def __init__(self, n_classes=1000, width_mult=1, bn_param=(0.1, 1e-3), dropout_rate=0.2,
ks=None, expand_ratio=None, depth_param=None, stage_width_list=None):
input_channel = 16
last_channel = 1280
input_channel = make_divisible(input_channel * width_mult, 8)
last_channel = make_divisible(last_channel * width_mult, 8) if width_mult > 1.0 else last_channel
cfg = {
# k, exp, c, se, nl, s, e,
'0': [
[3, 16, 16, False, 'relu', 1, 1],
],
'1': [
[3, 64, 24, False, 'relu', 2, None], # 4
[3, 72, 24, False, 'relu', 1, None], # 3
],
'2': [
[5, 72, 40, True, 'relu', 2, None], # 3
[5, 120, 40, True, 'relu', 1, None], # 3
[5, 120, 40, True, 'relu', 1, None], # 3
],
'3': [
[3, 240, 80, False, 'h_swish', 2, None], # 6
[3, 200, 80, False, 'h_swish', 1, None], # 2.5
[3, 184, 80, False, 'h_swish', 1, None], # 2.3
[3, 184, 80, False, 'h_swish', 1, None], # 2.3
],
'4': [
[3, 480, 112, True, 'h_swish', 1, None], # 6
[3, 672, 112, True, 'h_swish', 1, None], # 6
],
'5': [
[5, 672, 160, True, 'h_swish', 2, None], # 6
[5, 960, 160, True, 'h_swish', 1, None], # 6
[5, 960, 160, True, 'h_swish', 1, None], # 6
]
}
cfg = self.adjust_cfg(cfg, ks, expand_ratio, depth_param, stage_width_list)
# width multiplier on mobile setting, change `exp: 1` and `c: 2`
for stage_id, block_config_list in cfg.items():
for block_config in block_config_list:
if block_config[1] is not None:
block_config[1] = make_divisible(block_config[1] * width_mult, 8)
block_config[2] = make_divisible(block_config[2] * width_mult, 8)
first_conv, blocks, final_expand_layer, feature_mix_layer, classifier = self.build_net_via_cfg(
cfg, input_channel, last_channel, n_classes, dropout_rate
)
super(MobileNetV3Large, 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])
def forward(self, x):
in_channel = x.size(1)
num_mid = make_divisible(in_channel // self.reduction, divisor=8)
y = x.mean(3, keepdim=True).mean(2, keepdim=True)
# reduce
reduce_conv = self.fc.reduce
reduce_filter = reduce_conv.weight[:num_mid, :
in_channel, :, :].contiguous()
reduce_bias = reduce_conv.bias[:
num_mid] if reduce_conv.bias is not None else None
y = F.conv2d(y, reduce_filter, reduce_bias, 1, 0, 1, 1)
# relu
y = self.fc.relu(y)
# expand
expand_conv = self.fc.expand
expand_filter = expand_conv.weight[:in_channel, :
num_mid, :, :].contiguous()
expand_bias = expand_conv.bias[:
in_channel] if expand_conv.bias is not None else None
y = F.conv2d(y, expand_filter, expand_bias, 1, 0, 1, 1)
# hard sigmoid
y = self.fc.h_sigmoid(y)
return x * y
def get_active_net_config(self):
# first conv
first_conv_config = self.first_conv.config
first_block_config = self.blocks[0].config
if isinstance(self.first_conv, DynamicConvLayer):
first_conv_config = self.first_conv.get_active_subnet_config(3)
first_block_config = {
'name': MobileInvertedResidualBlock.__name__,
'mobile_inverted_conv': self.blocks[0].mobile_inverted_conv.get_active_subnet_config(
first_conv_config['out_channels']
),
'shortcut': self.blocks[0].shortcut.config if self.blocks[0].shortcut is not None else None,
}
final_expand_config = self.final_expand_layer.config
feature_mix_layer_config = self.feature_mix_layer.config
if isinstance(self.final_expand_layer, DynamicConvLayer):
final_expand_config = self.final_expand_layer.get_active_subnet_config(
self.blocks[-1].mobile_inverted_conv.active_out_channel)
feature_mix_layer_config = self.feature_mix_layer.get_active_subnet_config(
final_expand_config['out_channels'])
classifier_config = self.classifier.config
if isinstance(self.classifier, DynamicLinearLayer):
classifier_config = self.classifier.get_active_subnet_config(self.feature_mix_layer.active_out_channel)
block_config_list = [first_block_config]
input_channel = first_block_config['mobile_inverted_conv']['out_channels']
for stage_id, block_idx in enumerate(self.block_group_info):
depth = self.runtime_depth[stage_id]
