223 lines
7.4 KiB
Python
223 lines
7.4 KiB
Python
import math
|
|
|
|
import torch.nn as nn
|
|
|
|
from ..registry import BACKBONES
|
|
from ..utils import build_conv_layer, build_norm_layer
|
|
from .resnet import Bottleneck as _Bottleneck
|
|
from .resnet import ResNet
|
|
|
|
|
|
class Bottleneck(_Bottleneck):
|
|
|
|
def __init__(self, inplanes, planes, groups=1, base_width=4, **kwargs):
|
|
"""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.
|
|
"""
|
|
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
|
|
|
|
if groups == 1:
|
|
width = self.planes
|
|
else:
|
|
width = math.floor(self.planes * (base_width / 64)) * 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)
|
|
|
|
|
|
def make_res_layer(block,
|
|
inplanes,
|
|
planes,
|
|
blocks,
|
|
stride=1,
|
|
dilation=1,
|
|
groups=1,
|
|
base_width=4,
|
|
style='pytorch',
|
|
with_cp=False,
|
|
conv_cfg=None,
|
|
norm_cfg=dict(type='BN'),
|
|
dcn=None,
|
|
gcb=None):
|
|
downsample = None
|
|
if stride != 1 or inplanes != planes * block.expansion:
|
|
downsample = nn.Sequential(
|
|
build_conv_layer(
|
|
conv_cfg,
|
|
inplanes,
|
|
planes * block.expansion,
|
|
kernel_size=1,
|
|
stride=stride,
|
|
bias=False),
|
|
build_norm_layer(norm_cfg, planes * block.expansion)[1],
|
|
)
|
|
|
|
layers = []
|
|
layers.append(
|
|
block(
|
|
inplanes=inplanes,
|
|
planes=planes,
|
|
stride=stride,
|
|
dilation=dilation,
|
|
downsample=downsample,
|
|
groups=groups,
|
|
base_width=base_width,
|
|
style=style,
|
|
with_cp=with_cp,
|
|
conv_cfg=conv_cfg,
|
|
norm_cfg=norm_cfg,
|
|
dcn=dcn,
|
|
gcb=gcb))
|
|
inplanes = planes * block.expansion
|
|
for i in range(1, blocks):
|
|
layers.append(
|
|
block(
|
|
inplanes=inplanes,
|
|
planes=planes,
|
|
stride=1,
|
|
dilation=dilation,
|
|
groups=groups,
|
|
base_width=base_width,
|
|
style=style,
|
|
with_cp=with_cp,
|
|
conv_cfg=conv_cfg,
|
|
norm_cfg=norm_cfg,
|
|
dcn=dcn,
|
|
gcb=gcb))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
@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 openselfsup.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):
|
|
super(ResNeXt, self).__init__(**kwargs)
|
|
self.groups = groups
|
|
self.base_width = base_width
|
|
|
|
self.inplanes = 64
|
|
self.res_layers = []
|
|
for i, num_blocks in enumerate(self.stage_blocks):
|
|
stride = self.strides[i]
|
|
dilation = self.dilations[i]
|
|
dcn = self.dcn if self.stage_with_dcn[i] else None
|
|
gcb = self.gcb if self.stage_with_gcb[i] else None
|
|
planes = 64 * 2**i
|
|
res_layer = make_res_layer(
|
|
self.block,
|
|
self.inplanes,
|
|
planes,
|
|
num_blocks,
|
|
stride=stride,
|
|
dilation=dilation,
|
|
groups=self.groups,
|
|
base_width=self.base_width,
|
|
style=self.style,
|
|
with_cp=self.with_cp,
|
|
conv_cfg=self.conv_cfg,
|
|
norm_cfg=self.norm_cfg,
|
|
dcn=dcn,
|
|
gcb=gcb)
|
|
self.inplanes = planes * self.block.expansion
|
|
layer_name = 'layer{}'.format(i + 1)
|
|
self.add_module(layer_name, res_layer)
|
|
self.res_layers.append(layer_name)
|
|
|
|
self._freeze_stages()
|