def make_tensor(name, data_type, dims, vals, raw=False): ''' Make a TensorProto with specified arguments. If raw is False, this function will choose the corresponding proto field to store the values based on data_type. If raw is True, use "raw_data" proto field to store the values, and values should be of type bytes in this case. ''' tensor = TensorProto() tensor.data_type = data_type tensor.name = name if data_type == TensorProto.STRING: assert not raw, "Can not use raw_data to store string type" tensor.string_data.extend(vals) if (data_type == TensorProto.COMPLEX64 or data_type == TensorProto.COMPLEX128): vals = split_complex_to_pairs(vals) if raw: tensor.raw_data = vals else: field = mapping.STORAGE_TENSOR_TYPE_TO_FIELD[ mapping.TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE[data_type]] getattr(tensor, field).extend(vals) tensor.dims.extend(dims) return tensor
def make_tensor(name, data_type, dims, vals, raw=False): ''' Make a TensorProto with specified arguments. If raw is False, this function will choose the corresponding proto field to store the values based on data_type. If raw is True, use "raw_data" proto field to store the values, and values should be of type bytes in this case. ''' tensor = TensorProto() tensor.data_type = data_type tensor.name = name if data_type == TensorProto.STRING: assert not raw, "Can not use raw_data to store string type" tensor.string_data.extend(vals) elif data_type in [ TensorProto.UINT8, TensorProto.INT8, TensorProto.UINT16, TensorProto.INT16, TensorProto.INT32, TensorProto.FLOAT16, TensorProto.BOOL, TensorProto.FLOAT ]: if raw: tensor.raw_data = vals else: if data_type == TensorProto.FLOAT: tensor.float_data.extend(vals) elif data_type == TensorProto.INT64: tensor.int64_data.extend(vals) else: tensor.int32_data.extend(vals) else: raise RuntimeError('Unrecognized data_type: {}'.format(data_type)) tensor.dims.extend(dims) return tensor
def from_array(arr, name=None): """Converts a numpy array to a tensor def. Inputs: arr: a numpy array. name: (optional) the name of the tensor. Returns: tensor_def: the converted tensor def. """ tensor = TensorProto() tensor.dims.extend(arr.shape) if name: tensor.name = name if arr.dtype == np.object: # Special care for strings. raise NotImplementedError("Need to properly implement string.") # For numerical types, directly use numpy raw bytes. try: dtype = mapping.NP_TYPE_TO_TENSOR_TYPE[arr.dtype] except KeyError: raise RuntimeError("Numpy data type not understood yet: {}".format( str(arr.dtype))) tensor.data_type = dtype tensor.raw_data = arr.tobytes() # note: tobytes() is only after 1.9. return tensor
def test_check_string_tensor(self): tensor = TensorProto() tensor.data_type = TensorProto.STRING tensor.string_data.append('Test'.encode('utf-8')) checker.check_tensor(tensor) del tensor.string_data[:] tensor.raw_data = 'Test'.encode('utf-8') # string data should not be stored in raw_data field self.assertRaises(ValueError, checker.check_tensor, tensor)
def from_array(arr, name=None): """Converts a numpy array to a tensor def. Inputs: arr: a numpy array. name: (optional) the name of the tensor. Returns: tensor_def: the converted tensor def. """ tensor = TensorProto() tensor.dims.extend(arr.shape) if name: tensor.name = name dtype_map = { np.dtype("float32"): TensorProto.FLOAT, np.dtype("uint8"): TensorProto.UINT8, np.dtype("int8"): TensorProto.INT8, np.dtype("uint16"): TensorProto.UINT16, np.dtype("int16"): TensorProto.INT16, np.dtype("int32"): TensorProto.INT32, np.dtype("int64"): TensorProto.INT64, np.dtype("bool"): TensorProto.BOOL, np.dtype("float16"): TensorProto.FLOAT16, } if arr.dtype == np.object: # Special care for strings. raise NotImplementedError("Need to properly implement string.") # For numerical types, directly use numpy raw bytes. try: dtype = dtype_map[arr.dtype] except KeyError: raise RuntimeError( "Numpy data type not understood yet: {}".format(str(arr.dtype))) tensor.data_type = dtype tensor.raw_data = arr.tobytes() # note: tobytes() is only after 1.9. return tensor