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mobilenet_v3.py
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mobilenet_v3.py
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import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
import math
__all__ = [
'MobileNetV3', 'MobileNetV3_small_x0_25', 'MobileNetV3_small_x0_5',
'MobileNetV3_small_x0_75', 'MobileNetV3_small_x1_0',
'MobileNetV3_small_x1_25', 'MobileNetV3_large_x0_25',
'MobileNetV3_large_x0_5', 'MobileNetV3_large_x0_75',
'MobileNetV3_large_x1_0', 'MobileNetV3_large_x1_25',
'MobileNetV3_large_x2_0'
]
class MobileNetV3():
def __init__(self, scale=1.0, model_name='small'):
self.scale = scale
self.inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, 'relu', 1],
[3, 64, 24, False, 'relu', 2],
[3, 72, 24, False, 'relu', 1],
[5, 72, 40, True, 'relu', 2],
[5, 120, 40, True, 'relu', 1],
[5, 120, 40, True, 'relu', 1],
[3, 240, 80, False, 'hard_swish', 2],
[3, 200, 80, False, 'hard_swish', 1],
[3, 184, 80, False, 'hard_swish', 1],
[3, 184, 80, False, 'hard_swish', 1],
[3, 480, 112, True, 'hard_swish', 1],
[3, 672, 112, True, 'hard_swish', 1],
[5, 672, 160, True, 'hard_swish', 2],
[5, 960, 160, True, 'hard_swish', 1],
[5, 960, 160, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, 'relu', 2],
[3, 72, 24, False, 'relu', 2],
[3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hard_swish', 2],
[5, 240, 40, True, 'hard_swish', 1],
[5, 240, 40, True, 'hard_swish', 1],
[5, 120, 48, True, 'hard_swish', 1],
[5, 144, 48, True, 'hard_swish', 1],
[5, 288, 96, True, 'hard_swish', 2],
[5, 576, 96, True, 'hard_swish', 1],
[5, 576, 96, True, 'hard_swish', 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError
def net(self, input, class_dim=1000):
scale = self.scale
inplanes = self.inplanes
cfg = self.cfg
cls_ch_squeeze = self.cls_ch_squeeze
cls_ch_expand = self.cls_ch_expand
#conv1
conv = self.conv_bn_layer(
input,
filter_size=3,
#num_filters=int(scale*inplanes),
num_filters=inplanes if scale <= 1.0 else int(inplanes * scale),
stride=2,
padding=1,
num_groups=1,
if_act=True,
act='hard_swish',
name='conv1')
i = 0
for layer_cfg in cfg:
conv = self.residual_unit(
input=conv,
num_in_filter=inplanes,
num_mid_filter=int(scale * layer_cfg[1]),
num_out_filter=int(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
filter_size=layer_cfg[0],
use_se=layer_cfg[3],
name='conv' + str(i + 2))
inplanes = int(scale * layer_cfg[2])
i += 1
conv = self.conv_bn_layer(
input=conv,
filter_size=1,
num_filters=int(scale * cls_ch_squeeze),
stride=1,
padding=0,
num_groups=1,
if_act=True,
act='hard_swish',
name='conv_last')
conv = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True, use_cudnn=False)
conv = fluid.layers.conv2d(
input=conv,
num_filters=cls_ch_expand,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(name='last_1x1_conv_weights'),
bias_attr=False)
conv = fluid.layers.hard_swish(conv)
out = fluid.layers.fc(input=conv,
size=class_dim,
param_attr=ParamAttr(name='fc_weights'),
bias_attr=ParamAttr(name='fc_offset'))
return out
def conv_bn_layer(self,
input,
filter_size,
num_filters,
stride,
padding,
num_groups=1,
if_act=True,
act=None,
name=None,
use_cudnn=True):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(name=name + '_weights'),
bias_attr=False)
bn_name = name + '_bn'
bn = fluid.layers.batch_norm(
input=conv,
param_attr=ParamAttr(
name=bn_name + "_scale",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
bias_attr=ParamAttr(
name=bn_name + "_offset",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
if if_act:
if act == 'relu':
bn = fluid.layers.relu(bn)
elif act == 'hard_swish':
bn = fluid.layers.hard_swish(bn)
return bn
def hard_swish(self, x):
return x * fluid.layers.relu6(x + 3) / 6.
def se_block(self, input, num_out_filter, ratio=4, name=None):
num_mid_filter = int(num_out_filter // ratio)
pool = fluid.layers.pool2d(
input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
conv1 = fluid.layers.conv2d(
input=pool,
filter_size=1,
num_filters=num_mid_filter,
act='relu',
param_attr=ParamAttr(name=name + '_1_weights'),
bias_attr=ParamAttr(name=name + '_1_offset'))
conv2 = fluid.layers.conv2d(
input=conv1,
filter_size=1,
num_filters=num_out_filter,
act='hard_sigmoid',
param_attr=ParamAttr(name=name + '_2_weights'),
bias_attr=ParamAttr(name=name + '_2_offset'))
scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
return scale
def residual_unit(self,
input,
num_in_filter,
num_mid_filter,
num_out_filter,
stride,
filter_size,
act=None,
use_se=False,
name=None):
input_data = input
conv0 = self.conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_mid_filter,
stride=1,
padding=0,
if_act=True,
act=act,
name=name + '_expand')
conv1 = self.conv_bn_layer(
input=conv0,
filter_size=filter_size,
num_filters=num_mid_filter,
stride=stride,
padding=int((filter_size - 1) // 2),
if_act=True,
act=act,
num_groups=num_mid_filter,
use_cudnn=False,
name=name + '_depthwise')
if use_se:
with fluid.name_scope('se_block_skip'):
conv1 = self.se_block(
input=conv1,
num_out_filter=num_mid_filter,
name=name + '_se')
conv2 = self.conv_bn_layer(
input=conv1,
filter_size=1,
num_filters=num_out_filter,
stride=1,
padding=0,
if_act=False,
name=name + '_linear')
if num_in_filter != num_out_filter or stride != 1:
return conv2
else:
return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
def MobileNetV3_small_x0_25():
model = MobileNetV3(model_name='small', scale=0.25)
return model
def MobileNetV3_small_x0_5():
model = MobileNetV3(model_name='small', scale=0.5)
return model
def MobileNetV3_small_x0_75():
model = MobileNetV3(model_name='small', scale=0.75)
return model
def MobileNetV3_small_x1_0():
model = MobileNetV3(model_name='small', scale=1.0)
return model
def MobileNetV3_small_x1_25():
model = MobileNetV3(model_name='small', scale=1.25)
return model
def MobileNetV3_large_x0_25():
model = MobileNetV3(model_name='large', scale=0.25)
return model
def MobileNetV3_large_x0_5():
model = MobileNetV3(model_name='large', scale=0.5)
return model
def MobileNetV3_large_x0_75():
model = MobileNetV3(model_name='large', scale=0.75)
return model
def MobileNetV3_large_x1_0():
model = MobileNetV3(model_name='large', scale=1.0)
return model
def MobileNetV3_large_x1_25():
model = MobileNetV3(model_name='large', scale=1.25)
return model
def MobileNetV3_large_x2_0():
model = MobileNetV3(model_name='large', scale=2.0)
return model