import pyiqa
import torch
# list all available metrics
print(pyiqa.list_models())
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# create metric with default setting
iqa_metric = pyiqa.create_metric('lpips', device=device)
# Note that gradient propagation is disabled by default. set as_loss=True to enable it as a loss function.
iqa_loss = pyiqa.create_metric('lpips', device=device, as_loss=True)
# create metric with custom setting
iqa_metric = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr').to(device)
# check if lower better or higher better
print(iqa_metric.lower_better)
# example for iqa score inference
# Tensor inputs, img_tensor_x/y: (N, 3, H, W), RGB, 0 ~ 1
score_fr = iqa_metric(img_tensor_x, img_tensor_y)
score_nr = iqa_metric(img_tensor_x)
# img path as inputs.
score_fr = iqa_metric('./ResultsCalibra/dist_dir/I03.bmp', './ResultsCalibra/ref_dir/I03.bmp')
# For FID metric, use directory or precomputed statistics as inputs
# refer to clean-fid for more details: https://github.com/GaParmar/clean-fid
fid_metric = pyiqa.create_metric('fid')
score = fid_metric('./ResultsCalibra/dist_dir/', './ResultsCalibra/ref_dir')
score = fid_metric('./ResultsCalibra/dist_dir/', dataset_name="FFHQ", dataset_res=1024, dataset_split="trainval70k")