def test_suppress_automatic_save(self, patch_save_model):
model_name = "test_model"
model1 = RNNModel(
12,
"RNN",
10,
10,
model_name=model_name,
work_dir=self.temp_work_dir,
save_checkpoints=False,
)
model2 = RNNModel(
12,
"RNN",
10,
10,
model_name=model_name,
work_dir=self.temp_work_dir,
force_reset=True,
save_checkpoints=False,
)
model1.fit(self.series, epochs=1)
model2.fit(self.series, epochs=1)
model1.predict(n=1)
model2.predict(n=2)
patch_save_model.assert_not_called()
model1.save_model(
path=os.path.join(self.temp_work_dir, model_name))
patch_save_model.assert_called()
def test_prediction_custom_trainer(self):
model = RNNModel(12, "RNN", 10, 10, random_state=42)
model2 = RNNModel(12, "RNN", 10, 10, random_state=42)
# fit model with custom trainer
trainer = pl.Trainer(**self.trainer_params, precision=32)
model.fit(self.series, trainer=trainer)
# fit model with built-in trainer
model2.fit(self.series, epochs=1)
# both should produce identical prediction
self.assertEqual(model.predict(n=4), model2.predict(n=4))
def test_prediction_loaded_custom_trainer(self):
"""validate manual save with automatic save files by comparing output between the two"""
auto_name = "test_save_automatic"
model = RNNModel(
12,
"RNN",
10,
10,
model_name=auto_name,
work_dir=self.temp_work_dir,
save_checkpoints=True,
random_state=42,
)
# fit model with custom trainer
trainer = pl.Trainer(
max_epochs=1,
enable_checkpointing=True,
logger=False,
callbacks=model.trainer_params["callbacks"],
precision=32,
)
model.fit(self.series, trainer=trainer)
# load automatically saved model with manual load_model() and load_from_checkpoint()
model_loaded = RNNModel.load_from_checkpoint(
model_name=auto_name, work_dir=self.temp_work_dir, best=False
)
# compare prediction of loaded model with original model
self.assertEqual(model.predict(n=4), model_loaded.predict(n=4))
def test_manual_save_and_load(self):
"""validate manual save with automatic save files by comparing output between the two"""
manual_name = "test_save_manual"
auto_name = "test_save_automatic"
model_manual_save = RNNModel(
12,
"RNN",
10,
10,
model_name=manual_name,
work_dir=self.temp_work_dir,
save_checkpoints=False,
random_state=42,
)
model_auto_save = RNNModel(
12,
"RNN",
10,
10,
model_name=auto_name,
work_dir=self.temp_work_dir,
save_checkpoints=True,
random_state=42,
)
model_manual_save.fit(self.series, epochs=1)
model_auto_save.fit(self.series, epochs=1)
model_dir = os.path.join(self.temp_work_dir)
# check that file was not created with manual save
self.assertFalse(
os.path.exists(
os.path.join(model_dir, manual_name, "checkpoints")))
# check that file was created with automatic save
self.assertTrue(
os.path.exists(
os.path.join(model_dir, auto_name, "checkpoints")))
# create manually saved model checkpoints folder
checkpoint_path_manual = os.path.join(model_dir, manual_name)
os.mkdir(checkpoint_path_manual)
checkpoint_file_name = "checkpoint_0.pth.tar"
model_path_manual = os.path.join(checkpoint_path_manual,
checkpoint_file_name)
checkpoint_file_name_cpkt = "checkpoint_0_ptl-ckpt.pth.tar"
model_path_manual_ckpt = os.path.join(checkpoint_path_manual,
checkpoint_file_name_cpkt)
# save manually saved model
model_manual_save.save_model(model_path_manual)
self.assertTrue(os.path.exists(model_path_manual))
# check that the PTL checkpoint path is also there
self.assertTrue(os.path.exists(model_path_manual_ckpt))
# load manual save model and compare with automatic model results
model_manual_save = RNNModel.load_model(model_path_manual)
self.assertEqual(model_manual_save.predict(n=4),
model_auto_save.predict(n=4))
# load automatically saved model with manual load_model() and load_from_checkpoint()
model_auto_save1 = RNNModel.load_from_checkpoint(
model_name=auto_name, work_dir=self.temp_work_dir, best=False)
# compare loaded checkpoint with manual save
self.assertEqual(model_manual_save.predict(n=4),
model_auto_save1.predict(n=4))