def main(config):
num_step = config.num_step
data_loader = LSUNCatLoader(get_lsun_patterns(config.data_dir),
num_workers=4,
actions=lsun_process_actions())
names = []
fobjs = []
try:
data_loader.start_fetch()
print("generating images...")
for _ in xrange(num_step):
fd, name = tempfile.mkstemp(suffix=".npy")
fobj = os.fdopen(fd, "wb+")
names.append(name)
fobjs.append(fobj)
image_arr = data_loader.next_batch(config.batch_size)[0]
np.save(fobj, image_arr, allow_pickle=False)
fobj.close()
mean_score, std_score = get_resnet18_score(images_iter(names),
config.model_path,
batch_size=100,
split=10)
print("mean = %.4f, std = %.4f." % (mean_score, std_score))
if config.save_path is not None:
with open(config.save_path, "wb") as f:
cPickle.dump(dict(batch_size=config.batch_size,
scores=dict(mean=mean_score, std=std_score)), f)
finally:
data_loader.stop_fetch()
for name in names:
os.unlink(name)
for fobj in fobjs:
fobj.close()
eval_losses.append(eval_loss)
sess.close()
print("accuracy:", np.mean(eval_losses))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("val_data_dir", metavar="VALDATADIR")
parser.add_argument("model_path", metavar="MODELPATH")
parser.add_argument("--batch-size",
dest="batch_size",
type=int,
default=100)
parser.add_argument("--dim", dest="dim", default=64, type=int)
config = parser.parse_args()
print("config: %r" % config)
eval_data_loader = LSUNCatLoader(get_lsun_patterns(config.val_data_dir),
num_workers=2,
actions=lsun_process_actions())
try:
eval_data_loader.start_fetch()
run_task(config, eval_data_loader, classifier_forward,
tf.train.AdamOptimizer())
finally:
eval_data_loader.stop_fetch()
np.random.seed()
if config.enable_accounting:
config.sigma = np.sqrt(
2.0 * np.log(1.25 / config.delta)) / config.epsilon
print("Now with new sigma: %.4f" % config.sigma)
if config.image_size == 64:
patterns = get_lsun_patterns(config.data_dir)
print(patterns)
data_loader = LSUNCatLoader(patterns,
num_workers=4,
actions=lsun_process_actions(),
block_size=16,
max_blocks=256)
data_loader.start_fetch()
generator_forward = d64_resnet_dcgan.generator_forward
discriminator_forward = d64_resnet_dcgan.discriminator_forward
else:
raise NotImplementedError("Unsupported image size %d." %
config.image_size)
if config.enable_accounting:
accountant = GaussianMomentsAccountant(data_loader.num_steps(1),
config.moment)
if config.log_path:
open(config.log_path, "w").close()
else:
accountant = None
if config.adaptive_rate: