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Source code for mmpose.models.necks.cspnext_pafpn

# 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 mmpose.registry import MODELS
from mmpose.utils.typing import ConfigType, OptMultiConfig
from ..utils import CSPLayer


[docs]@MODELS.register_module() class CSPNeXtPAFPN(BaseModule): """Path Aggregation Network with CSPNeXt blocks. Modified from RTMDet. Args: in_channels (Sequence[int]): Number of input channels per scale. out_channels (int): Number of output channels (used at each scale) out_indices (Sequence[int]): Output from which stages. 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, out_indices=( 0, 1, 2, ), 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 self.out_indices = out_indices 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)) if self.out_channels is not None: 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)) self.out_convs = conv( in_channels[-1], out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
[docs] 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_high = inner_outs[0] feat_low = inputs[idx - 1] feat_high = self.reduce_layers[len(self.in_channels) - 1 - idx]( feat_high) inner_outs[0] = feat_high upsample_feat = self.upsample(feat_high) 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_high = inner_outs[idx + 1] downsample_feat = self.downsamples[idx](feat_low) out = self.bottom_up_blocks[idx]( torch.cat([downsample_feat, feat_high], 1)) outs.append(out) if self.out_channels is not None: # out convs for idx, conv in enumerate(self.out_convs): outs[idx] = conv(outs[idx]) return tuple([outs[i] for i in self.out_indices])