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generate.py
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generate.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import errno
import json
import os
import random
import cv2
import numpy as np
import tensorflow as tf
from video_prediction import datasets, models
from video_prediction.utils.ffmpeg_gif import save_gif
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--input_dir", type=str, required=True, help="either a directory containing subdirectories "
"train, val, test, etc, or a directory containing "
"the tfrecords")
parser.add_argument("--results_dir", type=str, default='results', help="ignored if output_gif_dir is specified")
parser.add_argument("--results_gif_dir", type=str, help="default is results_dir. ignored if output_gif_dir is specified")
parser.add_argument("--results_png_dir", type=str, help="default is results_dir. ignored if output_png_dir is specified")
parser.add_argument("--output_gif_dir", help="output directory where samples are saved as gifs. default is "
"results_gif_dir/model_fname")
parser.add_argument("--output_png_dir", help="output directory where samples are saved as pngs. default is "
"results_png_dir/model_fname")
parser.add_argument("--checkpoint", help="directory with checkpoint or checkpoint name (e.g. checkpoint_dir/model-200000)")
parser.add_argument("--mode", type=str, choices=['val', 'test'], default='val', help='mode for dataset, val or test.')
parser.add_argument("--dataset", type=str, help="dataset class name")
parser.add_argument("--dataset_hparams", type=str, help="a string of comma separated list of dataset hyperparameters")
parser.add_argument("--model", type=str, help="model class name")
parser.add_argument("--model_hparams", type=str, help="a string of comma separated list of model hyperparameters")
parser.add_argument("--batch_size", type=int, default=8, help="number of samples in batch")
parser.add_argument("--num_samples", type=int, help="number of samples in total (all of them by default)")
parser.add_argument("--num_epochs", type=int, default=1)
parser.add_argument("--num_stochastic_samples", type=int, default=5)
parser.add_argument("--gif_length", type=int, help="default is sequence_length")
parser.add_argument("--fps", type=int, default=4)
parser.add_argument("--gpu_mem_frac", type=float, default=0, help="fraction of gpu memory to use")
parser.add_argument("--seed", type=int, default=7)
args = parser.parse_args()
if args.seed is not None:
tf.set_random_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
args.results_gif_dir = args.results_gif_dir or args.results_dir
args.results_png_dir = args.results_png_dir or args.results_dir
dataset_hparams_dict = {}
model_hparams_dict = {}
if args.checkpoint:
checkpoint_dir = os.path.normpath(args.checkpoint)
if not os.path.isdir(args.checkpoint):
checkpoint_dir, _ = os.path.split(checkpoint_dir)
if not os.path.exists(checkpoint_dir):
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), checkpoint_dir)
with open(os.path.join(checkpoint_dir, "options.json")) as f:
print("loading options from checkpoint %s" % args.checkpoint)
options = json.loads(f.read())
args.dataset = args.dataset or options['dataset']
args.model = args.model or options['model']
try:
with open(os.path.join(checkpoint_dir, "dataset_hparams.json")) as f:
dataset_hparams_dict = json.loads(f.read())
except FileNotFoundError:
print("dataset_hparams.json was not loaded because it does not exist")
try:
with open(os.path.join(checkpoint_dir, "model_hparams.json")) as f:
model_hparams_dict = json.loads(f.read())
except FileNotFoundError:
print("model_hparams.json was not loaded because it does not exist")
args.output_gif_dir = args.output_gif_dir or os.path.join(args.results_gif_dir, os.path.split(checkpoint_dir)[1])
args.output_png_dir = args.output_png_dir or os.path.join(args.results_png_dir, os.path.split(checkpoint_dir)[1])
else:
if not args.dataset:
raise ValueError('dataset is required when checkpoint is not specified')
if not args.model:
