def convert_type(shapes, types):
ms_types = []
for np_shape, np_type in zip(shapes, types):
input_np = np.zeros(np_shape, np_type)
tensor = Tensor(input_np)
ms_types.append(tensor.dtype())
return ms_types
def _to_full_tensor(elem, device_num, global_rank, scaling_sens=None):
"""Conver numpy to tensor, expanding batch dimension according to device_num, adapt to minddata feed solution."""
lst = []
if not isinstance(elem, (tuple, list)):
elem = [elem]
if global_rank >= device_num:
raise ValueError(
"The global rank must be smaller than device number, the global rank is {}, "
"the device num is {}".format(global_rank, device_num))
for data in elem:
if isinstance(data, np.ndarray):
data = Tensor(data)
if not isinstance(data, Tensor):
raise ValueError("elements in tensors must be Tensor")
shape_ = data.shape()
type_ = data.dtype()
new_shape = ()
batchsize_per_device = 1
for i, item in enumerate(shape_):
if i == 0:
new_shape += (item * device_num, )
batchsize_per_device = item
else:
new_shape += (item, )
new_tensor_numpy = np.zeros(new_shape, dtype_to_nptype(type_))
start = global_rank * batchsize_per_device
new_tensor_numpy[start:start + batchsize_per_device] = data.asnumpy()
new_tensor = Tensor(new_tensor_numpy)
lst.append(new_tensor)
if scaling_sens:
lst.append(Tensor(scaling_sens, mstype.float32))
return tuple(lst)
