Python get_cached_data Examples

Example #1

from datasets import get_cached_data

parser = argparse.ArgumentParser()
parser.add_argument('--cfg_file', type=str)
parser.add_argument('--n_context', type=int, default=50)
parser.add_argument('--seed', type=int, default=1234)
parser.add_argument('--gpu', type=str, default='0')
args = parser.parse_args()
params = HParams(args.cfg_file)

os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
np.random.seed(args.seed)
tf.set_random_seed(args.seed)

# data
testset = get_cached_data(params, 'test')

# model
model = get_model(params)
model.load()

# run
save_dir = f'{params.exp_dir}/evaluate/imfns_imputation/'
os.makedirs(save_dir, exist_ok=True)
log_file = open(f'{save_dir}/log.txt', 'w')

def evaluate(batch):
    log_likel = model.execute(model.metric, batch)

    return -log_likel

Example #2

from models import get_model
from datasets import get_cached_data

parser = argparse.ArgumentParser()
parser.add_argument('--cfg_file', type=str)
parser.add_argument('--seed', type=int, default=1234)
parser.add_argument('--gpu', type=str, default='0')
args = parser.parse_args()
params = HParams(args.cfg_file)

os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
np.random.seed(args.seed)
tf.set_random_seed(args.seed)

# data
trainset = get_cached_data(params, 'train')
atoms = np.array([1,6,7,8,9])

# model
model = get_model(params)
model.load()

# run
save_dir = f'{params.exp_dir}/evaluate/set_generation/'
os.makedirs(save_dir, exist_ok=True)

# train
save_path = f'{save_dir}/train/'
os.makedirs(save_path, exist_ok=True)

res_p = defaultdict(list)

Example #3

from models import get_model
from datasets import get_cached_data

parser = argparse.ArgumentParser()
parser.add_argument('--cfg_file', type=str)
parser.add_argument('--seed', type=int, default=1234)
parser.add_argument('--gpu', type=str, default='0')
args = parser.parse_args()
params = HParams(args.cfg_file)

os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
np.random.seed(args.seed)
tf.set_random_seed(args.seed)

# data
validset = get_cached_data(params, 'valid')
testset = get_cached_data(params, 'test')

# model
model = get_model(params)
model.load()

# run
save_dir = f'{params.exp_dir}/evaluate/set_imputation/'
os.makedirs(save_dir, exist_ok=True)
log_file = open(f'{save_dir}/log.txt', 'w')

def evaluate(batch):
    average_mse = []
    for _ in range(5):
        sample = model.execute(model.sample, batch)

Example #4

File: pc_compression.py Project: lupalab/POEx

parser.add_argument('--n_points', type=int, default=128)
parser.add_argument('--seed', type=int, default=1234)
parser.add_argument('--gpu', type=str, default='0')
args = parser.parse_args()
params = HParams(args.cfg_file)
# modify config
params.dataset = 'modelnet'
params.mask_type = 'det_expand'

os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
np.random.seed(args.seed)
tf.set_random_seed(args.seed)

# data
if not args.biased:
    validset = get_cached_data(params, 'valid')
elif os.path.isfile('.modelnet_airplane_biased'):
    with open('.modelnet_airplane_biased', 'rb') as f:
        validset = pickle.load(f)
else:
    with h5py.File('/data/pointcloud/ModelNet40_cloud.h5', 'r') as f:
        train_cloud = np.array(f['tr_cloud'])
        train_labels = np.array(f['tr_labels'])
        test_cloud = np.array(f['test_cloud'])
        test_labels = np.array(f['test_labels'])
    inds = np.where(test_labels == 0)[0]
    pcs = []
    pcs_org = []
    for i in inds:
        x = test_cloud[i].astype(np.float32)
        # preprocess