else:
tolerate = tolerate+1
if tolerate==limit:
model.load()
test_mse = wrapper_test(model)
print('the best valid mse is:',str(best_mse))
print('the test mse is ',str(test_mse))
break
# if os.path.exists(args.save_dir):
# shutil.rmtree(args.save_dir)
# os.makedirs(args.save_dir)
#
# if os.path.exists(args.gen_frm_dir):
# shutil.rmtree(args.gen_frm_dir)
# os.makedirs(args.gen_frm_dir)
#
gpu_list = np.asarray(os.environ.get('CUDA_VISIBLE_DEVICES', '-1').split(','), dtype=np.int32)
args.n_gpu = len(gpu_list)
print('Initializing models')
model = Model(args)
model.load()
# test_mse = wrapper_test(model)
# print('test mse is:',str(test_mse))
# if args.is_training:
# wrapper_train(model)
# else:
# wrapper_test(model)
def test_wrapper(model):
model.load(args.pretrained_model)
test_input_handle = datasets_factory.data_provider(
args.dataset_name,
args.train_data_paths,
args.valid_data_paths,
args.batch_size,
args.img_width,
seq_length=args.total_length,
is_training=False,
)
trainer.test(model, test_input_handle, args, "test_result")
# if os.path.exists(args.save_dir):
# shutil.rmtree(args.save_dir)
# os.makedirs(args.save_dir)
# if os.path.exists(args.gen_frm_dir):
# shutil.rmtree(args.gen_frm_dir)
# os.makedirs(args.gen_frm_dir)
print("Initializing models")
model = Model(args)
if args.is_training:
train_wrapper(model)
else:
test_wrapper(model)