148 lines
5.5 KiB
C++
148 lines
5.5 KiB
C++
// Copyright 2020 The MediaPipe Authors.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#ifndef MEDIAPIPE_CALCULATORS_TFLITE_TFLITE_GPU_RUNNER_H_
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#define MEDIAPIPE_CALCULATORS_TFLITE_TFLITE_GPU_RUNNER_H_
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#include <cstdint>
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#include <memory>
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#include <vector>
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#include "absl/status/status.h"
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#include "mediapipe/framework/port.h"
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#include "mediapipe/framework/port/status.h"
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#include "mediapipe/framework/port/statusor.h"
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#include "tensorflow/lite/core/api/op_resolver.h"
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#include "tensorflow/lite/delegates/gpu/api.h"
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#include "tensorflow/lite/delegates/gpu/common/model.h"
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#include "tensorflow/lite/delegates/gpu/gl/api2.h"
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#include "tensorflow/lite/model.h"
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#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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#include "tensorflow/lite/delegates/gpu/cl/api.h"
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#endif // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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namespace tflite {
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namespace gpu {
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// Executes GPU based inference using the TFLite GPU delegate api2.
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// Currently supports only GPU inputs/outputs.
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//
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// Typical order of execution:
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// 1. Initialize with the flatbuffer model using InitializeWithModel().
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// 2. Bind OpenGL SSBO objects as inputs and outputs using
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// BindSSBOToInputTensor() and BindSSBOToOutputTensor().
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// 3. Build the inference runner with Build() method.
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// 4. Invoke() executes the inference, where inputs and outputs are those which
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// were specified earlier. Invoke() may be called in the loop.
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//
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// Note: All of these need to happen inside MediaPipe's RunInGlContext to make
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// sure that all steps from inference construction to execution are made using
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// same OpenGL context.
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class TFLiteGPURunner {
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public:
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explicit TFLiteGPURunner(const InferenceOptions& options)
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: options_(options) {}
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absl::Status InitializeWithModel(const tflite::FlatBufferModel& flatbuffer,
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const tflite::OpResolver& op_resolver,
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bool allow_quant_ops = false);
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void ForceOpenGL() { opengl_is_forced_ = true; }
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void ForceOpenCL() { opencl_is_forced_ = true; }
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absl::Status BindSSBOToInputTensor(GLuint ssbo_id, int input_id);
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absl::Status BindSSBOToOutputTensor(GLuint ssbo_id, int output_id);
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int inputs_size() const { return input_shapes_.size(); }
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int outputs_size() const { return output_shapes_.size(); }
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absl::StatusOr<int64_t> GetInputElements(int id);
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absl::StatusOr<int64_t> GetOutputElements(int id);
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absl::Status Build();
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absl::Status Invoke();
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std::vector<BHWC> GetInputShapes() { return input_shapes_; }
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std::vector<BHWC> GetOutputShapes() { return output_shapes_; }
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std::vector<std::vector<int>> GetTFLiteInputShapes() {
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return input_shape_from_model_;
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}
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std::vector<std::vector<int>> GetTFLiteOutputShapes() {
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return output_shape_from_model_;
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}
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#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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void SetSerializedBinaryCache(std::vector<uint8_t>&& cache) {
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serialized_binary_cache_ = std::move(cache);
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}
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std::vector<uint8_t> GetSerializedBinaryCache() {
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return cl_environment_->GetSerializedBinaryCache();
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}
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void SetSerializedModel(std::vector<uint8_t>&& serialized_model) {
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serialized_model_ = std::move(serialized_model);
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serialized_model_used_ = false;
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}
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absl::StatusOr<std::vector<uint8_t>> GetSerializedModel();
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#endif // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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private:
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absl::Status InitializeOpenGL(std::unique_ptr<InferenceBuilder>* builder);
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absl::Status InitializeOpenCL(std::unique_ptr<InferenceBuilder>* builder);
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#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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absl::Status InitializeOpenCLFromSerializedModel(
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std::unique_ptr<InferenceBuilder>* builder);
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#endif // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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InferenceOptions options_;
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std::unique_ptr<gl::InferenceEnvironment> gl_environment_;
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#if defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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std::unique_ptr<cl::InferenceEnvironment> cl_environment_;
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std::vector<uint8_t> serialized_binary_cache_;
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std::vector<uint8_t> serialized_model_;
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bool serialized_model_used_ = false;
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#endif // defined(__ANDROID__) || defined(MEDIAPIPE_CHROMIUMOS)
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// graph_gl_ is maintained temporarily and becomes invalid after runner_ is
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// ready
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std::unique_ptr<GraphFloat32> graph_gl_;
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std::unique_ptr<GraphFloat32> graph_cl_;
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std::unique_ptr<InferenceRunner> runner_;
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// We keep information about input/output shapes, because they are needed
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// after graph_ becomes "converted" into runner_.
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std::vector<BHWC> input_shapes_;
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std::vector<BHWC> output_shapes_;
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// Input/output shapes above belong to the internal graph representation. It
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// is handy in certain situations to have the original tflite model's
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// input/output shapes, which differ conceptually.
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std::vector<std::vector<int>> input_shape_from_model_;
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std::vector<std::vector<int>> output_shape_from_model_;
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bool opencl_is_forced_ = false;
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bool opengl_is_forced_ = false;
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};
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} // namespace gpu
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} // namespace tflite
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#endif // MEDIAPIPE_CALCULATORS_TFLITE_TFLITE_GPU_RUNNER_H_
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