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PaQ-2-PiQ

Code for our CVPR2020 paper "From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality"

We packaged our source code built on FastAI inside FastIQA. For the pure-python version, check it this repo!

Follow this notebook to reproduce the results in the paper.

Installation

• Linux system is recommended
• python 3.6 or higher
• for CPU-only usage, just install pytorch-cpu [detailed instruction coming soon]

PyPI Install

pip install fastiqa

By default pip will install the latest pytorch with the latest cudatoolkit as well as fastai. If your hardware doesn't support the latest cudatoolkit, follow the instructions here, to install a pytorch build that fits your hardware.

Bug Fix Install

If a bug fix was made in git and you can't wait till a new release is made, you can install the bleeding edge version with:

pip install git+https://github.com/baidut/PaQ-2-PiQ.git

Developer Install [Recommended]

git clone https://github.com/baidut/PaQ-2-PiQ
cd PaQ-2-PiQ
pip install -r requirements.txt

Demo

see demo.ipynb

Get Started with FastIQA 🚀

For brief examples, see the examples folder;

%matplotlib inline
from fastiqa.basics import *

# setup an experiment on gpu 0
e = IqaExp('test_different_models', gpu=0)

# pick a dataset: CLIVE data, Im2MOS format: input image, output MOS
data = Im2MOS(CLIVE, batch_size=16)

# add learners 
for model in [models.resnet18, models.resnet34, models.resnet50]:
	e += iqa_cnn_learner(data, model)
	
# start training all models
e.fit(10)

# validate on other databases
e.valid(on=[Im2MOS(KonIQ), Im2MOS(FLIVE)])

The sections about advanced materials will be marked with *

Prepare labels

First we need to prepare images and labels. All label classes are extended from IqaLabel, which stores all default configurations. By default, we assume all databases are put under !data folder. To pack your database:

1. make a short name for your database, e.g. PLIVE

2. put images under !data\PLIVE\images (or other places)

3. put label files !data\PLIVE\labels.csv, the label should contain a column name for filename and mos for scores

4. define your database label class:

   class PLIVE(IqaLabel):
       path = '!data/PLIVE'
       csv_labels = 'labels.csv'
       
       fn_col = 'name'
       label_cols = 'mos', # don't omit the comma here
       folder = 'images'
       valid_pct = 0.2  # how many percent of data for validation

5. define subset if you have any, now you can extend from PLIVE to get all its default settings :

   class PLIVE_256x256(PLIVE):
       folder = '256x256'

   For PLIVE_256x256, it will load images from 256x256 instead of images folder.

6. You are all set!

   Some IQA databases have been packed and ready to use:

graph LR
linkStyle default interpolate basis

%% IqaLabel ----------------------------

IqaLabel --> KonIQ
IqaLabel --> CLIVE
IqaLabel -->Rois0123Label


subgraph labels
    KonIQ --> KonIQ_dist
	CLIVE
    Rois0123Label-->FLIVE
    FLIVE-->FLIVE_8k
    FLIVE-->FLIVE_2k
    FLIVE-->FLIVE640
end

style IqaLabel fill:yellow
style CLIVE fill:lightgreen
style KonIQ fill:lightgreen
style KonIQ_dist fill:lightgreen
style FLIVE fill:lightgreen
style FLIVE640 fill:lightgreen
style FLIVE_2k fill:lightgreen
style FLIVE_8k fill:lightgreen

Loading

Advanced Label Format*

• ImageRoI contains the image size information so that we could do RoIPool on the whole image area and get image score prediction
• Rois0123 contains the roi information of patch0 (image), patch1, patch2, patch3

Analyze labels

Check out csv/vis.py you will find some useful visualization functions. Simply put

Vis(PLIVE). and press tab, a smart IDE will pull out those functions :)

Browse Images

You could use Browser(PLIVE) to go over images and labels:

Bunch labels to Datasets

How you gonna load the data for training/testing the model:

• Im2MOS input images, output MOSs
• RandCrop2MOS
• Rois0123

Or you could define your own way of bunching data:

class MyBunch(IqaDataBunch):
    def get_data(self):
        # write your code here
        # ...
        return data # return a fastai data object

Please follow fastai's tutorial to prepare your database.

Finally, you could also use the way existing model bunches it:

# use the way how NIMA processes the data to prepare CLIVE database
data = NIMA.bunch(CLIVE)

After bunching it:

db= MyBunch(PLIVE)

# check out information (e.g. # images in train/val split)
print(db.data)

# show a batch
db.show_batch()

Also check out other fastai functions that you can call :)

Prepare your models

you could use pretrained CNN models: model = models.resnet18

• fastai models (undocumented models)
• Pretrained models for Pytorch

or define your own model (in exactly Pytorch way but extend it from IqaModel):

class BodyHeadModel(IqaModel):
    def __init__(self):
        super().__init__()
        self.__name__ = self.__class__.__name__
        self.body = create_body(models.resnet18)
        
        nf = num_features_model(self.body) * 2
        self.head = create_head(nf, 1)
        
    def forward(self, img):
        feat = self.body(img)
        pred = self.head(feat)
        return pred
        
    @staticmethod
    def split_on(m):
        return [[m.body], [m.head]]
    
    @staticmethod
    def bunch(self, label, **kwargs):
        return Im2MOS(label, **kwargs)

Here, split_on function split the model into two groups and use smaller learning rate for pretrained CNN backbone and bigger learning rate for head layers. bunch function tells how this model bunch the labels.

Learner

Now you can train your model easily just as in Fastai :)

One thing to note, models.resnet18 and models.resnet18() is different, the former one is just a n architecture that should go to iqa_cnn_learner to create actual model object.

# iqa_cnn_learner is a sub class of cnn_learner in Fastai but with useful functins
data = Im2MOS(CLIVE)
model = models.resnet18

learn = iqa_cnn_learner(data, model)
learn.fit(10)

While the latter one is an model object that should go to IqaLearner:

# iqa_cnn_learner is just like IqaLearner in Fastai but with useful functins
data = Im2MOS(KonIQ)
model = models.resnet18()

learn = IqaLearner(data, model)
learn.fit(10)

By default, the best model and training history will be stored under the dataset folder, see!data\PLIVE\models\bestmodel.pth and ``!data\PLIVE\history.csv`

Setup Experiments

IqaExp is a bunch of learners (in a dictionary form)

Directory structure would be:

• data:
  • CLIVE
  • KonIQ
• experiment_name
  • model_name: one model, one folder, easy to manage
    • train@data_name
      • models
        • bestmodel.pth: parameters of current best model
      • history.csv: model training history
      • valid@data_name.csv: validation outputs
• fastiqa: the library
• your_code.py
• your_notebook.ipynb

Citation

If you use this code for your research, please cite our papers.

@article{ying2019patches,
  title={From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality},
  author={Ying, Zhenqi
  ang and Niu, Haoran and Gupta, Praful and Mahajan, Dhruv and Ghadiyaram, Deepti and Bovik, Alan},
  journal={arXiv preprint arXiv:1912.10088},
  year={2019}
}

Acknowledgments

Our code is built on fast.ai

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