712 lines
24 KiB
Python
712 lines
24 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.utils.checkpoint as cp
|
|
from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,
|
|
build_norm_layer)
|
|
from mmcv.cnn.bricks import DropPath
|
|
from mmengine.model import BaseModule
|
|
from mmengine.model.weight_init import constant_init
|
|
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
|
|
|
|
from mmcls.registry import MODELS
|
|
from .base_backbone import BaseBackbone
|
|
|
|
eps = 1.0e-5
|
|
|
|
|
|
class BasicBlock(BaseModule):
|
|
"""BasicBlock for ResNet.
|
|
|
|
Args:
|
|
in_channels (int): Input channels of this block.
|
|
out_channels (int): Output channels of this block.
|
|
expansion (int): The ratio of ``out_channels/mid_channels`` where
|
|
``mid_channels`` is the output channels of conv1. This is a
|
|
reserved argument in BasicBlock and should always be 1. Default: 1.
|
|
stride (int): stride of the block. Default: 1
|
|
dilation (int): dilation of convolution. Default: 1
|
|
downsample (nn.Module, optional): downsample operation on identity
|
|
branch. Default: None.
|
|
style (str): `pytorch` or `caffe`. It is unused and reserved for
|
|
unified API with Bottleneck.
|
|
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
|
|
memory while slowing down the training speed.
|
|
conv_cfg (dict, optional): dictionary to construct and config conv
|
|
layer. Default: None
|
|
norm_cfg (dict): dictionary to construct and config norm layer.
|
|
Default: dict(type='BN')
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
expansion=1,
|
|
stride=1,
|
|
dilation=1,
|
|
downsample=None,
|
|
style='pytorch',
|
|
with_cp=False,
|
|
conv_cfg=None,
|
|
norm_cfg=dict(type='BN'),
|
|
drop_path_rate=0.0,
|
|
act_cfg=dict(type='ReLU', inplace=True),
|
|
init_cfg=None):
|
|
super(BasicBlock, self).__init__(init_cfg=init_cfg)
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.expansion = expansion
|
|
assert self.expansion == 1
|
|
assert out_channels % expansion == 0
|
|
self.mid_channels = out_channels // expansion
|
|
self.stride = stride
|
|
self.dilation = dilation
|
|
self.style = style
|
|
self.with_cp = with_cp
|
|
self.conv_cfg = conv_cfg
|
|
self.norm_cfg = norm_cfg
|
|
|
|
self.norm1_name, norm1 = build_norm_layer(
|
|
norm_cfg, self.mid_channels, postfix=1)
|
|
self.norm2_name, norm2 = build_norm_layer(
|
|
norm_cfg, out_channels, postfix=2)
|
|
|
|
self.conv1 = build_conv_layer(
|
|
conv_cfg,
|
|
in_channels,
|
|
self.mid_channels,
|
|
3,
|
|
stride=stride,
|
|
padding=dilation,
|
|
dilation=dilation,
|
|
bias=False)
|
|
self.add_module(self.norm1_name, norm1)
|
|
self.conv2 = build_conv_layer(
|
|
conv_cfg,
|
|
self.mid_channels,
|
|
out_channels,
|
|
3,
|
|
padding=1,
|
|
bias=False)
|
|
self.add_module(self.norm2_name, norm2)
|
|
|
|
self.relu = build_activation_layer(act_cfg)
|
|
self.downsample = downsample
|
|
self.drop_path = DropPath(drop_prob=drop_path_rate
|
|
) if drop_path_rate > eps else nn.Identity()
|
|
|
|
@property
|
|
def norm1(self):
|
|
return getattr(self, self.norm1_name)
|
|
|
|
@property
|
|
def norm2(self):
|
|
return getattr(self, self.norm2_name)
|
|
|
|
def forward(self, x):
|
|
|
|
def _inner_forward(x):
|
|
identity = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.norm1(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv2(out)
|
|
out = self.norm2(out)
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out = self.drop_path(out)
|
|
|
|
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 Bottleneck(BaseModule):
|
|
"""Bottleneck block for ResNet.
