def set_active_subnet(self, d=None, e=None, w=None, **kwargs):
depth = val2list(d, len(ResNets.BASE_DEPTH_LIST) + 1)
expand_ratio = val2list(e, len(self.blocks))
width_mult = val2list(w, len(ResNets.BASE_DEPTH_LIST) + 2)
for block, e in zip(self.blocks, expand_ratio):
if e is not None:
block.active_expand_ratio = e
if width_mult[0] is not None:
self.input_stem[1].conv.active_out_channel = self.input_stem[0].active_out_channel = \
self.input_stem[0].out_channel_list[width_mult[0]]
if width_mult[1] is not None:
self.input_stem[2].active_out_channel = self.input_stem[
2].out_channel_list[width_mult[1]]
if depth[0] is not None:
self.input_stem_skipping = (depth[0] != max(self.depth_list))
for stage_id, (block_idx, d, w) in enumerate(
zip(self.grouped_block_index, depth[1:], width_mult[2:])):
if d is not None:
self.runtime_depth[stage_id] = max(self.depth_list) - d
if w is not None:
for idx in block_idx:
self.blocks[idx].active_out_channel = self.blocks[
idx].out_channel_list[w]
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 set_active_subnet(self, ks=None, e=None, d=None, **kwargs):
ks = val2list(ks, len(self.blocks) - 1)
expand_ratio = val2list(e, len(self.blocks) - 1)
depth = val2list(d, len(self.block_group_info))
for block, k, e in zip(self.blocks[1:], ks, expand_ratio):
if k is not None:
block.conv.active_kernel_size = k
if e is not None:
block.conv.active_expand_ratio = e
for i, d in enumerate(depth):
if d is not None:
self.runtime_depth[i] = min(len(self.block_group_info[i]), d)
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 __init__(self,
n_classes=1000,
bn_param=(0.1, 1e-3),
dropout_rate=0.1,
base_stage_width=None,
width_mult=1.0,
ks_list=3,
expand_ratio_list=6,
depth_list=4):
self.width_mult = width_mult
self.ks_list = val2list(ks_list, 1)
self.expand_ratio_list = val2list(expand_ratio_list, 1)
self.depth_list = val2list(depth_list, 1)
self.ks_list.sort()
self.expand_ratio_list.sort()
self.depth_list.sort()
if base_stage_width == 'google':
# MobileNetV2 Stage Width
base_stage_width = [32, 16, 24, 32, 64, 96, 160, 320, 1280]
else:
# ProxylessNAS Stage Width
base_stage_width = [32, 16, 24, 40, 80, 96, 192, 320, 1280]
input_channel = make_divisible(base_stage_width[0] * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
first_block_width = make_divisible(
base_stage_width[1] * self.width_mult, MyNetwork.CHANNEL_DIVISIBLE)
last_channel = make_divisible(base_stage_width[-1] * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
# first conv layer
first_conv = ConvLayer(3,
input_channel,
kernel_size=3,
stride=2,
use_bn=True,
act_func='relu6',
ops_order='weight_bn_act')
# first block
first_block_conv = MBConvLayer(
in_channels=input_channel,
out_channels=first_block_width,
kernel_size=3,
stride=1,
expand_ratio=1,
act_func='relu6',
)
first_block = ResidualBlock(first_block_conv, None)
input_channel = first_block_width
# inverted residual blocks
self.block_group_info = []
blocks = [first_block]
_block_index = 1
stride_stages = [2, 2, 2, 1, 2, 1]
n_block_list = [max(self.depth_list)] * 5 + [1]
width_list = []
for base_width in base_stage_width[2:-1]:
width = make_divisible(base_width * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
width_list.append(width)
for width, n_block, s in zip(width_list, n_block_list, stride_stages):
self.block_group_info.append(
[_block_index + i for i in range(n_block)])
_block_index += n_block
output_channel = width
for i in range(n_block):
if i == 0:
stride = s
else:
stride = 1
mobile_inverted_conv = DynamicMBConvLayer(
in_channel_list=val2list(input_channel, 1),
out_channel_list=val2list(output_channel, 1),
kernel_size_list=ks_list,
expand_ratio_list=expand_ratio_list,
stride=stride,
act_func='relu6',
)
if stride == 1 and input_channel == output_channel:
shortcut = IdentityLayer(input_channel, input_channel)
else:
shortcut = None
mb_inverted_block = ResidualBlock(mobile_inverted_conv,
shortcut)
blocks.append(mb_inverted_block)
input_channel = output_channel
