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# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import copy
import torch
import torch.nn as nn
from collections import OrderedDict
from proard.utils.layers import (
MBConvLayer,
ConvLayer,
IdentityLayer,
set_layer_from_config,
)
from proard.utils.layers import ResNetBottleneckBlock, LinearLayer
from proard.utils import (
MyModule,
val2list,
get_net_device,
build_activation,
make_divisible,
SEModule,
MyNetwork,
)
from .dynamic_op import (
DynamicSeparableConv2d,
DynamicConv2d,
DynamicBatchNorm2d,
DynamicSE,
DynamicGroupNorm,
)
from .dynamic_op import DynamicLinear
__all__ = [
"adjust_bn_according_to_idx",
"copy_bn",
"DynamicMBConvLayer",
"DynamicConvLayer",
"DynamicLinearLayer",
"DynamicResNetBottleneckBlock",
]
def adjust_bn_according_to_idx(bn, idx):
bn.weight.data = torch.index_select(bn.weight.data, 0, idx)
bn.bias.data = torch.index_select(bn.bias.data, 0, idx)
if type(bn) in [nn.BatchNorm1d, nn.BatchNorm2d]:
bn.running_mean.data = torch.index_select(bn.running_mean.data, 0, idx)
bn.running_var.data = torch.index_select(bn.running_var.data, 0, idx)
def copy_bn(target_bn, src_bn):
feature_dim = (
target_bn.num_channels
if isinstance(target_bn, nn.GroupNorm)
else target_bn.num_features
)
target_bn.weight.data.copy_(src_bn.weight.data[:feature_dim])
target_bn.bias.data.copy_(src_bn.bias.data[:feature_dim])
if type(src_bn) in [nn.BatchNorm1d, nn.BatchNorm2d]:
target_bn.running_mean.data.copy_(src_bn.running_mean.data[:feature_dim])
target_bn.running_var.data.copy_(src_bn.running_var.data[:feature_dim])
class DynamicLinearLayer(MyModule):
def __init__(self, in_features_list, out_features, bias=True, dropout_rate=0):
super(DynamicLinearLayer, self).__init__()
self.in_features_list = in_features_list
self.out_features = out_features
self.bias = bias
self.dropout_rate = dropout_rate
if self.dropout_rate > 0:
self.dropout = nn.Dropout(self.dropout_rate, inplace=True)
else:
self.dropout = None
self.linear = DynamicLinear(
max_in_features=max(self.in_features_list),
max_out_features=self.out_features,
bias=self.bias,
)
def forward(self, x):
if self.dropout is not None:
x = self.dropout(x)
return self.linear(x)
@property
def module_str(self):
return "DyLinear(%d, %d)" % (max(self.in_features_list), self.out_features)
@property
def config(self):
return {
"name": DynamicLinear.__name__,
"in_features_list": self.in_features_list,
"out_features": self.out_features,
"bias": self.bias,
"dropout_rate": self.dropout_rate,
}
@staticmethod
def build_from_config(config):
return DynamicLinearLayer(**config)
def get_active_subnet(self, in_features, preserve_weight=True):
sub_layer = LinearLayer(
in_features, self.out_features, self.bias, dropout_rate=self.dropout_rate
)
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
sub_layer.linear.weight.data.copy_(
self.linear.get_active_weight(self.out_features, in_features).data
)
if self.bias:
sub_layer.linear.bias.data.copy_(
self.linear.get_active_bias(self.out_features).data
)
return sub_layer
def get_active_subnet_config(self, in_features):
return {
"name": LinearLayer.__name__,
"in_features": in_features,
"out_features": self.out_features,
"bias": self.bias,
"dropout_rate": self.dropout_rate,
}
class DynamicMBConvLayer(MyModule):
def __init__(
self,
in_channel_list,
out_channel_list,
kernel_size_list=3,
expand_ratio_list=6,
stride=1,
act_func="relu6",
use_se=False,
):
