def _get_op_blob_info(self, job_name, op_name, blob_name):
self._check_status(self.SessionStatus.OPEN, self.SessionStatus.RUNNING)
if op_name in self.inferface_name2info_:
return self.inferface_name2info_[op_name]
job_name = job_name or self.cur_job_name_
if job_name is None:
raise ValueError("please specify job_name")
lbn = oneflow_api.JobBuildAndInferCtx_GetOpBlobLbn(job_name, op_name, blob_name)
shape = c_api_util.JobBuildAndInferCtx_GetStaticShape(job_name, lbn)
dtype = c_api_util.JobBuildAndInferCtx_GetDataType(job_name, lbn)
dtype = dtype_util.convert_proto_dtype_to_oneflow_dtype(dtype)
# TODO: other info
info = dict(shape=shape, dtype=dtype)
self.inferface_name2info_[op_name] = info
return info
def input_info(self, input_name):
self._check_status(self.SessionStatus.RUNNING)
if input_name in self.inferface_name2info_:
return self.inferface_name2info_[input_name]
input_lbn = "{}/out".format(input_name)
if input_name not in self.inferface_name2job_name_:
raise ValueError('can not find input with name "{}"'.format(input_name))
job_name = self.inferface_name2job_name_[input_name]
input_shape = c_api_util.JobBuildAndInferCtx_GetStaticShape(job_name, input_lbn)
input_dtype = c_api_util.JobBuildAndInferCtx_GetDataType(job_name, input_lbn)
input_dtype = dtype_util.convert_proto_dtype_to_oneflow_dtype(input_dtype)
# input_dtype = dtype_util.convert_oneflow_dtype_to_numpy_dtype(input_dtype)
# TODO: other info
input_info = dict(shape=input_shape, dtype=input_dtype)
self.inferface_name2info_[input_name] = input_info
return input_info
def __init__(
self,
var_dir: str,
dtype: Optional[dtype_util.dtype] = None,
shape: Optional[Sequence[int]] = None,
):
data_path = os.path.join(var_dir, DATA_FILENAME)
assert os.path.isfile(data_path)
self.var_dir_ = var_dir
meta_info_path = os.path.join(self.var_dir_, META_INFO_FILENAME)
if os.path.exists(meta_info_path):
meta_info = variable_meta_info_pb.VariableMetaInfo()
with open(meta_info_path) as f:
text_format.Parse(f.read(), meta_info)
self.has_meta_info_ = True
else:
self.has_meta_info_ = False
if self.has_meta_info_:
assert dtype is None and shape is None
self.shape_ = tuple(meta_info.shape.dim)
self.dtype_ = dtype_util.convert_proto_dtype_to_oneflow_dtype(
meta_info.data_type)
else:
if shape is not None and dtype is not None:
self.shape_ = shape
self.dtype_ = dtype
self.has_meta_info_ = True
elif shape is not None or dtype is not None:
raise RuntimeError(
"both or neither of shape and dtype should be None")
else:
pass
if self.has_meta_info_:
itemsize = np.dtype(
dtype_util.convert_oneflow_dtype_to_numpy_dtype(
self.dtype_)).itemsize
assert os.path.getsize(data_path) == np.prod(
self.shape).item() * itemsize
def dtype(self):
return convert_proto_dtype_to_oneflow_dtype(
self.blob_desc_.body.data_type)
def dtype(self):
return convert_proto_dtype_to_oneflow_dtype(self.get_dtype())
def dtype(self):
return convert_proto_dtype_to_oneflow_dtype(
oneflow_api.Ofblob_GetDataType(self.of_blob_ptr_))
def dtype(self):
return convert_proto_dtype_to_oneflow_dtype(
c_api_util.JobBuildAndInferCtx_MirroredBlobGetDataType(
self.job_name_, self.lbn_
)
)
def dtype(self):
ret = convert_proto_dtype_to_oneflow_dtype(self.get_dtype())
assert issubclass(ret, dtype_util.dtype)
return ret
def dtype(self):
ret = convert_proto_dtype_to_oneflow_dtype(self.get_dtype())
assert isinstance(ret, oneflow.dtype)
return ret
def dtype(self):
return convert_proto_dtype_to_oneflow_dtype(
c_api_util.JobBuildAndInferCtx_GetDataType(self.job_name_,
self.logical_blob_name))
