def test__generate_all_triplets_multiple_negative(self):
"""
Case where just one positive example and 1 negative sample
"""
# Arrange
input_target = torch.tensor([0, 0, 1, 2])
expected_triplet_indices = torch.tensor([[0, 1, 2], [0, 1, 3]])
sut = OnlineTripletLoss(.5)
# Act
actual = sut._generate_all_triplets(input_target)
# Assert
self.assertSequenceEqual(
expected_triplet_indices.cpu().numpy().tolist(),
actual.cpu().numpy().tolist())
def test__generate_all_triplets_three_clases_missing_target(self):
"""
Case where not all target classes are presented in the target ( can happen within a batch)
"""
# Arrange
input_target = torch.tensor([0, 0, 1, 4])
expected_triplet_indices = torch.tensor([[0, 1, 2], [0, 1, 3]])
sut = OnlineTripletLoss(.5)
# Act
actual = sut._generate_all_triplets(input_target)
# Assert
self.assertSequenceEqual(
expected_triplet_indices.cpu().numpy().tolist(),
actual.cpu().numpy().tolist())
def test__get_distance_single(self):
"""
Case where just a single item in each array
"""
# Arrange
input_x = torch.tensor([[1, 8, 7]], dtype=torch.float)
input_y = torch.tensor([[2, 3, 4]], dtype=torch.float)
expected = torch.tensor([35])
sut = OnlineTripletLoss(.5)
# Act
actual = sut._get_distance(input_x, input_y)
# Assert
self.assertSequenceEqual(expected.cpu().numpy().round(2).tolist(),
actual.cpu().numpy().round(2).tolist())
def test__get_distance_zero(self):
"""
Case where just a single item in each array that are the same
"""
# Arrange
input_x = torch.tensor([[0, 0, 1]], dtype=torch.float)
input_y = torch.tensor([[0, 0, 1]], dtype=torch.float)
expected = torch.tensor([0])
sut = OnlineTripletLoss(.5)
# Act
actual = sut._get_distance(input_x, input_y)
# Assert
self.assertSequenceEqual(expected.cpu().numpy().tolist(),
actual.cpu().numpy().tolist())
def test_forward_max_zero(self):
"""
Case where the difference between p and n sample is much greater than the margin
:return:
"""
# Arrange
margin = .1
sut = OnlineTripletLoss(margin)
predicted = torch.tensor([[0, 0, 1], [0, 0, 1], [1, 1, 2]],
dtype=torch.float)
target = torch.tensor([1, 1, 0])
# max ( 0, 0-3 + margin)
expected = 0
# Act
actual = sut.forward(predicted, target)
# Assert
self.assertEqual(round(expected, 2), round(actual.item(), 2))