|
|
|
|
Args:
|
|
in_channels (int): Input channels of this block.
|
|
out_channels (int): Output channels of this block.
|
|
expansion (int): The ratio of ``out_channels/mid_channels`` where
|
|
``mid_channels`` is the input/output channels of conv2. Default: 4.
|
|
stride (int): stride of the block. Default: 1
|
|
dilation (int): dilation of convolution. Default: 1
|
|
downsample (nn.Module, optional): downsample operation on identity
|
|
branch. Default: None.
|
|
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. Default: "pytorch".
|
|
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
|
|
memory while slowing down the training speed.
|
|
conv_cfg (dict, optional): dictionary to construct and config conv
|
|
layer. Default: None
|
|
norm_cfg (dict): dictionary to construct and config norm layer.
|
|
Default: dict(type='BN')
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
expansion=4,
|
|
stride=1,
|
|
dilation=1,
|
|
downsample=None,
|
|
style='pytorch',
|
|
with_cp=False,
|
|
conv_cfg=None,
|
|
norm_cfg=dict(type='BN'),
|
|
act_cfg=dict(type='ReLU', inplace=True),
|
|
drop_path_rate=0.0,
|
|
init_cfg=None):
|
|
super(Bottleneck, self).__init__(init_cfg=init_cfg)
|
|
assert style in ['pytorch', 'caffe']
|
|
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.expansion = expansion
|
|
assert out_channels % expansion == 0
|
|
self.mid_channels = out_channels // expansion
|
|
self.stride = stride
|
|
self.dilation = dilation
|
|
self.style = style
|
|
self.with_cp = with_cp
|
|
self.conv_cfg = conv_cfg
|
|
self.norm_cfg = norm_cfg
|
|
|
|
if self.style == 'pytorch':
|
|
self.conv1_stride = 1
|
|
self.conv2_stride = stride
|
|
else:
|
|
self.conv1_stride = stride
|
|
self.conv2_stride = 1
|
|
|
|
self.norm1_name, norm1 = build_norm_layer(
|
|
norm_cfg, self.mid_channels, postfix=1)
|
|
self.norm2_name, norm2 = build_norm_layer(
|
|
norm_cfg, self.mid_channels, postfix=2)
|
|
self.norm3_name, norm3 = build_norm_layer(
|
|
norm_cfg, out_channels, postfix=3)
|
|
|
|
self.conv1 = build_conv_layer(
|
|
conv_cfg,
|
|
in_channels,
|
|
self.mid_channels,
|
|
kernel_size=1,
|
|
stride=self.conv1_stride,
|
|
bias=False)
|
|
self.add_module(self.norm1_name, norm1)
|
|
self.conv2 = build_conv_layer(
|
|
conv_cfg,
|
|
self.mid_channels,
|
|
self.mid_channels,
|
|
kernel_size=3,
|
|
stride=self.conv2_stride,
|
|
padding=dilation,
|
|
dilation=dilation,
|
|
bias=False)
|
|
|
|
self.add_module(self.norm2_name, norm2)
|
|
self.conv3 = build_conv_layer(
|
|
conv_cfg,
|
|
self.mid_channels,
|
|
out_channels,
|
|
kernel_size=1,
|
|
bias=False)
|
|
self.add_module(self.norm3_name, norm3)
|
|
|
|
self.relu = build_activation_layer(act_cfg)
|
|
self.downsample = downsample
|
|
self.drop_path = DropPath(drop_prob=drop_path_rate
|
|
) if drop_path_rate > eps else nn.Identity()
|
|
|
|
@property
|
|
def norm1(self):
|
|
return getattr(self, self.norm1_name)
|
|
|
|
@property
|
|
def norm2(self):
|
|
return getattr(self, self.norm2_name)
|
|
|
|
@property
|
|
def norm3(self):
|
|
return getattr(self, self.norm3_name)
|
|
|
|
def forward(self, x):
|
|
|
|
def _inner_forward(x):
|
|
identity = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.norm1(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv2(out)
|
|
out = self.norm2(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv3(out)
|
|
out = self.norm3(out)
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out = self.drop_path(out)
|
|
|
|
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
|
|
|
|
|
|
def get_expansion(block, expansion=None):
|
|
"""Get the expansion of a residual block.
