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MODEL.py
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from __future__ import division
import keras
from keras.layers import Flatten,BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.merge import add
from keras.models import Sequential
from keras.layers import Input, merge, ZeroPadding2D
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import AveragePooling2D, GlobalAveragePooling2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import six
from keras.regularizers import l2
from keras.models import Model
from keras import backend as K
K.set_image_dim_ordering('tf')
try:
from keras import initializations
except ImportError:
from keras import initializers as initializations
import keras.backend as K
"""
author:tslgithub
email:[email protected]
time:2018-12-12
msg: You can choose the following model to train your image, and just switch in config.py:
VGG16,VGG19,InceptionV3,Xception,MobileNet,AlexNet,LeNet,ZF_Net,ResNet18,ResNet34,ResNet50,ResNet101,ResNet152,DenseNet
"""
class MODEL(object):
def __init__(self,config):
self.config = config
def input_shape_define(self):
return (self.config.normal_size, self.config.normal_size, self.config.channles)
def covn_block(self,model,kenal_number,kenal_size,padding,activation):
model.add(Convolution2D(kenal_number,kenal_size,padding=padding,activation=activation))
return model
def max_pooling_type(self,model,kenal_size,strides):
model.add(MaxPooling2D(pool_size=kenal_size,strides=strides))
return model
def mnist_net(self):
model = Sequential()
input_shape = (self.config.normal_size, self.config.normal_size, self.config.channles)
model.add(Convolution2D(96,(3,3),input_shape=input_shape,padding='same',activation='relu',kernel_initializer='uniform'))
model.add(Convolution2D(128,(3,3),padding='same',activation='relu'))
model.add(Convolution2D(128,(1,1),padding='same',activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Convolution2D(256,(3,3),padding='same',activation='relu'))
model.add(Convolution2D(256,(1,1),padding='same',activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Convolution2D(512, (3, 3), padding='same', activation='relu'))
model.add(Convolution2D(512, (3, 3), padding='same', activation='relu'))
model.add(Convolution2D(256, (1, 1), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Convolution2D(512, (3, 3), padding='same', activation='relu'))
model.add(Convolution2D(512, (3, 3), padding='same', activation='relu'))
model.add(Convolution2D(256, (1, 1), padding='same', activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(self.config.classNumber,activation='softmax'))
return model
#VGG16
def TSL16(self):
model = Sequential()
input_shape = (self.config.normal_size, self.config.normal_size, self.config.channles)
model.add(Convolution2D(64,kernel_size=(3,3),input_shape=input_shape,padding='same',activation='relu'))
model.add(Convolution2D(64,kernel_size=(3,3),padding='same',activation='relu'))
model = self.max_pooling_type(model,kenal_size=(2,2),strides=(2,2))
for i in range(2):
model = self.covn_block(model, kenal_number=128, kenal_size=(3, 3), padding='same', activation='relu')
model = self.max_pooling_type(model,kenal_size = (2,2),strides=(2,2))
for i in range(3):
model = self.covn_block(model,kenal_number=128, kenal_size=(3,3), padding='same',activation='relu')
model = self.max_pooling_type(model,kenal_size=(2,2),strides=(2,2))
for i in range(3):
model = self.covn_block(model,kenal_number=512,kenal_size=(3,3),padding='same',activation='relu')
model = self.max_pooling_type(model,kenal_size=(2,2),strides=(2,2))
for i in range(3):
model = self.covn_block(model,kenal_number=512,kenal_size=(3,3),padding='same',activation='relu')
model.add(Flatten())
model.add(Dense(4096,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1024,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(self.config.classNumber,activation='softmax'))
return model
# AlexNet
def AlexNet(self):
model = Sequential()
# input_shape = (64,64, self.config.channles)
input_shape = (self.config.normal_size, self.config.normal_size, self.config.channles)
model.add(Convolution2D(96, (11, 11), input_shape=input_shape,strides=(4, 4), padding='valid',activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))#26*26
model.add(Convolution2D(256, (5, 5), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
model.add(Convolution2D(384, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Convolution2D(384, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Convolution2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(self.config.classNumber, activation='softmax'))
# model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
def VGG16(self):
model = Sequential()
input_shape= (self.config.normal_size, self.config.normal_size, self.config.channles)
model.add(Convolution2D(64,(3,3),input_shape=input_shape,activation='relu',padding='same'))
model.add(Convolution2D(64,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(128,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(128,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Convolution2D(256,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(256,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(256,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Flatten())
