def build(builder):
if blob_object.op_arg_parallel_attr.is_mirrored():
input_blob_def = input_blob_def_util.MirroredTensorDef(
ndarray.shape,
dtype=dtype_util.convert_numpy_dtype_to_oneflow_dtype(ndarray.dtype),
)
else:
input_blob_def = input_blob_def_util.FixedTensorDef(
ndarray.shape,
dtype=dtype_util.convert_numpy_dtype_to_oneflow_dtype(ndarray.dtype),
)
push_util.FeedValueToEagerBlob(blob_object, input_blob_def, ndarray)
def build(builder):
blob_object = builder.MakeLazyRefBlobObject(op_name)
if blob_object.op_arg_blob_attr.is_tensor_list:
input_blob_def = input_blob_def_util.MirroredTensorListDef(
[x.shape for x in ndarray],
dtype=dtype_util.convert_numpy_dtype_to_oneflow_dtype(
ndarray.dtype),
)
elif blob_object.op_arg_parallel_attr.is_mirrored():
input_blob_def = input_blob_def_util.MirroredTensorDef(
ndarray.shape,
dtype=dtype_util.convert_numpy_dtype_to_oneflow_dtype(
ndarray.dtype),
)
else:
input_blob_def = input_blob_def_util.FixedTensorDef(
ndarray.shape,
dtype=dtype_util.convert_numpy_dtype_to_oneflow_dtype(
ndarray.dtype),
)
push_util.FeedValueToEagerBlob(blob_object, input_blob_def,
ndarray)
Yield()
def NewInputBlobDef(subclass):
return input_blob_def.MirroredTensorDef(subclass.shape,
dtype=subclass.dtype)