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unify the model config #14

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unify the model config #14

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78a7381
unify the model config
NHZlX Oct 20, 2017
91c61ae
modify readme in ./models
NHZlX Oct 23, 2017
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56 changes: 31 additions & 25 deletions model_compression/flowers102/mobilenet/mobilenet.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,10 @@
# edit-mode: -*- python -*-
import paddle.v2 as paddle


def conv_bn_layer(input, filter_size, num_filters,
stride, padding, channels=None, num_groups=1,
active_type=paddle.activation.Relu()):
active_type=paddle.activation.Relu(),
layer_type=None):
"""
A wrapper for conv layer with batch normalization layers.
Note:
Expand All @@ -18,43 +18,42 @@ def conv_bn_layer(input, filter_size, num_filters,
stride=stride,
padding=padding,
groups=num_groups,
#act=active_type,
act=paddle.activation.Linear(),
bias_attr=False)
#act=active_type,
#bias_attr=True)
bias_attr=False,
layer_type=layer_type)
return paddle.layer.batch_norm(
input=tmp,
act=active_type)
#use_global_stats=False,
def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride):

def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride, scale):
"""
"""
tmp = conv_bn_layer(
input=input,
filter_size=3,
num_filters=num_filters1,
num_filters=int(num_filters1*scale),
stride=stride,
padding=1,
num_groups=num_groups)
num_groups=int(num_groups*scale), layer_type='exconv')

tmp = conv_bn_layer(
input=tmp,
filter_size=1,
num_filters=num_filters2,
num_filters=int(num_filters2*scale),
stride=1,
padding=0)
return tmp

def mobile_net(img):
# For ImageNet
# image_data_layers(224, 1000, True)
def mobile_net(img_size, class_num, scale = 1.0):

img = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(img_size))

# conv1: 112x112
tmp = conv_bn_layer(img,
filter_size=3,
channels=3,
num_filters=32,
num_filters=int(32*scale),
stride=2,
padding=1)

Expand All @@ -63,57 +62,64 @@ def mobile_net(img):
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1)
stride=1, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2)
stride=2, scale = scale)
# 28x28
tmp = depthwise_separable(tmp,
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1)
stride=1, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2)
stride=2, scale = scale)
# 14x14
tmp = depthwise_separable(tmp,
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1)
stride=1, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2)
stride=2, scale = scale)
# 14x14
for i in range(5):
tmp = depthwise_separable(tmp,
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1)
stride=1, scale = scale)
# 7x7
tmp = depthwise_separable(tmp,
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2)
stride=2, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1)
stride=1, scale = scale)

tmp = paddle.layer.img_pool(
input=tmp,
pool_size=7,
stride=1,
pool_type=paddle.pooling.Avg())
out = paddle.layer.fc(
input=tmp, size=class_num, act=paddle.activation.Softmax())

return tmp
return out

if __name__ == '__main__':
img_size = 3 * 224 * 224
data_dim = 102
out = mobile_net(img_size, data_dim, 1.0)
27 changes: 2 additions & 25 deletions model_compression/flowers102/mobilenet/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,19 +14,16 @@

import sys
import gzip
from paddle.trainer_config_helpers import *

from paddle.trainer_config_helpers import *
import paddle.v2 as paddle

#from mobilenet_new import mobile_net
from mobilenet import mobile_net


BATCH = 40
def main():
datadim = 3 * 224 * 224
classdim = 102


# PaddlePaddle init
paddle.init(use_gpu=True, trainer_count=1, gpu_id = 1)
Expand All @@ -35,28 +32,9 @@ def main():
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 * BATCH),
learning_rate=0.005/ BATCH,
#learning_rate_decay_a=0.1,
#learning_rate_decay_b=50000 * 50,
learning_rate_schedule='constant')

image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(datadim))

net = mobile_net(image)
# option 2. vgg
#net = vgg_bn_drop(image)


out = paddle.layer.fc(
input=net, size=classdim, act=paddle.activation.Softmax())
'''
out = paddle.layer.img_conv(
input=net,
filter_size=1,
num_filters=classdim,
stride=1,
act=paddle.activation.Linear())
'''
out = mobile_net(datadim, classdim, 1.0)

lbl = paddle.layer.data(
name="label", type=paddle.data_type.integer_value(classdim))
Expand Down Expand Up @@ -104,6 +82,5 @@ def event_handler(event):
feeding={'image': 0,
'label': 1})


if __name__ == '__main__':
main()
58 changes: 35 additions & 23 deletions model_compression/flowers102/mobilenet_pruning/mobilenet_pruning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,8 @@

def conv_bn_layer(input, filter_size, num_filters,
stride, padding, channels=None, num_groups=1,
active_type=paddle.activation.Relu(), param_attr=None):
active_type=paddle.activation.Relu(), param_attr=None,
layer_type=None):
"""
A wrapper for conv layer with batch normalization layers.
Note:
Expand All @@ -22,43 +23,45 @@ def conv_bn_layer(input, filter_size, num_filters,
groups=num_groups,
param_attr=param_attr,
act=paddle.activation.Linear(),
bias_attr=False)
#act=active_type,
#bias_attr=True)
bias_attr=False,
layer_type=layer_type)

return paddle.layer.batch_norm(
input=tmp,
act=active_type)
#use_global_stats=False,
def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride):

def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride, scale):
"""
"""
tmp = conv_bn_layer(
input=input,
filter_size=3,
num_filters=num_filters1,
num_filters=int(num_filters1*scale),
stride=stride,
padding=1,
num_groups=num_groups)
num_groups=int(num_groups*scale),
layer_type='exconv')

pa0 = ParamAttr(update_hooks = Hook('dynamic_pruning', sparsity_upper_bound=0.75))
tmp = conv_bn_layer(
input=tmp,
filter_size=1,
num_filters=num_filters2,
num_filters=int(num_filters2*scale),
stride=1,
padding=0,
param_attr = pa0)
return tmp

def mobile_net(img):
# For ImageNet
# image_data_layers(224, 1000, True)
def mobile_net(img_size, class_num, scale = 1.0):

img = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(img_size))

# conv1: 112x112
tmp = conv_bn_layer(img,
filter_size=3,
channels=3,
num_filters=32,
num_filters=int(32*scale),
stride=2,
padding=1)

Expand All @@ -67,57 +70,66 @@ def mobile_net(img):
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1)
stride=1, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2)
stride=2, scale = scale)
# 28x28
tmp = depthwise_separable(tmp,
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1)
stride=1, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2)
stride=2, scale = scale)
# 14x14
tmp = depthwise_separable(tmp,
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1)
stride=1, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2)
stride=2, scale = scale)
# 14x14
for i in range(5):
tmp = depthwise_separable(tmp,
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1)
stride=1, scale = scale)
# 7x7
tmp = depthwise_separable(tmp,
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2)
stride=2, scale = scale)
tmp = depthwise_separable(tmp,
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1)
stride=1, scale = scale)

tmp = paddle.layer.img_pool(
input=tmp,
pool_size=7,
stride=1,
pool_type=paddle.pooling.Avg())

return tmp
out = paddle.layer.fc(
input=tmp, size=class_num, act=paddle.activation.Softmax(),
param_attr = ParamAttr(update_hooks=Hook('dynamic_pruning', sparsity_upper_bound=0.8)))

return out

if __name__ == '__main__':
img_size = 3 * 224 * 224
data_dim = 102
out = mobile_net(img_size, data_dim, 1.0)
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