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()
else:
clipper = clipper_ret
sampler = None
scheduler = get_scheduler(config.scheduler, config)
def callback_before_train(_0, _1, _2):
print(clipper.info())
supervisor = BasicSupervisorLSUN(
config,
clipper,
scheduler,
sampler=sampler,
callback_before_train=callback_before_train)
if config.adaptive_rate:
supervisor.put_key("lr", lr)
try:
train(config,
data_loader,
generator_forward,
discriminator_forward,
disc_optimizer=disc_optimizer,
gen_optimizer=gen_optimizer,
accountant=accountant,
supervisor=supervisor)
finally:
data_loader.stop_fetch()
if sampler is not None:
sampler.data_loader.stop_fetch()