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
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