def test_pieapp_supports_different_data_ranges(
input_tensors: Tuple[torch.Tensor, torch.Tensor], data_range, device: str) -> None:
x, y = input_tensors
x_scaled = x * data_range
y_scaled = y * data_range
measure_scaled = PieAPP(data_range=data_range, stride=27)(x_scaled.to(device), y_scaled.to(device))
measure = PieAPP(data_range=1.0, stride=27)(
x_scaled.to(device) / data_range,
y_scaled.to(device) / data_range,
)
diff = torch.abs(measure_scaled - measure)
assert diff <= 1e-6, f'Result for same tensor with different data_range should be the same, got {diff}'
def test_pieapp_fails_for_incorrect_data_range(x, y, device: str) -> None:
# Scale to [0, 255]
x_scaled = (x * 255).type(torch.uint8)
y_scaled = (y * 255).type(torch.uint8)
with pytest.raises(AssertionError):
PieAPP(data_range=1.0, stride=27)(x_scaled.to(device),
y_scaled.to(device))
def test_pieapp_loss_forward_for_special_cases(x, y, expectation: Any,
value: float) -> None:
loss = PieAPP()
with expectation:
loss_value = loss(x, y)
assert torch.isclose(loss_value, torch.tensor(value), atol=1e-6), \
f'Expected loss value to be equal to target value. Got {loss_value} and {value}'
def test_pieapp_simmilar_to_official_implementation() -> None:
loss = PieAPP(data_range=255, stride=27)
# RGB images
I01 = torch.tensor(imread('tests/assets/I01.BMP')).permute(2, 0, 1)
i1_01_5 = torch.tensor(imread('tests/assets/i01_01_5.bmp')).permute(2, 0, 1)
loss_value = loss(i1_01_5, I01)
# Baseline score from: https://github.com/prashnani/PerceptualImageError
baseline_value = torch.tensor(2.2393305)
assert torch.isclose(loss_value, baseline_value, atol=1e-5), \
f'Expected PIQ loss to be equal to original. Got {loss_value} and {baseline_value}'
def test_pieapp_computes_grad(x, y, device: str) -> None:
x.requires_grad_()
loss_value = PieAPP(enable_grad=True)(x.to(device), y.to(device))
loss_value.backward()
assert x.grad is not None, 'Expected non None gradient of leaf variable'
def test_pieapp_loss_forward(x, y, device: str) -> None:
loss = PieAPP()
loss(x.to(device), y.to(device))
def test_pieapp_fails_for_incorrect_data_range(prediction: torch.Tensor, target: torch.Tensor, device: str) -> None:
# Scale to [0, 255]
prediction_scaled = (prediction * 255).type(torch.uint8)
target_scaled = (target * 255).type(torch.uint8)
with pytest.raises(AssertionError):
PieAPP(data_range=1.0, stride=27)(prediction_scaled.to(device), target_scaled.to(device))
def test_pieapp_computes_grad(prediction: torch.Tensor, target: torch.Tensor, device: str) -> None:
prediction.requires_grad_()
loss_value = PieAPP(enable_grad=True)(prediction.to(device), target.to(device))
loss_value.backward()
assert prediction.grad is not None, 'Expected non None gradient of leaf variable'
def test_pieapp_loss_forward(prediction: torch.Tensor, target: torch.Tensor, device: str) -> None:
loss = PieAPP()
loss(prediction.to(device), target.to(device))
