def test_max_to_keep_tf_architecture(self):
save_dir = tempfile.mkdtemp()
model = fe.build(model_fn=one_layer_tf_model, optimizer_fn='adam')
model_saver = ModelSaver(model=model, save_dir=save_dir, max_to_keep=2, save_architecture=True)
model_saver.system = sample_system_object()
model_saver.on_epoch_end(data=Data())
model_saver.system.epoch_idx += 1
model_saver.on_epoch_end(data=Data())
model_saver.system.epoch_idx += 1
model_saver.on_epoch_end(data=Data())
model_name = "{}_epoch_{}".format(model_saver.model.model_name, model_saver.system.epoch_idx)
tf_model_path1 = os.path.join(save_dir, model_name + '.h5')
tf_architecture_path1 = os.path.join(save_dir, model_name)
model_saver.system.epoch_idx += 1
model_saver.on_epoch_end(data=Data())
model_name = "{}_epoch_{}".format(model_saver.model.model_name, model_saver.system.epoch_idx)
tf_model_path2 = os.path.join(save_dir, model_name + '.h5')
tf_architecture_path2 = os.path.join(save_dir, model_name)
with self.subTest('Check only four files are kept'):
self.assertEqual(len(os.listdir(save_dir)), 4)
with self.subTest('Check two latest models are kept'):
self.assertTrue(os.path.exists(tf_model_path1))
self.assertTrue(os.path.exists(tf_model_path2))
self.assertTrue(os.path.exists(tf_architecture_path1))
self.assertTrue(os.path.isdir(tf_architecture_path1))
self.assertTrue(os.path.exists(tf_architecture_path2))
self.assertTrue(os.path.isdir(tf_architecture_path2))
def test_tf_model(self):
model = fe.build(model_fn=one_layer_tf_model, optimizer_fn='adam')
model_saver = ModelSaver(model=model, save_dir=self.save_dir)
model_saver.system = sample_system_object()
model_saver.on_epoch_end(data=Data())
model_name = "{}_epoch_{}".format(model_saver.model.model_name, model_saver.system.epoch_idx)
tf_model_path = os.path.join(self.save_dir, model_name + '.h5')
with self.subTest('Check if model is saved'):
self.assertTrue(os.path.exists(tf_model_path))
with self.subTest('Validate model weights'):
m2 = fe.build(model_fn=one_layer_model_without_weights, optimizer_fn='adam')
fe.backend.load_model(m2, tf_model_path)
self.assertTrue(is_equal(m2.trainable_variables, model.trainable_variables))
def test_torch_model(self):
model = fe.build(model_fn=MultiLayerTorchModel, optimizer_fn='adam')
model_saver = ModelSaver(model=model, save_dir=self.save_dir)
model_saver.system = sample_system_object()
model_name = "{}_epoch_{}".format(model_saver.model.model_name, model_saver.system.epoch_idx)
torch_model_path = os.path.join(self.save_dir, model_name + '.pt')
if os.path.exists(torch_model_path):
os.remove(torch_model_path)
model_saver.on_epoch_end(data=Data())
with self.subTest('Check if model is saved'):
self.assertTrue(os.path.exists(torch_model_path))
with self.subTest('Validate model weights'):
m2 = fe.build(model_fn=MultiLayerTorchModelWithoutWeights, optimizer_fn='adam')
fe.backend.load_model(m2, torch_model_path)
self.assertTrue(is_equal(list(m2.parameters()), list(model.parameters())))
def test_max_to_keep_torch(self):
save_dir = tempfile.mkdtemp()
model = fe.build(model_fn=MultiLayerTorchModel, optimizer_fn='adam')
model_saver = ModelSaver(model=model, save_dir=save_dir, max_to_keep=2)
model_saver.system = sample_system_object()
model_saver.on_epoch_end(data=Data())
model_saver.system.epoch_idx += 1
model_saver.on_epoch_end(data=Data())
model_name = "{}_epoch_{}".format(model_saver.model.model_name, model_saver.system.epoch_idx)
torch_model_path1 = os.path.join(save_dir, model_name + '.pt')
model_saver.system.epoch_idx += 1
model_saver.on_epoch_end(data=Data())
model_name = "{}_epoch_{}".format(model_saver.model.model_name, model_saver.system.epoch_idx)
torch_model_path2 = os.path.join(save_dir, model_name + '.pt')
with self.subTest('Check only two file are kept'):
self.assertEqual(len(os.listdir(save_dir)), 2)
with self.subTest('Check two latest model are kept'):
self.assertTrue(os.path.exists(torch_model_path1))
self.assertTrue(os.path.exists(torch_model_path2))
