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mmdet.models.necks.yolox_pafpn 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import math

import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import BaseModule

from ..builder import NECKS
from ..utils import CSPLayer


[文档]@NECKS.register_module() class YOLOXPAFPN(BaseModule): """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) num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 3 use_depthwise (bool): Whether to depthwise separable convolution in blocks. Default: False 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, out_channels, num_csp_blocks=3, use_depthwise=False, upsample_cfg=dict(scale_factor=2, mode='nearest'), conv_cfg=None, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='Swish'), init_cfg=dict( type='Kaiming', layer='Conv2d', a=math.sqrt(5), distribution='uniform', mode='fan_in', nonlinearity='leaky_relu')): super(YOLOXPAFPN, self).__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, 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, 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( ConvModule( in_channels[i], out_channels, 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg))
[文档] def forward(self, inputs): """ 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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