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GitOrigin-RevId: 3ce19771d2586aeb611fff75bb7627721cf5d36b
This commit is contained in:
MediaPipe Team 2022-09-07 10:33:07 -07:00 committed by Sebastian Schmidt
parent 4dc4b19ddb
commit d3f98334bf
16 changed files with 590 additions and 199 deletions
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2
Dockerfile
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@ -51,6 +51,8 @@ RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 100 --slave /u
RUN pip3 install --upgrade setuptools
RUN pip3 install wheel
RUN pip3 install future
RUN pip3 install absl-py
RUN pip3 install numpy
RUN pip3 install six==1.14.0
RUN pip3 install tensorflow==2.2.0
RUN pip3 install tf_slim

93
mediapipe/calculators/tensor/BUILD
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@ -216,6 +216,50 @@ cc_library(
alwayslink = 1,
)
cc_library(
name = "inference_runner",
hdrs = ["inference_runner.h"],
copts = select({
# TODO: fix tensor.h not to require this, if possible
"//mediapipe:apple": [
"-x objective-c++",
"-fobjc-arc", # enable reference-counting
],
"//conditions:default": [],
}),
visibility = ["//visibility:public"],
deps = [
"//mediapipe/framework/formats:tensor",
"@com_google_absl//absl/status:statusor",
],
)
cc_library(
name = "inference_interpreter_delegate_runner",
srcs = ["inference_interpreter_delegate_runner.cc"],
hdrs = ["inference_interpreter_delegate_runner.h"],
copts = select({
# TODO: fix tensor.h not to require this, if possible
"//mediapipe:apple": [
"-x objective-c++",
"-fobjc-arc", # enable reference-counting
],
"//conditions:default": [],
}),
visibility = ["//visibility:public"],
deps = [
":inference_runner",
"//mediapipe/framework/api2:packet",
"//mediapipe/framework/formats:tensor",
"//mediapipe/framework/port:ret_check",
"//mediapipe/util/tflite:tflite_model_loader",
"@com_google_absl//absl/status",
"@com_google_absl//absl/status:statusor",
"@org_tensorflow//tensorflow/lite:framework_stable",
"@org_tensorflow//tensorflow/lite/core/api:op_resolver",
],
)
cc_library(
name = "inference_calculator_cpu",
srcs = [
@ -232,22 +276,63 @@ cc_library(
visibility = ["//visibility:public"],
deps = [
":inference_calculator_interface",
":inference_calculator_utils",
":inference_interpreter_delegate_runner",
":inference_runner",
"@com_google_absl//absl/memory",
"@com_google_absl//absl/status",
"@com_google_absl//absl/status:statusor",
"@org_tensorflow//tensorflow/lite/delegates/xnnpack:xnnpack_delegate",
"@org_tensorflow//tensorflow/lite:framework_stable",
"@org_tensorflow//tensorflow/lite/c:c_api_types",
] + select({
"//conditions:default": [
"//mediapipe/util:cpu_util",
],
}) + select({
"//conditions:default": [],
"//mediapipe:android": ["@org_tensorflow//tensorflow/lite/delegates/nnapi:nnapi_delegate"],
}),
alwayslink = 1,
)
cc_library(
name = "inference_calculator_utils",
srcs = ["inference_calculator_utils.cc"],
hdrs = ["inference_calculator_utils.h"],
deps = [
":inference_calculator_cc_proto",
] + select({
"//conditions:default": [
"//mediapipe/util:cpu_util",
],
}),
alwayslink = 1,
)
cc_library(
name = "inference_calculator_xnnpack",
srcs = [
