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Merge pull request #158 from LorenzoAgnolucci/arniqa
add ARNIQA
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| r"""ARNIQA: Learning Distortion Manifold for Image Quality Assessment | ||
| @inproceedings{agnolucci2024arniqa, | ||
| title={ARNIQA: Learning Distortion Manifold for Image Quality Assessment}, | ||
| author={Agnolucci, Lorenzo and Galteri, Leonardo and Bertini, Marco and Del Bimbo, Alberto}, | ||
| booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, | ||
| pages={189--198}, | ||
| year={2024} | ||
| } | ||
| Reference: | ||
| - Arxiv link: https://arxiv.org/abs/2310.14918 | ||
| - Official Github: https://github.com/miccunifi/ARNIQA | ||
| """ | ||
|
|
||
| import torch | ||
| from torch import nn | ||
| import torch.nn.functional as F | ||
| import torchvision.models | ||
| from typing import Tuple | ||
| import warnings | ||
| from collections import OrderedDict | ||
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| from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD | ||
| from pyiqa.utils.registry import ARCH_REGISTRY | ||
| from pyiqa.archs.arch_util import load_pretrained_network | ||
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| # Avoid warning related to loading a jit model from torch.hub | ||
| warnings.filterwarnings("ignore", category=UserWarning, module="torch.serialization") | ||
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| available_datasets_ranges = { | ||
| "live": (1, 100), | ||
| "csiq": (0, 1), | ||
| "tid": (0, 9), | ||
| "kadid": (1, 5), | ||
| "koniq": (1, 100), | ||
| "clive": (1, 100), | ||
| "flive": (1, 100), | ||
| "spaq": (1, 100) | ||
| } | ||
|
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| available_datasets_mos_types = { | ||
| "live": "dmos", | ||
| "csiq": "dmos", | ||
| "tid": "mos", | ||
| "kadid": "mos", | ||
| "koniq": "mos", | ||
| "clive": "mos", | ||
| "flive": "mos", | ||
| "spaq": "mos" | ||
| } | ||
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| base_url = "https://github.com/miccunifi/ARNIQA/releases/download/weights" | ||
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| default_model_urls = { | ||
| "ARNIQA": f"{base_url}/ARNIQA.pth", | ||
| "live": f"{base_url}/regressor_live.pth", | ||
| "csiq": f"{base_url}/regressor_csiq.pth", | ||
| "tid": f"{base_url}/regressor_tid2013.pth", | ||
| "kadid": f"{base_url}/regressor_kadid10k.pth", | ||
| "koniq": f"{base_url}/regressor_koniq10k.pth", | ||
| "clive": f"{base_url}/regressor_clive.pth", | ||
| "flive": f"{base_url}/regressor_flive.pth", | ||
| "spaq": f"{base_url}/regressor_spaq.pth" | ||
| } | ||
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| @ARCH_REGISTRY.register() | ||
| class ARNIQA(nn.Module): | ||
| def __init__(self, | ||
| regressor_dataset: str = "koniq", | ||
| ): | ||
| super().__init__() | ||
|
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| self.regressor_dataset = regressor_dataset | ||
|
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| self.encoder = torchvision.models.resnet50( | ||
| weights=torchvision.models.ResNet50_Weights.IMAGENET1K_V1) # V1 weights work better than V2 | ||
| self.feat_dim = self.encoder.fc.in_features | ||
| self.encoder = nn.Sequential(*list(self.encoder.children())[:-1]) | ||
|
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| encoder_state_dict = torch.hub.load_state_dict_from_url(default_model_urls["ARNIQA"], progress=True, | ||
| map_location="cpu") | ||
| cleaned_encoder_state_dict = OrderedDict() | ||
| for key, value in encoder_state_dict.items(): | ||
| # Remove the prefix | ||
| if key.startswith("model."): | ||
| new_key = key[6:] | ||
| cleaned_encoder_state_dict[new_key] = value | ||
|
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| self.encoder.load_state_dict(cleaned_encoder_state_dict) | ||
| self.encoder.eval() | ||
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| self.regressor: nn.Module = torch.hub.load_state_dict_from_url(default_model_urls[self.regressor_dataset], | ||
| progress=True, | ||
| map_location="cpu") # Load regressor from torch.hub as JIT model | ||
| self.regressor.eval() | ||
|
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| self.default_mean = torch.Tensor(IMAGENET_DEFAULT_MEAN).view(1, 3, 1, 1) | ||
| self.default_std = torch.Tensor(IMAGENET_DEFAULT_STD).view(1, 3, 1, 1) | ||
|
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| def forward(self, x: torch.Tensor) -> float: | ||
| x, x_ds = self._preprocess(x) | ||
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| f = F.normalize(self.encoder(x), dim=1) | ||
| f_ds = F.normalize(self.encoder(x_ds), dim=1) | ||
| f_combined = torch.hstack((f, f_ds)).view(-1, self.feat_dim * 2) | ||
|
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| score = self.regressor(f_combined) | ||
| score = self._scale_score(score) | ||
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| return score | ||
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| def _preprocess(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: | ||
| """ | ||
| Downsample the input image with a factor of 2 and normalize the original and downsampled images. | ||
| """ | ||
| x_ds = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) | ||
| x = (x - self.default_mean.to(x)) / self.default_std.to(x) | ||
| x_ds = (x_ds - self.default_mean.to(x_ds)) / self.default_std.to(x_ds) | ||
| return x, x_ds | ||
|
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| def _scale_score(self, score: torch.Tensor) -> torch.Tensor: | ||
| """ | ||
| Scale the score in the range [0, 1], where higher is better. | ||
| """ | ||
| new_range = (0., 1.) | ||
|
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| # Compute scaling factors | ||
| original_range = ( | ||
| available_datasets_ranges[self.regressor_dataset][0], available_datasets_ranges[self.regressor_dataset][1]) | ||
| original_width = original_range[1] - original_range[0] | ||
| new_width = new_range[1] - new_range[0] | ||
| scaling_factor = new_width / original_width | ||
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| # Scale score | ||
| scaled_score = new_range[0] + (score - original_range[0]) * scaling_factor | ||
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| # Invert the scale if needed | ||
| if available_datasets_mos_types[self.regressor_dataset] == "dmos": | ||
| scaled_score = new_range[1] - scaled_score | ||
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| return scaled_score |
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