torch.cuda.init()
cuda_available: bool = True
if torch.cuda.is_available():
print("CUDA available")
else:
print("CUDA not available")
device = torch.device("cuda:0" if cuda_available else "cpu")
# DATA
SPLIT_RATIO: float = 0.7
BATCH_SIZE: int = 32
NUM_EPOCHS: int = 15
movie_data_set: MovieSuccessDataset = MovieSuccessDataset(
MOVIE_DATA_FILE, POSTERS_DIR, Dictionary(DATA_DIR / 'dict2000.json'),
Compose([Resize((299, 299)), ToTensor()]))
data_set_size: int = len(movie_data_set)
print(f'Size of the data-set: {data_set_size}')
train_data_set_size: int = int(data_set_size * SPLIT_RATIO)
val_data_set_size: int = data_set_size - train_data_set_size
train_dataset, val_dataset = torch.utils.data.random_split(
movie_data_set, [train_data_set_size, val_data_set_size])
weights: np.ndarray = get_class_weights(train_dataset)
weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(
weights, len(weights))
train_data_set_loader: DataLoader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
cuda_available: bool = True
if torch.cuda.is_available():
print("CUDA available")
else:
print("CUDA not available")
device = torch.device("cuda:0" if cuda_available else "cpu")
# DATA
SPLIT_RATIO: float = 0.7
BATCH_SIZE: int = 32
NUM_EPOCHS: int = 15
movie_data_set: MovieSuccessDataset = MovieSuccessDataset(MOVIE_DATA_FILE,
POSTERS_DIR,
Dictionary(DATA_DIR / 'dict2000.json'),
Compose([Resize((299,
299)),
ToTensor()]))
data_set_size: int = len(movie_data_set)
print(f'Size of the data-set: {data_set_size}')
train_data_set_size: int = int(data_set_size * SPLIT_RATIO)
val_data_set_size: int = data_set_size - train_data_set_size
train_dataset, val_dataset = torch.utils.data.random_split(movie_data_set, [train_data_set_size,
val_data_set_size])
weights: np.ndarray = get_class_weights(train_dataset)
weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights))