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test_data_utils.py
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import os
import torch
from data_utils import datasets
from data_utils import distorters
from data_utils import normalization as norm
dataset_size = 42
distorter_config = {
'stds': [3.0, 5.0, 10.0],
'layer_probs': [0.7, 0.25, 0.05],
'layer_radii': [0.0, 6.0, 7.0],
'confusion_prob': 0.02
}
def test_single_pose_dataset():
plain_size = 20
dataset_plain = datasets.SinglePoseDataset('unit_test/dummy42_poses', num_samples=plain_size,
device='cpu')
dataset_dict = datasets.SinglePoseDataset('unit_test/dummy42_dict_poses', device='cuda:0')
all = dataset_plain[:]
single_sub1 = dataset_dict[0]
dataset_dict.select_subset('sub2')
single_sub2 = dataset_dict[1]
assert type(all) is datasets.PoseCorrectionBatch
assert len(dataset_plain) == plain_size
assert len(dataset_dict) == dataset_size
assert all.poses.shape == (plain_size, 21, 3)
assert all.poses.dtype == torch.float32
assert all.device == torch.device('cpu')
assert dataset_dict.device == torch.device('cuda:0')
assert single_sub1.device == torch.device('cuda:0')
assert single_sub1.poses.shape == (1, 21, 3)
assert not torch.allclose(single_sub1.poses, single_sub2.poses)
assert dataset_dict.get_subset_names() == ['sub1', 'sub2']
assert dataset_dict.has_subsets
def test_paired_pose_dataset():
plain_size = 20
some_size = 10
distorter = distorters.SyntheticDistorter(distorter_config)
dataset_plain = datasets.PairedPoseDataset('unit_test/dummy42', distorter, False, plain_size,
'cpu')
dataset_dict = datasets.PairedPoseDataset('unit_test/dummy42_dict', distorter, True,
device='cuda:0')
some = dataset_plain[:some_size]
single_sub1 = dataset_dict[0]
dataset_dict.select_subset('sub2')
single_sub2 = dataset_dict[1]
assert type(some) is datasets.PoseCorrectionBatch
assert len(dataset_plain) == plain_size
assert len(dataset_dict) == dataset_size
assert some.poses.shape == (some_size, 21, 3)
assert some.poses.dtype == torch.float32
assert some.poses.is_same_size(some.labels)
assert some.device == torch.device('cpu')
assert dataset_dict.device == torch.device('cuda:0')
assert single_sub1.device == torch.device('cuda:0')
assert single_sub1.poses.shape == (1, 21, 3)
assert not torch.allclose(single_sub1.poses, single_sub2.poses)
assert not torch.allclose(single_sub1.poses, single_sub1.labels)
assert dataset_dict.get_subset_names() == ['sub1', 'sub2']
assert dataset_dict.has_subsets
def test_normalized_paired_pose_dataset():
plain_size = 20
some_size = 10
distorter = distorters.SyntheticDistorter(distorter_config)
dataset_plain = datasets.NormalizedPairedPoseDataset('unit_test/dummy42', distorter,
norm.NoNorm, False, plain_size, 'cpu')
dataset_dict = datasets.NormalizedPairedPoseDataset('unit_test/dummy42_dict', distorter,
norm.NoNorm, True, device='cuda:0')
some = dataset_plain[:some_size]
single_sub1 = dataset_dict[0]
dataset_dict.select_subset('sub2')
single_sub2 = dataset_dict[1]
assert type(some) is datasets.PoseCorrectionBatch
assert len(dataset_plain) == plain_size
assert len(dataset_dict) == dataset_size
assert some.poses.shape == (some_size, 21, 3)
assert some.poses.dtype == torch.float32
assert some.poses.is_same_size(some.labels)
assert some.poses.is_same_size(some.original_poses)
assert some.poses.is_same_size(some.original_labels)
assert some.normalization_params is not None
assert some.device == torch.device('cpu')
assert dataset_dict.device == torch.device('cuda:0')
assert single_sub1.device == torch.device('cuda:0')
assert single_sub1.poses.shape == (1, 21, 3)
assert not torch.allclose(single_sub1.poses, single_sub2.poses)
assert not torch.allclose(single_sub1.poses, single_sub1.labels)
assert dataset_dict.get_subset_names() == ['sub1', 'sub2']
assert dataset_dict.has_subsets
def test_data_loader():
distorter = distorters.NoDistorter()
dataset_plain = datasets.NormalizedPairedPoseDataset('unit_test/dummy42', distorter,
norm.NoNorm, True, dataset_size, 'cuda:0')
dataset_dict = datasets.NormalizedPairedPoseDataset('unit_test/dummy42_dict', distorter,
norm.NoNorm, True, dataset_size, 'cuda:0')
true_sum_sub1 = dataset_dict[:].poses.sum()
dataset_dict.select_subset('sub2')
true_sum_sub2 = dataset_dict[:].poses.sum()
data_loader_plain = datasets.DataLoader(dataset_plain, 6)
data_loader_dict = datasets.DataLoader(dataset_dict, 6)
plain_batch = next(iter(data_loader_plain))
subset_names_plain = data_loader_plain.get_subset_names()
data_loader_plain.select_subset(subset_names_plain[0])
all_batches = {}
sum_of_subsets = {}
for subset_name in data_loader_dict.get_subset_names():
data_loader_dict.select_subset(subset_name)
all_batches[subset_name] = list(data_loader_dict)
sum_of_subsets[subset_name] = sum(batch.poses.sum() for batch in all_batches[subset_name])
assert type(plain_batch) is datasets.PoseCorrectionBatch
assert subset_names_plain == ['DEFAULT']
assert list(all_batches.keys()) == ['sub1', 'sub2']
assert len(all_batches['sub1']) == 7
assert type(all_batches['sub1'][0]) == datasets.PoseCorrectionBatch
assert all_batches['sub1'][0].labels.shape == (6, 21, 3)
assert torch.allclose(sum_of_subsets['sub1'], true_sum_sub1)
assert torch.allclose(sum_of_subsets['sub2'], true_sum_sub2)
def test_synthetic_distorter():
config = {'stds': [3.0, 5.0, 10.0],
'layer_probs': [0.7, 0.25, 0.05],
'layer_radii': [0.0, 6.0, 7.0],
'confusion_prob': 0.02}
poses = torch.normal(torch.zeros(dataset_size, 21, 3), 3.0).cuda()
passed_poses = poses.clone()
distorter = distorters.SyntheticDistorter(config)
distorted_poses = distorter(passed_poses)
assert poses.is_same_size(distorted_poses)
assert not torch.allclose(poses, distorted_poses)
assert torch.allclose(poses, passed_poses)
assert distorted_poses.device == torch.device('cuda:0')
def test_knn_predefined_distorter():
config = {
'source_name': os.path.join('unit_test', 'dummy42'),
'knn_name': os.path.join('unit_test', 'dummy42'),
'strength_alpha': -4.0,
'strength_loc': 0.85,
'strength_scale': 0.01,
'max_k': 2,
'device': 'cuda:0',
'stds': [3.0, 5.0, 10.0],
'layer_probs': [0.7, 0.25, 0.05],
'layer_radii': [0.0, 6.0, 7.0],
'confusion_prob': 0.02
}
distorter = distorters.KNNPredefinedDistorter(config)
distort_dataset = datasets.PairedPoseDataset(os.path.join('unit_test', 'dummy42'), distorter,
False, device='cuda:0')
no_distort_dataset = datasets.PairedPoseDataset(os.path.join('unit_test', 'dummy42'),
distorters.NoDistorter(), True, device='cuda:0')
distort_batch = distort_dataset[:]
no_distort_batch = no_distort_dataset[:]
assert not torch.allclose(distort_batch.poses, no_distort_batch.poses)
assert torch.equal(distort_batch.labels, no_distort_batch.labels)