# 1x1_conv before global average pooling
feature_mix_layer = ConvLayer(
input_channel,
last_channel,
kernel_size=1,
use_bn=True,
act_func='relu6',
)
classifier = LinearLayer(last_channel,
n_classes,
dropout_rate=dropout_rate)
super(OFAProxylessNASNets,
self).__init__(first_conv, blocks, feature_mix_layer, classifier)
# set bn param
self.set_bn_param(momentum=bn_param[0], eps=bn_param[1])
# runtime_depth
self.runtime_depth = [
len(block_idx) for block_idx in self.block_group_info
]
def __init__(self,
n_classes=1000,
bn_param=(0.1, 1e-5),
dropout_rate=0.1,
base_stage_width=None,
width_mult=1.0,
ks_list=3,
expand_ratio_list=6,
depth_list=4,
dropblock=False,
block_size=0):
self.width_mult = width_mult
self.ks_list = val2list(ks_list, 1)
self.expand_ratio_list = val2list(expand_ratio_list, 1)
self.depth_list = val2list(depth_list, 1)
self.ks_list.sort()
self.expand_ratio_list.sort()
self.depth_list.sort()
base_stage_width = [16, 16, 24, 40, 80, 112, 160, 960, 1280]
final_expand_width = make_divisible(
base_stage_width[-2] * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
last_channel = make_divisible(base_stage_width[-1] * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
stride_stages = [1, 2, 2, 2, 1, 2]
act_stages = ['relu', 'relu', 'relu', 'h_swish', 'h_swish', 'h_swish']
se_stages = [False, False, True, False, True, True]
n_block_list = [1] + [max(self.depth_list)] * 5
width_list = []
for base_width in base_stage_width[:-2]:
width = make_divisible(base_width * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
width_list.append(width)
input_channel, first_block_dim = width_list[0], width_list[1]
# first conv layer
first_conv = ConvLayer(3,
input_channel,
kernel_size=3,
stride=2,
act_func='h_swish')
first_block_conv = MBConvLayer(
in_channels=input_channel,
out_channels=first_block_dim,
kernel_size=3,
stride=stride_stages[0],
expand_ratio=1,
act_func=act_stages[0],
use_se=se_stages[0],
)
first_block = ResidualBlock(
first_block_conv,
IdentityLayer(first_block_dim, first_block_dim) if input_channel
== first_block_dim else None, dropout_rate, dropblock, block_size)
# inverted residual blocks
self.block_group_info = []
blocks = [first_block]
_block_index = 1
feature_dim = first_block_dim
for width, n_block, s, act_func, use_se in zip(width_list[2:],
n_block_list[1:],
stride_stages[1:],
act_stages[1:],
se_stages[1:]):
self.block_group_info.append(
[_block_index + i for i in range(n_block)])
_block_index += n_block
output_channel = width
for i in range(n_block):
if i == 0:
stride = s
else:
stride = 1
mobile_inverted_conv = DynamicMBConvLayer(
in_channel_list=val2list(feature_dim),
out_channel_list=val2list(output_channel),
kernel_size_list=ks_list,
expand_ratio_list=expand_ratio_list,
stride=stride,
act_func=act_func,
use_se=use_se,
)
if stride == 1 and feature_dim == output_channel:
shortcut = IdentityLayer(feature_dim, feature_dim)
else:
shortcut = None
blocks.append(
ResidualBlock(mobile_inverted_conv, shortcut, dropout_rate,
dropblock, block_size))
feature_dim = output_channel
# final expand layer, feature mix layer & classifier
final_expand_layer = ConvLayer(feature_dim,
final_expand_width,
kernel_size=1,
act_func='h_swish')
feature_mix_layer = ConvLayer(
final_expand_width,
last_channel,
kernel_size=1,
bias=False,
use_bn=False,
act_func='h_swish',
)
classifier = LinearLayer(last_channel,
n_classes,
dropout_rate=dropout_rate)
super(OFAMobileNetV3,
self).__init__(first_conv, blocks, final_expand_layer,
feature_mix_layer, classifier)
# set bn param
self.set_bn_param(momentum=bn_param[0], eps=bn_param[1])
# runtime_depth
self.runtime_depth = [
len(block_idx) for block_idx in self.block_group_info
]
def __init__(self,
n_classes=1000,
bn_param=(0.1, 1e-5),
dropout_rate=0,
depth_list=2,
expand_ratio_list=0.25,
width_mult_list=1.0):
self.depth_list = val2list(depth_list)
self.expand_ratio_list = val2list(expand_ratio_list)
self.width_mult_list = val2list(width_mult_list)
# sort
self.depth_list.sort()
self.expand_ratio_list.sort()
self.width_mult_list.sort()