"inference_calculator_xnnpack.cc",
],
copts = select({
# TODO: fix tensor.h not to require this, if possible
"//mediapipe:apple": [
"-x objective-c++",
"-fobjc-arc", # enable reference-counting
],
"//conditions:default": [],
}),
visibility = ["//visibility:public"],
deps = [
":inference_calculator_interface",
":inference_calculator_utils",
":inference_interpreter_delegate_runner",
":inference_runner",
"@com_google_absl//absl/status",
"@com_google_absl//absl/status:statusor",
"@org_tensorflow//tensorflow/lite:framework_stable",
"@org_tensorflow//tensorflow/lite/delegates/xnnpack:xnnpack_delegate",
],
alwayslink = 1,
)
cc_library(
name = "inference_calculator_gl_if_compute_shader_available",
visibility = ["//visibility:public"],

1
mediapipe/calculators/tensor/inference_calculator.cc
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@ -59,6 +59,7 @@ class InferenceCalculatorSelectorImpl
}
}
impls.emplace_back("Cpu");
impls.emplace_back("Xnnpack");
for (const auto& suffix : impls) {
const auto impl = absl::StrCat("InferenceCalculator", suffix);
if (!mediapipe::CalculatorBaseRegistry::IsRegistered(impl)) continue;

4
mediapipe/calculators/tensor/inference_calculator.h
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@ -141,6 +141,10 @@ struct InferenceCalculatorCpu : public InferenceCalculator {
static constexpr char kCalculatorName[] = "InferenceCalculatorCpu";
};
struct InferenceCalculatorXnnpack : public InferenceCalculator {
static constexpr char kCalculatorName[] = "InferenceCalculatorXnnpack";
};
} // namespace api2
} // namespace mediapipe

211
mediapipe/calculators/tensor/inference_calculator_cpu.cc
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@ -18,78 +18,21 @@
#include <string>
#include <vector>
#include "absl/memory/memory.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "mediapipe/calculators/tensor/inference_calculator.h"
#include "mediapipe/calculators/tensor/inference_calculator_utils.h"
#include "mediapipe/calculators/tensor/inference_interpreter_delegate_runner.h"
#include "mediapipe/calculators/tensor/inference_runner.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/interpreter_builder.h"
#if defined(MEDIAPIPE_ANDROID)
#include "tensorflow/lite/delegates/nnapi/nnapi_delegate.h"
#endif // ANDROID
#if !defined(__EMSCRIPTEN__) || defined(__EMSCRIPTEN_PTHREADS__)
#include "mediapipe/util/cpu_util.h"
#endif // !__EMSCRIPTEN__ || __EMSCRIPTEN_PTHREADS__
#include "tensorflow/lite/c/c_api_types.h"
#include "tensorflow/lite/delegates/xnnpack/xnnpack_delegate.h"
namespace mediapipe {
namespace api2 {
namespace {
int GetXnnpackDefaultNumThreads() {
#if defined(MEDIAPIPE_ANDROID) || defined(MEDIAPIPE_IOS) || \
defined(__EMSCRIPTEN_PTHREADS__)
constexpr int kMinNumThreadsByDefault = 1;
constexpr int kMaxNumThreadsByDefault = 4;
return std::clamp(NumCPUCores() / 2, kMinNumThreadsByDefault,
kMaxNumThreadsByDefault);
#else
return 1;
#endif // MEDIAPIPE_ANDROID || MEDIAPIPE_IOS || __EMSCRIPTEN_PTHREADS__
}
// Returns number of threads to configure XNNPACK delegate with.
// Returns user provided value if specified. Otherwise, tries to choose optimal
// number of threads depending on the device.