active_idx = block_idx[:depth]
stage_blocks = []
for idx in active_idx:
middle_channel = make_divisible(round(input_channel *
self.blocks[idx].mobile_inverted_conv.active_expand_ratio), 8)
stage_blocks.append({
'name': MobileInvertedResidualBlock.__name__,
'mobile_inverted_conv': {
'name': MBInvertedConvLayer.__name__,
'in_channels': input_channel,
'out_channels': self.blocks[idx].mobile_inverted_conv.active_out_channel,
'kernel_size': self.blocks[idx].mobile_inverted_conv.active_kernel_size,
'stride': self.blocks[idx].mobile_inverted_conv.stride,
'expand_ratio': self.blocks[idx].mobile_inverted_conv.active_expand_ratio,
'mid_channels': middle_channel,
'act_func': self.blocks[idx].mobile_inverted_conv.act_func,
'use_se': self.blocks[idx].mobile_inverted_conv.use_se,
},
'shortcut': self.blocks[idx].shortcut.config if self.blocks[idx].shortcut is not None else None,
})
input_channel = self.blocks[idx].mobile_inverted_conv.active_out_channel
block_config_list += stage_blocks
return {
'name': MobileNetV3.__name__,
'bn': self.get_bn_param(),
'first_conv': first_conv_config,
'blocks': block_config_list,
'final_expand_layer': final_expand_config,
'feature_mix_layer': feature_mix_layer_config,
'classifier': classifier_config,
}
def __init__(self,
bn_param=(0.1, 1e-5),
dropout_rate=0.1,
base_stage_width=None,
width_mult_list=1.0,
ks_list=3,
expand_ratio_list=6,
depth_list=4,
pixelshuffle_depth_list=2):
self.width_mult_list = int2list(width_mult_list,
1) # 이게 output width 조절하는 변수
self.ks_list = int2list(ks_list, 1)
self.expand_ratio_list = int2list(expand_ratio_list, 1)
self.depth_list = int2list(depth_list, 1)
self.pixelshuffle_depth_list = int2list(pixelshuffle_depth_list, 1)
self.base_stage_width = base_stage_width
self.width_mult_list.sort()
self.ks_list.sort()
self.expand_ratio_list.sort()
self.depth_list.sort()
self.pixelshuffle_depth_list.sort()
# FROM [3,64 64, 64, 64, 64, 64, 64, 64,64, 64, 64, 64, 64, 64, 64, 64, 64]
base_stage_width = [
16, 64, 64, 64, 64, 64, 64, 3, 64, 64, 64, 64, 64, 64, 64, 256, 3
]
# [Unshu ResBlock ResCon ResBlock ResCon Shu]
# [2, 4, 4, 4, 4, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 2, 1]
# [ Skip, Con, Skip, Con]
# final_expand_width = [
# make_divisible(base_stage_width[-2] * max(self.width_mult_list), 8) for _ in self.width_mult_list
# ]
# last_channel = [
# make_divisible(base_stage_width[-1] * max(self.width_mult_list), 8) for _ in self.width_mult_list
# ]
stride_stages = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
act_stages = [
'pixelunshuffle', 'relu6', 'relu6', 'relu6', 'relu6', None, None,
None, None, 'relu6', 'relu6', 'relu6', 'relu6', None, None,
'pixelshuffle', None
]
se_stages = [
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False
]
if depth_list is None:
n_block_list = [1, 2, 3, 4, 2, 3]
self.depth_list = [4, 4]
print('Use MobileNetV3 Depth Setting')
else:
n_block_list = [
max(self.pixelshuffle_depth_list)
] + [max(self.depth_list)] * 4 + [1] * 4 + [max(
self.depth_list)] * 4 + [1] * 2 + [
max(self.pixelshuffle_depth_list)
] + [1] # 2는 pixelshuffle, pixelunshuffle의 depth
# [2, 4, 4, 1, 1, 1, 1, 1, 4, 4, 1, 1, 2, 1]
width_list = []
for base_width in base_stage_width:
# width = [make_divisible(base_width * width_mult, 8) for width_mult in self.width_mult_list]
width = [
make_divisible(base_width * width_mult, 1)
for width_mult in self.width_mult_list
]
width_list.append(width)
#################################################################################################### encoder unshuffle
input_channel = width_list[0]
enc_first_pixelunshuffle = ConvLayer(3,
max(input_channel),
kernel_size=3,
stride=stride_stages[0],
act_func=act_stages[0],
use_bn=True)
enc_second_pixelunshuffle = ConvLayer(max(input_channel) * 4,
max(input_channel),
kernel_size=3,
stride=stride_stages[0],
act_func=act_stages[0],
