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提交 5a1c4210 编写于 作者: W wqz960
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add ghostnet

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configs/GhostNet/GhostNet_0_5.yaml 0 → 100644
浏览文件 @ 5a1c4210
mode: 'train'
ARCHITECTURE:
name: 'GhostNet_0_5'
pretrained_model: ""
model_save_dir: "./output/"
classes_num: 1000
total_images: 1281167
save_interval: 1
validate: True
valid_interval: 1
epochs: 360
topk: 5
image_shape: [3, 224, 224]
use_mix: False
ls_epsilon: 0.1
LEARNING_RATE:
function: 'CosineWarmup'
params:
lr: 0.8
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.0000400
TRAIN:
batch_size: 2048
num_workers: 4
file_list: "./dataset/ILSVRC2012/train_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
VALID:
batch_size: 64
num_workers: 4
file_list: "./dataset/ILSVRC2012/val_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
configs/GhostNet/GhostNet_1_0.yaml 0 → 100644
浏览文件 @ 5a1c4210
mode: 'train'
ARCHITECTURE:
name: 'GhostNet_1_0'
pretrained_model: ""
model_save_dir: "./output/"
classes_num: 1000
total_images: 1281167
save_interval: 1
validate: True
valid_interval: 1
epochs: 360
topk: 5
image_shape: [3, 224, 224]
use_mix: False
ls_epsilon: 0.1
LEARNING_RATE:
function: 'CosineWarmup'
params:
lr: 0.4
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.0000400
TRAIN:
batch_size: 1024
num_workers: 4
file_list: "./dataset/ILSVRC2012/train_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
VALID:
batch_size: 64
num_workers: 4
file_list: "./dataset/ILSVRC2012/val_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
configs/GhostNet/GhostNet_1_3.yaml 0 → 100644
浏览文件 @ 5a1c4210
mode: 'train'
ARCHITECTURE:
name: 'GhostNet_1_3'
pretrained_model: ""
model_save_dir: "./output/"
classes_num: 1000
total_images: 1281167
save_interval: 1
validate: True
valid_interval: 1
epochs: 360
topk: 5
image_shape: [3, 224, 224]
use_mix: False
ls_epsilon: 0.1
LEARNING_RATE:
function: 'CosineWarmup'
params:
lr: 0.4
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.0000400
TRAIN:
batch_size: 1024
num_workers: 4
file_list: "./dataset/ILSVRC2012/train_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- AutoAugment:
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
VALID:
batch_size: 64
num_workers: 4
file_list: "./dataset/ILSVRC2012/val_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
ppcls/modeling/architectures/ghostnet.py 0 → 100644
浏览文件 @ 5a1c4210
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.initializer import MSRA
from paddle.fluid.contrib.model_stat import summary
__all__ = ["GhostNet", "GhostNet_0_5", "GhostNet_1_0", "GhostNet_1_3"]
class GhostNet():
def __init__(self, width_mult):
cfgs = [
# k, t, c, SE, s
[3, 16, 16, 0, 1],
[3, 48, 24, 0, 2],
[3, 72, 24, 0, 1],
[5, 72, 40, 1, 2],
[5, 120, 40, 1, 1],
[3, 240, 80, 0, 2],
[3, 200, 80, 0, 1],
[3, 184, 80, 0, 1],
[3, 184, 80, 0, 1],
[3, 480, 112, 1, 1],
[3, 672, 112, 1, 1],
[5, 672, 160, 1, 2],
[5, 960, 160, 0, 1],
[5, 960, 160, 1, 1],
[5, 960, 160, 0, 1],
[5, 960, 160, 1, 1]
]
self.cfgs = cfgs
self.width_mult = width_mult
def _make_divisible(self, v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None,
data_format="NCHW"):
x = fluid.layers.conv2d(input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size-1)//2,
groups=groups,
act=None,
param_attr=ParamAttr(
initializer=fluid.initializer.MSRA(),name=name+"_weights"),
bias_attr=False,
name=name+"_conv_op",
data_format=data_format)
x = fluid.layers.batch_norm(input=x,
act=act,
name=name+"_bn",
param_attr=ParamAttr(name=name+"_bn_scale", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
bias_attr=ParamAttr(name=name+"_bn_offset", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
moving_mean_name=name+"_bn_mean",
moving_variance_name=name+"_bn_variance",
data_layout=data_format)
return x
def SElayer(self,
input,
num_channels,
reduction_ratio=4,
name=None):
pool = fluid.layers.pool2d(
input=input, pool_size=0, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
squeeze = fluid.layers.fc(
input=pool,
size=num_channels // reduction_ratio,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + '_sqz_weights'),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
excitation = fluid.layers.fc(
input=squeeze,
size=num_channels,
act=None,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + '_exc_weights'),
bias_attr=ParamAttr(name=name + '_exc_offset'))
#ones = fluid.layers.fill_constant(excitation.shape, "float32", 1)
#zeros = fluid.layers.fill_constant(excitation.shape, "float32", 0)
#excitation = fluid.layers.elementwise_max(excitation, zeros)
# excitation = fluid.layers.elementwise_min(excitation, ones)
excitation = fluid.layers.clip(x=excitation,