int GetXnnpackNumThreads(
const bool opts_has_delegate,
const mediapipe::InferenceCalculatorOptions::Delegate& opts_delegate) {
static constexpr int kDefaultNumThreads = -1;
if (opts_has_delegate && opts_delegate.has_xnnpack() &&
opts_delegate.xnnpack().num_threads() != kDefaultNumThreads) {
return opts_delegate.xnnpack().num_threads();
}
return GetXnnpackDefaultNumThreads();
}
template <typename T>
void CopyTensorBufferToInterpreter(const Tensor& input_tensor,
tflite::Interpreter* interpreter,
int input_tensor_index) {
auto input_tensor_view = input_tensor.GetCpuReadView();
auto input_tensor_buffer = input_tensor_view.buffer<T>();
T* local_tensor_buffer =
interpreter->typed_input_tensor<T>(input_tensor_index);
std::memcpy(local_tensor_buffer, input_tensor_buffer, input_tensor.bytes());
}
template <typename T>
void CopyTensorBufferFromInterpreter(tflite::Interpreter* interpreter,
int output_tensor_index,
Tensor* output_tensor) {
auto output_tensor_view = output_tensor->GetCpuWriteView();
auto output_tensor_buffer = output_tensor_view.buffer<T>();
T* local_tensor_buffer =
interpreter->typed_output_tensor<T>(output_tensor_index);
std::memcpy(output_tensor_buffer, local_tensor_buffer,
output_tensor->bytes());
}
} // namespace
class InferenceCalculatorCpuImpl
: public NodeImpl<InferenceCalculatorCpu, InferenceCalculatorCpuImpl> {
public:
@ -100,16 +43,11 @@ class InferenceCalculatorCpuImpl
absl::Status Close(CalculatorContext* cc) override;
private:
absl::Status InitInterpreter(CalculatorContext* cc);
absl::Status LoadDelegate(CalculatorContext* cc,
tflite::InterpreterBuilder* interpreter_builder);
absl::Status AllocateTensors();
absl::StatusOr<std::unique_ptr<InferenceRunner>> CreateInferenceRunner(
CalculatorContext* cc);
absl::StatusOr<TfLiteDelegatePtr> MaybeCreateDelegate(CalculatorContext* cc);
// TfLite requires us to keep the model alive as long as the interpreter is.
Packet<TfLiteModelPtr> model_packet_;
std::unique_ptr<tflite::Interpreter> interpreter_;
TfLiteDelegatePtr delegate_;
TfLiteType input_tensor_type_ = TfLiteType::kTfLiteNoType;
std::unique_ptr<InferenceRunner> inference_runner_;
};
absl::Status InferenceCalculatorCpuImpl::UpdateContract(
@ -122,7 +60,8 @@ absl::Status InferenceCalculatorCpuImpl::UpdateContract(
}
absl::Status InferenceCalculatorCpuImpl::Open(CalculatorContext* cc) {
return InitInterpreter(cc);
ASSIGN_OR_RETURN(inference_runner_, CreateInferenceRunner(cc));
return absl::OkStatus();
}
absl::Status InferenceCalculatorCpuImpl::Process(CalculatorContext* cc) {
@ -131,123 +70,32 @@ absl::Status InferenceCalculatorCpuImpl::Process(CalculatorContext* cc) {
}
const auto& input_tensors = *kInTensors(cc);
RET_CHECK(!input_tensors.empty());
auto output_tensors = absl::make_unique<std::vector<Tensor>>();
if (input_tensor_type_ == kTfLiteNoType) {
input_tensor_type_ = interpreter_->tensor(interpreter_->inputs()[0])->type;
}
// Read CPU input into tensors.
for (int i = 0; i < input_tensors.size(); ++i) {
switch (input_tensor_type_) {
case TfLiteType::kTfLiteFloat16:
case TfLiteType::kTfLiteFloat32: {
CopyTensorBufferToInterpreter<float>(input_tensors[i],
interpreter_.get(), i);
break;
}
case TfLiteType::kTfLiteUInt8: {
CopyTensorBufferToInterpreter<uint8>(input_tensors[i],
interpreter_.get(), i);
break;
}
case TfLiteType::kTfLiteInt8: {
CopyTensorBufferToInterpreter<int8>(input_tensors[i],
interpreter_.get(), i);
break;
}
case TfLiteType::kTfLiteInt32: {
CopyTensorBufferToInterpreter<int32_t>(input_tensors[i],
interpreter_.get(), i);
break;
}
default:
return absl::InvalidArgumentError(
absl::StrCat("Unsupported input tensor type:", input_tensor_type_));
}
}
// Run inference.
