Source code for archai.supergraph.datasets.providers.sport8_provider

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import os

import torchvision
from overrides import overrides
from torchvision.transforms import transforms

from archai.common import utils
from archai.common.config import Config
from archai.supergraph.datasets.dataset_provider import (
    DatasetProvider,
    ImgSize,
    TrainTestDatasets,
    register_dataset_provider,
)


[docs]class Sport8Provider(DatasetProvider): def __init__(self, conf_dataset:Config): super().__init__(conf_dataset) self._dataroot = utils.full_path(conf_dataset['dataroot'])
[docs] @overrides def get_datasets(self, load_train:bool, load_test:bool, transform_train, transform_test)->TrainTestDatasets: trainset, testset = None, None if load_train: trainpath = os.path.join(self._dataroot, 'sport8', 'train') trainset = torchvision.datasets.ImageFolder(trainpath, transform=transform_train) if load_test: testpath = os.path.join(self._dataroot, 'sport8', 'test') testset = torchvision.datasets.ImageFolder(testpath, transform=transform_test) return trainset, testset
[docs] @overrides def get_transforms(self, img_size:ImgSize)->tuple: # MEAN, STD computed for sport8 MEAN = [0.4734, 0.4856, 0.4526] STD = [0.2478, 0.2444, 0.2667] # transformations match that in # https://github.com/antoyang/NAS-Benchmark/blob/master/DARTS/preproc.py train_transf = [ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ColorJitter( brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2) ] test_transf = [transforms.Resize(256), transforms.CenterCrop(224)] normalize = [ transforms.ToTensor(), transforms.Normalize(MEAN, STD) ] train_transform = transforms.Compose(train_transf + normalize) test_transform = transforms.Compose(test_transf + normalize) return train_transform, test_transform
register_dataset_provider('sport8', Sport8Provider)