input_channel = [
make_divisible(64 * width_mult, MyNetwork.CHANNEL_DIVISIBLE)
for width_mult in self.width_mult_list
]
mid_input_channel = [
make_divisible(channel // 2, MyNetwork.CHANNEL_DIVISIBLE)
for channel in input_channel
]
stage_width_list = ResNets.STAGE_WIDTH_LIST.copy()
for i, width in enumerate(stage_width_list):
stage_width_list[i] = [
make_divisible(width * width_mult, MyNetwork.CHANNEL_DIVISIBLE)
for width_mult in self.width_mult_list
]
n_block_list = [
base_depth + max(self.depth_list)
for base_depth in ResNets.BASE_DEPTH_LIST
]
stride_list = [1, 2, 2, 2]
# build input stem
input_stem = [
DynamicConvLayer(val2list(3),
mid_input_channel,
3,
stride=2,
use_bn=True,
act_func='relu'),
ResidualBlock(
DynamicConvLayer(mid_input_channel,
mid_input_channel,
3,
stride=1,
use_bn=True,
act_func='relu'),
IdentityLayer(mid_input_channel, mid_input_channel)),
DynamicConvLayer(mid_input_channel,
input_channel,
3,
stride=1,
use_bn=True,
act_func='relu')
]
# blocks
blocks = []
for d, width, s in zip(n_block_list, stage_width_list, stride_list):
for i in range(d):
stride = s if i == 0 else 1
bottleneck_block = DynamicResNetBottleneckBlock(
input_channel,
width,
expand_ratio_list=self.expand_ratio_list,
kernel_size=3,
stride=stride,
act_func='relu',
downsample_mode='avgpool_conv',
)
blocks.append(bottleneck_block)
input_channel = width
# classifier
classifier = DynamicLinearLayer(input_channel,
n_classes,
dropout_rate=dropout_rate)
super(OFAResNets, self).__init__(input_stem, blocks, classifier)
# set bn param
self.set_bn_param(*bn_param)
# runtime_depth
self.input_stem_skipping = 0
self.runtime_depth = [0] * len(n_block_list)
def __init__(self,
n_classes=1000,
width_mult=1.0,
bn_param=(0.1, 1e-3),
dropout_rate=0.2,
ks=None,
expand_ratio=None,
depth_param=None,
stage_width_list=None):
ks = 3 if ks is None else ks
expand_ratio = 6 if expand_ratio is None else expand_ratio
input_channel = 32
last_channel = 1280
input_channel = make_divisible(input_channel * width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
last_channel = make_divisible(last_channel * width_mult, MyNetwork.CHANNEL_DIVISIBLE) \
if width_mult > 1.0 else last_channel
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[expand_ratio, 24, 2, 2],
[expand_ratio, 32, 3, 2],
[expand_ratio, 64, 4, 2],
[expand_ratio, 96, 3, 1],
[expand_ratio, 160, 3, 2],
[expand_ratio, 320, 1, 1],
]
if depth_param is not None:
assert isinstance(depth_param, int)
for i in range(1, len(inverted_residual_setting) - 1):
inverted_residual_setting[i][2] = depth_param
if stage_width_list is not None:
for i in range(len(inverted_residual_setting)):
inverted_residual_setting[i][1] = stage_width_list[i]
ks = val2list(ks,
sum([n for _, _, n, _ in inverted_residual_setting]) - 1)
_pt = 0
# first conv layer
first_conv = ConvLayer(3,
input_channel,
kernel_size=3,
stride=2,
use_bn=True,
act_func='relu6',
ops_order='weight_bn_act')
# inverted residual blocks
blocks = []
for t, c, n, s in inverted_residual_setting:
output_channel = make_divisible(c * width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
for i in range(n):
if i == 0:
stride = s
else:
stride = 1
if t == 1:
kernel_size = 3
else:
kernel_size = ks[_pt]
_pt += 1
mobile_inverted_conv = MBConvLayer(
in_channels=input_channel,
out_channels=output_channel,
kernel_size=kernel_size,
stride=stride,
expand_ratio=t,
)
if stride == 1:
if input_channel == output_channel:
shortcut = IdentityLayer(input_channel, input_channel)
else:
shortcut = None
else:
shortcut = None
blocks.append(ResidualBlock(mobile_inverted_conv, shortcut))
input_channel = output_channel
# 1x1_conv before global average pooling
feature_mix_layer = ConvLayer(
input_channel,
last_channel,
kernel_size=1,
use_bn=True,
act_func='relu6',
ops_order='weight_bn_act',
)
classifier = LinearLayer(last_channel,
n_classes,
dropout_rate=dropout_rate)
super(MobileNetV2, self).__init__(first_conv, blocks,
feature_mix_layer, classifier)
# set bn param
self.set_bn_param(*bn_param)