def load_parameters(params_path):
if not os.path.isfile(params_path):
from nnabla.utils.download import download
url = os.path.join("https://nnabla.org/pretrained-models/nnabla-examples/eval_metrics/lpips",
params_path.split("/")[-1])
download(url, params_path, False)
nn.load_parameters(params_path)
def _load_nnp(self, rel_name, rel_url):
'''
Args:
rel_name: relative path to where downloaded nnp is saved.
rel_url: relative url path to where nnp is downloaded from.
'''
from nnabla.utils.download import download
path_nnp = os.path.join(get_model_home(),
'imagenet/{}'.format(rel_name))
url = os.path.join(get_model_url_base(), 'imagenet/{}'.format(rel_url))
logger.info('Downloading {} from {}'.format(rel_name, url))
dir_nnp = os.path.dirname(path_nnp)
if not os.path.isdir(dir_nnp):
os.makedirs(dir_nnp)
download(url, path_nnp, open_file=False, allow_overwrite=False)
print('Loading {}.'.format(path_nnp))
self.nnp = NnpLoader(path_nnp)
def __init__(self, train=True, shuffle=False, rng=None):
super(Cifar10DataSource, self).__init__(shuffle=shuffle, rng=rng)
self._train = train
data_uri = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
logger.info('Getting labeled data from {}.'.format(data_uri))
r = download(data_uri) # file object returned
with tarfile.open(fileobj=r, mode="r:gz") as fpin:
# Training data
if train:
images = []
labels = []
for member in fpin.getmembers():
if "data_batch" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes", allow_pickle=True)
images.append(data[b"data"])
labels.append(data[b"labels"])
self._size = 50000
self._images = np.concatenate(images).reshape(
self._size, 3, 32, 32)
self._labels = np.concatenate(labels).reshape(-1, 1)
# Validation data
else:
for member in fpin.getmembers():
if "test_batch" not in member.name:
continue
fp = fpin.extractfile(member)
data = np.load(fp, encoding="bytes", allow_pickle=True)
images = data[b"data"]
labels = data[b"labels"]
self._size = 10000
self._images = images.reshape(self._size, 3, 32, 32)
self._labels = np.array(labels).reshape(-1, 1)
r.close()
logger.info('Getting labeled data from {}.'.format(data_uri))
self._size = self._labels.size
self._variables = ('x', 'y')
if rng is None:
rng = np.random.RandomState()
self.rng = rng
# keep data paths
self.data_history = queue.Queue(maxsize=128)
self.reset()
def load_parameters(params_path):
if not os.path.isfile(params_path):
from nnabla.utils.download import download
url = "https://nnabla.org/pretrained-models/nnabla-examples/eval_metrics/inceptions/original_inception_v3.h5"
download(url, params_path, False)
nn.load_parameters(params_path)
