def test_srsim_chromatic(device: str) -> None:
# Greyscale image
prediction = torch.tensor(
imread('tests/assets/goldhill.gif')).unsqueeze(0).unsqueeze(0)
target = torch.tensor(
imread('tests/assets/goldhill_jpeg.gif')).unsqueeze(0).unsqueeze(0)
with pytest.raises(ValueError):
srsim(prediction.to(device),
target.to(device),
data_range=255,
chromatic=True,
reduction='none')
# RBG image
prediction = torch.tensor(imread('tests/assets/I01.BMP')).permute(
2, 0, 1).unsqueeze(0)
target = torch.tensor(imread('tests/assets/i01_01_5.bmp')).permute(
2, 0, 1).unsqueeze(0)
predicted_score = srsim(prediction.to(device),
target.to(device),
data_range=255,
chromatic=True,
reduction='none')
target_score = torch.tensor([0.9546513]).to(predicted_score)
assert torch.allclose(predicted_score, target_score), f'Expected result for chromatic version,' \
f'got diff{predicted_score - target_score}'
def test_srsim_modes(input_tensors: Tuple[torch.Tensor, torch.Tensor],
device: str) -> None:
prediction, target = input_tensors
for reduction in ['mean', 'sum', 'none']:
srsim(prediction.to(device), target.to(device), reduction=reduction)
for reduction in ['DEADBEEF', 'random']:
with pytest.raises(ValueError):
srsim(prediction.to(device),
target.to(device),
reduction=reduction)
def test_srsim_zeros_ones_inputs(device: str) -> None:
zeros = torch.zeros(1, 3, 128, 128, device=device)
ones = torch.ones(1, 3, 128, 128, device=device)
srsim_zeros = srsim(zeros, zeros, data_range=1.)
assert torch.isfinite(srsim_zeros).all(
), f'Expected finite value for zeros tensors, got {srsim_zeros}'
srsim_ones = srsim(ones, ones, data_range=1.)
assert torch.isfinite(srsim_ones).all(
), f'Expected finite value for ones tensos, got {srsim_ones}'
srsim_zeros_ones = srsim(zeros, ones, data_range=1.)
assert torch.isfinite(srsim_zeros_ones).all(), \
f'Expected finite value for zeros and ones tensos, got {srsim_zeros_ones}'
def test_srsim_supports_different_data_ranges(
input_tensors: Tuple[torch.Tensor, torch.Tensor], device: str) -> None:
prediction, target = input_tensors
prediction_255 = (prediction * 255).type(torch.uint8)
target_255 = (target * 255).type(torch.uint8)
measure_255 = srsim(prediction_255.to(device),
target_255.to(device),
data_range=255)
measure = srsim((prediction_255 / 255.).to(device),
(target_255 / 255.).to(device))
diff = torch.abs(measure_255 - measure)
assert diff <= 1e-6, f'Result for same tensor with different data_range should be the same, got {diff}'
def test_srsim_symmetry(input_tensors: Tuple[torch.Tensor, torch.Tensor],
device: str) -> None:
prediction, target = input_tensors
result = srsim(prediction.to(device),
target.to(device),
data_range=1.,
reduction='none')
result_sym = srsim(target.to(device),
prediction.to(device),
data_range=1.,
reduction='none')
assert torch.allclose(
result_sym,
result), f'Expected the same results, got {result} and {result_sym}'
def test_srsim_compare_with_matlab(device: str) -> None:
# Greyscale image
prediction = torch.tensor(
imread('tests/assets/goldhill.gif')).unsqueeze(0).unsqueeze(0)
target = torch.tensor(
imread('tests/assets/goldhill_jpeg.gif')).unsqueeze(0).unsqueeze(0)
# odd kernel (exactly same as matlab)
predicted_score = srsim(prediction.to(device),
target.to(device),
