def predict_original_samples(self, batch, conv_type, output):
""" Takes the output generated by the NN and upsamples it to the original data
Arguments:
batch -- processed batch
conv_type -- Type of convolutio (DENSE, PARTIAL_DENSE, etc...)
output -- output predicted by the model
"""
full_res_results = {}
num_sample = BaseDataset.get_num_samples(batch, conv_type)
if conv_type == "DENSE":
output = output.reshape(num_sample, -1,
output.shape[-1]) # [B,N,L]
setattr(batch, "_pred", output)
for b in range(num_sample):
sampleid = batch.sampleid[b]
sample_raw_pos = self.test_dataset[0].get_raw(sampleid).pos.to(
output.device)
predicted = BaseDataset.get_sample(batch, "_pred", b, conv_type)
origindid = BaseDataset.get_sample(batch, SaveOriginalPosId.KEY, b,
conv_type)
full_prediction = knn_interpolate(predicted,
sample_raw_pos[origindid],
sample_raw_pos,
k=3)
labels = full_prediction.max(1)[1].unsqueeze(-1)
full_res_results[self.test_dataset[0].get_filename(
sampleid)] = np.hstack((
sample_raw_pos.cpu().numpy(),
labels.cpu().numpy(),
))
return full_res_results
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)