To compute SSIM index as a measure, use lower case function from the library:

import torch
from photosynthesis_metrics import ssim
from typing import Union, Tuple

prediction = torch.rand(3, 3, 256, 256)
target = torch.rand(3, 3, 256, 256) 
ssim_index: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] = ssim(prediction, target, data_range=1.)

In order to use SSIM as a loss function, use corresponding PyTorch module:

import torch
from photosynthesis_metrics import SSIMLoss

loss = SSIMLoss(data_range=1.)
prediction = torch.rand(3, 3, 256, 256, requires_grad=True)
target = torch.rand(3, 3, 256, 256)
output: torch.Tensor = loss(prediction, target)
output.backward()

To compute MS-SSIM index as a measure, use lower case function from the library:

import torch
from photosynthesis_metrics import multi_scale_ssim

prediction = torch.rand(3, 3, 256, 256)
target = torch.rand(3, 3, 256, 256) 
ms_ssim_index: torch.Tensor = multi_scale_ssim(prediction, target, data_range=1.)

In order to use MS-SSIM as a loss function, use corresponding PyTorch module:

import torch
from photosynthesis_metrics import MultiScaleSSIMLoss

loss = MultiScaleSSIMLoss(data_range=1.)
prediction = torch.rand(3, 3, 256, 256, requires_grad=True)
target = torch.rand(3, 3, 256, 256)
output: torch.Tensor = loss(prediction, target)
output.backward()

To compute TV as a measure, use lower case function from the library:

import torch
from photosynthesis_metrics import total_variation

data = torch.rand(3, 3, 256, 256) 
tv: torch.Tensor = total_variation(data)

In order to use TV as a loss function, use corresponding PyTorch module:

import torch
from photosynthesis_metrics import TVLoss

loss = TVLoss()
prediction = torch.rand(3, 3, 256, 256, requires_grad=True)
output: torch.Tensor = loss(prediction)
output.backward()

To compute VIF as a measure, use lower case function from the library:

import torch
from photosynthesis_metrics import vif_p

predicted = torch.rand(3, 3, 256, 256)
target = torch.rand(3, 3, 256, 256)
vif: torch.Tensor = vif_p(predicted, target, data_range=1.)

In order to use VIF as a loss function, use corresponding PyTorch class:

import torch
from photosynthesis_metrics import VIFLoss

loss = VIFLoss(sigma_n_sq=2.0, data_range=1.)
prediction = torch.rand(3, 3, 256, 256, requires_grad=True)
target = torch.rand(3, 3, 256, 256)
ouput: torch.Tensor = loss(prediction, target)
output.backward()

Note, that VIFLoss returns 1 - VIF value.

This is port of MATLAB version from the authors of original paper. It can be used both as a measure and as a loss function. In any case it should me minimized. Usually values of GMSD lie in [0, 0.35] interval.

import torch
from photosynthesis_metrics import GMSDLoss

loss = GMSDLoss(data_range=1.)
prediction = torch.rand(3, 3, 256, 256, requires_grad=True)
target = torch.rand(3, 3, 256, 256)
ouput: torch.Tensor = loss(prediction, target)
output.backward()

It can be used both as a measure and as a loss function. In any case it should me minimized. By defualt 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. Both GMSD and MS-GMSD computed for greyscale images, but to take contrast changes into account authors propoced to also add chromatic component. Use flag chromatic to use MS-GMSDc version of the loss

import torch
from photosynthesis_metrics import MultiScaleGMSDLoss

loss = MultiScaleGMSDLoss(chromatic=True, data_range=1.)
prediction = torch.rand(3, 3, 256, 256, requires_grad=True)
target = torch.rand(3, 3, 256, 256)
ouput: torch.Tensor = loss(prediction, target)
output.backward()

To compute BRISQUE score as a measure, use lower case function from the library:

import torch
from photosynthesis_metrics import brisque
from typing import Union, Tuple

prediction = torch.rand(3, 3, 256, 256)
brisque_index: torch.Tensor = brisque(prediction, data_range=1.)