gaussian_size=9,
data_range=255,
reduction='none')
target_score = torch.tensor([0.94623509
]).to(predicted_score) # from matlab code
assert torch.allclose(predicted_score, target_score), f'Expected MATLAB result {target_score.item():.8f},' \
f'got {predicted_score.item():.8f}'
# even kernel (a bit different as matlab)
predicted_score = srsim(prediction.to(device),
target.to(device),
data_range=255,
reduction='none')
target_score = torch.tensor([0.94652679
]).to(predicted_score) # from matlab code
assert torch.allclose(predicted_score, target_score),\
f'Expected MATLAB result {target_score.item():.8f}, got {predicted_score.item():.8f}'
# RBG image
prediction = torch.tensor(imread('tests/assets/I01.BMP')).permute(
2, 0, 1).unsqueeze(0)
target = torch.tensor(imread('tests/assets/i01_01_5.bmp')).permute(
2, 0, 1).unsqueeze(0)
# odd kernel (exactly same as matlab)
predicted_score = srsim(prediction.to(device),
target.to(device),
gaussian_size=9,
data_range=255,
reduction='none')
target_score = torch.tensor([0.96667468
]).to(predicted_score) # from matlab code
assert torch.allclose(predicted_score, target_score), \
f'Expected MATLAB result {target_score.item():.8f}, got {predicted_score.item():.8f}'
# even kernel (a bit different as matlab)
predicted_score = srsim(prediction.to(device),
target.to(device),
data_range=255,
reduction='none')
target_score = torch.tensor([0.9659730
]).to(predicted_score) # from matlab code
assert torch.allclose(predicted_score, target_score), \
f'Expected MATLAB result {target_score.item():.8f}, got {predicted_score.item():.8f}'
def test_srsim_preserves_dtype(input_tensors: Tuple[torch.Tensor,
torch.Tensor], dtype,
device: str) -> None:
x, y = input_tensors
output = srsim(x.to(device=device, dtype=dtype),
y.to(device=device, dtype=dtype))
assert output.dtype == dtype
def test_srsim_to_be_one_for_identical_inputs(
input_tensors: Tuple[torch.Tensor, torch.Tensor], device: str) -> None:
prediction, _ = input_tensors
index = srsim(prediction.to(device),
prediction.to(device),
data_range=1.,
reduction='none')
prediction_255 = (prediction * 255).type(torch.uint8)
index_255 = srsim(prediction_255,
prediction_255,
data_range=255,
reduction='none')
assert torch.allclose(index, torch.ones_like(index, device=device)), \
f'Expected index to be equal 1, got {index}'
assert torch.allclose(index_255, torch.ones_like(index_255, device=device)), \
f'Expected index to be equal 1, got {index_255}'
def test_ssim_raises_if_bigger_kernel(device: str) -> None:
# kernels bigger than image * scale
prediction = torch.rand(1, 3, 50, 50, device=device)
target = torch.rand(1, 3, 50, 50, device=device)
with pytest.raises(ValueError):
srsim(prediction, target, kernel_size=15)
with pytest.raises(ValueError):
srsim(prediction, target, gaussian_size=15)
assert torch.isfinite(srsim(prediction, target, kernel_size=15,
scale=0.5)).all()
assert torch.isfinite(
srsim(prediction, target, gaussian_size=15, scale=0.5)).all()
assert torch.isfinite(srsim(prediction, target)).all()
def main():
# Read RGB image and it's noisy version
x = torch.tensor(imread('tests/assets/i01_01_5.bmp')).permute(
2, 0, 1)[None, ...] / 255.
y = torch.tensor(imread('tests/assets/I01.BMP')).permute(
2, 0, 1)[None, ...] / 255.