super(DynamicMBConvLayer, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.kernel_size_list = val2list(kernel_size_list)
self.expand_ratio_list = val2list(expand_ratio_list)
self.stride = stride
self.act_func = act_func
self.use_se = use_se
# build modules
max_middle_channel = make_divisible(
round(max(self.in_channel_list) * max(self.expand_ratio_list)),
MyNetwork.CHANNEL_DIVISIBLE,
)
if max(self.expand_ratio_list) == 1:
self.inverted_bottleneck = None
else:
self.inverted_bottleneck = nn.Sequential(
OrderedDict(
[
(
"conv",
DynamicConv2d(
max(self.in_channel_list), max_middle_channel
),
),
("bn", DynamicBatchNorm2d(max_middle_channel)),
("act", build_activation(self.act_func)),
]
)
)
self.depth_conv = nn.Sequential(
OrderedDict(
[
(
"conv",
DynamicSeparableConv2d(
max_middle_channel, self.kernel_size_list, self.stride
),
),
("bn", DynamicBatchNorm2d(max_middle_channel)),
("act", build_activation(self.act_func)),
]
)
)
if self.use_se:
self.depth_conv.add_module("se", DynamicSE(max_middle_channel))
self.point_linear = nn.Sequential(
OrderedDict(
[
(
"conv",
DynamicConv2d(max_middle_channel, max(self.out_channel_list)),
),
("bn", DynamicBatchNorm2d(max(self.out_channel_list))),
]
)
)
self.active_kernel_size = max(self.kernel_size_list)
self.active_expand_ratio = max(self.expand_ratio_list)
self.active_out_channel = max(self.out_channel_list)
def forward(self, x):
in_channel = x.size(1)
if self.inverted_bottleneck is not None:
self.inverted_bottleneck.conv.active_out_channel = make_divisible(
round(in_channel * self.active_expand_ratio),
MyNetwork.CHANNEL_DIVISIBLE,
)
self.depth_conv.conv.active_kernel_size = self.active_kernel_size
self.point_linear.conv.active_out_channel = self.active_out_channel
if self.inverted_bottleneck is not None:
x = self.inverted_bottleneck(x)
x = self.depth_conv(x)
x = self.point_linear(x)
return x
@property
def module_str(self):
if self.use_se:
return "SE(O%d, E%.1f, K%d)" % (
self.active_out_channel,
self.active_expand_ratio,
self.active_kernel_size,
)
else:
return "(O%d, E%.1f, K%d)" % (
self.active_out_channel,
self.active_expand_ratio,
self.active_kernel_size,
)
@property
def config(self):
return {
"name": DynamicMBConvLayer.__name__,
"in_channel_list": self.in_channel_list,
"out_channel_list": self.out_channel_list,
"kernel_size_list": self.kernel_size_list,
"expand_ratio_list": self.expand_ratio_list,
"stride": self.stride,
"act_func": self.act_func,
"use_se": self.use_se,
}
@staticmethod
def build_from_config(config):
return DynamicMBConvLayer(**config)
############################################################################################
@property
def in_channels(self):
return max(self.in_channel_list)
@property
def out_channels(self):
return max(self.out_channel_list)
def active_middle_channel(self, in_channel):
return make_divisible(
round(in_channel * self.active_expand_ratio), MyNetwork.CHANNEL_DIVISIBLE
)
############################################################################################
def get_active_subnet(self, in_channel, preserve_weight=True):
# build the new layer
sub_layer = set_layer_from_config(self.get_active_subnet_config(in_channel))
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
middle_channel = self.active_middle_channel(in_channel)
# copy weight from current layer
if sub_layer.inverted_bottleneck is not None:
sub_layer.inverted_bottleneck.conv.weight.data.copy_(
self.inverted_bottleneck.conv.get_active_filter(
middle_channel, in_channel
).data,
)