|
|
|
|
The block expansion will be obtained by the following order:
|
|
|
|
1. If ``expansion`` is given, just return it.
|
|
2. If ``block`` has the attribute ``expansion``, then return
|
|
``block.expansion``.
|
|
3. Return the default value according the the block type:
|
|
1 for ``BasicBlock`` and 4 for ``Bottleneck``.
|
|
|
|
Args:
|
|
block (class): The block class.
|
|
expansion (int | None): The given expansion ratio.
|
|
|
|
Returns:
|
|
int: The expansion of the block.
|
|
"""
|
|
if isinstance(expansion, int):
|
|
assert expansion > 0
|
|
elif expansion is None:
|
|
if hasattr(block, 'expansion'):
|
|
expansion = block.expansion
|
|
elif issubclass(block, BasicBlock):
|
|
expansion = 1
|
|
elif issubclass(block, Bottleneck):
|
|
expansion = 4
|
|
else:
|
|
raise TypeError(f'expansion is not specified for {block.__name__}')
|
|
else:
|
|
raise TypeError('expansion must be an integer or None')
|
|
|
|
return expansion
|
|
|
|
|
|
class ResLayer(nn.Sequential):
|
|
"""ResLayer to build ResNet style backbone.
|
|
|
|
Args:
|
|
block (nn.Module): Residual block used to build ResLayer.
|
|
num_blocks (int): Number of blocks.
|
|
in_channels (int): Input channels of this block.
|
|
out_channels (int): Output channels of this block.
|
|
expansion (int, optional): The expansion for BasicBlock/Bottleneck.
|
|
If not specified, it will firstly be obtained via
|
|
``block.expansion``. If the block has no attribute "expansion",
|
|
the following default values will be used: 1 for BasicBlock and
|
|
4 for Bottleneck. Default: None.
|
|
stride (int): stride of the first block. Default: 1.
|
|
avg_down (bool): Use AvgPool instead of stride conv when
|
|
downsampling in the bottleneck. Default: False
|
|
conv_cfg (dict, optional): dictionary to construct and config conv
|
|
layer. Default: None
|
|
norm_cfg (dict): dictionary to construct and config norm layer.
|
|
Default: dict(type='BN')
|
|
drop_path_rate (float or list): stochastic depth rate.
|
|
Default: 0.
