| # 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) |