RET_CHECK_EQ(interpreter_->Invoke(), kTfLiteOk);
// Output result tensors (CPU).
const auto& tensor_indexes = interpreter_->outputs();
output_tensors->reserve(tensor_indexes.size());
for (int i = 0; i < tensor_indexes.size(); ++i) {
TfLiteTensor* tensor = interpreter_->tensor(tensor_indexes[i]);
Tensor::Shape shape{std::vector<int>{
tensor->dims->data, tensor->dims->data + tensor->dims->size}};
switch (tensor->type) {
case TfLiteType::kTfLiteFloat16:
case TfLiteType::kTfLiteFloat32:
output_tensors->emplace_back(Tensor::ElementType::kFloat32, shape);
CopyTensorBufferFromInterpreter<float>(interpreter_.get(), i,
&output_tensors->back());
break;
case TfLiteType::kTfLiteUInt8:
output_tensors->emplace_back(
Tensor::ElementType::kUInt8, shape,
Tensor::QuantizationParameters{tensor->params.scale,
tensor->params.zero_point});
CopyTensorBufferFromInterpreter<uint8>(interpreter_.get(), i,
&output_tensors->back());
break;
case TfLiteType::kTfLiteInt8:
output_tensors->emplace_back(
Tensor::ElementType::kInt8, shape,
Tensor::QuantizationParameters{tensor->params.scale,
tensor->params.zero_point});
CopyTensorBufferFromInterpreter<int8>(interpreter_.get(), i,
&output_tensors->back());
break;
case TfLiteType::kTfLiteInt32:
output_tensors->emplace_back(Tensor::ElementType::kInt32, shape);
CopyTensorBufferFromInterpreter<int32_t>(interpreter_.get(), i,
&output_tensors->back());
break;
default:
return absl::InvalidArgumentError(
absl::StrCat("Unsupported output tensor type:",
TfLiteTypeGetName(tensor->type)));
}
}
ASSIGN_OR_RETURN(std::vector<Tensor> output_tensors,
inference_runner_->Run(input_tensors));
kOutTensors(cc).Send(std::move(output_tensors));
return absl::OkStatus();
}
absl::Status InferenceCalculatorCpuImpl::Close(CalculatorContext* cc) {
interpreter_ = nullptr;
delegate_ = nullptr;
inference_runner_ = nullptr;
return absl::OkStatus();
}
absl::Status InferenceCalculatorCpuImpl::InitInterpreter(
CalculatorContext* cc) {
ASSIGN_OR_RETURN(model_packet_, GetModelAsPacket(cc));
const auto& model = *model_packet_.Get();
absl::StatusOr<std::unique_ptr<InferenceRunner>>
InferenceCalculatorCpuImpl::CreateInferenceRunner(CalculatorContext* cc) {
ASSIGN_OR_RETURN(auto model_packet, GetModelAsPacket(cc));
ASSIGN_OR_RETURN(auto op_resolver_packet, GetOpResolverAsPacket(cc));
const auto& op_resolver = op_resolver_packet.Get();
tflite::InterpreterBuilder interpreter_builder(model, op_resolver);
MP_RETURN_IF_ERROR(LoadDelegate(cc, &interpreter_builder));
#if defined(__EMSCRIPTEN__)
interpreter_builder.SetNumThreads(1);
#else
interpreter_builder.SetNumThreads(
cc->Options<mediapipe::InferenceCalculatorOptions>().cpu_num_thread());
#endif // __EMSCRIPTEN__
RET_CHECK_EQ(interpreter_builder(&interpreter_), kTfLiteOk);
RET_CHECK(interpreter_);
return AllocateTensors();
const int interpreter_num_threads =
cc->Options<mediapipe::InferenceCalculatorOptions>().cpu_num_thread();
ASSIGN_OR_RETURN(TfLiteDelegatePtr delegate, MaybeCreateDelegate(cc));
return CreateInferenceInterpreterDelegateRunner(
std::move(model_packet), std::move(op_resolver_packet),
std::move(delegate), interpreter_num_threads);
}
absl::Status InferenceCalculatorCpuImpl::AllocateTensors() {
RET_CHECK_EQ(interpreter_->AllocateTensors(), kTfLiteOk);
return absl::OkStatus();
}
absl::Status InferenceCalculatorCpuImpl::LoadDelegate(
CalculatorContext* cc, tflite::InterpreterBuilder* interpreter_builder) {
absl::StatusOr<TfLiteDelegatePtr>
InferenceCalculatorCpuImpl::MaybeCreateDelegate(CalculatorContext* cc) {
const auto& calculator_opts =
cc->Options<mediapipe::InferenceCalculatorOptions>();
auto opts_delegate = calculator_opts.delegate();
@ -268,7 +116,7 @@ absl::Status InferenceCalculatorCpuImpl::LoadDelegate(
calculator_opts.has_delegate() || !kDelegate(cc).IsEmpty();
if (opts_has_delegate && opts_delegate.has_tflite()) {
// Default tflite inference requeqsted - no need to modify graph.
