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import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
"""Bottleneck block for ResNeXt.
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.
"""
def __init__(self,
inplanes,
planes,
groups=1,
base_width=4,
base_channels=64,
**kwargs):
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
else:
width = math.floor(self.planes *
(base_width / base_channels)) * groups
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, width, postfix=1)
self.norm2_name, norm2 = build_norm_layer(
self.norm_cfg, width, postfix=2)
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,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
self.with_modulated_dcn = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
self.conv_cfg,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
self.conv2 = build_conv_layer(
self.dcn,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
self.conv_cfg,
width,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
@BACKBONES.register_module()
class ResNeXt(ResNet):
"""ResNeXt backbone.
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
in_channels (int): Number of input image channels. Normally 3.
num_stages (int): Resnet stages, normally 4.
groups (int): Group of resnext.
base_width (int): Base width of resnext.
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.
frozen_stages (int): Stages to be frozen (all param fixed). -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.
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.
Example:
>>> from mmseg.models import ResNeXt
>>> import torch
>>> self = ResNeXt(depth=50)
>>> 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: (Bottleneck, (3, 4, 6, 3)),
101: (Bottleneck, (3, 4, 23, 3)),
152: (Bottleneck, (3, 8, 36, 3))
}
def __init__(self, groups=1, base_width=4, **kwargs):
self.groups = groups
self.base_width = base_width
super(ResNeXt, self).__init__(**kwargs)
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer``"""
return ResLayer(
groups=self.groups,
base_width=self.base_width,
base_channels=self.base_channels,
**kwargs)
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