Merge pull request #14 from NHZlX/unify_config

unify the model config

Author: NHZlX

model_compression/flowers102/mobilenet/mobilenet.py

@@ -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:
@@ -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)
 
@@ -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)

model_compression/flowers102/mobilenet/train.py

@@ -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)
@@ -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))
@@ -104,6 +82,5 @@ def event_handler(event):
  feeding={'image': 0,
  'label': 1})
 
-
 if __name__ == '__main__':
  main()

model_compression/flowers102/mobilenet_pruning/mobilenet_pruning.py

@@ -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:
@@ -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)
 
@@ -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)