|
|
"""
|
|
|
|
def __init__(self,
|
|
block,
|
|
num_blocks,
|
|
in_channels,
|
|
out_channels,
|
|
expansion=None,
|
|
stride=1,
|
|
avg_down=False,
|
|
conv_cfg=None,
|
|
norm_cfg=dict(type='BN'),
|
|
drop_path_rate=0.0,
|
|
**kwargs):
|
|
self.block = block
|
|
self.expansion = get_expansion(block, expansion)
|
|
|
|
if isinstance(drop_path_rate, float):
|
|
drop_path_rate = [drop_path_rate] * num_blocks
|
|
|
|
assert len(drop_path_rate
|
|
) == num_blocks, 'Please check the length of drop_path_rate'
|
|
|
|
downsample = None
|
|
if stride != 1 or in_channels != out_channels:
|
|
downsample = []
|
|
conv_stride = stride
|
|
if avg_down and stride != 1:
|
|
conv_stride = 1
|
|
downsample.append(
|
|
nn.AvgPool2d(
|
|
kernel_size=stride,
|
|
stride=stride,
|
|
ceil_mode=True,
|
|
count_include_pad=False))
|
|
downsample.extend([
|
|
build_conv_layer(
|
|
conv_cfg,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size=1,
|
|
stride=conv_stride,
|
|
bias=False),
|
|
build_norm_layer(norm_cfg, out_channels)[1]
|
|
])
|
|
downsample = nn.Sequential(*downsample)
|
|
|
|
layers = []
|
|
layers.append(
|
|
block(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
expansion=self.expansion,
|
|
stride=stride,
|
|
downsample=downsample,
|
|
conv_cfg=conv_cfg,
|
|
norm_cfg=norm_cfg,
|
|
drop_path_rate=drop_path_rate[0],
|
|
**kwargs))
|
|
in_channels = out_channels
|
|
for i in range(1, num_blocks):
|
|
layers.append(
|
|
block(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
expansion=self.expansion,
|
|
stride=1,
|
|
conv_cfg=conv_cfg,
|
|
norm_cfg=norm_cfg,
|
|
drop_path_rate=drop_path_rate[i],
|
|
**kwargs))
|
|
super(ResLayer, self).__init__(*layers)
|
|
|
|
|
|
@MODELS.register_module()
|
|
class ResNet(BaseBackbone):
|
|
"""ResNet backbone.
|
|
|
|
Please refer to the `paper <https://arxiv.org/abs/1512.03385>`__ for
|
|
details.
|
|
|
|
Args:
|
|
depth (int): Network depth, from {18, 34, 50, 101, 152}.
|
|
in_channels (int): Number of input image channels. Default: 3.
|
|
stem_channels (int): Output channels of the stem layer. Default: 64.
|
|
base_channels (int): Middle channels of the first stage. Default: 64.
|
|
num_stages (int): Stages of the network. Default: 4.
|
|
strides (Sequence[int]): Strides of the first block of each stage.
|
|
Default: ``(1, 2, 2, 2)``.
|
|
dilations (Sequence[int]): Dilation of each stage.
|
|
Default: ``(1, 1, 1, 1)``.
|
|
out_indices (Sequence[int]): Output from which stages.
|
|
Default: ``(3, )``.
|
|
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.
|
|
Default: False.
|
|
avg_down (bool): Use AvgPool instead of stride conv when
|
|
downsampling in the bottleneck. Default: False.
|
|
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
|
-1 means not freezing any parameters. Default: -1.
|
|
conv_cfg (dict | None): The config dict for conv layers. Default: None.
|
|
norm_cfg (dict): The config dict for norm layers.
|
|
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. Default: False.
|
|
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
|
|
memory while slowing down the training speed. Default: False.
|
|
zero_init_residual (bool): Whether to use zero init for last norm layer
|
|
in resblocks to let them behave as identity. Default: True.
|
|
|
|
Example:
|
|
>>> from mmcls.models import ResNet
|
|
>>> import torch
|
|
>>> self = ResNet(depth=18)
|
|
