107 lines
3.9 KiB
C++
107 lines
3.9 KiB
C++
/* Copyright 2017 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/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/kernels/op_macros.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace reshape {
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constexpr int kInputTensor = 0;
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constexpr int kOutputTensor = 0;
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TfLiteStatus ReshapeOutput(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input = GetInput(context, node, kInputTensor);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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// Tensorflow's Reshape allows one of the shape components to have the
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// special -1 value, meaning it will be calculated automatically based on the
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// input. Here we calculate what that dimension should be so that the number
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// of output elements in the same as the number of input elements.
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int num_input_elements = NumElements(input);
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TfLiteIntArray* output_shape = output->dims;
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if (NumInputs(node) == 1 && // Legacy scalar supported with params.
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output_shape->size == 1 && output_shape->data[0] == 0) {
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// Legacy tflite models use a shape parameter of [0] to indicate scalars,
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// so adjust accordingly. TODO(b/111614235): Allow zero-sized buffers during
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// toco conversion.
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output_shape->size = 0;
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}
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int num_output_elements = 1;
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int stretch_dim = -1;
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for (int i = 0; i < output_shape->size; ++i) {
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int value = output_shape->data[i];
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if (value == -1) {
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TF_LITE_ENSURE_EQ(context, stretch_dim, -1);
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stretch_dim = i;
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} else {
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num_output_elements *= value;
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}
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}
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if (stretch_dim != -1) {
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output_shape->data[stretch_dim] = num_input_elements / num_output_elements;
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num_output_elements *= output_shape->data[stretch_dim];
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}
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TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
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TF_LITE_ENSURE_EQ(context, num_input_elements, num_output_elements);
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return kTfLiteOk;
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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TF_LITE_ENSURE_EQ(context, ReshapeOutput(context, node), kTfLiteOk);
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input = GetInput(context, node, kInputTensor);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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// Do nothing for in-place reshape.
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if (input->data.raw != output->data.raw) {
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// Otherwise perform reshape with copy.
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for (size_t i = 0; i < input->bytes; ++i) {
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output->data.raw[i] = input->data.raw[i];
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}
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}
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return kTfLiteOk;
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}
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} // namespace reshape
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TfLiteRegistration* Register_RESHAPE() {
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static TfLiteRegistration r = {/*init=*/nullptr,
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/*free=*/nullptr,
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/*prepare=*/reshape::Prepare,
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/*invoke=*/reshape::Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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return &r;
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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