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# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptMultiConfig
from ..layers import CSPLayer
@MODELS.register_module()
class CSPNeXtPAFPN(BaseModule):
"""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)
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. Default: 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='Swish')
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
def __init__(
self,
in_channels: Sequence[int],
out_channels: int,
num_csp_blocks: int = 3,
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', momentum=0.03, eps=0.001),
act_cfg: ConfigType = dict(type='Swish'),
init_cfg: OptMultiConfig = dict(
type='Kaiming',
layer='Conv2d',
a=math.sqrt(5),
distribution='uniform',
mode='fan_in',
nonlinearity='leaky_relu')
) -> None:
super().__init__(init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
# build top-down blocks
self.upsample = nn.Upsample(**upsample_cfg)
self.reduce_layers = nn.ModuleList()
self.top_down_blocks = nn.ModuleList()
for idx in range(len(in_channels) - 1, 0, -1):
self.reduce_layers.append(
ConvModule(
in_channels[idx],
in_channels[idx - 1],
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.top_down_blocks.append(
CSPLayer(
in_channels[idx - 1] * 2,
in_channels[idx - 1],
num_blocks=num_csp_blocks,
add_identity=False,
use_depthwise=use_depthwise,
use_cspnext_block=True,
expand_ratio=expand_ratio,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
# build bottom-up blocks
self.downsamples = nn.ModuleList()
self.bottom_up_blocks = nn.ModuleList()
for idx in range(len(in_channels) - 1):
self.downsamples.append(
conv(
in_channels[idx],
in_channels[idx],
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.bottom_up_blocks.append(
CSPLayer(
in_channels[idx] * 2,
in_channels[idx + 1],
num_blocks=num_csp_blocks,
add_identity=False,
use_depthwise=use_depthwise,
use_cspnext_block=True,
expand_ratio=expand_ratio,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.out_convs = nn.ModuleList()
for i in range(len(in_channels)):
self.out_convs.append(
conv(
in_channels[i],
out_channels,
3,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
def forward(self, inputs: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]:
"""
Args:
inputs (tuple[Tensor]): input features.
Returns:
tuple[Tensor]: YOLOXPAFPN features.
"""
assert len(inputs) == len(self.in_channels)
# top-down path
inner_outs = [inputs[-1]]
for idx in range(len(self.in_channels) - 1, 0, -1):
feat_heigh = inner_outs[0]
feat_low = inputs[idx - 1]
feat_heigh = self.reduce_layers[len(self.in_channels) - 1 - idx](
feat_heigh)
inner_outs[0] = feat_heigh
upsample_feat = self.upsample(feat_heigh)
inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx](
torch.cat([upsample_feat, feat_low], 1))
inner_outs.insert(0, inner_out)
# bottom-up path
outs = [inner_outs[0]]
for idx in range(len(self.in_channels) - 1):
feat_low = outs[-1]
feat_height = inner_outs[idx + 1]
downsample_feat = self.downsamples[idx](feat_low)
out = self.bottom_up_blocks[idx](
torch.cat([downsample_feat, feat_height], 1))
outs.append(out)
# out convs
for idx, conv in enumerate(self.out_convs):
outs[idx] = conv(outs[idx])
return tuple(outs)
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