model.add(Dense(4096,activation='relu'))
model.add(Dense(4096,activation='relu'))
model.add(Dense(self.config.classNumber,activation='softmax'))
return model
def VGG19(self):
model = Sequential()
input_shape= (self.config.normal_size, self.config.normal_size, self.config.channles)
model.add(Convolution2D(64,(3,3),input_shape=input_shape,activation='relu',padding='same'))
model.add(Convolution2D(64,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(128,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(128,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Convolution2D(256,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(256,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(256,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(256,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(Convolution2D(512,(3,3),activation='relu',padding='same'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Flatten())
model.add(Dense(4096,activation='relu'))
model.add(Dense(4096,activation='relu'))
model.add(Dense(self.config.classNumber,activation='softmax'))
return model
#LeNet
def LeNet(self):
# initialize the model
model = Sequential()
inputShape = (self.config.normal_size, self.config.normal_size, self.config.channles)
model.add(Conv2D(20, (5, 5), padding="same", input_shape=inputShape))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# second set of CONV => RELU => POOL layers
model.add(Conv2D(50, (5, 5), padding="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# first (and only) set of FC => RELU layers
model.add(Flatten())
model.add(Dense(500))
model.add(Activation("relu"))
# softmax classifier
model.add(Dense(self.config.classNumber))
model.add(Activation("softmax"))
# model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
# return the constructed network architecture
return model
#ZF_Net,8 layers
def ZF_Net(self):
model = Sequential()
model.add(
Conv2D(96, (7, 7), strides=(2, 2),
input_shape=(self.config.normal_size, self.config.normal_size,self.config.channles),
padding='valid',
activation='relu',
kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
model.add(Conv2D(256, (5, 5), strides=(2, 2), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
model.add(Conv2D(384, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Conv2D(384, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(self.config.classNumber, activation='softmax'))
# model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
#RESNET
class ResnetBuilder(object):
# @staticmethod
def build(self,config, block_fn, repetitions):
"""Builds a custom ResNet like architecture.
Args:
input_shape: The input shape in the form (nb_channels, nb_rows, nb_cols)
num_outputs: The number of outputs at final softmax layer
block_fn: The block function to use. This is either `basic_block` or `bottleneck`.
The original paper used basic_block for layers < 50
repetitions: Number of repetitions of various block units.
At each block unit, the number of filters are doubled and the input size is halved
Returns:
The keras `Model`.
"""
input_shape = (config.normal_size,config.normal_size,config.channles)
num_outputs = config.classNumber
self._handle_dim_ordering()
block_fn = self._get_block(block_fn)
input = Input(shape=input_shape)
conv1 = self._conv_bn_relu(filters=64, kernel_size=(7, 7), strides=(2, 2))(input)
pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same")(conv1)
block = pool1
filters = 64
for i, r in enumerate(repetitions):
block = self._residual_block(block_fn, filters=filters, repetitions=r, is_first_layer=(i == 0))(block)
filters *= 2
# Last activation
block = self._bn_relu(block)
# Classifier block
block_shape = K.int_shape(block)
pool2 = AveragePooling2D(pool_size=(block_shape[ROW_AXIS], block_shape[COL_AXIS]),
strides=(1, 1))(block)
flatten1 = Flatten()(pool2)
dense = Dense(units=num_outputs,
activation="softmax")(flatten1)
model = Model(inputs=input, outputs=dense)
# model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
# @staticmethod
def build_resnet18(self,params):
return self.build(params, self.basic_block, [2, 2, 2, 2])
# @staticmethod
def build_resnet34(self,params):
return self.build(params, self.basic_block, [3, 4, 6, 3])
# @staticmethod
def build_resnet50(self,params):
return self.build(params, self.bottleneck, [3, 4, 6, 3])
# @staticmethod
def build_resnet101(self,params):
return self.build(params, self.bottleneck, [3, 4, 23, 3])
# @staticmethod
def build_resnet152(self,params):
return self.build(params, self.bottleneck, [3, 8, 36, 3])
def _bn_relu(self,input):
"""Helper to build a BN -> relu block
"""
norm = BatchNormalization(axis=CHANNEL_AXIS)(input)
return Activation("relu")(norm)
def _conv_bn_relu(self,**conv_params):
"""Helper to build a conv -> BN -> relu block
"""
filters = conv_params["filters"]
kernel_size = conv_params["kernel_size"]
strides = conv_params.setdefault("strides", (1, 1))
kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal")
padding = conv_params.setdefault("padding", "same")
kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(1.e-4))
def f(input):
conv = Conv2D(filters=filters, kernel_size=kernel_size,
strides=strides, padding=padding,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)(input)
return self._bn_relu(conv)
return f
def _bn_relu_conv(self,**conv_params):
"""Helper to build a BN -> relu -> conv block.