copy_bn(sub_layer.inverted_bottleneck.bn, self.inverted_bottleneck.bn.bn)
sub_layer.depth_conv.conv.weight.data.copy_(
self.depth_conv.conv.get_active_filter(
middle_channel, self.active_kernel_size
).data
)
copy_bn(sub_layer.depth_conv.bn, self.depth_conv.bn.bn)
if self.use_se:
se_mid = make_divisible(
middle_channel // SEModule.REDUCTION,
divisor=MyNetwork.CHANNEL_DIVISIBLE,
)
sub_layer.depth_conv.se.fc.reduce.weight.data.copy_(
self.depth_conv.se.get_active_reduce_weight(se_mid, middle_channel).data
)
sub_layer.depth_conv.se.fc.reduce.bias.data.copy_(
self.depth_conv.se.get_active_reduce_bias(se_mid).data
)
sub_layer.depth_conv.se.fc.expand.weight.data.copy_(
self.depth_conv.se.get_active_expand_weight(se_mid, middle_channel).data
)
sub_layer.depth_conv.se.fc.expand.bias.data.copy_(
self.depth_conv.se.get_active_expand_bias(middle_channel).data
)
sub_layer.point_linear.conv.weight.data.copy_(
self.point_linear.conv.get_active_filter(
self.active_out_channel, middle_channel
).data
)
copy_bn(sub_layer.point_linear.bn, self.point_linear.bn.bn)
return sub_layer
def get_active_subnet_config(self, in_channel):
return {
"name": MBConvLayer.__name__,
"in_channels": in_channel,
"out_channels": self.active_out_channel,
"kernel_size": self.active_kernel_size,
"stride": self.stride,
"expand_ratio": self.active_expand_ratio,
"mid_channels": self.active_middle_channel(in_channel),
"act_func": self.act_func,
"use_se": self.use_se,
}
def re_organize_middle_weights(self, expand_ratio_stage=0):
importance = torch.sum(
torch.abs(self.point_linear.conv.conv.weight.data), dim=(0, 2, 3)
)
if isinstance(self.depth_conv.bn, DynamicGroupNorm):
channel_per_group = self.depth_conv.bn.channel_per_group
importance_chunks = torch.split(importance, channel_per_group)
for chunk in importance_chunks:
chunk.data.fill_(torch.mean(chunk))
importance = torch.cat(importance_chunks, dim=0)
if expand_ratio_stage > 0:
sorted_expand_list = copy.deepcopy(self.expand_ratio_list)
sorted_expand_list.sort(reverse=True)
target_width_list = [
make_divisible(
round(max(self.in_channel_list) * expand),
MyNetwork.CHANNEL_DIVISIBLE,
)
for expand in sorted_expand_list
]
right = len(importance)
base = -len(target_width_list) * 1e5
for i in range(expand_ratio_stage + 1):
left = target_width_list[i]
importance[left:right] += base
base += 1e5
right = left
sorted_importance, sorted_idx = torch.sort(importance, dim=0, descending=True)
self.point_linear.conv.conv.weight.data = torch.index_select(
self.point_linear.conv.conv.weight.data, 1, sorted_idx
)
adjust_bn_according_to_idx(self.depth_conv.bn.bn, sorted_idx)
self.depth_conv.conv.conv.weight.data = torch.index_select(
self.depth_conv.conv.conv.weight.data, 0, sorted_idx
)
if self.use_se:
# se expand: output dim 0 reorganize
se_expand = self.depth_conv.se.fc.expand
se_expand.weight.data = torch.index_select(
se_expand.weight.data, 0, sorted_idx
)
se_expand.bias.data = torch.index_select(se_expand.bias.data, 0, sorted_idx)
# se reduce: input dim 1 reorganize
se_reduce = self.depth_conv.se.fc.reduce
se_reduce.weight.data = torch.index_select(
se_reduce.weight.data, 1, sorted_idx
)
# middle weight reorganize
se_importance = torch.sum(torch.abs(se_expand.weight.data), dim=(0, 2, 3))
se_importance, se_idx = torch.sort(se_importance, dim=0, descending=True)
se_expand.weight.data = torch.index_select(se_expand.weight.data, 1, se_idx)
se_reduce.weight.data = torch.index_select(se_reduce.weight.data, 0, se_idx)
se_reduce.bias.data = torch.index_select(se_reduce.bias.data, 0, se_idx)
