262 lines
9.7 KiB
C++
262 lines
9.7 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 <math.h>
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#include <stdint.h>
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#include <algorithm>
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#include <vector>
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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/optimized/optimized_ops.h"
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#include "tensorflow/lite/kernels/internal/strided_slice_logic.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/internal/types.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 strided_slice {
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enum KernelType {
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kReference,
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kGenericOptimized,
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};
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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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StridedSliceParams BuildStridedSliceParams(StridedSliceContext* op_context) {
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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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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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int begin_count = GetTensorShape(op_context->begin).Dims(0);
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for (int i = 0; i < begin_count; ++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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// If the length of begin and end smaller than number of input dims, set the
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// mask bit of begin and end for that index.
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for (int i = begin_count; i < op_context->dims; ++i) {
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op_params.start_indices[i] = op_params.stop_indices[i] = 0;
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op_params.strides[i] = 1;
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op_params.begin_mask |= (1 << i);
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op_params.end_mask |= (1 << i);
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}
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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 ResizeOutputTensor(TfLiteContext* context,
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StridedSliceContext* op_context) {
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std::vector<int> output_shape_vector;
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StridedSliceParams op_params = BuildStridedSliceParams(op_context);
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RuntimeShape input_shape = GetTensorShape(op_context->input);
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for (int idx = op_context->dims - 1; idx >= 0; --idx) {
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int32_t stride = op_params.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 =
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::tflite::strided_slice::StartForAxis(op_params, input_shape, idx);
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int32_t end = ::tflite::strided_slice::StopForAxis(op_params, input_shape,
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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 GetBeginValueAtIndex.
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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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output_shape_vector.push_back(dim_shape);
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}
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}
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TfLiteIntArray* output_shape =
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TfLiteIntArrayCreate(output_shape_vector.size());
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std::reverse_copy(output_shape_vector.begin(), output_shape_vector.end(),
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output_shape->data);
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TF_LITE_ENSURE_STATUS(
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context->ResizeTensor(context, op_context->output, output_shape));
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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_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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// Ensure validity of input tensor and its dimension
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TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.begin), 1);
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TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.end), 1);
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TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.strides), 1);
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TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type);
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// Only INT32 begin/end/strides are supported
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// TODO(b/175642009): add support for INT64
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TF_LITE_ENSURE_TYPES_EQ(context, op_context.begin->type, kTfLiteInt32);
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TF_LITE_ENSURE_TYPES_EQ(context, op_context.end->type, kTfLiteInt32);
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TF_LITE_ENSURE_TYPES_EQ(context, op_context.strides->type, kTfLiteInt32);
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TF_LITE_ENSURE_MSG(context, op_context.dims <= 5,
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"StridedSlice op only supports 1D-5D input arrays.");
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// TODO(b/138098220): Remove when bug is resolved.
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// Currently, working on using the compiler to cannonize strided_slice,
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// so ellipis_mask will become part of begin/end mask, new_axis_mask will
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// involve in a reshape to pad the dimensions.
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TF_LITE_ENSURE_MSG(context, op_context.params->ellipsis_mask == 0,
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"ellipsis_mask is not implemented yet.");
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TF_LITE_ENSURE_MSG(context, op_context.params->new_axis_mask == 0,
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"new_axis_mask is not implemented yet.");
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// Postpone allocation of output if any of the indexing tensors is not
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// constant
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if (!(IsConstantTensor(op_context.begin) &&
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IsConstantTensor(op_context.end) &&
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IsConstantTensor(op_context.strides))) {
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SetTensorToDynamic(op_context.output);
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return kTfLiteOk;
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}
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return ResizeOutputTensor(context, &op_context);
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}
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template <KernelType kernel_type>
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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StridedSliceContext op_context(context, node);
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if (IsDynamicTensor(op_context.output)) {
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TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context));
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}
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StridedSliceParams op_params = BuildStridedSliceParams(&op_context);
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#define TF_LITE_STRIDED_SLICE(data_type) \
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{ \
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if (kernel_type == kGenericOptimized) { \
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optimized_ops::StridedSlice<data_type>( \
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op_params, GetTensorShape(op_context.input), op_context.input, \
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GetTensorShape(op_context.output), op_context.output); \
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} else { \
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reference_ops::StridedSlice<data_type>( \
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op_params, GetTensorShape(op_context.input), op_context.input, \
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GetTensorShape(op_context.output), op_context.output); \
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} \
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}
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switch (op_context.input->type) {
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case kTfLiteFloat32:
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TF_LITE_STRIDED_SLICE(float);
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break;
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case kTfLiteInt32:
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TF_LITE_STRIDED_SLICE(int32_t);
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break;
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case kTfLiteInt64:
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TF_LITE_STRIDED_SLICE(int64_t);
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break;
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case kTfLiteUInt8:
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TF_LITE_STRIDED_SLICE(uint8_t);
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break;
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case kTfLiteInt8:
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TF_LITE_STRIDED_SLICE(int8_t);
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break;
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case kTfLiteInt16:
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TF_LITE_STRIDED_SLICE(int16_t);
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break;
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case kTfLiteBool:
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TF_LITE_STRIDED_SLICE(bool);
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break;
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case kTfLiteString:
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TF_LITE_STRIDED_SLICE(string);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Type %s is currently not supported "
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"by StridedSlice.",
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TfLiteTypeGetName(op_context.input->type));
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return kTfLiteError;
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}
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#undef TF_LITE_STRIDED_SLICE
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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_REF() {
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static TfLiteRegistration r = {
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nullptr, nullptr, strided_slice::Prepare,
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strided_slice::Eval<strided_slice::kReference>};
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return &r;
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}
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TfLiteRegistration* Register_STRIDED_SLICE() {
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static TfLiteRegistration r = {
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nullptr, nullptr, strided_slice::Prepare,
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strided_slice::Eval<strided_slice::kGenericOptimized>};
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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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