def setup_class(cls): """Setup method.""" cls.optimizer = Momentum(Tensor(0.12)) cls.loss_fn = SoftmaxCrossEntropyWithLogits() cls.net = ResNet() cls.run_context = dict() cls.run_context['train_network'] = cls.net cls.run_context['loss_fn'] = cls.loss_fn cls.run_context['net_outputs'] = Tensor(np.array([0.03])) cls.run_context['optimizer'] = cls.optimizer cls.run_context['train_dataset'] = MindDataset(dataset_size=32) cls.run_context['epoch_num'] = 10 cls.run_context['cur_step_num'] = 320 cls.run_context['parallel_mode'] = "stand_alone" cls.run_context['device_number'] = 2 cls.run_context['batch_num'] = 32 cls.summary_record = SummaryRecord(SUMMARY_DIR) callback = [ ModelCheckpoint(directory=SUMMARY_DIR), SummaryStep(cls.summary_record), TrainLineage(cls.summary_record) ] cls.run_context['list_callback'] = _ListCallback(callback) cls.user_defined_info = {"info": "info1", "version": "v1"}
def test_get_dataset_path_wrapped(self): """Test get_dataset_path_wrapped method.""" dataset = Dataset() dataset.input.append( MindDataset(dataset_size=10, dataset_file='/path/to/cifar10')) res = self.analyzer.get_dataset_path_wrapped(dataset) assert res == '/path/to/cifar10'
def test_analyze_dataset(self, mock_get_path): """Test analyze_dataset method.""" mock_get_path.return_value = '/path/to/mindinsightset' dataset = MindDataset(dataset_size=10, dataset_file='/path/to/mindinsightset') res1 = self.analyzer.analyze_dataset(dataset, { 'step_num': 10, 'epoch': 2 }, 'train') res2 = self.analyzer.analyze_dataset(dataset, {'step_num': 5}, 'valid') assert res1 == { 'step_num': 10, 'train_dataset_path': '/path/to', 'train_dataset_size': 50, 'epoch': 2 } assert res2 == { 'step_num': 5, 'valid_dataset_path': '/path/to', 'valid_dataset_size': 50 }
def test_analyze_dataset(self, mock_get_path, mock_isfile): """Test analyze_dataset method.""" mock_get_path.return_value = '/path/to/mindinsightset' mock_isfile.return_value = True dataset = MindDataset(dataset_size=10, dataset_file='/path/to/mindinsightset') res1 = self.analyzer.analyze_dataset(dataset, { 'step_num': 10, 'epoch': 2 }, 'train') res2 = self.analyzer.analyze_dataset(dataset, {'step_num': 5}, 'valid') # batch_size is mocked as 32. assert res1 == { 'step_num': 10, 'train_dataset_path': '/path/to', 'train_dataset_size': 320, 'epoch': 2 } assert res2 == { 'step_num': 5, 'valid_dataset_path': '/path/to', 'valid_dataset_size': 320 }
def test_get_dataset_path_mindrecord(self): """Test get_dataset_path method with MindDataset.""" dataset = MindDataset(dataset_file='/path/to/cifar10') dataset_path = self.analyzer.get_dataset_path(output_dataset=dataset) self.assertEqual(dataset_path, '/path/to/cifar10')
def test_get_dataset_path(self): """Test get_dataset_path method.""" dataset = MindDataset(dataset_file='/path/to/mindrecord') res = self.analyzer.get_dataset_path(dataset) assert res == '/path/to/mindrecord'