121 lines
3.9 KiB
C++
121 lines
3.9 KiB
C++
/* Copyright 2019 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/densify.h"
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#include <stddef.h>
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#include <cstdint>
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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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namespace tflite {
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namespace ops {
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namespace builtin {
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namespace densify {
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struct OpContext {
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OpContext(TfLiteContext* context, TfLiteNode* node) {
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input = GetInput(context, node, 0);
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output = GetOutput(context, node, 0);
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}
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const TfLiteTensor* input;
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TfLiteTensor* output;
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};
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struct OpData {
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bool dense_weights_initialized;
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};
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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auto* op_data = new OpData();
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op_data->dense_weights_initialized = false;
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return op_data;
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}
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void Free(TfLiteContext* context, void* buffer) {
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delete reinterpret_cast<OpData*>(buffer);
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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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OpContext op_context(context, node);
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TF_LITE_ENSURE(context, op_context.input->type != kTfLiteString);
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TF_LITE_ENSURE(context, IsConstantTensor(op_context.input));
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TF_LITE_ENSURE(context, op_context.input->sparsity != nullptr);
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op_context.output->type = op_context.input->type;
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op_context.output->allocation_type = kTfLiteArenaRwPersistent;
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return context->ResizeTensor(context, op_context.output,
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TfLiteIntArrayCopy(op_context.input->dims));
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
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OpContext op_context(context, node);
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if (op_data->dense_weights_initialized) {
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return kTfLiteOk;
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}
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switch (op_context.input->type) {
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case kTfLiteFloat32:
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reference_ops::Densify(op_context.input->sparsity,
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GetTensorShape(op_context.input),
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GetTensorData<float>(op_context.input),
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GetTensorShape(op_context.output),
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GetTensorData<float>(op_context.output), context);
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break;
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case kTfLiteFloat16:
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reference_ops::Densify(
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op_context.input->sparsity, GetTensorShape(op_context.input),
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GetTensorData<Eigen::half>(op_context.input),
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GetTensorShape(op_context.output),
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GetTensorData<Eigen::half>(op_context.output), context);
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break;
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case kTfLiteInt8:
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reference_ops::Densify(op_context.input->sparsity,
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GetTensorShape(op_context.input),
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GetTensorData<int8_t>(op_context.input),
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GetTensorShape(op_context.output),
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GetTensorData<int8_t>(op_context.output), context);
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break;
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default:
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context->ReportError(context, "Type %d not supported.",
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op_context.input->type);
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return kTfLiteError;
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}
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op_data->dense_weights_initialized = true;
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return kTfLiteOk;
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}
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} // namespace densify
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TfLiteRegistration* Register_DENSIFY() {
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static TfLiteRegistration r = {densify::Init, densify::Free, densify::Prepare,
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densify::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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