>>> 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, 64, 8, 8)
|
|
(1, 128, 4, 4)
|
|
(1, 256, 2, 2)
|
|
(1, 512, 1, 1)
|
|
"""
|
|
|
|
arch_settings = {
|
|
18: (BasicBlock, (2, 2, 2, 2)),
|
|
34: (BasicBlock, (3, 4, 6, 3)),
|
|
50: (Bottleneck, (3, 4, 6, 3)),
|
|
101: (Bottleneck, (3, 4, 23, 3)),
|
|
152: (Bottleneck, (3, 8, 36, 3))
|
|
}
|
|
|
|
def __init__(self,
|
|
depth,
|
|
in_channels=3,
|
|
stem_channels=64,
|
|
base_channels=64,
|
|
expansion=None,
|
|
num_stages=4,
|
|
strides=(1, 2, 2, 2),
|
|
dilations=(1, 1, 1, 1),
|
|
out_indices=(3, ),
|
|
style='pytorch',
|
|
deep_stem=False,
|
|
avg_down=False,
|
|
frozen_stages=-1,
|
|
conv_cfg=None,
|
|
norm_cfg=dict(type='BN', requires_grad=True),
|
|
norm_eval=False,
|
|
with_cp=False,
|
|
zero_init_residual=True,
|
|
init_cfg=[
|
|
dict(type='Kaiming', layer=['Conv2d']),
|
|
dict(
|
|
type='Constant',
|
|
val=1,
|
|
layer=['_BatchNorm', 'GroupNorm'])
|
|
],
|
|
drop_path_rate=0.0):
|
|
super(ResNet, self).__init__(init_cfg)
|
|
if depth not in self.arch_settings:
|
|
raise KeyError(f'invalid depth {depth} for resnet')
|
|
self.depth = depth
|
|
self.stem_channels = stem_channels
|
|
self.base_channels = base_channels
|
|
self.num_stages = num_stages
|
|
assert num_stages >= 1 and num_stages <= 4
|
|
self.strides = strides
|
|
self.dilations = dilations
|
|
assert len(strides) == len(dilations) == num_stages
|
|
self.out_indices = out_indices
|
|
assert max(out_indices) < num_stages
|
|
self.style = style
|
|
self.deep_stem = deep_stem
|
|
self.avg_down = avg_down
|
|
self.frozen_stages = frozen_stages
|
|
self.conv_cfg = conv_cfg
|
|
self.norm_cfg = norm_cfg
|
|
self.with_cp = with_cp
|
|
self.norm_eval = norm_eval
|
|
self.zero_init_residual = zero_init_residual
|
|
self.block, stage_blocks = self.arch_settings[depth]
|
|
self.stage_blocks = stage_blocks[:num_stages]
|
|
self.expansion = get_expansion(self.block, expansion)
|
|
|
|
self._make_stem_layer(in_channels, stem_channels)
|
|
|
|
self.res_layers = []
|
|
_in_channels = stem_channels
|
|
_out_channels = base_channels * self.expansion
|
|
|
|
# stochastic depth decay rule
|
|
total_depth = sum(stage_blocks)
|
|
dpr = [
|
|
x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
|
|
]
|
|
# net_num_blocks = sum(stage_blocks)
|
|
# dpr = np.linspace(0, drop_path_rate, net_num_blocks)
|
|
# block_id = 0
|
|
|
|
for i, num_blocks in enumerate(self.stage_blocks):
|
|
stride = strides[i]
|
|
dilation = dilations[i]
|
|
res_layer = self.make_res_layer(
|
|
block=self.block,
|
|
num_blocks=num_blocks,
|
|
in_channels=_in_channels,
|
|
out_channels=_out_channels,
|
|
expansion=self.expansion,
|
|
stride=stride,
|
|
dilation=dilation,
|
|
style=self.style,
|
|
avg_down=self.avg_down,
|
|
with_cp=with_cp,
|
|
conv_cfg=conv_cfg,
|
|
norm_cfg=norm_cfg,
|
|
drop_path_rate=dpr[:num_blocks])
|
|
_in_channels = _out_channels
|
|
_out_channels *= 2
|
|
dpr = dpr[num_blocks:]
|
|
layer_name = f'layer{i + 1}'
|
|
self.add_module(layer_name, res_layer)
|
|
self.res_layers.append(layer_name)
|
|
|
|
self._freeze_stages()
|
|
|
|
self.feat_dim = res_layer[-1].out_channels
|
|
|
|
def make_res_layer(self, **kwargs):
|
|
return ResLayer(**kwargs)
|
|
|
|
@property
|
|
def norm1(self):
|
|
return getattr(self, self.norm1_name)
|
|
|
|
def _make_stem_layer(self, in_channels, stem_channels):
|
|
if self.deep_stem:
|
|
self.stem = nn.Sequential(
