193 lines
7.5 KiB
C++
193 lines
7.5 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 "tensorflow/lite/kernels/internal/reference/strided_slice.h"
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#include <cmath>
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#include <cstring>
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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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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace strided_slice {
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constexpr int kInputTensor = 0;
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constexpr int kBeginTensor = 1;
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constexpr int kEndTensor = 2;
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constexpr int kStridesTensor = 3;
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constexpr int kOutputTensor = 0;
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struct StridedSliceContext {
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StridedSliceContext(TfLiteContext* context, TfLiteNode* node) {
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params = reinterpret_cast<TfLiteStridedSliceParams*>(node->builtin_data);
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input = GetInput(context, node, kInputTensor);
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begin = GetInput(context, node, kBeginTensor);
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end = GetInput(context, node, kEndTensor);
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strides = GetInput(context, node, kStridesTensor);
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output = GetOutput(context, node, kOutputTensor);
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dims = NumDimensions(input);
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}
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const TfLiteStridedSliceParams* params;
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const TfLiteTensor* input;
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const TfLiteTensor* begin;
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const TfLiteTensor* end;
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const TfLiteTensor* strides;
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TfLiteTensor* output;
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int dims;
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};
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// This Op only supports 1-4D cases and since we use the reference 4D
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// implementation, the 1-3D tensors are mapped to 4D.
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const int kMaxDim = 4;
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tflite::StridedSliceParams BuildStridedSliceParams(
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StridedSliceContext* op_context) {
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tflite::StridedSliceParams op_params;
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op_params.start_indices_count = op_context->dims;
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op_params.stop_indices_count = op_context->dims;
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op_params.strides_count = op_context->dims;
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for (int i = 0; i < op_context->dims; ++i) {
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op_params.start_indices[i] = GetTensorData<int32_t>(op_context->begin)[i];
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op_params.stop_indices[i] = GetTensorData<int32_t>(op_context->end)[i];
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op_params.strides[i] = GetTensorData<int32_t>(op_context->strides)[i];
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}
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op_params.begin_mask = op_context->params->begin_mask;
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op_params.ellipsis_mask = 0;
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op_params.end_mask = op_context->params->end_mask;
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op_params.new_axis_mask = 0;
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op_params.shrink_axis_mask = op_context->params->shrink_axis_mask;
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return op_params;
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}
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// Processes the indexing tensors (begin, end and strides) to resize the
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// output tensor. This function is callable from both Prepare() and Eval() as
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// long as the caller ensures the indexing tensors are present.
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TfLiteStatus CheckOutputSize(TfLiteContext* context,
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StridedSliceContext* op_context) {
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using ::tflite::strided_slice::StartForAxis;
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using ::tflite::strided_slice::StopForAxis;
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TfLiteIntArray* output_shape = op_context->output->dims;
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int shape_size = 0;
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auto op_params = BuildStridedSliceParams(op_context);
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auto input_shape = GetTensorShape(op_context->input);
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for (int idx = 0; idx < op_context->dims; ++idx) {
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int32_t stride = GetTensorData<int32_t>(op_context->strides)[idx];
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TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero");
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int32_t begin = StartForAxis(op_params, input_shape, idx);
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int32_t end = StopForAxis(op_params, input_shape, idx, begin);
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// When shrinking an axis, the end position does not matter (and can be
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// incorrect when negative indexing is used, see Issue #19260). Always use
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// begin + 1 to generate a length 1 slice, since begin has
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// already been adjusted for negative indices by StartForAxis.
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const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx);
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if (shrink_axis) {
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end = begin + 1;
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}
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// This is valid for both positive and negative strides
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int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride));
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dim_shape = dim_shape < 0 ? 0 : dim_shape;
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if (!shrink_axis) {
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TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape);
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shape_size++;
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}
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}
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TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size);
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return kTfLiteOk;
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}
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams));
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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StridedSliceParams* op_params =
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static_cast<StridedSliceParams*>(node->user_data);
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 4);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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StridedSliceContext op_context(context, node);
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TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
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"input dim should not exceed 4");
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auto params = BuildStridedSliceParams(&op_context);
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memcpy(op_params, ¶ms, sizeof(StridedSliceParams));
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return CheckOutputSize(context, &op_context);
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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const StridedSliceParams& op_params =
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*(static_cast<const StridedSliceParams*>(node->user_data));
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const TfLiteEvalTensor* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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switch (output->type) {
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case kTfLiteFloat32:
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reference_ops::StridedSlice(op_params,
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tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output));
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break;
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case kTfLiteUInt8:
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reference_ops::StridedSlice(
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op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<uint8_t>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output));
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break;
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case kTfLiteInt8:
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reference_ops::StridedSlice(op_params,
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tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<int8_t>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
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TfLiteTypeGetName(input->type), input->type);
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace strided_slice
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TfLiteRegistration Register_STRIDED_SLICE() {
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return {/*init=*/strided_slice::Init,
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/*free=*/nullptr,
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/*prepare=*/strided_slice::Prepare,
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/*invoke=*/strided_slice::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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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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