Python get_test_data_loader 예제들

예제 #1

OUTPUT_PATH = Path("output") / "val_probas"
dataset_path = Path(
    "/home/fast_storage/imaterialist-challenge-furniture-2018/")

SAVE_PROBAS = True
# SAMPLE_SUBMISSION_PATH = dataset_path / "sample_submission_randomlabel.csv"

TEST_TRANSFORMS = [
    RandomResizedCrop(350, scale=(0.7, 1.0), interpolation=3),
    RandomHorizontalFlip(p=0.5),
    ColorJitter(hue=0.12, brightness=0.12),
    ToTensor(),
    Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]

N_CLASSES = 128
BATCH_SIZE = 64
NUM_WORKERS = 15

TEST_LOADER = get_test_data_loader(dataset_path=dataset_path / "validation",
                                   test_data_transform=TEST_TRANSFORMS,
                                   batch_size=BATCH_SIZE,
                                   num_workers=NUM_WORKERS,
                                   pin_memory=True)

MODEL = (Path("output") / "train" /
         "train_nasnetalarge_350_random_resized_crop" / "20180509_1544" /
         "model_FurnitureNASNetALarge350_5_val_loss=0.5331665.pth").as_posix()

N_TTA = 12

예제 #2

파일: test_probas_inceptionresnetv2_350_resized_crop.py 프로젝트: vfdev-5/ignite-examples

OUTPUT_PATH = "output"
dataset_path = Path(
    "/home/fast_storage/imaterialist-challenge-furniture-2018/")

SAVE_PROBAS = True
# SAMPLE_SUBMISSION_PATH = dataset_path / "sample_submission_randomlabel.csv"

TEST_TRANSFORMS = [
    RandomResizedCrop(350, scale=(0.7, 1.0), interpolation=3),
    RandomVerticalFlip(p=0.5),
    RandomHorizontalFlip(p=0.5),
    ToTensor(),
    Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]

N_CLASSES = 128
BATCH_SIZE = 24
NUM_WORKERS = 15

TEST_LOADER = get_test_data_loader(dataset_path=dataset_path / "test",
                                   test_data_transform=TEST_TRANSFORMS,
                                   batch_size=BATCH_SIZE,
                                   num_workers=NUM_WORKERS,
                                   cuda=True)

MODEL = (
    Path(OUTPUT_PATH) / "training_FurnitureInceptionResNet299_20180425_2324" /
    "model_FurnitureInceptionResNet299_10_val_loss=0.5324794.pth").as_posix()

N_TTA = 12