本文整理汇总了Python中chainer.dataset.download.cache_or_load_file方法的典型用法代码示例。如果您正苦于以下问题：Python download.cache_or_load_file方法的具体用法？Python download.cache_or_load_file怎么用？Python download.cache_or_load_file使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chainer.dataset.download的用法示例。
在下文中一共展示了download.cache_or_load_file方法的10个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _retrieve
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve(n_layers, name_npz, name_caffemodel, model):
root = download.get_dataset_directory('pfnet/chainer/models/')
path = os.path.join(root, name_npz)
path_caffemodel = os.path.join(root, name_caffemodel)
return download.cache_or_load_file(
path, lambda path: _make_npz(path, path_caffemodel, model, n_layers),
lambda path: npz.load_npz(path, model)) 示例2: _retrieve
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve(name, url, model):
root = download.get_dataset_directory('pfnet/chainer/models/')
path = os.path.join(root, name)
return download.cache_or_load_file(
path, lambda path: _make_npz(path, url, model),
lambda path: npz.load_npz(path, model)) 示例3: _retrieve
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve(name_npz, url, model):
root = download.get_dataset_directory('pfnet/chainer/models/')
path = os.path.join(root, name_npz)
return download.cache_or_load_file(
path, lambda path: _make_npz(path, url, model),
lambda path: npz.load_npz(path, model)) 示例4: _retrieve_ptb_words
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve_ptb_words(name, url):
def creator(path):
vocab = _retrieve_word_vocabulary()
words = _load_words(url)
x = numpy.empty(len(words), dtype=numpy.int32)
for i, word in enumerate(words):
x[i] = vocab[word]
numpy.savez_compressed(path, x=x)
return {'x': x}
root = download.get_dataset_directory('pfnet/chainer/ptb')
path = os.path.join(root, name)
loaded = download.cache_or_load_file(path, creator, numpy.load)
return loaded['x'] 示例5: _retrieve_fashion_mnist
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve_fashion_mnist(name, urls):
root = download.get_dataset_directory('pfnet/chainer/fashion-mnist')
path = os.path.join(root, name)
return download.cache_or_load_file(
path, lambda path: make_npz(path, urls), numpy.load) 示例6: _retrieve_kuzushiji_mnist
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve_kuzushiji_mnist(name, urls):
root = download.get_dataset_directory('pfnet/chainer/kuzushiji_mnist')
path = os.path.join(root, name)
return download.cache_or_load_file(
path, lambda path: make_npz(path, urls), numpy.load) 示例7: _retrieve_svhn
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve_svhn(name, url):
root = download.get_dataset_directory('pfnet/chainer/svhn')
path = os.path.join(root, name)
return download.cache_or_load_file(
path, lambda path: _make_npz(path, url), numpy.load) 示例8: _retrieve_mnist
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve_mnist(name, urls):
root = download.get_dataset_directory('pfnet/chainer/mnist')
path = os.path.join(root, name)
return download.cache_or_load_file(
path, lambda path: make_npz(path, urls), numpy.load) 示例9: _retrieve
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _retrieve(name, url, model):
root = download.get_dataset_directory('pfnet/chainer/models/')
path = os.path.join(root, name)
return download.cache_or_load_file(
path, lambda path: _make_npz(path, url, model),
lambda path: npz.load_npz(path, model)) 示例10: _get_cifar
# 需要导入模块: from chainer.dataset import download [as 别名]
# 或者: from chainer.dataset.download import cache_or_load_file [as 别名]
def _get_cifar(name, withlabel, ndim, scale, dtype):
root = download.get_dataset_directory(os.path.join('pfnet', 'chainer',
'cifar'))
npz_path = os.path.join(root, '{}.npz'.format(name))
url = 'https://www.cs.toronto.edu/~kriz/{}-python.tar.gz'.format(name)
def creator(path):
archive_path = download.cached_download(url)
if name == 'cifar-10':
train_x = numpy.empty((5, 10000, 3072), dtype=numpy.uint8)
train_y = numpy.empty((5, 10000), dtype=numpy.uint8)
test_y = numpy.empty(10000, dtype=numpy.uint8)
dir_name = '{}-batches-py'.format(name)
with tarfile.open(archive_path, 'r:gz') as archive:
# training set
for i in range(5):
file_name = '{}/data_batch_{}'.format(dir_name, i + 1)
d = _pickle_load(archive.extractfile(file_name))
train_x[i] = d['data']
train_y[i] = d['labels']
# test set
file_name = '{}/test_batch'.format(dir_name)
d = _pickle_load(archive.extractfile(file_name))
test_x = d['data']
test_y[...] = d['labels'] # copy to array
train_x = train_x.reshape(50000, 3072)
train_y = train_y.reshape(50000)
else:
# name == 'cifar-100'
def load(archive, file_name):
d = _pickle_load(archive.extractfile(file_name))
x = d['data'].reshape((-1, 3072))
y = numpy.array(d['fine_labels'], dtype=numpy.uint8)
return x, y
with tarfile.open(archive_path, 'r:gz') as archive:
train_x, train_y = load(archive, 'cifar-100-python/train')
test_x, test_y = load(archive, 'cifar-100-python/test')
numpy.savez_compressed(path, train_x=train_x, train_y=train_y,
test_x=test_x, test_y=test_y)
return {'train_x': train_x, 'train_y': train_y,
'test_x': test_x, 'test_y': test_y}
raw = download.cache_or_load_file(npz_path, creator, numpy.load)
train = _preprocess_cifar(raw['train_x'], raw['train_y'], withlabel,
ndim, scale, dtype)
test = _preprocess_cifar(raw['test_x'], raw['test_y'], withlabel, ndim,
scale, dtype)
return train, test