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train.py
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train.py
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from __future__ import print_function, division
import argparse
import functools
import time
import tensorflow as tf, numpy as np, os, random
from utils import get_files, get_img_random_crop
from model import WCTModel
import threading
parser = argparse.ArgumentParser()
### Directories
parser.add_argument('--checkpoint', type=str,
dest='checkpoint', help='Checkpoint save dir',
required=True)
parser.add_argument('--log-path', type=str,
dest='log_path', help='Logging dir path')
parser.add_argument('--relu-target', type=str, required=True,
help='Target VGG19 relu layer to decode from, e.g. relu4_1')
parser.add_argument('--content-path', type=str, required=True,
dest='content_path', help='Content images folder')
parser.add_argument('--val-path', type=str, default=None,
dest='val_path', help='Validation images folder')
parser.add_argument('--vgg-path', type=str,
dest='vgg_path', help='Path to vgg_normalised.t7',
default='models/vgg_normalised.t7')
### Loss weights
parser.add_argument('--feature-weight', type=float,
dest='feature_weight', help='Feature loss weight',
default=1)
parser.add_argument('--pixel-weight', type=float,
dest='pixel_weight', help='Pixel reconstruction loss weight',
default=1)
parser.add_argument('--tv-weight', type=float,
dest='tv_weight', help='Total variation loss weight',
default=0)
### Train opts
parser.add_argument('--learning-rate', type=float,
dest='learning_rate', help='Learning rate',
default=1e-4)
parser.add_argument('--lr-decay', type=float,
dest='lr_decay', help='Learning rate decay',
default=0)
parser.add_argument('--max-iter', type=int,
dest='max_iter', help='Max # of training iterations',
default=16000)
parser.add_argument('--batch-size', type=int,
dest='batch_size', help='Batch size',
default=8)
parser.add_argument('--save-iter', type=int,
dest='save_iter', help='Checkpoint save frequency',
default=200)
parser.add_argument('--summary-iter', type=int,
dest='summary_iter', help='Summary write frequency',
default=20)
parser.add_argument('--max-to-keep', type=int,
dest='max_to_keep', help='Max # of checkpoints to keep around',
default=10)
args = parser.parse_args()
def batch_gen(folder, batch_shape):
'''Resize images to 512, randomly crop a 256 square, and normalize'''
files = np.asarray(get_files(folder))
while True:
X_batch = np.zeros(batch_shape, dtype=np.float32)
idx = 0
while idx < batch_shape[0]: # Build batch sample by sample
try:
f = np.random.choice(files)
X_batch[idx] = get_img_random_crop(f, resize=512, crop=256).astype(np.float32)
X_batch[idx] /= 255. # Normalize between [0,1]
assert(not np.isnan(X_batch[idx].min()))
except Exception as e:
# Do not increment idx if we failed
print(e)
continue
idx += 1
yield X_batch
def train():
batch_shape = (args.batch_size,256,256,3)
with tf.Graph().as_default():
tf.logging.set_verbosity(tf.logging.INFO)
### Setup data loading queue
queue_input_content = tf.placeholder(tf.float32, shape=batch_shape)
queue_input_val = tf.placeholder(tf.float32, shape=batch_shape)
queue = tf.FIFOQueue(capacity=100, dtypes=[tf.float32, tf.float32], shapes=[[256,256,3], [256,256,3]])
enqueue_op = queue.enqueue_many([queue_input_content, queue_input_val])
dequeue_op = queue.dequeue()
content_batch_op, val_batch_op = tf.train.batch(dequeue_op, batch_size=args.batch_size, capacity=100)
def enqueue(sess):
content_images = batch_gen(args.content_path, batch_shape)
val_path = args.val_path if args.val_path is not None else args.content_path
val_images = batch_gen(val_path, batch_shape)
while True:
content_batch = next(content_images)
val_batch = next(val_images)
sess.run(enqueue_op, feed_dict={queue_input_content: content_batch,
queue_input_val: val_batch})
### Build the model graph & train/summary ops, and get the EncoderDecoder
model = WCTModel(mode='train',
relu_targets=[args.relu_target],
vgg_path=args.vgg_path,
batch_size=args.batch_size,
feature_weight=args.feature_weight,
pixel_weight=args.pixel_weight,
tv_weight=args.tv_weight,
learning_rate=args.learning_rate,
lr_decay=args.lr_decay).encoder_decoders[0]
saver = tf.train.Saver(max_to_keep=args.max_to_keep)
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
enqueue_thread = threading.Thread(target=enqueue, args=[sess])
enqueue_thread.isDaemon()
enqueue_thread.start()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
log_path = args.log_path if args.log_path is not None else os.path.join(args.checkpoint,'log')
summary_writer = tf.summary.FileWriter(log_path, sess.graph)
sess.run(tf.global_variables_initializer())
def load_latest():
if os.path.exists(os.path.join(args.checkpoint,'checkpoint')):
print("Restoring checkpoint")
saver.restore(sess, tf.train.latest_checkpoint(args.checkpoint))
load_latest()
for iteration in range(args.max_iter):
start = time.time()
content_batch = sess.run(content_batch_op)
fetches = {
'train': model.train_op,
'global_step': model.global_step,
# 'summary': model.summary_op,
'lr': model.learning_rate,
'feature_loss': model.feature_loss,
'pixel_loss': model.pixel_loss,
'tv_loss': model.tv_loss
}
feed_dict = { model.content_input: content_batch }
try:
results = sess.run(fetches, feed_dict=feed_dict)
except Exception as e:
print(e)
print("Exception encountered, re-loading latest checkpoint")
load_latest()
continue
### Run a val batch and log the summaries
if iteration % args.summary_iter == 0:
val_batch = sess.run(val_batch_op)
summary = sess.run(model.summary_op, feed_dict={ model.content_input: val_batch })
summary_writer.add_summary(summary, results['global_step'])
### Save checkpoint
if iteration % args.save_iter == 0:
save_path = saver.save(sess, os.path.join(args.checkpoint, 'model.ckpt'), results['global_step'])
print("Model saved in file: %s" % save_path)
### Log training stats
print("Step: {} LR: {:.7f} Feature: {:.5f} Pixel: {:.5f} TV: {:.5f} Time: {:.5f}".format(results['global_step'],
results['lr'],
results['feature_loss'],
results['pixel_loss'],
results['tv_loss'],
time.time() - start))
# Last save
save_path = saver.save(sess, os.path.join(args.checkpoint, 'model.ckpt'), results['global_step'])
print("Model saved in file: %s" % save_path)
if __name__ == '__main__':
train()