raise ValueError('model is required when checkpoint is not specified')
args.output_gif_dir = args.output_gif_dir or os.path.join(args.results_gif_dir, 'model.%s' % args.model)
args.output_png_dir = args.output_png_dir or os.path.join(args.results_png_dir, 'model.%s' % args.model)
print('----------------------------------- Options ------------------------------------')
for k, v in args._get_kwargs():
print(k, "=", v)
print('------------------------------------- End --------------------------------------')
VideoDataset = datasets.get_dataset_class(args.dataset)
dataset = VideoDataset(
args.input_dir,
mode=args.mode,
num_epochs=args.num_epochs,
seed=args.seed,
hparams_dict=dataset_hparams_dict,
hparams=args.dataset_hparams)
VideoPredictionModel = models.get_model_class(args.model)
hparams_dict = dict(model_hparams_dict)
hparams_dict.update({
'context_frames': dataset.hparams.context_frames,
'sequence_length': dataset.hparams.sequence_length,
'repeat': dataset.hparams.time_shift,
})
model = VideoPredictionModel(
mode=args.mode,
hparams_dict=hparams_dict,
hparams=args.model_hparams)
sequence_length = model.hparams.sequence_length
context_frames = model.hparams.context_frames
future_length = sequence_length - context_frames
if args.num_samples:
if args.num_samples > dataset.num_examples_per_epoch():
raise ValueError('num_samples cannot be larger than the dataset')
num_examples_per_epoch = args.num_samples
else:
num_examples_per_epoch = dataset.num_examples_per_epoch()
if num_examples_per_epoch % args.batch_size != 0:
raise ValueError('batch_size should evenly divide the dataset size %d' % num_examples_per_epoch)
inputs = dataset.make_batch(args.batch_size)
input_phs = {k: tf.placeholder(v.dtype, v.shape, '%s_ph' % k) for k, v in inputs.items()}
with tf.variable_scope(''):
model.build_graph(input_phs)
for output_dir in (args.output_gif_dir, args.output_png_dir):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(args), sort_keys=True, indent=4))
with open(os.path.join(output_dir, "dataset_hparams.json"), "w") as f:
f.write(json.dumps(dataset.hparams.values(), sort_keys=True, indent=4))
with open(os.path.join(output_dir, "model_hparams.json"), "w") as f:
f.write(json.dumps(model.hparams.values(), sort_keys=True, indent=4))
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_mem_frac)
config = tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)
sess = tf.Session(config=config)
sess.graph.as_default()
model.restore(sess, args.checkpoint)
sample_ind = 0
while True:
if args.num_samples and sample_ind >= args.num_samples:
break
try:
input_results = sess.run(inputs)
except tf.errors.OutOfRangeError:
break
print("evaluation samples from %d to %d" % (sample_ind, sample_ind + args.batch_size))
feed_dict = {input_ph: input_results[name] for name, input_ph in input_phs.items()}
for stochastic_sample_ind in range(args.num_stochastic_samples):
gen_images = sess.run(model.outputs['gen_images'], feed_dict=feed_dict)
# only keep the future frames
gen_images = gen_images[:, -future_length:]
for i, gen_images_ in enumerate(gen_images):
context_images_ = (input_results['images'][i] * 255.0).astype(np.uint8)
gen_images_ = (gen_images_ * 255.0).astype(np.uint8)
gen_images_fname = 'gen_image_%05d_%02d.gif' % (sample_ind + i, stochastic_sample_ind)
context_and_gen_images = list(context_images_[:context_frames]) + list(gen_images_)
if args.gif_length:
context_and_gen_images = context_and_gen_images[:args.gif_length]
save_gif(os.path.join(args.output_gif_dir, gen_images_fname),
context_and_gen_images, fps=args.fps)
gen_image_fname_pattern = 'gen_image_%%05d_%%02d_%%0%dd.png' % max(2, len(str(len(gen_images_) - 1)))
for t, gen_image in enumerate(gen_images_):
gen_image_fname = gen_image_fname_pattern % (sample_ind + i, stochastic_sample_ind, t)
if gen_image.shape[-1] == 1:
gen_image = np.tile(gen_image, (1, 1, 3))
else:
gen_image = cv2.cvtColor(gen_image, cv2.COLOR_RGB2BGR)
cv2.imwrite(os.path.join(args.output_png_dir, gen_image_fname), gen_image)
sample_ind += args.batch_size
if __name__ == '__main__':
main()