use_bn=True)
#################################################################################################### encoder inverted residual blocks
self.block_group_info = [[0, 1]]
blocks = [enc_first_pixelunshuffle, enc_second_pixelunshuffle]
_block_index = 2
feature_dim = width_list[1] # pixelunshuffle 해서 x4 되기때문에 그냥 이렇게함
for width, n_block, s, act_func, use_se in zip(width_list[1:5],
n_block_list[1:5],
stride_stages[1:5],
act_stages[1:5],
se_stages[1:5]):
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=feature_dim,
out_channel_list=output_channel,
kernel_size_list=ks_list,
expand_ratio_list=expand_ratio_list,
stride=stride,
act_func=act_func,
use_se=use_se,
)
shortcut = IdentityLayer(feature_dim, feature_dim)
blocks.append(
MobileInvertedResidualBlock(mobile_inverted_conv,
shortcut))
feature_dim = output_channel
#################################################################################################### encoder final conv blocks
enc_final_conv_blocks = []
for width, n_block, s, act_func, use_se in zip(width_list[5:8],
n_block_list[5:8],
stride_stages[5:8],
act_stages[5:8],
se_stages[5:8]):
# 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
enc_final_conv_blocks.append(
ConvLayer(max(feature_dim),
max(output_channel),
kernel_size=3,
stride=s,
act_func=act_func,
use_bn=True))
feature_dim = output_channel
#################################################################################################### decoder first conv block
dec_first_conv_block = ConvLayer(max(feature_dim),
max(width_list[8]),
kernel_size=3,
stride=stride_stages[8],
act_func=act_stages[8],
use_bn=True)
#################################################################################################### decoder inverted residual blocks
feature_dim = width_list[6]
for width, n_block, s, act_func, use_se in zip(width_list[9:13],
n_block_list[9:13],
stride_stages[9:13],
act_stages[9:13],
se_stages[9:13]):
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=feature_dim,
out_channel_list=output_channel,
kernel_size_list=ks_list,
expand_ratio_list=expand_ratio_list,
stride=stride,
act_func=act_func,
use_se=use_se,
)
shortcut = IdentityLayer(feature_dim, feature_dim)
blocks.append(
MobileInvertedResidualBlock(mobile_inverted_conv,
shortcut))
feature_dim = output_channel
#################################################################################################### decoder final conv blocks
dec_final_conv_blocks = []
for width, n_block, s, act_func, use_se in zip(width_list[13:15],
n_block_list[13:15],
stride_stages[13:15],
act_stages[13:15],
se_stages[13:15]):
# 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
dec_final_conv_blocks.append(
ConvLayer(max(feature_dim),
max(output_channel),
kernel_size=3,
stride=s,
act_func=act_func,
use_bn=True))
feature_dim = output_channel
#################################################################################################### decoder shuffle
# for width, n_block, s, act_func, use_se in zip(width_list[11], n_block_list[11],
# stride_stages[11], act_stages[11], se_stages[11]):
self.block_group_info.append(
[_block_index + i for i in range(n_block_list[15])])
_block_index += n_block_list[15]
output_channel = width_list[15]
for i in range(n_block_list[15]):
if i == 0:
stride = stride_stages[15]
else:
stride = 1
blocks.append(
ConvLayer(max(feature_dim),
max(output_channel),
kernel_size=3,
stride=s,
act_func=act_stages[15],
use_bn=True))
#################################################################################################### decoder final output conv block
dec_final_output_conv_block = ConvLayer(max(feature_dim),
max(width_list[16]),
kernel_size=3,
stride=stride_stages[16],
act_func=act_stages[16],
use_bn=True)
####################################################################################################