min=0,
max=1,
name=name+'_clip')
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
return scale
def depthwise_conv(self,
inp,
oup,
kernel_size,
stride=1,
relu=False,
name=None,
data_format="NCHW"):
return self.conv_bn_layer(input=inp,
num_filters=oup,
filter_size=kernel_size,
stride=stride,
groups=inp.shape[1] if data_format=="NCHW" else inp.shape[-1],
act="relu" if relu else None,
name=name+"_dw",
data_format=data_format)
def GhostModule(self,
inp,
oup,
kernel_size=1,
ratio=2,
dw_size=3,
stride=1,
relu=True,
name=None,
data_format="NCHW"):
self.oup=oup
init_channels = int(math.ceil(oup/ratio))
new_channels = int(init_channels*(ratio-1))
primary_conv = self.conv_bn_layer(input=inp,
num_filters=init_channels,
filter_size=kernel_size,
stride=stride,
groups=1,
act="relu" if relu else None,
name=name+"_primary_conv",
data_format="NCHW")
cheap_operation = self.conv_bn_layer(input=primary_conv,
num_filters=new_channels,
filter_size=dw_size,
stride=1,
groups=init_channels,
act="relu" if relu else None,
name=name+"_cheap_operation",
data_format=data_format)
out = fluid.layers.concat([primary_conv, cheap_operation], axis=1, name=name+"_concat")
# return out[:, :self.oup, :, :]
print(self.oup)
print(out.shape)
return fluid.layers.slice(out, axes=[1], starts=[0], ends=[self.oup])
def GhostBottleneck(self,
inp,
hidden_dim,
oup,
kernel_size,
stride,
use_se,
name=None,
data_format="NCHW"):
inp_channels = inp.shape[1]
x = self.GhostModule(inp=inp,
oup=hidden_dim,
kernel_size=1,
stride=1,
relu=True,
name=name+"GhostBottle_1",
data_format="NCHW")
if stride==2:
x = self.depthwise_conv(inp=x,
oup=hidden_dim,
kernel_size=kernel_size,
stride=stride,
relu=False,
name=name+"_dw2",
data_format="NCHW")
if use_se:
x = self.SElayer(input=x,
num_channels=hidden_dim,
name=name+"SElayer")
x = self.GhostModule(inp=x,
oup=oup,
kernel_size=1,
relu=False,
name=name+"GhostModule_2")
if stride==1 and inp_channels==oup:
shortcut = inp
else:
shortcut = self.depthwise_conv(inp=inp,
oup=inp_channels,
kernel_size=kernel_size,
stride=stride,
relu=False,
name=name+"shortcut_depthwise_conv",
data_format="NCHW")
shortcut = self.conv_bn_layer(input=shortcut,
num_filters=oup,
filter_size=1,
stride=1,
groups=1,
act=None,
name=name+"shortcut_conv_bn",
data_format="NCHW")
return fluid.layers.elementwise_add(x=x,
y=shortcut,
axis=-1,
act=None,
name=name+"elementwise_add")
def net(self,
input,
class_dim=1000):
#build first layer:
output_channel = int(self._make_divisible(16*self.width_mult, 4))
#print(output_channel)
x = self.conv_bn_layer(input=input,
num_filters=output_channel,
filter_size=3,
stride=2,
groups=1,
act="relu",
name="firstlayer",
data_format="NCHW")
input_channel = output_channel
#build inverted residual blocks
idx = 0
fm = {}
for k, exp_size, c, use_se, s in self.cfgs:
output_channel = int(self._make_divisible(c*self.width_mult, 4))
hidden_channel = int(self._make_divisible(exp_size*self.width_mult, 4))
#print(output_channel)
#print(hidden_channel)
x = self.GhostBottleneck(inp=x,
hidden_dim=hidden_channel,
oup=output_channel,
kernel_size=k,
stride=s,
use_se=use_se,
name="GhostBottle_"+str(idx),
data_format="NCHW")
input_channel = output_channel
fm[str(idx)] = x
idx+=1
#build last several layers
output_channel = int(self._make_divisible(exp_size * self.width_mult, 4))
x = self.conv_bn_layer(input=x,
num_filters=output_channel,
filter_size=1,
stride=1,
groups=1,
act="relu",
name="lastlayer",
data_format="NCHW")
x = fluid.layers.pool2d(input=x,
pool_type='avg',
global_pooling=True,
data_format="NCHW")
input_channel = output_channel
output_channel = 1280
stdv = 1.0/math.sqrt(x.shape[1]*1.0)
out = fluid.layers.conv2d(input=x,
num_filters=output_channel,
filter_size=1,
groups=1,
param_attr=ParamAttr(name="fc_0_w", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=False,
name="fc_0")
out = fluid.layers.batch_norm(input=out,
act="relu",
name="fc_0_bn",
param_attr=ParamAttr(name="fc_0_bn_scale", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
bias_attr=ParamAttr(name="fc_0_bn_offset", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
moving_mean_name="fc_0_bn_mean",
moving_variance_name="fc_0_bn_variance",
data_layout="NCHW")
out = fluid.layers.dropout(x=out, dropout_prob=0.2)
stdv = 1.0/math.sqrt(out.shape[1]*1.0)
out = fluid.layers.fc(input=out,
size=class_dim,
param_attr=ParamAttr(name="fc_1_w", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_1_bias"))
return out, fm
def GhostNet_0_5():
model = GhostNet(width_mult=0.5)
return model
def GhostNet_1_0():
model = GhostNet(width_mult=1.0)
return model
def GhostNet_1_3():
model = GhostNet(width_mult=1.3)
return model
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