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