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# -*- coding: utf-8 -*-
"""Train the Segnet model"""
import argparse
from os import listdir, environ
import pandas as pd
from keras.callbacks import ModelCheckpoint
from segnet import create_segnet
from generator import segnet_generator, domain_generator
from configuration import CONFIG
#Set CUDA device for tensorflow
environ["CUDA_VISIBLE_DEVICES"] = CONFIG['segnet']['cuda_device']
def main(args):
"""Training"""
training_list = pd.DataFrame(listdir(args.trainimg_dir))
val_list = pd.DataFrame(listdir(args.valimg_dir))
#from sklearn.model_selection import train_test_split
#training_list, val_list = train_test_split(pd.DataFrame(listdir(args.trainimg_dir)), test_size=0.1)
# Training generator
segnet_train_gen = segnet_generator(img_dir=args.trainimg_dir,
mask_dir=args.trainmsk_dir,
lists=training_list,
batch_size=args.batch_size,
dims=[args.input_shape[0], args.input_shape[1]],
n_labels=args.n_labels,
crop=args.crop,
flip=args.flip,
motion_blur=args.motion_blur,
sp_noise=args.sp_noise)
# Validation generator
segnet_val_gen = segnet_generator(img_dir=args.valimg_dir,
mask_dir=args.valmsk_dir,
lists=val_list,
batch_size=args.batch_size,
dims=[args.input_shape[0], args.input_shape[1]],
n_labels=args.n_labels,
crop=args.crop,
flip=args.flip,
motion_blur=args.motion_blur,
sp_noise=args.sp_noise)
# Domain adaptation generator
if args.domain_adaptation:
domain_train_gen = domain_generator(img_dir=args.trainimg_dir,
domain_dir=args.domainimg_dir,
img_list=pd.DataFrame(listdir(args.trainimg_dir)),
domain_list=pd.DataFrame(listdir(args.domainimg_dir)),
batch_size=args.batch_size,
dims=[args.input_shape[0], args.input_shape[1]],
crop=args.crop,
flip=args.flip,
motion_blur=args.motion_blur,
sp_noise=args.sp_noise)
# Create the complete network
segnet, domain_adapt = create_segnet(input_shape=args.input_shape,
n_labels=args.n_labels,
kernel=args.kernel,
pool_size=args.pool_size,
output_mode=args.output_mode,
reverse_ratio=args.reverse_ratio)
print("SegNet/DANN created")
# Load weights if specified in args
if args.segnet_weights and args.domain_weights:
segnet.load_weights(args.segnet_weights)
domain_adapt.load_weights(args.domain_weights)
# Compile both models
segnet.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
if args.domain_adaptation:
domain_adapt.compile(loss=args.loss,
optimizer=args.optimizer,
metrics=["accuracy"])
#Set callbacks
checkpoint = ModelCheckpoint(filepath="./weights/weights.{epoch:02d}.hdf5",
monitor='val_acc',
verbose=1,
save_best_only=False,
save_weights_only=True)
callbacks_list = [checkpoint]
# Custom training loop
# Each complete epoch is one epoch on segnet, one epoch on dann
for i in range(0, args.n_epochs):
print("")
print("--- MAIN EPOCH " + str(i + 1) + " / " + str(args.n_epochs) + " ---")
print("--- SEGNET ---")
segnet.fit_generator(segnet_train_gen,
steps_per_epoch=args.epoch_steps,
epochs=i+1,
initial_epoch=i,
validation_data=segnet_val_gen,
validation_steps=args.val_steps,
workers=2,
max_queue_size=2 * args.batch_size,
callbacks=callbacks_list)
#class_weight = args.class_weight)
if args.domain_adaptation:
print("--- ADAPTATION ---")
domain_adapt.fit_generator(domain_train_gen,
steps_per_epoch=args.epoch_steps / 2,
epochs=1,
workers=2,
max_queue_size=2 * args.batch_size)
# Save weights of both models on completion
segnet.save_weights("./weights/SegNet-"+str(args.n_epochs)+".hdf5")
if args.domain_adaptation:
domain_adapt.save_weights("./weights/Domain_adapt-"+str(args.n_epochs)+".hdf5")
print("Weights saved")
if __name__ == "__main__":
# command line argments
parser = argparse.ArgumentParser(description="SegNet dataset")
parser.add_argument("--segnet_weights",
default=None,
help="Segnet branch starting weights path")
parser.add_argument("--domain_weights",
default=None,
help="Domain adaptation branch starting weights path")
parser.add_argument("--trainimg_dir",
default=CONFIG['dataset']['train']['images_dir'],
help="train image dir path")
parser.add_argument("--trainmsk_dir",
default=CONFIG['dataset']['train']['masks_dir'],
help="train mask dir path")
parser.add_argument("--valimg_dir",
default=CONFIG['dataset']['val']['images_dir'],
help="val image dir path")
parser.add_argument("--valmsk_dir",
default=CONFIG['dataset']['val']['masks_dir'],
help="val mask dir path")
parser.add_argument("--domainimg_dir",
default=CONFIG['dataset']['other_domain']['images_dir'],
help="domain image dir path")
parser.add_argument("--batch_size",
default=CONFIG['training']['batch_size'],
type=int,
help="batch size")
parser.add_argument("--n_epochs",
default=CONFIG['training']['n_epochs'],
type=int,
help="number of epoch")
parser.add_argument("--epoch_steps",
default=CONFIG['training']['train_steps'],
type=int,
help="number of epoch step")
parser.add_argument("--val_steps",
default=CONFIG['training']['val_steps'],
type=int,
help="number of validation step")
parser.add_argument("--n_labels",
default=CONFIG['dataset']['n_labels'],
type=int,
help="Number of label")
parser.add_argument("--class_weight",
default=CONFIG['dataset']['class_weight'],
help="Weight of segmentation classes")
parser.add_argument("--crop",
default=CONFIG['training']['crop'],
help="Crop to input shape, otherwise resize")
parser.add_argument("--flip",
default=CONFIG['training']['flip'],
help="Random flip of training images")
parser.add_argument("--sp_noise",
default=CONFIG['training']['sp_noise'],
help="Fraction of images with added noise")
parser.add_argument("--motion_blur",
default=CONFIG['training']['motion_blur'],
help="Fraction of images with added blur")
parser.add_argument("--input_shape",
default=CONFIG['segnet']['input_shape'],
help="Input images shape")
parser.add_argument("--kernel",
default=CONFIG['segnet']['kernel'],
type=int,
help="Kernel size")
parser.add_argument("--pool_size",
default=CONFIG['segnet']['pool_size'],
help="pooling and unpooling size")
parser.add_argument("--output_mode",
default=CONFIG['segnet']['output_mode'],
type=str,
help="output activation")
parser.add_argument("--loss",
default=CONFIG['segnet']['loss'],
type=str,
help="loss function")
parser.add_argument("--optimizer",
default=CONFIG['segnet']['optimizer'],
type=str,
help="optimizer")
parser.add_argument("--domain_adaptation",
default=CONFIG['training']['domain_adaptation'],
help="True/False for domain adaptation to the network")
parser.add_argument("--reverse_ratio",
default=CONFIG['segnet']['reverse_ratio'],
type=int,
help="Gradient multiplier for the dann branch")
args = parser.parse_args()
main(args)