103 lines
3.9 KiB
C++
103 lines
3.9 KiB
C++
// Copyright 2019 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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#include "mediapipe/calculators/tensor/inference_calculator.h"
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#include <cstring>
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#include <memory>
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#include <string>
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#include <vector>
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#include "absl/status/status.h"
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#include "absl/strings/string_view.h"
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#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
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#include "mediapipe/framework/api2/packet.h"
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#include "mediapipe/framework/tool/subgraph_expansion.h"
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#include "tensorflow/lite/core/api/op_resolver.h"
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namespace mediapipe {
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namespace api2 {
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class InferenceCalculatorSelectorImpl
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: public SubgraphImpl<InferenceCalculatorSelector,
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InferenceCalculatorSelectorImpl> {
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public:
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absl::StatusOr<CalculatorGraphConfig> GetConfig(
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const CalculatorGraphConfig::Node& subgraph_node) {
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const auto& options =
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Subgraph::GetOptions<mediapipe::InferenceCalculatorOptions>(
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subgraph_node);
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std::vector<absl::string_view> impls;
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const bool should_use_gpu =
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!options.has_delegate() || // Use GPU delegate if not specified
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(options.has_delegate() && options.delegate().has_gpu());
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if (should_use_gpu) {
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const auto& api = options.delegate().gpu().api();
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using Gpu = ::mediapipe::InferenceCalculatorOptions::Delegate::Gpu;
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impls.emplace_back("Metal");
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const bool prefer_gl_advanced =
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options.delegate().gpu().use_advanced_gpu_api() &&
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(api == Gpu::ANY || api == Gpu::OPENGL || api == Gpu::OPENCL);
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if (prefer_gl_advanced) {
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impls.emplace_back("GlAdvanced");
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impls.emplace_back("Gl");
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} else {
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impls.emplace_back("Gl");
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impls.emplace_back("GlAdvanced");
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}
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}
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impls.emplace_back("Cpu");
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impls.emplace_back("Xnnpack");
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for (const auto& suffix : impls) {
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const auto impl = absl::StrCat("InferenceCalculator", suffix);
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if (!mediapipe::CalculatorBaseRegistry::IsRegistered(impl)) continue;
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VLOG(1) << "Using " << suffix << " for InferenceCalculator with "
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<< (options.has_model_path()
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? "model " + options.model_path()
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: "output_stream " + subgraph_node.output_stream(0));
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CalculatorGraphConfig::Node impl_node = subgraph_node;
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impl_node.set_calculator(impl);
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return tool::MakeSingleNodeGraph(std::move(impl_node));
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}
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return absl::UnimplementedError("no implementation available");
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}
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};
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absl::StatusOr<Packet<TfLiteModelPtr>> InferenceCalculator::GetModelAsPacket(
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CalculatorContext* cc) {
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const auto& options = cc->Options<mediapipe::InferenceCalculatorOptions>();
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if (!options.model_path().empty()) {
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return TfLiteModelLoader::LoadFromPath(options.model_path());
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}
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if (!kSideInModel(cc).IsEmpty()) return kSideInModel(cc);
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return absl::Status(mediapipe::StatusCode::kNotFound,
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"Must specify TFLite model as path or loaded model.");
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}
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absl::StatusOr<Packet<tflite::OpResolver>>
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InferenceCalculator::GetOpResolverAsPacket(CalculatorContext* cc) {
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if (kSideInOpResolver(cc).IsConnected()) {
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return kSideInOpResolver(cc).As<tflite::OpResolver>();
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} else if (kSideInCustomOpResolver(cc).IsConnected()) {
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return kSideInCustomOpResolver(cc).As<tflite::OpResolver>();
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}
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return PacketAdopting<tflite::OpResolver>(
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std::make_unique<
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tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates>());
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}
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} // namespace api2
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} // namespace mediapipe
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