.use_cspsppf = use_cspsppf
super().__init__(
arch=arch,
deepen_factor=deepen_factor,
widen_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,
block_cfg=block_cfg,
init_cfg=init_cfg)
[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, num_blocks, use_spp = setting
in_channels = int(in_channels * self.widen_factor)
out_channels = int(out_channels * self.widen_factor)
num_blocks = make_round(num_blocks, self.deepen_factor)
rep_stage_block = BepC3StageBlock(
in_channels=out_channels,
out_channels=out_channels,
num_blocks=num_blocks,
hidden_ratio=self.hidden_ratio,
block_cfg=self.block_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
block_cfg = self.block_cfg.copy()
block_cfg.update(
dict(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=2))
stage = []
ef_block = nn.Sequential(MODELS.build(block_cfg), rep_stage_block)
stage.append(ef_block)
if use_spp:
spp = SPPFBottleneck(
in_channels=out_channels,
out_channels=out_channels,
kernel_sizes=5,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
if self.use_cspsppf:
spp = CSPSPPFBottleneck(
in_channels=out_channels,
out_channels=out_channels,
kernel_sizes=5,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
stage.append(spp)
return stage