352 lines
12 KiB
Python
352 lines
12 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
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kaiming_init)
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from mmcv.runner import load_checkpoint
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmdet.utils import get_root_logger
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from ..builder import BACKBONES
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from .resnet import Bottleneck as _Bottleneck
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from .resnet import ResNet
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class Bottle2neck(_Bottleneck):
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expansion = 4
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def __init__(self,
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inplanes,
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planes,
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scales=4,
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base_width=26,
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base_channels=64,
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stage_type='normal',
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**kwargs):
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"""Bottle2neck block for Res2Net.
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If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
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it is "caffe", the stride-two layer is the first 1x1 conv layer.
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"""
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super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
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assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.'
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width = int(math.floor(self.planes * (base_width / base_channels)))
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self.norm1_name, norm1 = build_norm_layer(
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self.norm_cfg, width * scales, postfix=1)
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self.norm3_name, norm3 = build_norm_layer(
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self.norm_cfg, self.planes * self.expansion, postfix=3)
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self.conv1 = build_conv_layer(
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self.conv_cfg,
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self.inplanes,
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width * scales,
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kernel_size=1,
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stride=self.conv1_stride,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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if stage_type == 'stage' and self.conv2_stride != 1:
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self.pool = nn.AvgPool2d(
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kernel_size=3, stride=self.conv2_stride, padding=1)
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convs = []
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bns = []
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fallback_on_stride = False
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if self.with_dcn:
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fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
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if not self.with_dcn or fallback_on_stride:
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for i in range(scales - 1):
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convs.append(
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build_conv_layer(
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self.conv_cfg,
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width,
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width,
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kernel_size=3,
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stride=self.conv2_stride,
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padding=self.dilation,
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dilation=self.dilation,
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bias=False))
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bns.append(
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build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
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self.convs = nn.ModuleList(convs)
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self.bns = nn.ModuleList(bns)
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else:
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assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
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for i in range(scales - 1):
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convs.append(
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build_conv_layer(
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self.dcn,
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width,
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width,
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kernel_size=3,
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stride=self.conv2_stride,
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padding=self.dilation,
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dilation=self.dilation,
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bias=False))
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bns.append(
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build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
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self.convs = nn.ModuleList(convs)
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self.bns = nn.ModuleList(bns)
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self.conv3 = build_conv_layer(
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self.conv_cfg,
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width * scales,
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self.planes * self.expansion,
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kernel_size=1,
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bias=False)
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self.add_module(self.norm3_name, norm3)
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self.stage_type = stage_type
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self.scales = scales
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self.width = width
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delattr(self, 'conv2')
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delattr(self, self.norm2_name)
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def forward(self, x):
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"""Forward function."""
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def _inner_forward(x):
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identity = x
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.relu(out)
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if self.with_plugins:
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out = self.forward_plugin(out, self.after_conv1_plugin_names)
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spx = torch.split(out, self.width, 1)
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sp = self.convs[0](spx[0].contiguous())
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sp = self.relu(self.bns[0](sp))
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out = sp
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for i in range(1, self.scales - 1):
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if self.stage_type == 'stage':
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sp = spx[i]
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else:
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sp = sp + spx[i]
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sp = self.convs[i](sp.contiguous())
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sp = self.relu(self.bns[i](sp))
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out = torch.cat((out, sp), 1)
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if self.stage_type == 'normal' or self.conv2_stride == 1:
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out = torch.cat((out, spx[self.scales - 1]), 1)
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elif self.stage_type == 'stage':
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out = torch.cat((out, self.pool(spx[self.scales - 1])), 1)
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if self.with_plugins:
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out = self.forward_plugin(out, self.after_conv2_plugin_names)
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out = self.conv3(out)
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out = self.norm3(out)
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if self.with_plugins:
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out = self.forward_plugin(out, self.after_conv3_plugin_names)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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return out
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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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out = self.relu(out)
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return out
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class Res2Layer(nn.Sequential):
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"""Res2Layer to build Res2Net style backbone.
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Args:
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block (nn.Module): block used to build ResLayer.
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inplanes (int): inplanes of block.
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planes (int): planes of block.
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num_blocks (int): number of blocks.
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stride (int): stride of the first block. Default: 1
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avg_down (bool): Use AvgPool instead of stride conv when
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downsampling in the bottle2neck. Default: False
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conv_cfg (dict): dictionary to construct and config conv layer.
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Default: None
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norm_cfg (dict): dictionary to construct and config norm layer.
