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cls.py
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from tensorflow.keras.layers import Dropout,Dense,Conv2D,GlobalAveragePooling2D,Flatten,Reshape,Activation
from tensorflow.keras import Model
from tensorflow.keras.applications import VGG16,VGG19,ResNet50,InceptionResNetV2,DenseNet121,MobileNet
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import TensorBoard,ReduceLROnPlateau,ModelCheckpoint,EarlyStopping
from tensorflow.keras.optimizers import SGD,Adam
import os
class classifier(object):
def __init__(self,img_dir,split_radio,batch_size,val_batch_size,base_model='vgg16',img_shape=(None,None,3),cls_num=21):
self.img_shape=img_shape
self.img_dir=img_dir
self.base_model=base_model
self.split_radio=split_radio
self.batch_size=batch_size if self.img_shape!=(None,None,3) else 1
self.val_batch_size=val_batch_size if self.img_shape!=(None,None,3) else 1
self.cls_num=cls_num
def model(self,freeze=True):
if self.base_model.lower()=='vgg16':
base_model=VGG16(weights='imagenet',include_top=False,input_shape=self.img_shape)
input_layer=base_model.input
layer=base_model.get_layer('block5_pool').output
elif self.base_model.lower()=='vgg19':
base_model=VGG19(weights='imagenet',include_top=False,input_shape=self.img_shape)
input_layer = base_model.input
layer = base_model.output
elif self.base_model.lower()=='resnet50':
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=self.img_shape)
input_layer = base_model.input
layer = base_model.output
elif 'inception' in self.base_model.lower():
base_model = InceptionResNetV2(weights='imagenet', include_top=False, input_shape=self.img_shape)
input_layer = base_model.input
layer = base_model.output
elif self.base_model.lower()=='mobilenet':
base_model=MobileNet(weights='imagenet', include_top=False, input_shape=self.img_shape,pooling='avg')
input_layer=base_model.input
layer=base_model.output
else:
base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=self.img_shape)
input_layer = base_model.input
layer = base_model.output
if self.base_model.lower()=='vgg16':
layer = Flatten()(layer)
layer = Dense(4096, activation='relu', name='fc1', kernel_initializer='he_normal')(layer)
layer = Dropout(0.5)(layer)
layer = Dense(4096, activation='relu', name='fc2', kernel_initializer='he_normal')(layer)
layer = Dropout(0.5)(layer)
prediction = Dense(self.cls_num, activation='softmax', name='predictions')(layer)
elif self.base_model.lower()=='mobilenet':
# layer=GlobalAveragePooling2D()(layer)
layer=Reshape((1,1,1024),name='reshape_1')(layer)
layer=Dropout(1e-3)(layer)
layer=Conv2D(self.cls_num, (1, 1),
padding='same',
name='conv_preds')(layer)
layer=Activation('softmax')(layer)
prediction=Reshape((self.cls_num,))(layer)
elif self.base_model.lower()=='resnet50':
layer=GlobalAveragePooling2D()(layer)
prediction = Dense(self.cls_num, activation='softmax', name='predictions')(layer)
else:
layer = GlobalAveragePooling2D()(layer)
prediction = Dense(self.cls_num, activation='softmax', name='predictions')(layer)
if freeze==True:
for layer in base_model.layers:
layer.trainable=False
else:
for layer in base_model.layers:
layer.trainable=True
model_finetune = Model(input_layer, prediction)
return model_finetune
def train(self,initial_epoch=0,epochs=20,opt='sgd'):
gen=ImageDataGenerator(
rotation_range=360,horizontal_flip=True,vertical_flip=True,shear_range=0.2,zoom_range=0.2,
rescale=1/255,validation_split=self.split_radio
)
train_data = gen.flow_from_directory(
directory=self.img_dir,
batch_size=self.batch_size,
target_size=(self.img_shape[0],self.img_shape[1]),
shuffle=True,
class_mode='categorical',
subset='training'
)
val_data = gen.flow_from_directory(
directory=self.img_dir,
batch_size=self.val_batch_size,
target_size=(self.img_shape[0],self.img_shape[1]),
shuffle=True,
class_mode='categorical',
subset='validation'
)
model_finetune=self.model(freeze=False)
optimizer=SGD(lr=1e-3,momentum=0.9,nesterov=True) if opt=='sgd' else Adam(lr=0.0001)
model_finetune.compile(
optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['acc']
)
model_finetune.summary()
tb=TensorBoard(log_dir='logs',batch_size=self.batch_size)
es=EarlyStopping(monitor='val_acc',patience=15,verbose=1,min_delta=0.0001)
cp=ModelCheckpoint(filepath=os.path.join('model','freeze_vgg16_cls--epoch_{epoch:02d}--val_loss_{val_loss:.5f}--val_acc_{val_acc:.5f}--train_loss_{loss:.5f}--train_acc_{acc:.5f}.hdf5'),
monitor='val_acc', save_best_only=True,save_weights_only=True,
verbose=1, period=1)
lr=ReduceLROnPlateau(monitor='val_acc',patience=5,verbose=1)
his=model_finetune.fit_generator(
generator=train_data,
steps_per_epoch=train_data.samples//self.batch_size,
validation_data=val_data,
validation_steps=val_data.samples//self.val_batch_size,
initial_epoch=initial_epoch,
epochs=epochs,
callbacks=[tb,es,cp,lr]
)
print(his)
if __name__=='__main__':
model=classifier(
img_dir='train_data_2',
split_radio=0.1,
batch_size=19,
val_batch_size=19,
base_model='vgg16',
img_shape=(224,224,3),
cls_num=77
)
model.train(initial_epoch=0,epochs=100,opt='sgd')