def test_dataloaders(self):
dataset = ForwardShapenetDataset(self.config)
dataset.create_dataloaders(MockModel(DictConfig({"conv_type": "DENSE"})), 2, False, 1, False)
forward_set = dataset.test_dataloaders()[0]
for b in forward_set:
self.assertEqual(b.origin_id.shape, (2, 2))
sparseconfig = DictConfig({"dataroot": self.datadir, "category": "Airplane", "forward_category": "Airplane"})
dataset = ForwardShapenetDataset(sparseconfig)
dataset.create_dataloaders(MockModel(DictConfig({"conv_type": "PARTIAL_DENSE"})), 2, False, 1, False)
forward_set = dataset.test_dataloaders()[0]
for b in forward_set:
torch.testing.assert_allclose(b.origin_id, torch.tensor([0, 1, 2, 0, 1, 2, 3]))
torch.testing.assert_allclose(b.sampleid, torch.tensor([0, 1]))
def test_simple_datasets(self):
opt = Options()
opt.dataset_name = os.path.join(os.getcwd(), "test")
opt.dataroot = os.path.join(os.getcwd(), "test")
class SimpleDataset(BaseDataset):
def __init__(self, dataset_opt):
super(SimpleDataset, self).__init__(dataset_opt)
self.train_dataset = CustomMockDataset(10, 1, 3, 10)
self.test_dataset = CustomMockDataset(10, 1, 3, 10)
dataset = SimpleDataset(opt)
model_config = MockModelConfig()
model_config.conv_type = "dense"
model = MockModel(model_config)
dataset.create_dataloaders(model, 5, True, 0, False)
self.assertEqual(dataset.pre_transform, None)
self.assertEqual(dataset.test_transform, None)
self.assertEqual(dataset.train_transform, None)
self.assertEqual(dataset.val_transform, None)
self.assertNotEqual(dataset.train_dataset, None)
self.assertNotEqual(dataset.test_dataset, None)
self.assertTrue(dataset.has_test_loaders)
self.assertFalse(dataset.has_val_loader)
def test_predictupsamplepartialdense(self):
dataset = ForwardShapenetDataset(self.config)
dataset.create_dataloaders(MockModel(DictConfig({"conv_type": "PARTIAL_DENSE"})), 2, False, 1, False)
forward_set = dataset.test_dataloaders()[0]
for b in forward_set:
output = torch.tensor([[1, 0], [1, 0], [0, 1], [0, 1]])
predicted = dataset.predict_original_samples(b, "PARTIAL_DENSE", output)
self.assertEqual(len(predicted), 2)
self.assertEqual(predicted["example1.txt"].shape, (3, 4))
self.assertEqual(predicted["example2.txt"].shape, (4, 4))
npt.assert_allclose(predicted["example1.txt"][:, -1], np.asarray([0, 0, 0]))
npt.assert_allclose(predicted["example2.txt"][:, -1], np.asarray([1, 1, 1, 1]))
def test_multiple_test_datasets(self):
opt = Options()
opt.dataset_name = os.path.join(os.getcwd(), "test")
opt.dataroot = os.path.join(os.getcwd(), "test")
class MultiTestDataset(BaseDataset):
def __init__(self, dataset_opt):
super(MultiTestDataset, self).__init__(dataset_opt)
self.train_dataset = CustomMockDataset(10, 1, 3, 10)
self.val_dataset = CustomMockDataset(10, 1, 3, 10)
self.test_dataset = [
CustomMockDataset(10, 1, 3, 10),
CustomMockDataset(10, 1, 3, 20)
]
dataset = MultiTestDataset(opt)
model_config = MockModelConfig()
model_config.conv_type = "dense"
model = MockModel(model_config)
dataset.create_dataloaders(model, 5, True, 0, False)
loaders = dataset.test_dataloaders
self.assertEqual(len(loaders), 2)
self.assertEqual(len(loaders[0].dataset), 10)
self.assertEqual(len(loaders[1].dataset), 20)
self.assertEqual(dataset.num_classes, 3)
self.assertEqual(dataset.is_hierarchical, False)
self.assertEqual(dataset.has_fixed_points_transform, False)
self.assertEqual(dataset.has_val_loader, True)
self.assertEqual(dataset.class_to_segments, None)
self.assertEqual(dataset.feature_dimension, 1)
batch = next(iter(loaders[0]))
num_samples = BaseDataset.get_num_samples(batch, "dense")
self.assertEqual(num_samples, 5)
sample = BaseDataset.get_sample(batch, "pos", 1, "dense")
self.assertEqual(sample.shape, (10, 3))
sample = BaseDataset.get_sample(batch, "x", 1, "dense")
self.assertEqual(sample.shape, (10, 1))
self.assertEqual(dataset.num_batches, {
"train": 2,
"val": 2,
"test_0": 2,
"test_1": 4
})
repr = "Dataset: MultiTestDataset \n\x1b[0;95mpre_transform \x1b[0m= None\n\x1b[0;95mtest_transform \x1b[0m= None\n\x1b[0;95mtrain_transform \x1b[0m= None\n\x1b[0;95mval_transform \x1b[0m= None\n\x1b[0;95minference_transform \x1b[0m= None\nSize of \x1b[0;95mtrain_dataset \x1b[0m= 10\nSize of \x1b[0;95mtest_dataset \x1b[0m= 10, 20\nSize of \x1b[0;95mval_dataset \x1b[0m= 10\n\x1b[0;95mBatch size =\x1b[0m 5"
self.assertEqual(dataset.__repr__(), repr)
def test_normal(self):
dataset_opt = MockDatasetConfig()
setattr(dataset_opt, "dataroot", os.path.join(DIR, "temp_dataset"))
mock_base_dataset = MockBaseDataset(dataset_opt)
mock_base_dataset.test_dataset = MockDataset()
model_config = MockModelConfig()
setattr(model_config, "conv_type", "dense")
model = MockModel(model_config)
mock_base_dataset.create_dataloaders(model, 2, True, 0, False)
datasets = mock_base_dataset.test_dataloaders
self.assertEqual(len(datasets), 1)