In order to use BRISQUE as a loss function, use corresponding PyTorch module:

import torch
from photosynthesis_metrics import BRISQUELoss

loss = BRISQUELoss(data_range=1.)
prediction = torch.rand(3, 3, 256, 256, requires_grad=True)
output: torch.Tensor = loss(prediction)
output.backward()

Use MSID class to compute MSID score from image features, pre-extracted from some feature extractor network:

import torch
from photosynthesis_metrics import MSID

msid_metric = MSID()
prediction_feats = torch.rand(10000, 1024)
target_feats = torch.rand(10000, 1024)
msid: torch.Tensor = msid_metric(prediction_feats, target_feats)

If image features are not available, extract them using _compute_feats of MSID class. Please note that _compute_feats consumes a data loader of predefined format.

import torch
from  torch.utils.data import DataLoader
from photosynthesis_metrics import MSID

first_dl, second_dl = DataLoader(), DataLoader()
msid_metric = MSID() 
first_feats = msid_metric._compute_feats(first_dl)
second_feats = msid_metric._compute_feats(second_dl)
msid: torch.Tensor = msid_metric(first_feats, second_feats)

Use FID class to compute FID score from image features, pre-extracted from some feature extractor network:

import torch
from photosynthesis_metrics import FID

fid_metric = FID()
prediction_feats = torch.rand(10000, 1024)
target_feats = torch.rand(10000, 1024)
msid: torch.Tensor = fid_metric(prediction_feats, target_feats)

If image features are not available, extract them using _compute_feats of FID class. Please note that _compute_feats consumes a data loader of predefined format.

import torch
from  torch.utils.data import DataLoader
from photosynthesis_metrics import FID

first_dl, second_dl = DataLoader(), DataLoader()
fid_metric = FID() 
first_feats = fid_metric._compute_feats(first_dl)
second_feats = fid_metric._compute_feats(second_dl)
msid: torch.Tensor = fid_metric(first_feats, second_feats)

Use KID class to compute KID score from image features, pre-extracted from some feature extractor network:

import torch
from photosynthesis_metrics import KID

kid_metric = KID()
prediction_feats = torch.rand(10000, 1024)
target_feats = torch.rand(10000, 1024)
kid: torch.Tensor = kid_metric(prediction_feats, target_feats)

If image features are not available, extract them using _compute_feats of KID class. Please note that _compute_feats consumes a data loader of predefined format.

import torch
from  torch.utils.data import DataLoader
from photosynthesis_metrics import KID

first_dl, second_dl = DataLoader(), DataLoader()
kid_metric = KID() 
first_feats = kid_metric._compute_feats(first_dl)
second_feats = kid_metric._compute_feats(second_dl)
kid: torch.Tensor = kid_metric(first_feats, second_feats)

Use GS class to compute Geometry Score from image features, pre-extracted from some feature extractor network. Computation is heavily CPU dependent, adjust num_workers parameter according to your system configuration:

import torch
from photosynthesis_metrics import GS

gs_metric = GS(sample_size=64, num_iters=100, i_max=100, num_workers=4)
prediction_feats = torch.rand(10000, 1024)
target_feats = torch.rand(10000, 1024)
gs: torch.Tensor = gs_metric(prediction_feats, target_feats)

GS metric requiers gudhi library which is not installed by default. If you use conda, write: conda install -c conda-forge gudhi, otherwise follow installation guide.

Use inception_score function to compute IS from image features, pre-extracted from some feature extractor network. Note, that we follow recomendations from paper A Note on the Inception Score, which proposed small modification to original algorithm:

import torch
from photosynthesis_metrics import inception_score

prediction_feats = torch.rand(10000, 1024)
mean: torch.Tensor, variance: torch.Tensor = inception_score(prediction_feats, num_splits=10)

To compute difference between IS for 2 sets of image features, use IS class.

import torch
from photosynthesis_metrics import IS


is_metric = IS(distance='l1') 
prediction_feats = torch.rand(10000, 1024)
target_feats = torch.rand(10000, 1024)
distance: torch.Tensor = is_metric(prediction_feats, target_feats)