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mmdet.models.necks.pafpn 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import auto_fp16

from ..builder import NECKS
from .fpn import FPN


[文档]@NECKS.register_module() class PAFPN(FPN): """Path Aggregation Network for Instance Segmentation. This is an implementation of the `PAFPN in Path Aggregation Network <https://arxiv.org/abs/1803.01534>`_. Args: in_channels (List[int]): Number of input channels per scale. out_channels (int): Number of output channels (used at each scale) num_outs (int): Number of output scales. start_level (int): Index of the start input backbone level used to build the feature pyramid. Default: 0. end_level (int): Index of the end input backbone level (exclusive) to build the feature pyramid. Default: -1, which means the last level. add_extra_convs (bool | str): If bool, it decides whether to add conv layers on top of the original feature maps. Default to False. If True, it is equivalent to `add_extra_convs='on_input'`. If str, it specifies the source feature map of the extra convs. Only the following options are allowed - 'on_input': Last feat map of neck inputs (i.e. backbone feature). - 'on_lateral': Last feature map after lateral convs. - 'on_output': The last output feature map after fpn convs. relu_before_extra_convs (bool): Whether to apply relu before the extra conv. Default: False. no_norm_on_lateral (bool): Whether to apply norm on lateral. Default: False. conv_cfg (dict): Config dict for convolution layer. Default: None. norm_cfg (dict): Config dict for normalization layer. Default: None. act_cfg (str): Config dict for activation layer in ConvModule. Default: None. init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, in_channels, out_channels, num_outs, start_level=0, end_level=-1, add_extra_convs=False, relu_before_extra_convs=False, no_norm_on_lateral=False, conv_cfg=None, norm_cfg=None, act_cfg=None, init_cfg=dict( type='Xavier', layer='Conv2d', distribution='uniform')): super(PAFPN, self).__init__( in_channels, out_channels, num_outs, start_level, end_level, add_extra_convs, relu_before_extra_convs, no_norm_on_lateral, conv_cfg, norm_cfg, act_cfg, init_cfg=init_cfg) # add extra bottom up pathway self.downsample_convs = nn.ModuleList() self.pafpn_convs = nn.ModuleList() for i in range(self.start_level + 1, self.backbone_end_level): d_conv = ConvModule( out_channels, out_channels, 3, stride=2, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg, inplace=False) pafpn_conv = ConvModule( out_channels, out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg, inplace=False) self.downsample_convs.append(d_conv) self.pafpn_convs.append(pafpn_conv)
[文档] @auto_fp16() def forward(self, inputs): """Forward function.""" assert len(inputs) == len(self.in_channels) # build laterals laterals = [ lateral_conv(inputs[i + self.start_level]) for i, lateral_conv in enumerate(self.lateral_convs) ] # build top-down path used_backbone_levels = len(laterals) for i in range(used_backbone_levels - 1, 0, -1): prev_shape = laterals[i - 1].shape[2:] laterals[i - 1] += F.interpolate( laterals[i], size=prev_shape, mode='nearest') # build outputs # part 1: from original levels inter_outs = [ self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels) ] # part 2: add bottom-up path for i in range(0, used_backbone_levels - 1): inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i]) outs = [] outs.append(inter_outs[0]) outs.extend([ self.pafpn_convs[i - 1](inter_outs[i]) for i in range(1, used_backbone_levels) ]) # part 3: add extra levels if self.num_outs > len(outs): # use max pool to get more levels on top of outputs # (e.g., Faster R-CNN, Mask R-CNN) if not self.add_extra_convs: for i in range(self.num_outs - used_backbone_levels): outs.append(F.max_pool2d(outs[-1], 1, stride=2)) # add conv layers on top of original feature maps (RetinaNet) else: if self.add_extra_convs == 'on_input': orig = inputs[self.backbone_end_level - 1] outs.append(self.fpn_convs[used_backbone_levels](orig)) elif self.add_extra_convs == 'on_lateral': outs.append(self.fpn_convs[used_backbone_levels]( laterals[-1])) elif self.add_extra_convs == 'on_output': outs.append(self.fpn_convs[used_backbone_levels](outs[-1])) else: raise NotImplementedError for i in range(used_backbone_levels + 1, self.num_outs): if self.relu_before_extra_convs: outs.append(self.fpn_convs[i](F.relu(outs[-1]))) else: outs.append(self.fpn_convs[i](outs[-1])) return tuple(outs)
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