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'
