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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List
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 YOLOXPAFPN(BaseYOLONeck):
"""Path Aggregation Network used in YOLOX.
Args:
in_channels (List[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 1.
use_depthwise (bool): Whether to use depthwise separable convolution.
Defaults to False.
freeze_all(bool): Whether to freeze the model. Defaults to False.
norm_cfg (dict): Config dict for normalization layer.
Defaults to dict(type='BN', momentum=0.03, eps=0.001).
act_cfg (dict): Config dict for activation layer.
Defaults to dict(type='SiLU', inplace=True).
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
in_channels: List[int],
out_channels: int,
deepen_factor: float = 1.0,
widen_factor: float = 1.0,
num_csp_blocks: int = 3,
use_depthwise: bool = False,
freeze_all: bool = False,
norm_cfg: ConfigType = dict(
type='BN', momentum=0.03, eps=0.001),
act_cfg: ConfigType = dict(type='SiLU', inplace=True),
init_cfg: OptMultiConfig = None):
self.num_csp_blocks = round(num_csp_blocks * deepen_factor)
self.use_depthwise = use_depthwise
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 == 2:
layer = ConvModule(
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(scale_factor=2, mode='nearest')
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,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
elif idx == 2:
return nn.Sequential(
CSPLayer(
self.in_channels[idx - 1] * 2,
self.in_channels[idx - 1],
num_blocks=self.num_csp_blocks,
add_identity=False,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg),
ConvModule(
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.
"""
conv = DepthwiseSeparableConvModule \
if self.use_depthwise else ConvModule
return 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,
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 ConvModule(
self.in_channels[idx],
self.out_channels,
1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
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