if self.inverted_bottleneck is not None:
adjust_bn_according_to_idx(self.inverted_bottleneck.bn.bn, sorted_idx)
self.inverted_bottleneck.conv.conv.weight.data = torch.index_select(
self.inverted_bottleneck.conv.conv.weight.data, 0, sorted_idx
)
return None
else:
return sorted_idx
class DynamicConvLayer(MyModule):
def __init__(
self,
in_channel_list,
out_channel_list,
kernel_size=3,
stride=1,
dilation=1,
use_bn=True,
act_func="relu6",
):
super(DynamicConvLayer, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.use_bn = use_bn
self.act_func = act_func
self.conv = DynamicConv2d(
max_in_channels=max(self.in_channel_list),
max_out_channels=max(self.out_channel_list),
kernel_size=self.kernel_size,
stride=self.stride,
dilation=self.dilation,
)
if self.use_bn:
self.bn = DynamicBatchNorm2d(max(self.out_channel_list))
self.act = build_activation(self.act_func)
self.active_out_channel = max(self.out_channel_list)
def forward(self, x):
self.conv.active_out_channel = self.active_out_channel
x = self.conv(x)
if self.use_bn:
x = self.bn(x)
x = self.act(x)
return x
@property
def module_str(self):
return "DyConv(O%d, K%d, S%d)" % (
self.active_out_channel,
self.kernel_size,
self.stride,
)
@property
def config(self):
return {
"name": DynamicConvLayer.__name__,
"in_channel_list": self.in_channel_list,
"out_channel_list": self.out_channel_list,
"kernel_size": self.kernel_size,
"stride": self.stride,
"dilation": self.dilation,
"use_bn": self.use_bn,
"act_func": self.act_func,
}
@staticmethod
def build_from_config(config):
return DynamicConvLayer(**config)
############################################################################################
@property
def in_channels(self):
return max(self.in_channel_list)
@property
def out_channels(self):
return max(self.out_channel_list)
############################################################################################
def get_active_subnet(self, in_channel, preserve_weight=True):
sub_layer = set_layer_from_config(self.get_active_subnet_config(in_channel))
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
sub_layer.conv.weight.data.copy_(
self.conv.get_active_filter(self.active_out_channel, in_channel).data
)
if self.use_bn:
copy_bn(sub_layer.bn, self.bn.bn)
return sub_layer
def get_active_subnet_config(self, in_channel):
return {
"name": ConvLayer.__name__,
"in_channels": in_channel,
"out_channels": self.active_out_channel,
"kernel_size": self.kernel_size,
"stride": self.stride,
"dilation": self.dilation,
"use_bn": self.use_bn,
"act_func": self.act_func,
}
class DynamicResNetBottleneckBlock(MyModule):
def __init__(
self,
in_channel_list,
out_channel_list,
expand_ratio_list=0.25,
kernel_size=3,
stride=1,
act_func="relu",
downsample_mode="avgpool_conv",
):
super(DynamicResNetBottleneckBlock, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.expand_ratio_list = val2list(expand_ratio_list)
self.kernel_size = kernel_size
self.stride = stride
self.act_func = act_func
self.downsample_mode = downsample_mode
# build modules
max_middle_channel = make_divisible(
round(max(self.out_channel_list) * max(self.expand_ratio_list)),
MyNetwork.CHANNEL_DIVISIBLE,
)
self.conv1 = nn.Sequential(
OrderedDict(
[
(
"conv",
DynamicConv2d(max(self.in_channel_list), max_middle_channel),
),
("bn", DynamicBatchNorm2d(max_middle_channel)),
("act", build_activation(self.act_func, inplace=True)),
]
)
)
self.conv2 = nn.Sequential(
OrderedDict(
[
(
"conv",
DynamicConv2d(