return absl::OkStatus();
return nullptr;
}
#if defined(MEDIAPIPE_ANDROID)
@ -288,10 +136,8 @@ absl::Status InferenceCalculatorCpuImpl::LoadDelegate(
options.accelerator_name = nnapi.has_accelerator_name()
? nnapi.accelerator_name().c_str()
: nullptr;
delegate_ = TfLiteDelegatePtr(new tflite::StatefulNnApiDelegate(options),
return TfLiteDelegatePtr(new tflite::StatefulNnApiDelegate(options),
[](TfLiteDelegate*) {});
interpreter_builder->AddDelegate(delegate_.get());
return absl::OkStatus();
}
#endif // MEDIAPIPE_ANDROID
@ -305,12 +151,11 @@ absl::Status InferenceCalculatorCpuImpl::LoadDelegate(
auto xnnpack_opts = TfLiteXNNPackDelegateOptionsDefault();
xnnpack_opts.num_threads =
GetXnnpackNumThreads(opts_has_delegate, opts_delegate);
delegate_ = TfLiteDelegatePtr(TfLiteXNNPackDelegateCreate(&xnnpack_opts),
return TfLiteDelegatePtr(TfLiteXNNPackDelegateCreate(&xnnpack_opts),
&TfLiteXNNPackDelegateDelete);
interpreter_builder->AddDelegate(delegate_.get());
}
return absl::OkStatus();
return nullptr;
}
} // namespace api2

52
mediapipe/calculators/tensor/inference_calculator_utils.cc Normal file
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@ -0,0 +1,52 @@
// Copyright 2022 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "mediapipe/calculators/tensor/inference_calculator_utils.h"
#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
#if !defined(__EMSCRIPTEN__) || defined(__EMSCRIPTEN_PTHREADS__)
#include "mediapipe/util/cpu_util.h"
#endif // !__EMSCRIPTEN__ || __EMSCRIPTEN_PTHREADS__
namespace mediapipe {
namespace {
int GetXnnpackDefaultNumThreads() {
#if defined(MEDIAPIPE_ANDROID) || defined(MEDIAPIPE_IOS) || \
defined(__EMSCRIPTEN_PTHREADS__)
constexpr int kMinNumThreadsByDefault = 1;
constexpr int kMaxNumThreadsByDefault = 4;
return std::clamp(NumCPUCores() / 2, kMinNumThreadsByDefault,
kMaxNumThreadsByDefault);
#else
return 1;
#endif // MEDIAPIPE_ANDROID || MEDIAPIPE_IOS || __EMSCRIPTEN_PTHREADS__
}
} // namespace
int GetXnnpackNumThreads(
const bool opts_has_delegate,
const mediapipe::InferenceCalculatorOptions::Delegate& opts_delegate) {
static constexpr int kDefaultNumThreads = -1;
if (opts_has_delegate && opts_delegate.has_xnnpack() &&
opts_delegate.xnnpack().num_threads() != kDefaultNumThreads) {
return opts_delegate.xnnpack().num_threads();
}
return GetXnnpackDefaultNumThreads();
}
} // namespace mediapipe

31
mediapipe/calculators/tensor/inference_calculator_utils.h Normal file
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@ -0,0 +1,31 @@
// Copyright 2022 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_CALCULATOR_UTILS_H_
#define MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_CALCULATOR_UTILS_H_
#include "mediapipe/calculators/tensor/inference_calculator.pb.h"
namespace mediapipe {
// Returns number of threads to configure XNNPACK delegate with.
