def test_predict(self):
model = SimpleModel().train()
dp = DataProcessor(model=model)
self.assertFalse(model.fc.weight.is_cuda)
self.assertTrue(model.training)
res = dp.predict({'data': torch.rand(1, 3)})
self.assertFalse(model.training)
self.assertFalse(res.requires_grad)
self.assertIsNone(res.grad)
def test_prediction_output(self):
model = SimpleModel()
dp = DataProcessor(model=model)
self.assertFalse(model.fc.weight.is_cuda)
res = dp.predict({'data': torch.rand(1, 3)})
self.assertIs(type(res), torch.Tensor)
model = NonStandardIOModel()
dp = DataProcessor(model=model)
self.assertFalse(model.fc.weight.is_cuda)
res = dp.predict({'data': {'data1': torch.rand(1, 3), 'data2': torch.rand(1, 3)}})
self.assertIs(type(res), dict)
self.assertIn('res1', res)
self.assertIs(type(res['res1']), torch.Tensor)
self.assertIn('res2', res)
self.assertIs(type(res['res2']), torch.Tensor)
def test_continue_from_checkpoint(self):
def on_node(n1, n2):
self.assertTrue(np.array_equal(n1.numpy(), n2.numpy()))
model = SimpleModel().train()
dp = DataProcessor(model=model)
with self.assertRaises(Model.ModelException):
dp.save_state()
try:
fsm = FileStructManager(self.base_dir, is_continue=False)
dp.set_checkpoints_manager(CheckpointsManager(fsm))
dp.save_state()
except:
self.fail(
'Fail to DataProcessor load when CheckpointsManager was defined'
)
del dp
model_new = SimpleModel().train()
dp = DataProcessor(model=model_new)
with self.assertRaises(Model.ModelException):
dp.load()
fsm = FileStructManager(base_dir=self.base_dir, is_continue=True)
dp.set_checkpoints_manager(CheckpointsManager(fsm))
try:
fsm = FileStructManager(self.base_dir, is_continue=True)
dp.set_checkpoints_manager(CheckpointsManager(fsm))
dp.load()
except:
self.fail(
'Fail to DataProcessor load when CheckpointsManager was defined'
)
compare_two_models(self, model, model_new)
def test_initialisation(self):
try:
DataProcessor(model=SimpleModel())
except:
self.fail('DataProcessor initialisation raises exception')