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# Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the License); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Description:
# Utlity function for reading .tosa and .tflite files
from .operation import Op
from .operation import Operation
def decode_str(s):
if s is None:
return ""
return s.decode("utf-8")
def clone_and_reshape_tensor(src_tens, reorder, set_unique):
tens = src_tens.clone("_reshape", set_unique)
tens.shape = [src_tens.shape[idx] for idx in reorder]
tens.bandwidth_shape = tens.shape
tens.storage_shape = tens.shape
if tens.values is not None:
tens.values = tens.values.transpose(reorder)
op = Operation(Op.Const, tens.name)
op.set_output_tensor(tens)
return tens
# Fix up tensors without operations. Generate either Placeholder or Constant ops
def fixup_tensors(input_tensors, tensors):
for tens in input_tensors:
if len(tens.ops) and tens.ops[0].type == Op.Const:
break
if tens.ops != []:
tens.error("This subgraph input tensor has unexpected driving operators.")
op = Operation(Op.Placeholder, tens.name)
op.set_output_tensor(tens)
for tens in tensors:
if not tens.ops:
op = Operation(Op.Const, tens.name)
op.set_output_tensor(tens)
def align_inputs_indices(from_indices, to_indices, inputs):
to_list = to_indices.ifms + to_indices.weights + to_indices.biases
from_list = from_indices.ifms + from_indices.weights + from_indices.biases
assert len(to_list) == len(from_list)
if to_list != from_list:
for idx, t_idx in enumerate(to_list):
if t_idx >= len(inputs):
# Biases are allowed to be left out
assert t_idx in from_indices.biases and t_idx in to_indices.biases
continue
if to_list[idx] != from_list[idx]:
# find t_idx in from list and swap.
for jdx in from_list[idx:]:
if from_list[jdx] == t_idx:
inputs[idx], inputs[jdx] = inputs[jdx], inputs[idx]
from_list[idx], from_list[jdx] = from_list[jdx], from_list[idx]
break
assert from_list == to_list
return inputs
def align_tensor_indices_to_nng(op_type, indices, inputs):
nng_op = Op(op_type)
return align_inputs_indices(indices, nng_op.info.indices, inputs)
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