# runtime_depth
self.runtime_depth = [
len(block_idx) for block_idx in self.block_group_info
]
super(OFAMobileNetX4,
self).__init__(blocks, enc_final_conv_blocks,
dec_first_conv_block, dec_final_conv_blocks,
dec_final_output_conv_block, self.runtime_depth)
# set bn param
self.set_bn_param(momentum=bn_param[0], eps=bn_param[1])
def __init__(self, n_classes=1000, bn_param=(0.1, 1e-5), dropout_rate=0.1, base_stage_width=None,
width_mult_list=1.0, ks_list=3, expand_ratio_list=6, depth_list=4):
self.width_mult_list = int2list(width_mult_list, 1)
self.ks_list = int2list(ks_list, 1)
self.expand_ratio_list = int2list(expand_ratio_list, 1)
self.depth_list = int2list(depth_list, 1)
self.base_stage_width = base_stage_width
self.width_mult_list.sort()
self.ks_list.sort()
self.expand_ratio_list.sort()
self.depth_list.sort()
# base_stage_width = [16, 24, 40, 80, 112, 160, 960, 1280]
base_stage_width = [16, 24, 40, 80, 112, 160, 192, 224, 256, 320, 480, 960, 1280]
stride_stages = [1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2]
act_stages = ['relu', 'relu', 'relu', 'h_swish', 'h_swish', 'h_swish', 'relu', 'relu', 'h_swish', 'h_swish',
'h_swish']
se_stages = [False, False, True, False, True, True, False, True, False, True, True]
final_expand_width = [
make_divisible(base_stage_width[-2] * max(self.width_mult_list), 8) for _ in self.width_mult_list
]
last_channel = [
make_divisible(base_stage_width[-1] * max(self.width_mult_list), 8) for _ in self.width_mult_list
]
# 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]
if depth_list is None:
n_block_list = [1, 2, 3, 4, 2, 3]
self.depth_list = [4, 4]
print('Use MobileNetV3 Depth Setting')
else:
n_block_list = [1] + [max(self.depth_list)] * 10 # depth_list = [,12,3,4]
# [1, 4,4,4,4,..........]
width_list = []
for base_width in base_stage_width[:-2]:
width = [make_divisible(base_width * width_mult, 8) for width_mult in self.width_mult_list]
width_list.append(width)
input_channel = width_list[0]
# first conv layer
if len(set(input_channel)) == 1:
first_conv = ConvLayer(3, max(input_channel), kernel_size=3, stride=2, act_func='h_swish')
first_block_conv = MBInvertedConvLayer(
in_channels=max(input_channel), out_channels=max(input_channel), kernel_size=3, stride=stride_stages[0],
expand_ratio=1, act_func=act_stages[0], use_se=se_stages[0],
)
else:
first_conv = DynamicConvLayer(
in_channel_list=int2list(3, len(input_channel)), out_channel_list=input_channel, kernel_size=3,
stride=2, act_func='h_swish',
)
first_block_conv = DynamicMBConvLayer(
in_channel_list=input_channel, out_channel_list=input_channel, kernel_size_list=3, expand_ratio_list=1,
stride=stride_stages[0], act_func=act_stages[0], use_se=se_stages[0],
)
first_block = MobileInvertedResidualBlock(first_block_conv, IdentityLayer(input_channel, input_channel))
# inverted residual blocks
self.block_group_info = []
blocks = [first_block]
_block_index = 1
feature_dim = input_channel
for width, n_block, s, act_func, use_se in zip(width_list[1:], 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=feature_dim, out_channel_list=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(MobileInvertedResidualBlock(mobile_inverted_conv, shortcut))
feature_dim = output_channel
# final expand layer, feature mix layer & classifier
if len(final_expand_width) == 1:
final_expand_layer = ConvLayer(max(feature_dim), max(final_expand_width), kernel_size=1, act_func='h_swish')
feature_mix_layer = ConvLayer(
max(final_expand_width), max(last_channel), kernel_size=1, bias=False, use_bn=False, act_func='h_swish',
)
else:
final_expand_layer = DynamicConvLayer(
in_channel_list=feature_dim, out_channel_list=final_expand_width, kernel_size=1, act_func='h_swish'
)
feature_mix_layer = DynamicConvLayer(
in_channel_list=final_expand_width, out_channel_list=last_channel, kernel_size=1,
use_bn=False, act_func='h_swish',
)