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Default: dict(type='BN')
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scales (int): Scales used in Res2Net. Default: 4
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base_width (int): Basic width of each scale. Default: 26
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"""
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def __init__(self,
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block,
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inplanes,
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planes,
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num_blocks,
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stride=1,
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avg_down=True,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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scales=4,
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base_width=26,
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**kwargs):
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self.block = block
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downsample = None
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if stride != 1 or inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.AvgPool2d(
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kernel_size=stride,
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stride=stride,
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ceil_mode=True,
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count_include_pad=False),
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build_conv_layer(
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conv_cfg,
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inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=1,
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bias=False),
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build_norm_layer(norm_cfg, planes * block.expansion)[1],
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)
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layers = []
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layers.append(
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block(
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inplanes=inplanes,
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planes=planes,
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stride=stride,
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downsample=downsample,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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scales=scales,
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base_width=base_width,
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stage_type='stage',
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**kwargs))
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inplanes = planes * block.expansion
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for i in range(1, num_blocks):
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layers.append(
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block(
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inplanes=inplanes,
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planes=planes,
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stride=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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scales=scales,
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base_width=base_width,
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**kwargs))
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super(Res2Layer, self).__init__(*layers)
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@BACKBONES.register_module()
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class Res2Net(ResNet):
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"""Res2Net backbone.
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Args:
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scales (int): Scales used in Res2Net. Default: 4
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base_width (int): Basic width of each scale. Default: 26
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depth (int): Depth of res2net, from {50, 101, 152}.
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in_channels (int): Number of input image channels. Default: 3.
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num_stages (int): Res2net stages. Default: 4.
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strides (Sequence[int]): Strides of the first block of each stage.
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dilations (Sequence[int]): Dilation of each stage.
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out_indices (Sequence[int]): Output from which stages.
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style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
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layer is the 3x3 conv layer, otherwise the stride-two layer is
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the first 1x1 conv layer.
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deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
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avg_down (bool): Use AvgPool instead of stride conv when
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downsampling in the bottle2neck.
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frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
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-1 means not freezing any parameters.
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norm_cfg (dict): Dictionary to construct and config norm layer.
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only.
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plugins (list[dict]): List of plugins for stages, each dict contains:
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- cfg (dict, required): Cfg dict to build plugin.
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- position (str, required): Position inside block to insert
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plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
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- stages (tuple[bool], optional): Stages to apply plugin, length
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should be same as 'num_stages'.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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zero_init_residual (bool): Whether to use zero init for last norm layer
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in resblocks to let them behave as identity.
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Example:
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>>> from mmdet.models import Res2Net
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>>> import torch
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>>> self = Res2Net(depth=50, scales=4, base_width=26)
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>>> self.eval()
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>>> inputs = torch.rand(1, 3, 32, 32)
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>>> level_outputs = self.forward(inputs)
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>>> for level_out in level_outputs:
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... print(tuple(level_out.shape))
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(1, 256, 8, 8)
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(1, 512, 4, 4)
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(1, 1024, 2, 2)
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(1, 2048, 1, 1)
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"""
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arch_settings = {
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50: (Bottle2neck, (3, 4, 6, 3)),
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101: (Bottle2neck, (3, 4, 23, 3)),
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152: (Bottle2neck, (3, 8, 36, 3))
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}
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def __init__(self,
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scales=4,
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base_width=26,
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style='pytorch',
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deep_stem=True,
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avg_down=True,
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**kwargs):
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self.scales = scales
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self.base_width = base_width
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super(Res2Net, self).__init__(
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style='pytorch', deep_stem=True, avg_down=True, **kwargs)
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def make_res_layer(self, **kwargs):
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return Res2Layer(
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scales=self.scales,
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base_width=self.base_width,
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base_channels=self.base_channels,
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**kwargs)
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def init_weights(self, pretrained=None):
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"""Initialize the weights in backbone.
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Args:
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pretrained (str, optional): Path to pre-trained weights.
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Defaults to None.
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"""
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if isinstance(pretrained, str):
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logger = get_root_logger()
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load_checkpoint(self, pretrained, strict=False, logger=logger)
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elif pretrained is None:
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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kaiming_init(m)
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elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
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constant_init(m, 1)
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if self.dcn is not None:
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for m in self.modules():
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if isinstance(m, Bottle2neck):
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# dcn in Res2Net bottle2neck is in ModuleList
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for n in m.convs:
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if hasattr(n, 'conv_offset'):
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constant_init(n.conv_offset, 0)
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if self.zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottle2neck):
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constant_init(m.norm3, 0)
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else:
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raise TypeError('pretrained must be a str or None')
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