tftk.Context.init_context(training_name='ssim_catsdog_deep_autoencoder')
tftk.ENABLE_MIXED_PRECISION()
tftk.ENABLE_SUSPEND_RESUME_TRAINING()
IMAGE_SIZE = 128
EPOCHS = 80
BATCH_SIZE = 50
# mvtec_ad, len = MVTecAd.get_train_dataset("bottle")
# mvtec_ad = mvtec_ad.map(ImageDatasetUtil.resize(IMAGE_SIZE,IMAGE_SIZE))
# (train, len),(validation, validation_len) =ImageDatasetUtil.devide_train_validation(mvtec_ad,len,0.9)
cats_vs_dogs, total_len = CatsVsDogs.get_train_dataset()
cats_vs_dogs = cats_vs_dogs.map(
ImageDatasetUtil.map_max_square_crop_and_resize(IMAGE_SIZE, IMAGE_SIZE))
(train, len), (validation,
validation_len) = ImageDatasetUtil.devide_train_validation(
cats_vs_dogs, total_len, 0.9)
train = train.map(ImageDatasetUtil.image_reguralization(),
num_parallel_calls=tf.data.experimental.AUTOTUNE
) # .map(ImageDatasetUtil.resize(64,64))
validation_r = validation.map(ImageDatasetUtil.image_reguralization(),
num_parallel_calls=tf.data.experimental.AUTOTUNE
) # .map(ImageDatasetUtil.resize(64,64))
model = SSIMAutoEncoderModel.get_model(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))
optimizer = OptimizerBuilder.get_optimizer("rmsprop")
callback = CallbackBuilder.get_callbacks()
if __name__ == '__main__':
context = Context.init_context(
TRAINING_BASE_DIR="tmp",
TRAINING_NAME="food101"
)
tftk.ENABLE_MIXED_PRECISION()
BATCH_SIZE = 64
CLASS_NUM = 101
IMAGE_SIZE = 224
CHANNELS = 3
EPOCHS = 100
SHUFFLE_SIZE = 1000
train, train_len = Food101.get_train_dataset()
validation, validation_len = Food101.get_validation_dataset()
train = train.map(ImageDatasetUtil.map_max_square_crop_and_resize(IMAGE_SIZE,IMAGE_SIZE),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageAugument.randaugment_map(1,2))
train = train.map(ImageDatasetUtil.image_reguralization(),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageDatasetUtil.one_hot(CLASS_NUM),num_parallel_calls=tf.data.experimental.AUTOTUNE).apply(ImageAugument.mixup_apply(200,0.1))
validation = validation.map(ImageDatasetUtil.map_max_square_crop_and_resize(IMAGE_SIZE,IMAGE_SIZE),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageDatasetUtil.image_reguralization(),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageDatasetUtil.one_hot(CLASS_NUM),num_parallel_calls=tf.data.experimental.AUTOTUNE)
optimizer = OptimizerBuilder.get_optimizer(name="rmsprop", lr=0.05)
model = KerasResNet50V2.get_model(input_shape=(IMAGE_SIZE,IMAGE_SIZE,CHANNELS),classes=CLASS_NUM) # resnest=True,resnet_c=True,resnet_d=True,mish=True)
callbacks = CallbackBuilder.get_callbacks(tensorboard=False, consine_annealing=False, reduce_lr_on_plateau=True,reduce_patience=5,reduce_factor=0.25,early_stopping_patience=8)
ImageTrain.train_image_classification(train_data=train,train_size=train_len,batch_size=BATCH_SIZE,validation_data=validation,validation_size=validation_len,shuffle_size=SHUFFLE_SIZE,model=model,callbacks=callbacks,optimizer=optimizer,loss="categorical_crossentropy",max_epoch=EPOCHS)