max_middle_channel, max_middle_channel, kernel_size, stride
),
),
("bn", DynamicBatchNorm2d(max_middle_channel)),
("act", build_activation(self.act_func, inplace=True)),
]
)
)
self.conv3 = nn.Sequential(
OrderedDict(
[
(
"conv",
DynamicConv2d(max_middle_channel, max(self.out_channel_list)),
),
("bn", DynamicBatchNorm2d(max(self.out_channel_list))),
]
)
)
if self.stride == 1 and self.in_channel_list == self.out_channel_list:
self.downsample = IdentityLayer(
max(self.in_channel_list), max(self.out_channel_list)
)
elif self.downsample_mode == "conv":
self.downsample = nn.Sequential(
OrderedDict(
[
(
"conv",
DynamicConv2d(
max(self.in_channel_list),
max(self.out_channel_list),
stride=stride,
),
),
("bn", DynamicBatchNorm2d(max(self.out_channel_list))),
]
)
)
elif self.downsample_mode == "avgpool_conv":
self.downsample = nn.Sequential(
OrderedDict(
[
(
"avg_pool",
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
padding=0,
ceil_mode=True,
),
),
(
"conv",
DynamicConv2d(
max(self.in_channel_list), max(self.out_channel_list)
),
),
("bn", DynamicBatchNorm2d(max(self.out_channel_list))),
]
)
)
else:
raise NotImplementedError
self.final_act = build_activation(self.act_func, inplace=True)
self.active_expand_ratio = max(self.expand_ratio_list)
self.active_out_channel = max(self.out_channel_list)
def forward(self, x):
feature_dim = self.active_middle_channels
self.conv1.conv.active_out_channel = feature_dim
self.conv2.conv.active_out_channel = feature_dim
self.conv3.conv.active_out_channel = self.active_out_channel
if not isinstance(self.downsample, IdentityLayer):
self.downsample.conv.active_out_channel = self.active_out_channel
residual = self.downsample(x)
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = x + residual
x = self.final_act(x)
return x
@property
def module_str(self):
return "(%s, %s)" % (
"%dx%d_BottleneckConv_in->%d->%d_S%d"
% (
self.kernel_size,
self.kernel_size,
self.active_middle_channels,
self.active_out_channel,
self.stride,
),
"Identity"
if isinstance(self.downsample, IdentityLayer)
else self.downsample_mode,
)
@property
def config(self):
return {
"name": DynamicResNetBottleneckBlock.__name__,
"in_channel_list": self.in_channel_list,
"out_channel_list": self.out_channel_list,
"expand_ratio_list": self.expand_ratio_list,
"kernel_size": self.kernel_size,
"stride": self.stride,
"act_func": self.act_func,
"downsample_mode": self.downsample_mode,
}
@staticmethod
def build_from_config(config):
return DynamicResNetBottleneckBlock(**config)
############################################################################################
@property
def in_channels(self):
return max(self.in_channel_list)
@property
def out_channels(self):
return max(self.out_channel_list)
@property
def active_middle_channels(self):
feature_dim = round(self.active_out_channel * self.active_expand_ratio)
feature_dim = make_divisible(feature_dim, MyNetwork.CHANNEL_DIVISIBLE)
return feature_dim
############################################################################################
def get_active_subnet(self, in_channel, preserve_weight=True):
# build the new layer
sub_layer = set_layer_from_config(self.get_active_subnet_config(in_channel))
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
# copy weight from current layer
sub_layer.conv1.conv.weight.data.copy_(
self.conv1.conv.get_active_filter(
self.active_middle_channels, in_channel
).data
)
copy_bn(sub_layer.conv1.bn, self.conv1.bn.bn)
sub_layer.conv2.conv.weight.data.copy_(
