length_model_train.py

from sidekick.nn.conv.length_model import MiniVgg
from sidekick.io.hdf5datagen import Hdf5DataGen
from sidekick.callbs.manualcheckpoint import ManualCheckpoint
from tensorflow.keras.models import load_model
from sidekick.prepro.process import Process
from sidekick.prepro.imgtoarrayprepro import ImgtoArrPrePro
from tensorflow.keras.optimizers import SGD
import argparse

ap= argparse.ArgumentParser()
ap.add_argument('-o','--output', type=str, required=True ,help="Path to output directory")
ap.add_argument('-m', '--model', help='Path to checkpointed model')
ap.add_argument('-e','--epoch', type=int, default=0, help="Starting epoch of training")
args= vars(ap.parse_args())

hdf5_train_path= "train.hdf5"
hdf5_val_path= "val.hdf5"
epochs= 50
lr= 1e-2
batch_size= 32
num_classes= 1
fig_path= args['output']+"train_plot.jpg"
json_path= args['output']+"train_values.json"

print('[NOTE]:- Building Dataset...\n')
pro= Process(224, 224)
i2a= ImgtoArrPrePro()

train_gen= Hdf5DataGen(hdf5_train_path, batch_size, num_classes, encode=False, preprocessors=[pro, i2a])
val_gen= Hdf5DataGen(hdf5_val_path, batch_size, num_classes, encode=False, preprocessors=[pro, i2a])

if args['model'] is None:
    print("[NOTE]:- Building model from scratch...")
    model= MiniVgg.build(224, 224, 1, num_classes)
    opt= SGD(learning_rate=lr, momentum=0.9, nesterov=True)
    model.compile(loss="mean_absolute_percentage_error", optimizer=opt)
else:
    print("[NOTE]:- Building model {}\n".format(args['model']))
    model= load_model(args['model'])

callbacks= [ManualCheckpoint(args['output'], save_at=1, start_from=args['epoch'])]

print("[NOTE]:- Training model...\n")

model.fit_generator(train_gen.generator(),
                    steps_per_epoch=train_gen.data_length//batch_size,
                    validation_data= val_gen.generator(),
                    validation_steps= val_gen.data_length//batch_size,
                    epochs=epochs,
                    max_queue_size=10,
                    callbacks=callbacks,
                    initial_epoch=args['epoch'])