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ResNetCIFAR.py
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ResNetCIFAR.py
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import keras
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
from keras.layers import AveragePooling2D, Input, Flatten
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator
from keras.regularizers import l2
from keras import backend as K
from keras.models import Model
from keras.datasets import cifar10
from modules.resnet_modules import ResNetModules
from keras.optimizers import Adam
import numpy as np
import os
class ResNetCIFAR:
def __init__(self, version=1, n=3):
self.version = version
self.input_shape = (32, 32, 3)
self.num_classes = 10
self.depth = n
self.model = self.build_model()
def build_model(self):
if self.version == 1:
return self.resnet_v1()
else:
return self.resnet_v2()
def resnet_v1(self):
if (self.depth - 2) % 6 != 0:
raise ValueError('Depth should be 6n+2 (20, 26, 32, ..)')
num_filters = 16
num_res_blocks = int((self.depth - 2) / 6)
inputs = Input(shape=self.input_shape)
x = ResNetModules.resnet_layer(input_tensor=inputs)
for stack in range(3):
for res_block in range(num_res_blocks):
strides = 1
if stack > 0 and res_block == 0:
strides = 2
y = ResNetModules.resnet_layer(input_tensor=x, num_filters=num_filters, strides=strides)
y = ResNetModules.resnet_layer(input_tensor=y, num_filters=num_filters, activation=None)
if stack > 0 and res_block==0:
x = ResNetModules.resnet_layer(input_tensor=x, num_filters=num_filters, kernel_size=1, strides=strides, activation=None, batch_normalization=False)
x = keras.layers.add([x, y])
x = Activation('relu')(x)
num_filters *= 2
x = AveragePooling2D(pool_size=8)(x)
y = Flatten()(x)
outputs = Dense(units=self.num_classes,
activation='softmax',
kernel_initializer='he_normal')(y)
model = Model(inputs=inputs, outputs=outputs, name='ResNet v1 - CIFAR10')
return model
def resnet_v2(self):
if (self.depth - 2) % 9 != 0:
raise ValueError('Depth should be 9n+2 (56, 92, 101, ..)')
num_filters_in = 16
num_res_blocks = int((self.depth - 2) / 9)
inputs = Input(shape=self.input_shape)
x = ResNetModules.resnet_layer(input_tensor=inputs,
num_filters=num_filters_in,
conv_first=True)
for stage in range(3):
for res_block in range(num_res_blocks):
activation = 'relu'
batch_normalization = True
strides = 1
if stage==0:
num_filters_out = num_filters_in * 4
if res_block == 0:
activation = None
batch_normalization = False
else:
num_filters_out = num_filters_in * 2
if res_block == 0:
strides = 2
y = ResNetModules.resnet_layer(input_tensor=x, num_filters=num_filters_in, kernel_size=1, activation=activation, strides=strides, batch_normalization=batch_normalization, conv_first=False)
y = ResNetModules.resnet_layer(input_tensor=y, num_filters=num_filters_in, conv_first=False)
y = ResNetModules.resnet_layer(input_tensor=y, num_filters=num_filters_out, kernel_size=1, conv_first=False)
if res_block == 0:
x = ResNetModules.resnet_layer(input_tensor=x, num_filters=num_filters_out, kernel_size=1, strides=strides, activation=None, batch_normalization=False)
x = keras.layers.add([x, y])
num_filters_in = num_filters_out
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = AveragePooling2D(pool_size=8)(x)
y = Flatten()(x)
outputs = Dense(units=self.num_classes, activation='softmax', kernel_initializer='he_normal')(y)
model = Model(inputs=inputs, outputs=outputs, name='ResNet v2 - CIFAR10')
return model
def train(self, epochs=200, batch_size=32, summary=False):
print("[INFO]: Downloading dataset")
(trainX, trainY), (testX, testY) = cifar10.load_data()
trainX = trainX.astype(np.float32) / 255.
testX = testX.astype(np.float32) / 255.
mean = np.mean(trainX, axis=0)
trainX -= mean
testX -= mean
trainY = keras.utils.to_categorical(trainY, self.num_classes)
testY = keras.utils.to_categorical(testY, self.num_classes)
def lr_scheduler(epoch):
lr = 1e-3
if epoch > 180:
lr *= 0.5e-3
elif epoch > 160:
lr *= 1e-3
elif epoch > 120:
lr *= 1e-2
elif epoch > 80:
lr *= 1e-1
print('Learning rate: ', lr)
return lr
print("[INFO]: Compiling model...")
self.model.compile(optimizer=Adam(learning_rate=lr_scheduler(0)),
loss='categorical_crossentropy',
metrics=['accuracy'])
if summary:
self.model.summary()
filepath=r"ResNet-weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', save_best_only=True, mode='max')
callbacks = [LearningRateScheduler(lr_scheduler), checkpoint]
datagen = ImageDataGenerator(featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=False,
rotation_range=0,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.0,
zoom_range=0.0,
channel_shift_range=0,
fill_mode='nearest',
cval=0,
horizontal_flip=True,
vertical_flip=False,
rescale=None,
preprocessing_function=None,
data_format=None,
)
datagen.fit(trainX)
print("[INFO]: Training model")
history = self.model.fit_generator(datagen.flow(trainX, trainY, batch_size=batch_size),
validation_data=(testX, testY),
epochs=epochs,
verbose=1,
workers=4,
callbacks=callbacks)
return history