self.conv2.conv.get_active_filter(
self.active_middle_channels, self.active_middle_channels
).data
)
copy_bn(sub_layer.conv2.bn, self.conv2.bn.bn)
sub_layer.conv3.conv.weight.data.copy_(
self.conv3.conv.get_active_filter(
self.active_out_channel, self.active_middle_channels
).data
)
copy_bn(sub_layer.conv3.bn, self.conv3.bn.bn)
if not isinstance(self.downsample, IdentityLayer):
sub_layer.downsample.conv.weight.data.copy_(
self.downsample.conv.get_active_filter(
self.active_out_channel, in_channel
).data
)
copy_bn(sub_layer.downsample.bn, self.downsample.bn.bn)
return sub_layer
def get_active_subnet_config(self, in_channel):
return {
"name": ResNetBottleneckBlock.__name__,
"in_channels": in_channel,
"out_channels": self.active_out_channel,
"kernel_size": self.kernel_size,
"stride": self.stride,
"expand_ratio": self.active_expand_ratio,
"mid_channels": self.active_middle_channels,
"act_func": self.act_func,
"groups": 1,
"downsample_mode": self.downsample_mode,
}
def re_organize_middle_weights(self, expand_ratio_stage=0):
# conv3 -> conv2
importance = torch.sum(
torch.abs(self.conv3.conv.conv.weight.data), dim=(0, 2, 3)
)
if isinstance(self.conv2.bn, DynamicGroupNorm):
channel_per_group = self.conv2.bn.channel_per_group
importance_chunks = torch.split(importance, channel_per_group)
for chunk in importance_chunks:
chunk.data.fill_(torch.mean(chunk))
importance = torch.cat(importance_chunks, dim=0)
if expand_ratio_stage > 0:
sorted_expand_list = copy.deepcopy(self.expand_ratio_list)
sorted_expand_list.sort(reverse=True)
target_width_list = [
make_divisible(
round(max(self.out_channel_list) * expand),
MyNetwork.CHANNEL_DIVISIBLE,
)
for expand in sorted_expand_list
]
right = len(importance)
base = -len(target_width_list) * 1e5
for i in range(expand_ratio_stage + 1):
left = target_width_list[i]
importance[left:right] += base
base += 1e5
right = left
sorted_importance, sorted_idx = torch.sort(importance, dim=0, descending=True)
self.conv3.conv.conv.weight.data = torch.index_select(
self.conv3.conv.conv.weight.data, 1, sorted_idx
)
adjust_bn_according_to_idx(self.conv2.bn.bn, sorted_idx)
self.conv2.conv.conv.weight.data = torch.index_select(
self.conv2.conv.conv.weight.data, 0, sorted_idx
)
# conv2 -> conv1
importance = torch.sum(
torch.abs(self.conv2.conv.conv.weight.data), dim=(0, 2, 3)
)
if isinstance(self.conv1.bn, DynamicGroupNorm):
channel_per_group = self.conv1.bn.channel_per_group
importance_chunks = torch.split(importance, channel_per_group)
for chunk in importance_chunks:
chunk.data.fill_(torch.mean(chunk))
importance = torch.cat(importance_chunks, dim=0)
if expand_ratio_stage > 0:
sorted_expand_list = copy.deepcopy(self.expand_ratio_list)
sorted_expand_list.sort(reverse=True)
target_width_list = [
make_divisible(
round(max(self.out_channel_list) * expand),
MyNetwork.CHANNEL_DIVISIBLE,
)
for expand in sorted_expand_list
]
right = len(importance)
base = -len(target_width_list) * 1e5
for i in range(expand_ratio_stage + 1):
left = target_width_list[i]
importance[left:right] += base
base += 1e5
right = left
sorted_importance, sorted_idx = torch.sort(importance, dim=0, descending=True)
self.conv2.conv.conv.weight.data = torch.index_select(
self.conv2.conv.conv.weight.data, 1, sorted_idx
)
adjust_bn_according_to_idx(self.conv1.bn.bn, sorted_idx)
self.conv1.conv.conv.weight.data = torch.index_select(
self.conv1.conv.conv.weight.data, 0, sorted_idx
)
return None
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