108 lines
4.2 KiB
C++
108 lines
4.2 KiB
C++
/* Copyright 2021 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 <stddef.h>
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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/compatibility.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/memory_helpers.h"
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#include "tensorflow/lite/micro/micro_graph.h"
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#include "tensorflow/lite/micro/micro_log.h"
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#include "tensorflow/lite/micro/micro_resource_variable.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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constexpr int kInputVariableId = 0;
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constexpr int kInputValue = 1;
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 0);
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// This must be a TfLiteEvalTensor despite this being in Prepare, because
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// CreateTensor allocates a temp tensor from the flatbuffer, which does not
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// contain the correct ID generated within the VAR_HANDLE op. EvalTensors are
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// all allocated during StartModelAllocation which happens before
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// init/prepare, and VAR_HANDLE Prepare() references its own op_data in the
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// TfLiteEvalTensor, so reading the ID here is valid.
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const TfLiteEvalTensor* input_resource_id_tensor =
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tflite::micro::GetEvalInput(context, node, kInputVariableId);
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TFLITE_DCHECK(input_resource_id_tensor != nullptr);
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TF_LITE_ENSURE(context, (input_resource_id_tensor->type == kTfLiteResource ||
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input_resource_id_tensor->type == kTfLiteInt32));
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TF_LITE_ENSURE_EQ(context, NumElements(input_resource_id_tensor->dims), 1);
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tflite::MicroContext* micro_context = tflite::GetMicroContext(context);
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TfLiteTensor* input_value =
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micro_context->AllocateTempInputTensor(node, kInputValue);
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TFLITE_DCHECK(input_value != nullptr);
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MicroGraph& graph_info = micro_context->graph();
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MicroResourceVariables* resources = graph_info.GetResourceVariables();
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// If the data field of this tensor is nullptr, we assume that this is a case
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// of using resource variables in another subgraph, and the resource_id
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// will be valid during Eval time. In case it wasn't valid, this will
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// still be caught during Invoke. More info in b/277231654.
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if (input_resource_id_tensor->data.i32 != nullptr) {
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TF_LITE_ENSURE_OK(context,
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resources->Allocate(input_resource_id_tensor->data.i32[0],
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context, input_value));
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}
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micro_context->DeallocateTempTfLiteTensor(input_value);
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteEvalTensor* input_id =
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tflite::micro::GetEvalInput(context, node, kInputVariableId);
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TFLITE_DCHECK(input_id != nullptr);
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const TfLiteEvalTensor* input_value =
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tflite::micro::GetEvalInput(context, node, kInputValue);
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TFLITE_DCHECK(input_value != nullptr);
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tflite::MicroContext* micro_context = tflite::GetMicroContext(context);
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MicroGraph& graph_info = micro_context->graph();
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MicroResourceVariables* resources = graph_info.GetResourceVariables();
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if (resources == nullptr) {
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MicroPrintf(
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"ASSIGN_VARIABLE requires resource variables. Please create "
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"ResourceVariables and pass it to the interpreter.");
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return kTfLiteError;
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}
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TF_LITE_ENSURE_OK(context,
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resources->Assign(input_id->data.i32[0], input_value));
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return kTfLiteOk;
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}
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} // namespace.
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TFLMRegistration Register_ASSIGN_VARIABLE() {
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return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
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}
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} // namespace tflite
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