Hello! I was trying to open the both trained models with torch.load('MetaIQA/model_IQA/TID2013_IQA_Meta_resnet18.pt') and got this error:

AttributeError Traceback (most recent call last) in () ----> 1 model = torch.load('MetaIQA/model_IQA/TID2013_IQA_Meta_resnet18.pt')

/usr/local/lib/python3.6/dist-packages/torch/serialization.py in load(f, map_location, pickle_module, **pickle_load_args) 591 return torch.jit.load(f) 592 return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) --> 593 return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) 594 595

/usr/local/lib/python3.6/dist-packages/torch/serialization.py in _legacy_load(f, map_location, pickle_module, **pickle_load_args) 771 unpickler = pickle_module.Unpickler(f, **pickle_load_args) 772 unpickler.persistent_load = persistent_load --> 773 result = unpickler.load() 774 775 deserialized_storage_keys = pickle_module.load(f, **pickle_load_args)

AttributeError: Can't get attribute 'Net' on <module 'main'>

I tried it in my local environment and using colab, so the same error occurs with different python and pycharm versions. Could you, please, specify the exact versions or maybe check the both models.

when I use the project with torch==1.6.0. it will show me that: torch.nn.modules.module.ModuleAttributeError: 'AvgPool2d' object has no attribute 'divisor_override' and then it will be right when I change torch version to 0.4.0. So that means MetaIQA use the 0.4.0 version ?

First of all. thanks for your work. I want known how to convert tid 2013 data range to 0-1. The orignal tid 2013 range is max: 7.21429 min: 0.24242. value / max value ?

Hi!Congratulations to your work! In the process of reproducing the code, I trained the prior knowledge model on TID2013 and Kadid10k, and fine-tuned on the CLIVE data set, but the SROCC was about 0.48, which did not achieve the results in the paper. Do you get similar results? I didn't make any change but the results were not ideal.

Hello! The overall framework of the MetaIQA is composed of two steps: 1. meta-training for quality prior model and fine-tuning for NR-IQA of unknown distortions. When will the first part of the code can be released? Thanks!

Thank you very much for your nice work. It is a quiet great guidance for NR-IQA. Excuse me.
I want to consult where can we get the relevant 5 datasets? Thanks alot.

Hello! We kindly invite you to participate in our video quality metrics benchmark. You can submit MetaIQA (or any other your metrics) to the benchmark, following the submission steps, described here. The dataset distortions refer to compression artifacts on professional and user-generated content. The full dataset is used to measure methods overall performance, so we do not share it to avoid overfitting. Nevertheless, we provided the open part of it (around 1,000 videos) within our paper "Video compression dataset and benchmark of learning-based video-quality metrics", accepted to NeurIPS 2022.

Excuse me, zhuhancheng, I want to know how you fine-tuning the quality prior model to get the final quality modelmodel_IQA/TID2013_IQA_Meta_resnet18.pt? Hope to see the reply!

Excuse me, I want to train and test new datasets (such as training TID2008, live, csiq, pipal, and so on) with your model. How should I do it? Thanks so much for your help

Hi, zhuhancheng. Thanks for your excellent work, but I'm a little bit confused about the meta model update location, and the counter location. Why they are in the most inner loop of the train_dataloader? Should not they be in the loop of the task? https://github.com/zhuhancheng/MetaIQA/blob/30eeeb179bc72cf9408f91e2096726a2713de028/MetaIQA_On_TID2013_KADID.py#L360-366