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https://github.com/vee1e/tflite-micro.git
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2791785562
* Check for the case where the Operators vector in a tflite model is missing Some of our tools remove the vector if it's empty (i.e. the subgraph doesn't contain ops). The check is added until the tools are fixed. BUG=http://b/192589496 * Move NumSubgraphOperators to flatbuffer_utils.h/cc * Fix the CI errors. * Remove `#define FLATBUFFERS_LOCALE_INDEPENDENT 0` We will handle that via the Makefile instead. * Add -DFLATBUFFERS_LOCALE_INDEPENDENT=0 to makefile. * remove references to flatbuffer from kernel_utils.cc/h * Fix the build. * run buildifier Co-authored-by: Advait Jain <advaitjain@google.com> Co-authored-by: Advait Jain <advaitjain@users.noreply.github.com>
78 lines
2.8 KiB
C++
78 lines
2.8 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/c/common.h"
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namespace tflite {
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namespace micro {
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bool HaveSameShapes(const TfLiteEvalTensor* input1,
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const TfLiteEvalTensor* input2) {
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TFLITE_DCHECK(input1 != nullptr);
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TFLITE_DCHECK(input2 != nullptr);
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return TfLiteIntArrayEqual(input1->dims, input2->dims);
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}
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const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor) {
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if (tensor == nullptr || tensor->dims == nullptr) {
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return RuntimeShape();
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}
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TfLiteIntArray* dims = tensor->dims;
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const int dims_size = dims->size;
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const int32_t* dims_data = reinterpret_cast<const int32_t*>(dims->data);
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return RuntimeShape(dims_size, dims_data);
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}
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PaddingType RuntimePaddingType(TfLitePadding padding) {
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switch (padding) {
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case TfLitePadding::kTfLitePaddingSame:
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return PaddingType::kSame;
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case TfLitePadding::kTfLitePaddingValid:
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return PaddingType::kValid;
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case TfLitePadding::kTfLitePaddingUnknown:
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default:
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return PaddingType::kNone;
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}
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}
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// Relocate tensor dims from FlatBuffer to the persistent storage arena.
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// The old dims data is copied to the new storage area.
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// The tensor and eval_tensor must be the same tensor.
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// Only use during Prepare phase.
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TfLiteStatus CreateWritableTensorDimsWithCopy(TfLiteContext* context,
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TfLiteTensor* tensor,
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TfLiteEvalTensor* eval_tensor) {
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TF_LITE_ENSURE(context, tensor != nullptr);
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TF_LITE_ENSURE(context, eval_tensor != nullptr);
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TF_LITE_ENSURE(context, context->AllocatePersistentBuffer != nullptr);
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int ranks = tensor->dims->size;
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size_t alloc_size = TfLiteIntArrayGetSizeInBytes(ranks);
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TfLiteIntArray* new_dims = static_cast<TfLiteIntArray*>(
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context->AllocatePersistentBuffer(context, alloc_size));
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TfLiteIntArray* old_dims = tensor->dims;
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new_dims->size = ranks;
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tensor->dims = new_dims;
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eval_tensor->dims = new_dims;
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for (int i = 0; i < ranks; i++) {
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new_dims->data[i] = old_dims->data[i];
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}
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return kTfLiteOk;
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}
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} // namespace micro
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} // namespace tflite
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