Shortcuts

mmdet.models.necks.nasfcos_fpn 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, caffe2_xavier_init
from mmcv.ops.merge_cells import ConcatCell
from mmcv.runner import BaseModule

from ..builder import NECKS


[文档]@NECKS.register_module() class NASFCOS_FPN(BaseModule): """FPN structure in NASFPN. Implementation of paper `NAS-FCOS: Fast Neural Architecture Search for Object Detection <https://arxiv.org/abs/1906.04423>`_ 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): It decides whether to add conv layers on top of the original feature maps. Default to False. If True, its actual mode is specified by `extra_convs_on_inputs`. conv_cfg (dict): dictionary to construct and config conv layer. norm_cfg (dict): dictionary to construct and config norm layer. init_cfg (dict or list[dict], optional): Initialization config dict. Default: None """ def __init__(self, in_channels, out_channels, num_outs, start_level=1, end_level=-1, add_extra_convs=False, conv_cfg=None, norm_cfg=None, init_cfg=None): assert init_cfg is None, 'To prevent abnormal initialization ' \ 'behavior, init_cfg is not allowed to be set' super(NASFCOS_FPN, self).__init__(init_cfg) assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) self.num_outs = num_outs self.norm_cfg = norm_cfg self.conv_cfg = conv_cfg if end_level == -1: self.backbone_end_level = self.num_ins assert num_outs >= self.num_ins - start_level else: self.backbone_end_level = end_level assert end_level <= len(in_channels) assert num_outs == end_level - start_level self.start_level = start_level self.end_level = end_level self.add_extra_convs = add_extra_convs self.adapt_convs = nn.ModuleList() for i in range(self.start_level, self.backbone_end_level): adapt_conv = ConvModule( in_channels[i], out_channels, 1, stride=1, padding=0, bias=False, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU', inplace=False)) self.adapt_convs.append(adapt_conv) # C2 is omitted according to the paper extra_levels = num_outs - self.backbone_end_level + self.start_level def build_concat_cell(with_input1_conv, with_input2_conv): cell_conv_cfg = dict( kernel_size=1, padding=0, bias=False, groups=out_channels) return ConcatCell( in_channels=out_channels, out_channels=out_channels, with_out_conv=True, out_conv_cfg=cell_conv_cfg, out_norm_cfg=dict(type='BN'), out_conv_order=('norm', 'act', 'conv'), with_input1_conv=with_input1_conv, with_input2_conv=with_input2_conv, input_conv_cfg=conv_cfg, input_norm_cfg=norm_cfg, upsample_mode='nearest') # Denote c3=f0, c4=f1, c5=f2 for convince self.fpn = nn.ModuleDict() self.fpn['c22_1'] = build_concat_cell(True, True) self.fpn['c22_2'] = build_concat_cell(True, True) self.fpn['c32'] = build_concat_cell(True, False) self.fpn['c02'] = build_concat_cell(True, False) self.fpn['c42'] = build_concat_cell(True, True) self.fpn['c36'] = build_concat_cell(True, True) self.fpn['c61'] = build_concat_cell(True, True) # f9 self.extra_downsamples = nn.ModuleList() for i in range(extra_levels): extra_act_cfg = None if i == 0 \ else dict(type='ReLU', inplace=False) self.extra_downsamples.append( ConvModule( out_channels, out_channels, 3, stride=2, padding=1, act_cfg=extra_act_cfg, order=('act', 'norm', 'conv')))
[文档] def forward(self, inputs): """Forward function.""" feats = [ adapt_conv(inputs[i + self.start_level]) for i, adapt_conv in enumerate(self.adapt_convs) ] for (i, module_name) in enumerate(self.fpn): idx_1, idx_2 = int(module_name[1]), int(module_name[2]) res = self.fpn[module_name](feats[idx_1], feats[idx_2]) feats.append(res) ret = [] for (idx, input_idx) in zip([9, 8, 7], [1, 2, 3]): # add P3, P4, P5 feats1, feats2 = feats[idx], feats[5] feats2_resize = F.interpolate( feats2, size=feats1.size()[2:], mode='bilinear', align_corners=False) feats_sum = feats1 + feats2_resize ret.append( F.interpolate( feats_sum, size=inputs[input_idx].size()[2:], mode='bilinear', align_corners=False)) for submodule in self.extra_downsamples: ret.append(submodule(ret[-1])) return tuple(ret)
[文档] def init_weights(self): """Initialize the weights of module.""" super(NASFCOS_FPN, self).init_weights() for module in self.fpn.values(): if hasattr(module, 'conv_out'): caffe2_xavier_init(module.out_conv.conv) for modules in [ self.adapt_convs.modules(), self.extra_downsamples.modules() ]: for module in modules: if isinstance(module, nn.Conv2d): caffe2_xavier_init(module)
Read the Docs v: latest
Versions
latest
stable
v2.19.1
v2.19.0
v2.18.1
v2.18.0
v2.17.0
v2.16.0
v2.15.1
v2.15.0
v2.14.0
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.