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train.py
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import tensorflow as tf
from tensorflow.python.lib.io import file_io
from imports.data_utils import create_one_shot_iterator, augment_dataset, create_initializable_iterator
from imports.losses import loss_fun
from imports.models import segmentation_block, feathering_block
from imports.metrics import iou
import os
import argparse
def args_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--mode', default=1, type=int)
parser.add_argument('--train_files', nargs='+', required=False, default="train-00001-of-00001")
parser.add_argument('--test_files', nargs='+', required=False, default="val-00001-of-00001")
parser.add_argument('--log_dir', default='./logs', type=str)
parser.add_argument('--ckpt_dir', default='./ckpts', type=str)
parser.add_argument('--train_batch_size', default=256, type=int)
parser.add_argument('--test_batch_size', default=256, type=int)
parser.add_argument('--num_epochs', default=10000, type=int)
parser.add_argument('--learning_rate', default=1e-3, type=float)
parser.add_argument('--resume', default=None, type=int)
return parser.parse_args()
if __name__ == '__main__':
args = args_parser()
mode = args.mode
if mode is None or mode <= 0 or mode > 3:
raise Exception("Invalid mode")
train_files = args.train_files
test_files = args.test_files
train_batch_size = args.train_batch_size
test_batch_size = args.test_batch_size
num_train_samples = sum(1 for f in file_io.get_matching_files(train_files)
for n in tf.python_io.tf_record_iterator(f))
num_test_samples = sum(1 for f in file_io.get_matching_files(test_files)
for n in tf.python_io.tf_record_iterator(f))
num_epochs = args.num_epochs
train_iterator = create_one_shot_iterator(train_files, train_batch_size, num_epoch=num_epochs)
test_iterator = create_initializable_iterator(test_files, batch_size=num_test_samples)
next_images, next_masks = train_iterator.get_next()
next_images, next_masks = augment_dataset(next_images, next_masks, size=[128, 128])
coarse_masks = segmentation_block(next_images)
alpha_mattes = feathering_block(next_images, coarse_masks)
loss = loss_fun(next_images, next_masks, alpha_mattes)
test_images, test_masks = test_iterator.get_next()
test_images, test_masks = augment_dataset(test_images, test_masks, size=[128, 128], augment=False)
test_coarse_masks = segmentation_block(test_images)
test_alpha_mattes = feathering_block(test_images, test_coarse_masks)
test_loss = loss_fun(test_images, test_masks, test_alpha_mattes)
train_iou = iou(next_masks, alpha_mattes)
test_iou = iou(test_masks, test_alpha_mattes)
all_trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
train_op = tf.train.AdamOptimizer(learning_rate=args.learning_rate).minimize(loss, var_list=all_trainable_vars)
summary = tf.summary.FileWriter(logdir=args.log_dir)
image_summary = tf.summary.image("image", next_images)
gt_summary = tf.summary.image("gt", next_masks * next_images)
result_summary = tf.summary.image("result", alpha_mattes * next_images)
images_summary = tf.summary.merge([image_summary, gt_summary, result_summary])
test_image_summary = tf.summary.image("test_image", test_images)
test_gt_summary = tf.summary.image("test_gt", test_masks * test_images)
test_result_summary = tf.summary.image("test_result", test_alpha_mattes * test_images)
test_images_summary = tf.summary.merge([test_image_summary, test_gt_summary, test_result_summary])
loss_summary = tf.summary.scalar("loss", loss)
test_loss_summary = tf.summary.scalar("test_loss", test_loss)
train_iou_sum = tf.summary.scalar("train_iou", train_iou)
test_iou_sum = tf.summary.scalar("test_iou", test_iou)
saver = tf.train.Saver(var_list=tf.trainable_variables())
resume = args.resume
def get_session(sess):
session = sess
while type(session).__name__ != 'Session':
session = session._sess
return session
with tf.train.MonitoredTrainingSession() as sess:
it = 0
if resume is not None and resume > 0:
saver.restore(sess, os.path.join(args.ckpt_dir, "ckpt") + "-{it}".format(it=resume))
it = resume + 1
while not sess.should_stop():
_, cur_loss, cur_images_summary, cur_loss_summary, cur_train_iou = sess.run([train_op, loss, images_summary, loss_summary, train_iou_sum])
summary.add_summary(cur_loss_summary, it)
summary.add_summary(cur_train_iou, it)
if it % 10 == 0:
summary.add_summary(cur_images_summary, it)
if it % 200 == 0:
sess.run(test_iterator.initializer)
cur_test_loss_summary, cur_test_images_summary, cur_test_iou = sess.run([test_loss_summary, test_images_summary, test_iou_sum])
summary.add_summary(cur_test_loss_summary, it)
summary.add_summary(cur_test_images_summary, it)
summary.add_summary(cur_test_iou, it)
summary.flush()
if it % 5000 == 0:
ckpt_path = saver.save(get_session(sess), save_path=os.path.join(args.ckpt_dir, "ckpt"),
write_meta_graph=False, global_step=it)
print("Checkpoint saved as: {ckpt_path}".format(ckpt_path=ckpt_path))
it += 1
sess.run(test_iterator.initializer)
cur_test_loss_summary, zzz = sess.run([test_loss_summary, test_images_summary])
summary.add_summary(cur_test_loss_summary, it)
summary.add_summary(zzz, it)
ckpt_path = saver.save(get_session(sess), save_path=os.path.join(args.ckpt_dir, "ckpt"), write_meta_graph=False,
global_step=it)
print("Checkpoint saved as: {ckpt_path}".format(ckpt_path=ckpt_path))