if len(set(last_channel)) == 1:
classifier = LinearLayer(max(last_channel), n_classes, dropout_rate=dropout_rate)
else:
classifier = DynamicLinearLayer(
in_features_list=last_channel, out_features=n_classes, bias=True, dropout_rate=dropout_rate
)
super(OFAMobileNetV3_depth, 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-3),
dropout_rate=0.1,
base_stage_width=None,
width_mult_list=1.0,
ks_list=3,
expand_ratio_list=6,
depth_list=4):
self.width_mult_list = int2list(width_mult_list, 1)
self.ks_list = int2list(ks_list, 1)
self.expand_ratio_list = int2list(expand_ratio_list, 1)
self.depth_list = int2list(depth_list, 1)
self.base_stage_width = base_stage_width
self.width_mult_list.sort()
self.ks_list.sort()
self.expand_ratio_list.sort()
self.depth_list.sort()
if base_stage_width == 'google':
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] * width_mult, 8)
for width_mult in self.width_mult_list
]
first_block_width = [
make_divisible(base_stage_width[1] * width_mult, 8)
for width_mult in self.width_mult_list
]
last_channel = [
make_divisible(base_stage_width[-1] * width_mult, 8)
if width_mult > 1.0 else base_stage_width[-1]
for width_mult in self.width_mult_list
]
# first conv layer
if len(input_channel) == 1:
first_conv = ConvLayer(3,
max(input_channel),
kernel_size=3,
stride=2,
use_bn=True,
act_func='relu6',
ops_order='weight_bn_act')
else:
first_conv = DynamicConvLayer(in_channel_list=int2list(
3, len(input_channel)),
out_channel_list=input_channel,
kernel_size=3,
stride=2,
act_func='relu6')
# first block
if len(first_block_width) == 1:
first_block_conv = MBInvertedConvLayer(
in_channels=max(input_channel),
out_channels=max(first_block_width),
kernel_size=3,
stride=1,
expand_ratio=1,
act_func='relu6',
)
else:
first_block_conv = DynamicMBConvLayer(
in_channel_list=input_channel,
out_channel_list=first_block_width,
kernel_size_list=3,
expand_ratio_list=1,
stride=1,
act_func='relu6',
)
first_block = MobileInvertedResidualBlock(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]
if depth_list is None:
n_block_list = [2, 3, 4, 3, 3, 1]
self.depth_list = [4, 4]
print('Use MobileNetV2 Depth Setting')
else:
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 * width_mult, 8)
for width_mult in self.width_mult_list
]
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=int2list(input_channel, 1),
out_channel_list=int2list(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 = MobileInvertedResidualBlock(
mobile_inverted_conv, shortcut)
blocks.append(mb_inverted_block)
input_channel = output_channel
# 1x1_conv before global average pooling
if len(last_channel) == 1:
feature_mix_layer = ConvLayer(
max(input_channel),
max(last_channel),
kernel_size=1,
use_bn=True,
act_func='relu6',
)
classifier = LinearLayer(max(last_channel),
n_classes,
dropout_rate=dropout_rate)
else:
feature_mix_layer = DynamicConvLayer(
in_channel_list=input_channel,
out_channel_list=last_channel,
kernel_size=1,
stride=1,
act_func='relu6',
)
classifier = DynamicLinearLayer(in_features_list=last_channel,
out_features=n_classes,
bias=True,
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,
width_mult=1,
bn_param=(0.1, 1e-3),
dropout_rate=0.2,
ks=None,
expand_ratio=None,
depth_param=None,
stage_width_list=None):
if ks is None:
ks = 3
if expand_ratio is None:
expand_ratio = 6
input_channel = 32
last_channel = 1280
input_channel = make_divisible(input_channel * width_mult, 8)
last_channel = make_divisible(last_channel * width_mult,
8) 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 = int2list(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, 8)
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 = MBInvertedConvLayer(
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(
MobileInvertedResidualBlock(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(momentum=bn_param[0], eps=bn_param[1])