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# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.backbones.csp_darknet import CSPLayer
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from .base_yolo_neck import BaseYOLONeck
@MODELS.register_module()
class CSPNeXtPAFPN(BaseYOLONeck):
"""Path Aggregation Network with CSPNeXt blocks.
Args:
in_channels (Sequence[int]): Number of input channels per scale.
out_channels (int): Number of output channels (used at each scale)
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.
num_csp_blocks (int): Number of bottlenecks in CSPLayer.
Defaults to 3.
use_depthwise (bool): Whether to use depthwise separable convolution in
blocks. Defaults to False.
expand_ratio (float): Ratio to adjust the number of channels of the
hidden layer. Defaults to 0.5.
upsample_cfg (dict): Config dict for interpolate layer.
Default: `dict(scale_factor=2, mode='nearest')`
conv_cfg (dict, optional): 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='SiLU', inplace=True)
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
def __init__(
self,
in_channels: Sequence[int],
out_channels: int,
deepen_factor: float = 1.0,
widen_factor: float = 1.0,
num_csp_blocks: int = 3,
freeze_all: bool = False,
use_depthwise: bool = False,
expand_ratio: float = 0.5,
upsample_cfg: ConfigType = dict(scale_factor=2, mode='nearest'),
conv_cfg: bool = None,
norm_cfg: ConfigType = dict(type='BN'),
act_cfg: ConfigType = dict(type='SiLU', inplace=True),
init_cfg: OptMultiConfig = dict(
type='Kaiming',
layer='Conv2d',
a=math.sqrt(5),
distribution='uniform',
mode='fan_in',
nonlinearity='leaky_relu')
) -> None:
self.num_csp_blocks = round(num_csp_blocks * deepen_factor)
self.conv = DepthwiseSeparableConvModule \
if use_depthwise else ConvModule
self.upsample_cfg = upsample_cfg
self.expand_ratio = expand_ratio
self.conv_cfg = conv_cfg
super().__init__(
in_channels=[
int(channel * widen_factor) for channel in in_channels
],
out_channels=int(out_channels * widen_factor),
deepen_factor=deepen_factor,
widen_factor=widen_factor,
freeze_all=freeze_all,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
init_cfg=init_cfg)
def build_reduce_layer(self, idx: int) -> nn.Module:
"""build reduce layer.
Args:
idx (int): layer idx.
Returns:
nn.Module: The reduce layer.
"""
if idx == len(self.in_channels) - 1:
layer = self.conv(
self.in_channels[idx],
self.in_channels[idx - 1],
1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
else:
layer = nn.Identity()
return layer
def build_upsample_layer(self, *args, **kwargs) -> nn.Module:
"""build upsample layer."""
return nn.Upsample(**self.upsample_cfg)
def build_top_down_layer(self, idx: int) -> nn.Module:
"""build top down layer.
Args:
idx (int): layer idx.
Returns:
nn.Module: The top down layer.
"""
if idx == 1:
return CSPLayer(
self.in_channels[idx - 1] * 2,
self.in_channels[idx - 1],
num_blocks=self.num_csp_blocks,
add_identity=False,
use_cspnext_block=True,
expand_ratio=self.expand_ratio,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
else:
return nn.Sequential(
CSPLayer(
self.in_channels[idx - 1] * 2,
self.in_channels[idx - 1],
num_blocks=self.num_csp_blocks,
add_identity=False,
use_cspnext_block=True,
expand_ratio=self.expand_ratio,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg),
self.conv(
self.in_channels[idx - 1],
self.in_channels[idx - 2],
kernel_size=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
def build_downsample_layer(self, idx: int) -> nn.Module:
"""build downsample layer.
Args:
idx (int): layer idx.
Returns:
nn.Module: The downsample layer.
"""
return self.conv(
self.in_channels[idx],
self.in_channels[idx],
kernel_size=3,
stride=2,
padding=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
def build_bottom_up_layer(self, idx: int) -> nn.Module:
"""build bottom up layer.
Args:
idx (int): layer idx.
Returns:
nn.Module: The bottom up layer.
"""
return CSPLayer(
self.in_channels[idx] * 2,
self.in_channels[idx + 1],
num_blocks=self.num_csp_blocks,
add_identity=False,
use_cspnext_block=True,
expand_ratio=self.expand_ratio,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
def build_out_layer(self, idx: int) -> nn.Module:
"""build out layer.
Args:
idx (int): layer idx.
Returns:
nn.Module: The out layer.
"""
return self.conv(
self.in_channels[idx],
self.out_channels,
3,
padding=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
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