if torch.cuda.is_available():
# Move to GPU to make computaions faster
x = x.cuda()
y = y.cuda()
# To compute BRISQUE score as a measure, use lower case function from the library
brisque_index: torch.Tensor = piq.brisque(x,
data_range=1.,
reduction='none')
# In order to use BRISQUE as a loss function, use corresponding PyTorch module.
# Note: the back propagation is not available using torch==1.5.0.
# Update the environment with latest torch and torchvision.
brisque_loss: torch.Tensor = piq.BRISQUELoss(data_range=1.,
reduction='none')(x)
print(
f"BRISQUE index: {brisque_index.item():0.4f}, loss: {brisque_loss.item():0.4f}"
)
# To compute Content score as a loss function, use corresponding PyTorch module
# By default VGG16 model is used, but any feature extractor model is supported.
# Don't forget to adjust layers names accordingly. Features from different layers can be weighted differently.
# Use weights parameter. See other options in class docstring.
content_loss = piq.ContentLoss(feature_extractor="vgg16",
layers=("relu3_3", ),
reduction='none')(x, y)
print(f"ContentLoss: {content_loss.item():0.4f}")
# To compute DISTS as a loss function, use corresponding PyTorch module
# By default input images are normalized with ImageNet statistics before forwarding through VGG16 model.
# If there is no need to normalize the data, use mean=[0.0, 0.0, 0.0] and std=[1.0, 1.0, 1.0].
dists_loss = piq.DISTS(reduction='none')(x, y)
print(f"DISTS: {dists_loss.item():0.4f}")
# To compute DSS as a measure, use lower case function from the library
dss_index: torch.Tensor = piq.dss(x, y, data_range=1., reduction='none')
# In order to use DSS as a loss function, use corresponding PyTorch module
dss_loss = piq.DSSLoss(data_range=1., reduction='none')(x, y)
print(f"DSS index: {dss_index.item():0.4f}, loss: {dss_loss.item():0.4f}")
# To compute FSIM as a measure, use lower case function from the library
fsim_index: torch.Tensor = piq.fsim(x, y, data_range=1., reduction='none')
# In order to use FSIM as a loss function, use corresponding PyTorch module
fsim_loss = piq.FSIMLoss(data_range=1., reduction='none')(x, y)
print(
f"FSIM index: {fsim_index.item():0.4f}, loss: {fsim_loss.item():0.4f}")
# To compute GMSD as a measure, use lower case function from the library
# This is port of MATLAB version from the authors of original paper.
# In any case it should me minimized. Usually values of GMSD lie in [0, 0.35] interval.
gmsd_index: torch.Tensor = piq.gmsd(x, y, data_range=1., reduction='none')
# In order to use GMSD as a loss function, use corresponding PyTorch module:
gmsd_loss: torch.Tensor = piq.GMSDLoss(data_range=1., reduction='none')(x,
y)
print(
f"GMSD index: {gmsd_index.item():0.4f}, loss: {gmsd_loss.item():0.4f}")
# To compute HaarPSI as a measure, use lower case function from the library
# This is port of MATLAB version from the authors of original paper.
haarpsi_index: torch.Tensor = piq.haarpsi(x,
y,
data_range=1.,
reduction='none')
# In order to use HaarPSI as a loss function, use corresponding PyTorch module
haarpsi_loss: torch.Tensor = piq.HaarPSILoss(data_range=1.,
reduction='none')(x, y)
print(
f"HaarPSI index: {haarpsi_index.item():0.4f}, loss: {haarpsi_loss.item():0.4f}"
)
# To compute LPIPS as a loss function, use corresponding PyTorch module
lpips_loss: torch.Tensor = piq.LPIPS(reduction='none')(x, y)
print(f"LPIPS: {lpips_loss.item():0.4f}")
# To compute MDSI as a measure, use lower case function from the library
mdsi_index: torch.Tensor = piq.mdsi(x, y, data_range=1., reduction='none')
# In order to use MDSI as a loss function, use corresponding PyTorch module
mdsi_loss: torch.Tensor = piq.MDSILoss(data_range=1., reduction='none')(x,
y)
print(
f"MDSI index: {mdsi_index.item():0.4f}, loss: {mdsi_loss.item():0.4f}")
# To compute MS-SSIM index as a measure, use lower case function from the library:
ms_ssim_index: torch.Tensor = piq.multi_scale_ssim(x, y, data_range=1.)
