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style.py
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style.py
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from __future__ import print_function
import sys, os, pdb
sys.path.insert(0, 'src')
import numpy as np, scipy.misc
from optimize import optimize
from argparse import ArgumentParser
from utils import save_img, get_img, exists, list_files
import evaluate
CONTENT_WEIGHT = 7.5e0
STYLE_WEIGHT = 1e2
TV_WEIGHT = 2e2
LEARNING_RATE = 1e-3
NUM_EPOCHS = 2
CHECKPOINT_DIR = 'checkpoints'
CHECKPOINT_ITERATIONS = 2000
VGG_PATH = 'data/imagenet-vgg-verydeep-19.mat'
TRAIN_PATH = 'data/train2014'
BATCH_SIZE = 4
# DEVICE = '/gpu:0'
FRAC_GPU = 1
def build_parser():
parser = ArgumentParser()
parser.add_argument('--checkpoint-dir', type=str,
dest='checkpoint_dir', help='dir to save checkpoint in',
metavar='CHECKPOINT_DIR', required=True)
parser.add_argument('--style', type=str,
dest='style', help='style image path',
metavar='STYLE', required=True)
parser.add_argument('--train-path', type=str,
dest='train_path', help='path to training images folder',
metavar='TRAIN_PATH', default=TRAIN_PATH)
parser.add_argument('--test', type=str,
dest='test', help='test image path',
metavar='TEST', default=False)
parser.add_argument('--test-dir', type=str,
dest='test_dir', help='test image save dir',
metavar='TEST_DIR', default=False)
parser.add_argument('--slow', dest='slow', action='store_true',
help='gatys\' approach (for debugging, not supported)',
default=False)
parser.add_argument('--epochs', type=int,
dest='epochs', help='num epochs',
metavar='EPOCHS', default=NUM_EPOCHS)
parser.add_argument('--batch-size', type=int,
dest='batch_size', help='batch size',
metavar='BATCH_SIZE', default=BATCH_SIZE)
parser.add_argument('--checkpoint-iterations', type=int,
dest='checkpoint_iterations', help='checkpoint frequency',
metavar='CHECKPOINT_ITERATIONS',
default=CHECKPOINT_ITERATIONS)
parser.add_argument('--vgg-path', type=str,
dest='vgg_path',
help='path to VGG19 network (default %(default)s)',
metavar='VGG_PATH', default=VGG_PATH)
parser.add_argument('--content-weight', type=float,
dest='content_weight',
help='content weight (default %(default)s)',
metavar='CONTENT_WEIGHT', default=CONTENT_WEIGHT)
parser.add_argument('--style-weight', type=float,
dest='style_weight',
help='style weight (default %(default)s)',
metavar='STYLE_WEIGHT', default=STYLE_WEIGHT)
parser.add_argument('--tv-weight', type=float,
dest='tv_weight',
help='total variation regularization weight (default %(default)s)',
metavar='TV_WEIGHT', default=TV_WEIGHT)
parser.add_argument('--learning-rate', type=float,
dest='learning_rate',
help='learning rate (default %(default)s)',
metavar='LEARNING_RATE', default=LEARNING_RATE)
parser.add_argument('--device', type=str,
dest='device_and_number',
help='device and its associated GPU number or CPU count',
metavar='DEVICE', required=True)
return parser
def check_opts(opts):
exists(opts.checkpoint_dir, "checkpoint dir not found!")
exists(opts.style, "style path not found!")
exists(opts.train_path, "train path not found!")
if opts.test or opts.test_dir:
exists(opts.test, "test img not found!")
exists(opts.test_dir, "test directory not found!")
exists(opts.vgg_path, "vgg network data not found!")
assert opts.epochs > 0
assert opts.batch_size > 0
assert opts.checkpoint_iterations > 0
assert os.path.exists(opts.vgg_path)
assert opts.content_weight >= 0
assert opts.style_weight >= 0
assert opts.tv_weight >= 0
assert opts.learning_rate >= 0
def _get_files(img_dir):
files = list_files(img_dir)
return [os.path.join(img_dir,x) for x in files]
def main():
parser = build_parser()
options = parser.parse_args()
check_opts(options)
style_target = get_img(options.style)
if not options.slow:
content_targets = _get_files(options.train_path)
elif options.test:
content_targets = [options.test]
kwargs = {
"slow":options.slow,
"epochs":options.epochs,
"print_iterations":options.checkpoint_iterations,
"batch_size":options.batch_size,
"save_path":os.path.join(options.checkpoint_dir,'fns.ckpt'),
"learning_rate":options.learning_rate,
"device_and_number":options.device_and_number
}
if options.slow:
if options.epochs < 10:
kwargs['epochs'] = 1000
if options.learning_rate < 1:
kwargs['learning_rate'] = 1e1
args = [
content_targets,
style_target,
options.content_weight,
options.style_weight,
options.tv_weight,
options.vgg_path
]
import time
from datetime import datetime
start_time = time.time()
for preds, losses, i, epoch in optimize(*args, **kwargs):
style_loss, content_loss, tv_loss, loss = losses
delta_time, start_time = time.time() - start_time, time.time()
print('Current Time = {}; Time Elapsed = {}; Epoch = {}; Iteration = {}; Loss = {}'.format(
datetime.now().strftime("%Y %B %d, %H:%M:%S"), delta_time, epoch, i, loss))
to_print = (style_loss, content_loss, tv_loss)
print('Loss values: style = %s; content = %s; tv = %s' % to_print)
sys.stdout.flush()
if options.test:
assert options.test_dir != False
preds_path = '%s/%s_%s.png' % (options.test_dir,epoch,i)
if not options.slow: # if uses GPU, uses RAM that it doesn't have, so it's slow here
ckpt_dir = os.path.dirname(options.checkpoint_dir)
evaluate.ffwd_to_img(options.test,preds_path,
options.checkpoint_dir)
else:
save_img(preds_path, img)
ckpt_dir = options.checkpoint_dir
cmd_text = 'python evaluate.py --checkpoint %s ...' % ckpt_dir
print("Training complete. For evaluation:\n `%s`" % cmd_text)
if __name__ == '__main__':
main()