Metadata-Version: 2.1
Name: torchdatasetutil
Version: 0.1.2
Summary: Utilities to load and use pytorch datasets stored in Minio S3
Home-page: https://github.com/bhlarson/torchdatasetutil
Author: Brad Larson
Author-email: <bhlarson@gmail.com>
License: UNKNOWN
Description: # Torch Dataset Utilities
        
        The python library [torchdatasetutils](https://pypi.org/project/torchdatasetutil/) produces torch [DataLoader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) classes and utility functions for several imaging datasets.  This currently includes sets of images and annotations from [CVAT](https://github.com/openvinotoolkit/cvat), [COCO dataset](https://cocodataset.org/).  "torchdatasetutil" uses an s3 object storage to hold dataset data.  This enables training and test to be performed on nodes different from where the dataset is stored with application defined credentials.  It uses torch PyTorch worker threads to prefetch data for efficient GPU or CPU training and inference.
        
        "torchdatasetutils" takes as an input the [pymlutil](https://pypi.org/project/pymlutil/).s3 object to access the object storage.
        
        Two json or yaml dictionaries are loaded from the object storage to identify and process the dataset: the dataset description and class dictionary.  The the dataset description is unique for each type of dataset.  The class dictionary is common to all datasets and describes data transformation and data augmentation.
        
        ## Library structure
        - pymlutil.s3: access to object storage
        - [torchdatasetutil](https://pypi.org/project/torchdatasetutil/)
            - [gitcoco.getcoco](https://github.com/bhlarson/torchdatasetutil/blob/main/torchdatasetutil/getcoco.py#L25): function to load the [COCO dataset](https://cocodataset.org/) from internet archives into object storage
            - [cocostore](https://github.com/bhlarson/torchdatasetutil/blob/main/torchdatasetutil/cocostore.py)
                - [CocoStore](https://github.com/bhlarson/torchdatasetutil/blob/main/torchdatasetutil/cocostore.py#L17): class providing a python iterator over the coco dataset in object storage
                - [CocoDataset](https://github.com/bhlarson/torchdatasetutil/blob/main/torchdatasetutil/cocostore.py)" class implementing the pytorch [Dataset class](https://pytorch.org/docs/stable/data.html#dataset-types) for the CocoStore iterator
            - [imstore](https://github.com/bhlarson/torchdatasetutil/blob/main/torchdatasetutil/imstore.py)
        
        See [torchdatasetutil.ipynb](https://github.com/bhlarson/torchdatasetutil/blob/main/torchdatasetutil.ipynb) for library interface and usage
        
        ## Class Dictionary
        
        ## COCO Dataset
        To load coco dataset you must have a credentials yaml file identifying the final s3 location and credentials for the dataset with the following keys:
        
        ```yaml
        s3:
        - name: store
          type: trainer
          address: <address>:<port>
          access key: <access key>
          secret key: <secret key>
          tls: false
          cert verify: false
          cert path: null
          sets:
            dataset: {"bucket":"imgml","prefix":"data", "dataset_filter":"" }
            trainingset: {"bucket":"imgml","prefix":"training", "dataset_filter":"" }
            model: {"bucket":"imgml","prefix":"model", "dataset_filter":"" }
            test: {"bucket":"imgml","prefix":"test", "dataset_filter":"" }
        ```
        
        Call torchdatasetutil.getcoco to retrieve the COCO dataset and stage it into object storage
        ```cmd
        python3 -m torchdatasetutil.getcoco
        ```
        
        To train with the coco dataset, first create dataset loaders
        ```python
        from torchdatasetutil.cocostore import CreateCocoLoaders
        
        # Create dataset loaders
        dataset_bucket = s3def['sets']['dataset']['bucket']
        if args.dataset=='coco':
            class_dictionary = s3.GetDict(s3def['sets']['dataset']['bucket'],args.coco_class_dict)
            loaders = CreateCocoLoaders(s3, dataset_bucket, 
                class_dict=args.coco_class_dict, 
                batch_size=args.batch_size,
                num_workers=args.num_workers,
                cuda = args.cuda,
                height = args.height,
                width = args.width,
            )
        
        # Identify training and test loaders
        trainloader = next(filter(lambda d: d.get('set') == 'train', loaders), None)
        testloader = next(filter(lambda d: d.get('set') == 'test' or d.get('set') == 'val', loaders), None)
        
        # Iterate through the dataset
        for i, data in tqdm(enumerate(trainloader['dataloader']), 
                            bar_format='{desc:<8.5}{percentage:3.0f}%|{bar:50}{r_bar}', 
                            total=trainloader['batches'], desc="Train batches", disable=args.job):
        
            # Extract dataset data
            inputs, labels, mean, stdev = data
        
            # Remaining steps
        
        ```
        
        # Cityscapes Dataset
        To download cityscapes, your cityscapes credentials must be included in you credentials yaml file with the following structure
        
        ```yaml
        cityscapes:
          username: <username>
          password: <password>
        ```
        Call torchdatasetutil.getcityscapes to retrieve the cityscapes dataset and stage it into object storage
        ```cmd
        python3 -m torchdatasetutil.getcityscapes
        ```
        ```python
        if args.dataset=='cityscapes':
            class_dictionary = s3.GetDict(s3def['sets']['dataset']['bucket'],args.cityscapes_class_dict)
            loaders = CreateCityscapesLoaders(s3, s3def, 
                src = args.cityscapes_data,
                dest = args.dataset_path+'/cityscapes',
                class_dictionary = class_dictionary,
                batch_size = args.batch_size, 
                num_workers=args.num_workers,
                height=args.height,
                width=args.width, 
            )
        ```
        
        # Imagenet:
        1. Data from kaggle:
            # Data from https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_sample_submission.csv
        1. Extract and move validation folder data:
            https://discuss.pytorch.org/t/issues-with-dataloader-for-imagenet-should-i-use-datasets-imagefolder-or-datasets-imagenet/115742/7
        1. Zip ILSVRC/Data/CLS-LOC/ to ILSVRC2012_devkit_t12.tar.gz
            ```cmd
            tar -czvf ILSVRC2012_devkit_t12.tar.gz ILSVRC/Data/CLS-LOC
            ```
Keywords: python,Machine Learning,Utilities
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
