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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, build_activation_layer, constant_init,
normal_init)
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
from .utils import channel_shuffle, load_checkpoint, make_divisible
class ShuffleUnit(nn.Module):
"""ShuffleUnit block.
ShuffleNet unit with pointwise group convolution (GConv) and channel
shuffle.
Args:
in_channels (int): The input channels of the ShuffleUnit.
out_channels (int): The output channels of the ShuffleUnit.
groups (int, optional): The number of groups to be used in grouped 1x1
convolutions in each ShuffleUnit. Default: 3
first_block (bool, optional): Whether it is the first ShuffleUnit of a
sequential ShuffleUnits. Default: True, which means not using the
grouped 1x1 convolution.
combine (str, optional): The ways to combine the input and output
branches. Default: 'add'.
conv_cfg (dict): Config dict for convolution layer. Default: None,
which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU').
with_cp (bool, optional): Use checkpoint or not. Using checkpoint
will save some memory while slowing down the training speed.
Default: False.
Returns:
Tensor: The output tensor.
"""
def __init__(self,
in_channels,
out_channels,
groups=3,
first_block=True,
combine='add',
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
with_cp=False):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
act_cfg = copy.deepcopy(act_cfg)
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.first_block = first_block
self.combine = combine
self.groups = groups
self.bottleneck_channels = self.out_channels // 4
self.with_cp = with_cp
if self.combine == 'add':
self.depthwise_stride = 1
self._combine_func = self._add
assert in_channels == out_channels, (
'in_channels must be equal to out_channels when combine '
'is add')
elif self.combine == 'concat':
self.depthwise_stride = 2
self._combine_func = self._concat
self.out_channels -= self.in_channels
self.avgpool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
else:
raise ValueError(f'Cannot combine tensors with {self.combine}. '
'Only "add" and "concat" are supported')
self.first_1x1_groups = 1 if first_block else self.groups
self.g_conv_1x1_compress = ConvModule(
in_channels=self.in_channels,
out_channels=self.bottleneck_channels,
kernel_size=1,
groups=self.first_1x1_groups,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.depthwise_conv3x3_bn = ConvModule(
in_channels=self.bottleneck_channels,
out_channels=self.bottleneck_channels,
kernel_size=3,
stride=self.depthwise_stride,
padding=1,
groups=self.bottleneck_channels,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
self.g_conv_1x1_expand = ConvModule(
in_channels=self.bottleneck_channels,
out_channels=self.out_channels,
kernel_size=1,
groups=self.groups,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
self.act = build_activation_layer(act_cfg)
@staticmethod
def _add(x, out):
# residual connection
return x + out
@staticmethod
def _concat(x, out):
# concatenate along channel axis
return torch.cat((x, out), 1)
def forward(self, x):
def _inner_forward(x):
residual = x
out = self.g_conv_1x1_compress(x)
out = self.depthwise_conv3x3_bn(out)
if self.groups > 1:
out = channel_shuffle(out, self.groups)
out = self.g_conv_1x1_expand(out)
if self.combine == 'concat':
residual = self.avgpool(residual)
out = self.act(out)
out = self._combine_func(residual, out)
else:
out = self._combine_func(residual, out)
out = self.act(out)
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
return out
@BACKBONES.register_module()
class ShuffleNetV1(BaseBackbone):
"""ShuffleNetV1 backbone.
Args:
groups (int, optional): The number of groups to be used in grouped 1x1
convolutions in each ShuffleUnit. Default: 3.
widen_factor (float, optional): Width multiplier - adjusts the number
of channels in each layer by this amount. Default: 1.0.
out_indices (Sequence[int]): Output from which stages.
Default: (2, )
frozen_stages (int): Stages to be frozen (all param fixed).
Default: -1, which means not freezing any parameters.
conv_cfg (dict): Config dict for convolution layer. Default: None,
which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU').
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.
"""
def __init__(self,
groups=3,
widen_factor=1.0,
out_indices=(2, ),
frozen_stages=-1,
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
norm_eval=False,
with_cp=False):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
act_cfg = copy.deepcopy(act_cfg)
super().__init__()
self.stage_blocks = [4, 8, 4]
self.groups = groups
for index in out_indices:
if index not in range(0, 3):
raise ValueError('the item in out_indices must in '
f'range(0, 3). But received {index}')
if frozen_stages not in range(-1, 3):
raise ValueError('frozen_stages must be in range(-1, 3). '
f'But received {frozen_stages}')
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
if groups == 1:
channels = (144, 288, 576)
elif groups == 2:
channels = (200, 400, 800)
elif groups == 3:
channels = (240, 480, 960)
elif groups == 4:
channels = (272, 544, 1088)
elif groups == 8:
channels = (384, 768, 1536)
else:
raise ValueError(f'{groups} groups is not supported for 1x1 '
'Grouped Convolutions')
channels = [make_divisible(ch * widen_factor, 8) for ch in channels]
self.in_channels = int(24 * widen_factor)
self.conv1 = ConvModule(
in_channels=3,
out_channels=self.in_channels,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layers = nn.ModuleList()
for i, num_blocks in enumerate(self.stage_blocks):
first_block = (i == 0)
layer = self.make_layer(channels[i], num_blocks, first_block)
self.layers.append(layer)
def _freeze_stages(self):
if self.frozen_stages >= 0:
for param in self.conv1.parameters():
param.requires_grad = False
for i in range(self.frozen_stages):
layer = self.layers[i]
layer.eval()
for param in layer.parameters():
param.requires_grad = False
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for name, m in self.named_modules():
if isinstance(m, nn.Conv2d):
if 'conv1' in name:
normal_init(m, mean=0, std=0.01)
else:
normal_init(m, mean=0, std=1.0 / m.weight.shape[1])
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, val=1, bias=0.0001)
if isinstance(m, _BatchNorm):
if m.running_mean is not None:
nn.init.constant_(m.running_mean, 0)
else:
raise TypeError('pretrained must be a str or None. But received '
f'{type(pretrained)}')
def make_layer(self, out_channels, num_blocks, first_block=False):
"""Stack ShuffleUnit blocks to make a layer.
Args:
out_channels (int): out_channels of the block.
num_blocks (int): Number of blocks.
first_block (bool, optional): Whether is the first ShuffleUnit of a
sequential ShuffleUnits. Default: False, which means using
the grouped 1x1 convolution.
"""
layers = []
for i in range(num_blocks):
first_block = first_block if i == 0 else False
combine_mode = 'concat' if i == 0 else 'add'
layers.append(
ShuffleUnit(
self.in_channels,
out_channels,
groups=self.groups,
first_block=first_block,
combine=combine_mode,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
with_cp=self.with_cp))
self.in_channels = out_channels
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.maxpool(x)
outs = []
for i, layer in enumerate(self.layers):
x = layer(x)
if i in self.out_indices:
outs.append(x)
if len(outs) == 1:
return outs[0]
return tuple(outs)
def train(self, mode=True):
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
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