104 lines
3.4 KiB
C++
104 lines
3.4 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 <stdint.h>
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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/compatibility.h"
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#include "tensorflow/lite/kernels/internal/tensor.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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namespace tflite {
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namespace ops {
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namespace builtin {
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namespace shape {
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constexpr int kInputTensor = 0;
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constexpr int kOutputTensor = 0;
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template <typename OutType>
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void ExtractShape(const TfLiteTensor* input, OutType* output_data) {
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for (int i = 0; i < NumDimensions(input); ++i) {
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output_data[i] = SizeOfDimension(input, i);
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}
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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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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auto* params = reinterpret_cast<TfLiteShapeParams*>(node->builtin_data);
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switch (params->out_type) {
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case kTfLiteInt32:
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output->type = kTfLiteInt32;
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break;
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case kTfLiteInt64:
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output->type = kTfLiteInt64;
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break;
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default:
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context->ReportError(context, "Unknown shape output data type: %d",
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params->out_type);
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return kTfLiteError;
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}
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// By design, the input shape is always known at the time of Prepare, even
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// if the preceding op that generates |input| is dynamic. Thus, we can
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// always compute the shape immediately, without waiting for Eval.
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SetTensorToPersistentRo(output);
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// Shape always produces a 1-dimensional output tensor, where each output
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// element is the length of the corresponding input tensor's dimension.
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TfLiteIntArray* output_size = TfLiteIntArrayCreate(1);
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output_size->data[0] = NumDimensions(input);
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TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_size));
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TFLITE_DCHECK_EQ(NumDimensions(output), 1);
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TFLITE_DCHECK_EQ(SizeOfDimension(output, 0), NumDimensions(input));
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// Immediately propagate the known shape to the output tensor. This allows
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// downstream ops that rely on the value to use it during prepare.
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switch (output->type) {
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case kTfLiteInt32:
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ExtractShape(input, GetTensorData<int32_t>(output));
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break;
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case kTfLiteInt64:
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ExtractShape(input, GetTensorData<int64_t>(output));
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break;
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default:
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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return kTfLiteOk;
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}
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} // namespace shape
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TfLiteRegistration* Register_SHAPE() {
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static TfLiteRegistration r = {nullptr, nullptr, shape::Prepare, shape::Eval};
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return &r;
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}
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} // namespace builtin
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} // namespace ops
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} // namespace tflite
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