|
|
ConvModule(
|
|
in_channels,
|
|
stem_channels // 2,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
conv_cfg=self.conv_cfg,
|
|
norm_cfg=self.norm_cfg,
|
|
inplace=True),
|
|
ConvModule(
|
|
stem_channels // 2,
|
|
stem_channels // 2,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
conv_cfg=self.conv_cfg,
|
|
norm_cfg=self.norm_cfg,
|
|
inplace=True),
|
|
ConvModule(
|
|
stem_channels // 2,
|
|
stem_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
conv_cfg=self.conv_cfg,
|
|
norm_cfg=self.norm_cfg,
|
|
inplace=True))
|
|
else:
|
|
self.conv1 = build_conv_layer(
|
|
self.conv_cfg,
|
|
in_channels,
|
|
stem_channels,
|
|
kernel_size=7,
|
|
stride=2,
|
|
padding=3,
|
|
bias=False)
|
|
self.norm1_name, norm1 = build_norm_layer(
|
|
self.norm_cfg, stem_channels, postfix=1)
|
|
self.add_module(self.norm1_name, norm1)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
|
|
def _freeze_stages(self):
|
|
if self.frozen_stages >= 0:
|
|
if self.deep_stem:
|
|
self.stem.eval()
|
|
for param in self.stem.parameters():
|
|
param.requires_grad = False
|
|
else:
|
|
self.norm1.eval()
|
|
for m in [self.conv1, self.norm1]:
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
for i in range(1, self.frozen_stages + 1):
|
|
m = getattr(self, f'layer{i}')
|
|
m.eval()
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
def init_weights(self):
|
|
super(ResNet, self).init_weights()
|
|
|
|
if (isinstance(self.init_cfg, dict)
|
|
and self.init_cfg['type'] == 'Pretrained'):
|
|
# Suppress zero_init_residual if use pretrained model.
|
|
return
|
|
|
|
if self.zero_init_residual:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck):
|
|
constant_init(m.norm3, 0)
|
|
elif isinstance(m, BasicBlock):
|
|
constant_init(m.norm2, 0)
|
|
|
|
def forward(self, x):
|
|
if self.deep_stem:
|
|
x = self.stem(x)
|
|
else:
|
|
x = self.conv1(x)
|
|
x = self.norm1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)
|
|
outs = []
|
|
for i, layer_name in enumerate(self.res_layers):
|
|
res_layer = getattr(self, layer_name)
|
|
x = res_layer(x)
|
|
if i in self.out_indices:
|
|
outs.append(x)
|
|
return tuple(outs)
|
|
|
|
def train(self, mode=True):
|
|
super(ResNet, self).train(mode)
|
|
self._freeze_stages()
|
|
if mode and self.norm_eval:
|
|
for m in self.modules():
|
|
# trick: eval have effect on BatchNorm only
|
|
if isinstance(m, _BatchNorm):
|
|
m.eval()
|
|
|
|
|
|
@MODELS.register_module()
|
|
class ResNetV1c(ResNet):
|
|
"""ResNetV1c backbone.
|
|
|
|
This variant is described in `Bag of Tricks.
|
|
<https://arxiv.org/pdf/1812.01187.pdf>`_.
|
|
|
|
Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv
|
|
in the input stem with three 3x3 convs.
|
|
"""
|
|
|
|
def __init__(self, **kwargs):
|
|
super(ResNetV1c, self).__init__(
|
|
deep_stem=True, avg_down=False, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
class ResNetV1d(ResNet):
|
|
"""ResNetV1d backbone.
|
|
|
|
This variant is described in `Bag of Tricks.
|
|
<https://arxiv.org/pdf/1812.01187.pdf>`_.
|
|
|
|
Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in
|
|
the input stem with three 3x3 convs. And in the downsampling block, a 2x2
|
|
avg_pool with stride 2 is added before conv, whose stride is changed to 1.
|
|
"""
|
|
|
|
def __init__(self, **kwargs):
|
|
super(ResNetV1d, self).__init__(
|
|
deep_stem=True, avg_down=True, **kwargs)
|