def tensor(self, shape, variant=None):
key = construct_minmax_tensor_key(shape)
# If a tensor with these properties exists already, then load it.
if os.path.exists(key):
return self.loader.load(key)
else:
# Otherwise, we must create a random tensor with the desired properties,
# dump it to the output file, then return it.
crds = self.generate_crds(shape)
values = dict()
for c in crds:
ind_list = numpy.random.rand(2) * shape[-1]
ind_list = ind_list.astype('int')
start = numpy.min(ind_list)
stop = numpy.max(ind_list)
for i in range(start, stop):
temp = tuple(c[1:] + [i])
values[temp] = int(20 * numpy.random.rand() - 10)
dok = sparse.DOK(shape, values)
ind = tuple([e - 1 for e in shape])
write_shape = False if ind in values.keys() else True
TnsFileDumper().dump_dict_to_file(shape, dok.data, key,
write_shape)
result = dok.asformat('coo')
return result
def random(self, shape, sparsity, variant=None):
key = construct_random_tensor_key(shape, sparsity, variant)
# If a tensor with these properties exists already, then load it.
if os.path.exists(key):
return self.loader.load(key)
else:
# Otherwise, we must create a random tensor with the desired properties,
# dump it to the output file, then return it.
result = sparse.random(shape, density=sparsity)
dok = sparse.DOK(result)
TnsFileDumper().dump_dict_to_file(shape, dok.data, key)
return result
def sparse_window(self, num, variant=3):
path = TENSOR_PATH
key = "image" + str(num) + "-" + str(variant) + ".tns"
key = os.path.join(path, "image", "tensors", key)
shape = self.shape[num]
if os.path.exists(key):
return self.loader.load(key)
else:
result_np = self.dense_window(num)
result = sparse.COO.from_numpy(result_np)
dok = sparse.DOK(result)
write_shape = result_np.flat[-1] == 0
TnsFileDumper().dump_dict_to_file(shape, dok.data, key,
write_shape)
return result
def sparse_image(self, num, pt, variant=None, path='no'):
key = construct_image_tensor_key(num, pt, variant)
# If an image with these properties exists already, then load it.
if os.path.exists(key):
result = self.loader.load(key)
self.shape[num] = result.shape
return result
else:
# Otherwise, we must create load the image and preprocess it with the desired properties.
# dump it to the output file, then return it.
bin_img = self.dense_image(num, pt, variant, path)
result = sparse.COO.from_numpy(bin_img)
dok = sparse.DOK(result)
write_shape = bin_img.flat[-1] == 0
TnsFileDumper().dump_dict_to_file(self.shape[num], dok.data, key,
write_shape)
return result
def dump(self, tensor, path):
self.dumper.dump_dict_to_file(tensor.shape,
sparse.DOK(tensor).data, path)