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modelsets.py
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#coding:utf-8
'''
author:Wang Haibo
at: Pingan Tec.
email: [email protected]
!!!
代码中会有少量中文注释，无需在意
'''
from pruner import Pruner
__all__ = ["CifarModelZoo"]
class CifarModelZoo():
def __init__(self):
# resnet config
self.model_config = {"resnet18":[2,2,2,2],"resnet34":[3,4,6,3],"resnet50":[3,4,6,3]}
self.init_channels = 64
self.block_version = 2
self.support_model = ["simpleNet","DenseNet40","MobileNetV1","vgg19","resnet18","resnet34"]
# end
@classmethod
def getModel(cls,model_name=None,params=None):
if not isinstance(model_name,str) and not isinstance(params,dict):
raise TypeError("model_name must be str, params must be dict.")
if len(params)!=4:
raise ValueError("params error.")
if model_name not in cls().support_model:
raise ValueError(model_name+" not implemented in modelsets.")
return_func = None
if model_name=="simpleNet":
return_func = cls().simpleNet
elif model_name=="DenseNet40":
return_func = cls().DenseNet40
elif model_name=="MobileNetV1":
return_func = cls().MobileNetV1
elif model_name=="vgg19":
return_func = cls().vgg19
elif model_name=="resnet18":
return_func = cls().ResNet18
elif model_name=="resnet34":
return_func = cls().ResNet34
return return_func(inputs=params["inputs"],
is_train=params["is_train"],
reload_w=params["reload_w"],
num_classes=params["num_classes"])
def simpleNet(self,inputs=None,is_train=True,reload_w=None,num_classes=None):
model = Pruner(reload_file=reload_w)
k_size = 5
x = model._add_layer(inputs, mode='conv', out_c=32, k_size=k_size, strides=1, is_train=is_train)
x = model._add_layer(x, mode='conv', out_c=64, k_size=k_size, strides=2, is_train=is_train)
x1 = model._add_layer(x, mode='conv', out_c=64, k_size=k_size, strides=1, is_train=is_train)
x2 = model._add_layer(x, mode='conv', out_c=64, k_size=k_size, strides=1, is_train=is_train)
x = model.Add_layer(x1,x2)
x = model._add_layer(x, mode='conv', out_c=64, k_size=k_size, strides=1, is_train=is_train)
x = model._add_layer(x, mode='conv', out_c=96, k_size=k_size, strides=2, is_train=is_train)
x = model._add_layer(x, mode='conv', out_c=96, k_size=k_size, strides=1, is_train=is_train)
x = model._add_layer(x, mode='conv', out_c=128, k_size=k_size, strides=2, is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x, mode="fc", out_c=num_classes, with_bn=False, act=None)
return x,model
def __resnet_block_v2(self,model, inputs, out_c, strides, is_train):
output = model.bn_act_layer(inputs,is_train=is_train)
if strides==2:
shortcut = model._add_layer(output, mode="conv", out_c=out_c, k_size=3, strides=2, with_bn=False,
act=None)
else:
shortcut = inputs
output = model._add_layer(output, mode="conv", out_c=out_c, k_size=3, strides=strides, with_bn=True,
is_train=is_train)
output = model._add_layer(output, mode="conv", out_c=out_c, k_size=3, strides=1, with_bn=False,
act=None)
output = model.Add_layer(shortcut, output)
return output
def __resnet_block_v1(self,model, inputs, out_c, strides, is_train):
output = model._add_layer(inputs, mode="conv", out_c=out_c, k_size=3, strides=strides, with_bn=True,
is_train=is_train)
output = model._add_layer(output, mode="conv", out_c=out_c, k_size=3, strides=1, with_bn=True,
is_train=is_train)
if strides==2:
shortcut = model._add_layer(inputs, mode="conv", out_c=out_c, k_size=3, strides=strides, with_bn=True,
is_train=is_train)
else:
shortcut = inputs
output = model.Add_layer(shortcut,output)
return output
def ResNet34(self,inputs=None,is_train=True,reload_w=None,num_classes=None):
model = Pruner(reload_file=reload_w)
num_block = self.model_config["resnet34"]
block_func = self.__resnet_block_v1 if self.block_version==1 else self.__resnet_block_v2
if self.block_version==1:
x = model._add_layer(inputs,mode="conv",out_c=self.init_channels,k_size=3,strides=1,with_bn=True,is_train=is_train)
else:
x = model._add_layer(inputs,mode="conv",out_c=self.init_channels,k_size=3,strides=1,with_bn=False,act=None)
# stage 1 out size = 32
for _ in range(num_block[0]):
x = block_func(model,inputs=x,out_c=self.init_channels,strides=1,is_train=is_train)
# stage 2 out_size = 16
x = block_func(model, inputs=x, out_c=self.init_channels*2, strides=2, is_train=is_train)
for _ in range(num_block[1]-1):
x = block_func(model, inputs=x, out_c=self.init_channels*2, strides=1, is_train=is_train)
# stage 3 out_size = 8
x = block_func(model, inputs=x, out_c=self.init_channels*4, strides=2, is_train=is_train)
for _ in range(num_block[2]-1):
x = block_func(model, inputs=x, out_c=self.init_channels*4, strides=1, is_train=is_train)
# stage 4 out_size = 4
x = block_func(model, inputs=x, out_c=self.init_channels*8, strides=2, is_train=is_train)
for _ in range(num_block[3]-1):
x = block_func(model, inputs=x, out_c=self.init_channels*8, strides=1, is_train=is_train)
if self.block_version==2:
x = model.bn_act_layer(x,is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x,mode="fc",out_c=num_classes,act=None,with_bn=False)
