110 lines
4.0 KiB
C++
110 lines
4.0 KiB
C++
/* Copyright 2018 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 <string.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/portable_tensor.h"
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#include "tensorflow/lite/kernels/internal/tensor.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 squeeze {
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struct SqueezeContext {
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SqueezeContext(TfLiteContext* context, TfLiteNode* node)
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: params(reinterpret_cast<TfLiteSqueezeParams*>(node->builtin_data)),
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input(GetInput(context, node, 0)),
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output(GetOutput(context, node, 0)) {}
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TfLiteSqueezeParams* params;
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const TfLiteTensor* const input;
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TfLiteTensor* output;
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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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SqueezeContext op_context(context, node);
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int input_num_dims = NumDimensions(op_context.input);
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int num_squeeze_dims = op_context.params->num_squeeze_dims;
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// Determines number of dimensions of output tensor after squeeze.
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const TfLiteIntArray* input_dims = op_context.input->dims;
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const int* squeeze_dims = op_context.params->squeeze_dims;
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TF_LITE_ENSURE(context, input_num_dims <= 8);
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bool should_squeeze[8] = {false};
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int num_squeezed_dims = 0;
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if (num_squeeze_dims == 0) {
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for (int idx = 0; idx < input_num_dims; ++idx) {
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if (input_dims->data[idx] == 1) {
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should_squeeze[idx] = true;
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++num_squeezed_dims;
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}
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}
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} else {
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for (int idx = 0; idx < num_squeeze_dims; ++idx) {
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int current = squeeze_dims[idx] < 0 ? squeeze_dims[idx] + input_num_dims
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: squeeze_dims[idx];
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TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims &&
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input_dims->data[current] == 1);
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if (!should_squeeze[current]) ++num_squeezed_dims;
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should_squeeze[current] = true;
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}
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}
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// Sets output dimensions.
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TfLiteIntArray* output_dims =
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TfLiteIntArrayCreate(input_num_dims - num_squeezed_dims);
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for (int in_idx = 0, out_idx = 0; in_idx < input_num_dims; ++in_idx) {
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if (!should_squeeze[in_idx]) {
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output_dims->data[out_idx++] = input_dims->data[in_idx];
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}
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}
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return context->ResizeTensor(context, op_context.output, output_dims);
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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SqueezeContext op_context(context, node);
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if (op_context.input->type == kTfLiteString) {
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const int input_flat_size = GetTensorShape(op_context.input).FlatSize();
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const int output_flat_size = GetTensorShape(op_context.output).FlatSize();
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TF_LITE_ENSURE_EQ(context, input_flat_size, output_flat_size);
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SequentialTensorWriter<string> writer(op_context.input, op_context.output);
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for (int i = 0; i < input_flat_size; i++) {
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writer.Write(i);
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}
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return kTfLiteOk;
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}
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TF_LITE_ENSURE_EQ(context, op_context.input->bytes, op_context.output->bytes);
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memcpy(op_context.output->data.raw, op_context.input->data.raw,
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op_context.input->bytes);
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return kTfLiteOk;
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}
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} // namespace squeeze
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TfLiteRegistration* Register_SQUEEZE() {
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static TfLiteRegistration r = {nullptr, nullptr, squeeze::Prepare,
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squeeze::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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