This is an improved scheme proposed in http://arxiv.org/pdf/1603.05027v2.pdf
"""
filters = conv_params["filters"]
kernel_size = conv_params["kernel_size"]
strides = conv_params.setdefault("strides", (1, 1))
kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal")
padding = conv_params.setdefault("padding", "same")
kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(1.e-4))
def f(input):
activation = self._bn_relu(input)
return Conv2D(filters=filters, kernel_size=kernel_size,
strides=strides, padding=padding,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)(activation)
return f
def _shortcut(self,input, residual):
"""Adds a shortcut between input and residual block and merges them with "sum"
"""
# Expand channles of shortcut to match residual.
# Stride appropriately to match residual (width, height)
# Should be int if network architecture is correctly configured.
input_shape = K.int_shape(input)
residual_shape = K.int_shape(residual)
stride_width = int(round(input_shape[ROW_AXIS] / residual_shape[ROW_AXIS]))
stride_height = int(round(input_shape[COL_AXIS] / residual_shape[COL_AXIS]))
equal_channels = input_shape[CHANNEL_AXIS] == residual_shape[CHANNEL_AXIS]
shortcut = input
# 1 X 1 conv if shape is different. Else identity.
if stride_width > 1 or stride_height > 1 or not equal_channels:
shortcut = Conv2D(filters=residual_shape[CHANNEL_AXIS],
kernel_size=(1, 1),
strides=(stride_width, stride_height),
padding="valid",
kernel_initializer="he_normal",
kernel_regularizer=l2(0.0001))(input)
return add([shortcut, residual])
def _residual_block(self,block_function, filters, repetitions, is_first_layer=False):
"""Builds a residual block with repeating bottleneck blocks.
"""
def f(input):
for i in range(repetitions):
init_strides = (1, 1)
if i == 0 and not is_first_layer:
init_strides = (2, 2)
input = block_function(filters=filters, init_strides=init_strides,
is_first_block_of_first_layer=(is_first_layer and i == 0))(input)
return input
return f
def basic_block(self,filters, init_strides=(1, 1), is_first_block_of_first_layer=False):
"""Basic 3 X 3 convolution blocks for use on resnets with layers <= 34.
Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf
"""
def f(input):
if is_first_block_of_first_layer:
# don't repeat bn->relu since we just did bn->relu->maxpool
conv1 = Conv2D(filters=filters, kernel_size=(3, 3),
strides=init_strides,
padding="same",
kernel_initializer="he_normal",
kernel_regularizer=l2(1e-4))(input)
else:
conv1 = self._bn_relu_conv(filters=filters, kernel_size=(3, 3),
strides=init_strides)(input)
residual = self._bn_relu_conv(filters=filters, kernel_size=(3, 3))(conv1)
return self._shortcut(input, residual)
return f
def bottleneck(self,filters, init_strides=(1, 1), is_first_block_of_first_layer=False):
"""Bottleneck architecture for > 34 layer resnet.
Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf
Returns:
A final conv layer of filters * 4
"""
def f(input):
if is_first_block_of_first_layer:
# don't repeat bn->relu since we just did bn->relu->maxpool
conv_1_1 = Conv2D(filters=filters, kernel_size=(1, 1),
strides=init_strides,
padding="same",
kernel_initializer="he_normal",
kernel_regularizer=l2(1e-4))(input)
else:
conv_1_1 = self._bn_relu_conv(filters=filters, kernel_size=(1, 1),
strides=init_strides)(input)
conv_3_3 = self._bn_relu_conv(filters=filters, kernel_size=(3, 3))(conv_1_1)
residual = self._bn_relu_conv(filters=filters * 4, kernel_size=(1, 1))(conv_3_3)
return self._shortcut(input, residual)
return f
def _handle_dim_ordering(self):
global ROW_AXIS
global COL_AXIS
global CHANNEL_AXIS
if K.image_dim_ordering() == 'tf':
ROW_AXIS = 1
COL_AXIS = 2
CHANNEL_AXIS = 3
else:
CHANNEL_AXIS = 1
ROW_AXIS = 2
COL_AXIS = 3
def _get_block(self,identifier):
if isinstance(identifier, six.string_types):
res = globals().get(identifier)
if not res:
raise ValueError('Invalid {}'.format(identifier))
return res
return identifier