return x,model
def ResNet18(self,inputs=None,is_train=True,reload_w=None,num_classes=None):
model = Pruner(reload_file=reload_w)
num_block = self.model_config["resnet18"]
block_func = self.__resnet_block_v1 if self.block_version==1 else self.__resnet_block_v2
if self.block_version==1:
x = model._add_layer(inputs,mode="conv",out_c=self.init_channels,k_size=3,strides=1,with_bn=True,is_train=is_train)
else:
x = model._add_layer(inputs,mode="conv",out_c=self.init_channels,k_size=3,strides=1,with_bn=False,act=None)
# stage 1 out size = 32
for _ in range(num_block[0]):
x = block_func(model,inputs=x,out_c=self.init_channels,strides=1,is_train=is_train)
# stage 2 out_size = 16
x = block_func(model, inputs=x, out_c=self.init_channels*2, strides=2, is_train=is_train)
for _ in range(num_block[1]-1):
x = block_func(model, inputs=x, out_c=self.init_channels*2, strides=1, is_train=is_train)
# stage 3 out_size = 8
x = block_func(model, inputs=x, out_c=self.init_channels*4, strides=2, is_train=is_train)
for _ in range(num_block[2]-1):
x = block_func(model, inputs=x, out_c=self.init_channels*4, strides=1, is_train=is_train)
# stage 4 out_size = 4
x = block_func(model, inputs=x, out_c=self.init_channels*8, strides=2, is_train=is_train)
for _ in range(num_block[3]-1):
x = block_func(model, inputs=x, out_c=self.init_channels*8, strides=1, is_train=is_train)
if self.block_version==2:
x = model.bn_act_layer(x,is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x,mode="fc",out_c=num_classes,act=None,with_bn=False)
return x,model
def densenet_block(self,model,inputs=None,is_train=True):
def _add_dense_layer(in_):
in_c = in_.get_shape().as_list()[-1]
c = model.bn_act_layer(in_,is_train=is_train)
c = model._add_layer(c, mode="conv", out_c=12, k_size=3, strides=1, with_bn=False,act=None)
out = model.concat_layer([in_,c],3)
return out
for i in range(12):
inputs = _add_dense_layer(inputs)
return inputs
def densenet_trans(self,model,inputs=None,is_train=True):
in_c = inputs.get_shape().as_list()[-1]
c = model.bn_act_layer(inputs,is_train=is_train)
c = model._add_layer(c, mode="conv", out_c=in_c, k_size=1, strides=1, with_bn=False,act=None)
c = model.pool_layer(c,"avg",pool_size=2,strides=2)
return c
def DenseNet40(self,inputs=None,is_train=True,reload_w=None,num_classes=None):
model = Pruner(reload_file=reload_w)
# net header
x = model._add_layer(inputs, mode="conv", out_c=32, k_size=3, strides=1, with_bn=False,act=None)
# stage 1
x = self.densenet_block(model,x,is_train=is_train)
x = self.densenet_trans(model,x,is_train=is_train) #16
# stage 2
x = self.densenet_block(model,x,is_train=is_train)
x = self.densenet_trans(model,x,is_train=is_train) #8
# stage 3
x = self.densenet_block(model,x,is_train=is_train)
x = model.bn_act_layer(x,is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x,mode="fc",out_c=num_classes,act=None,with_bn=False)
return x,model
def vgg19(self,inputs=None,is_train=True,reload_w=None,num_classes=None):
model = Pruner(reload_file=reload_w)
x = inputs
init_size = 64
for i in range(4):
x = model._add_layer(x, mode="conv", out_c=init_size*(2**i), k_size=3, strides=1, with_bn=False)
x = model._add_layer(x, mode="conv", out_c=init_size*(2**i), k_size=3, strides=1, with_bn=False)
x = model.pool_layer(x,"max",pool_size=2,strides=2)
x = model._add_layer(x, mode="conv", out_c=512, k_size=3, strides=1, with_bn=False)
x = model._add_layer(x, mode="conv", out_c=512, k_size=3, strides=1, with_bn=False)
x = model._add_layer(x, mode="conv", out_c=512, k_size=3, strides=1, with_bn=False)
x = model.gmp_layer(x)
x = model._add_layer(x, mode="fc", out_c=1024, with_bn=False)
x = model._add_layer(x, mode="fc", out_c=1024, with_bn=False)
x = model._add_layer(x, mode="fc", out_c=num_classes, with_bn=False,act=None)
return x,model
def MobileNetV1(self,inputs=None,is_train=None,reload_w=None,num_classes=None):
model = Pruner(reload_file=reload_w)
x = model._add_layer(inputs,mode="conv",out_c=32,k_size=3,strides=1)
x = model._add_layer(x, mode="dconv", out_c=64, k_size=3, strides=1,with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=128, k_size=3, strides=2,with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=128, k_size=3, strides=1,with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=256, k_size=3, strides=2,with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=256, k_size=3, strides=1,with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=512, k_size=3, strides=2,with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=512, k_size=3, strides=1, with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=512, k_size=3, strides=1, with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=512, k_size=3, strides=1, with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=512, k_size=3, strides=1, with_bn=True,is_train=is_train)
x = model._add_layer(x, mode="dconv", out_c=512, k_size=3, strides=1, with_bn=True,is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x,mode="fc",out_c=num_classes,act=None)
return x,model