# In order to use MS-SSIM as a loss function, use corresponding PyTorch module:
ms_ssim_loss = piq.MultiScaleSSIMLoss(data_range=1., reduction='none')(x,
y)
print(
f"MS-SSIM index: {ms_ssim_index.item():0.4f}, loss: {ms_ssim_loss.item():0.4f}"
)
# To compute Multi-Scale GMSD as a measure, use lower case function from the library
# It can be used both as a measure and as a loss function. In any case it should me minimized.
# By default scale weights are initialized with values from the paper.
# You can change them by passing a list of 4 variables to scale_weights argument during initialization
# Note that input tensors should contain images with height and width equal 2 ** number_of_scales + 1 at least.
ms_gmsd_index: torch.Tensor = piq.multi_scale_gmsd(x,
y,
data_range=1.,
chromatic=True,
reduction='none')
# In order to use Multi-Scale GMSD as a loss function, use corresponding PyTorch module
ms_gmsd_loss: torch.Tensor = piq.MultiScaleGMSDLoss(chromatic=True,
data_range=1.,
reduction='none')(x, y)
print(
f"MS-GMSDc index: {ms_gmsd_index.item():0.4f}, loss: {ms_gmsd_loss.item():0.4f}"
)
# To compute PSNR as a measure, use lower case function from the library.
psnr_index = piq.psnr(x, y, data_range=1., reduction='none')
print(f"PSNR index: {psnr_index.item():0.4f}")
# To compute PieAPP as a loss function, use corresponding PyTorch module:
pieapp_loss: torch.Tensor = piq.PieAPP(reduction='none', stride=32)(x, y)
print(f"PieAPP loss: {pieapp_loss.item():0.4f}")
# To compute SSIM index as a measure, use lower case function from the library:
ssim_index = piq.ssim(x, y, data_range=1.)
# In order to use SSIM as a loss function, use corresponding PyTorch module:
ssim_loss: torch.Tensor = piq.SSIMLoss(data_range=1.)(x, y)
print(
f"SSIM index: {ssim_index.item():0.4f}, loss: {ssim_loss.item():0.4f}")
# To compute Style score as a loss function, use corresponding PyTorch module:
# By default VGG16 model is used, but any feature extractor model is supported.
# Don't forget to adjust layers names accordingly. Features from different layers can be weighted differently.
# Use weights parameter. See other options in class docstring.
style_loss = piq.StyleLoss(feature_extractor="vgg16",
layers=("relu3_3", ))(x, y)
print(f"Style: {style_loss.item():0.4f}")
# To compute TV as a measure, use lower case function from the library:
tv_index: torch.Tensor = piq.total_variation(x)
# In order to use TV as a loss function, use corresponding PyTorch module:
tv_loss: torch.Tensor = piq.TVLoss(reduction='none')(x)
print(f"TV index: {tv_index.item():0.4f}, loss: {tv_loss.item():0.4f}")
# To compute VIF as a measure, use lower case function from the library:
vif_index: torch.Tensor = piq.vif_p(x, y, data_range=1.)
# In order to use VIF as a loss function, use corresponding PyTorch class:
vif_loss: torch.Tensor = piq.VIFLoss(sigma_n_sq=2.0, data_range=1.)(x, y)
print(f"VIFp index: {vif_index.item():0.4f}, loss: {vif_loss.item():0.4f}")
# To compute VSI score as a measure, use lower case function from the library:
vsi_index: torch.Tensor = piq.vsi(x, y, data_range=1.)
# In order to use VSI as a loss function, use corresponding PyTorch module:
vsi_loss: torch.Tensor = piq.VSILoss(data_range=1.)(x, y)
print(f"VSI index: {vsi_index.item():0.4f}, loss: {vsi_loss.item():0.4f}")
# To compute SR-SIM score as a measure, use lower case function from the library:
srsim_index: torch.Tensor = piq.srsim(x, y, data_range=1.)
# In order to use SR-SIM as a loss function, use corresponding PyTorch module:
srsim_loss: torch.Tensor = piq.SRSIMLoss(data_range=1.)(x, y)
print(
f"SR-SIM index: {srsim_index.item():0.4f}, loss: {srsim_loss.item():0.4f}"
)