// Returns user provided value if specified. Otherwise, tries to choose optimal
// number of threads depending on the device.
int GetXnnpackNumThreads(
const bool opts_has_delegate,
const mediapipe::InferenceCalculatorOptions::Delegate& opts_delegate);
} // namespace mediapipe
#endif // MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_CALCULATOR_UTILS_H_

122
mediapipe/calculators/tensor/inference_calculator_xnnpack.cc Normal file
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@ -0,0 +1,122 @@
// Copyright 2022 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <cstdint>
#include <cstring>
#include <memory>
#include <string>
#include <vector>
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "mediapipe/calculators/tensor/inference_calculator.h"
#include "mediapipe/calculators/tensor/inference_calculator_utils.h"
#include "mediapipe/calculators/tensor/inference_interpreter_delegate_runner.h"
#include "mediapipe/calculators/tensor/inference_runner.h"
#include "tensorflow/lite/delegates/xnnpack/xnnpack_delegate.h"
#include "tensorflow/lite/interpreter.h"
namespace mediapipe {
namespace api2 {
class InferenceCalculatorXnnpackImpl
: public NodeImpl<InferenceCalculatorXnnpack,
InferenceCalculatorXnnpackImpl> {
public:
static absl::Status UpdateContract(CalculatorContract* cc);
absl::Status Open(CalculatorContext* cc) override;
absl::Status Process(CalculatorContext* cc) override;
absl::Status Close(CalculatorContext* cc) override;
private:
absl::StatusOr<std::unique_ptr<InferenceRunner>> CreateInferenceRunner(
CalculatorContext* cc);
absl::StatusOr<TfLiteDelegatePtr> CreateDelegate(CalculatorContext* cc);
std::unique_ptr<InferenceRunner> inference_runner_;
};
absl::Status InferenceCalculatorXnnpackImpl::UpdateContract(
CalculatorContract* cc) {
const auto& options = cc->Options<mediapipe::InferenceCalculatorOptions>();
RET_CHECK(!options.model_path().empty() ^ kSideInModel(cc).IsConnected())
<< "Either model as side packet or model path in options is required.";
return absl::OkStatus();
}
absl::Status InferenceCalculatorXnnpackImpl::Open(CalculatorContext* cc) {
ASSIGN_OR_RETURN(inference_runner_, CreateInferenceRunner(cc));
return absl::OkStatus();
}
absl::Status InferenceCalculatorXnnpackImpl::Process(CalculatorContext* cc) {
if (kInTensors(cc).IsEmpty()) {
return absl::OkStatus();
}
const auto& input_tensors = *kInTensors(cc);
RET_CHECK(!input_tensors.empty());
ASSIGN_OR_RETURN(std::vector<Tensor> output_tensors,
inference_runner_->Run(input_tensors));
kOutTensors(cc).Send(std::move(output_tensors));
return absl::OkStatus();
}
absl::Status InferenceCalculatorXnnpackImpl::Close(CalculatorContext* cc) {
inference_runner_ = nullptr;
return absl::OkStatus();
}
absl::StatusOr<std::unique_ptr<InferenceRunner>>
InferenceCalculatorXnnpackImpl::CreateInferenceRunner(CalculatorContext* cc) {
ASSIGN_OR_RETURN(auto model_packet, GetModelAsPacket(cc));
ASSIGN_OR_RETURN(auto op_resolver_packet, GetOpResolverAsPacket(cc));
const int interpreter_num_threads =
cc->Options<mediapipe::InferenceCalculatorOptions>().cpu_num_thread();
ASSIGN_OR_RETURN(TfLiteDelegatePtr delegate, CreateDelegate(cc));
return CreateInferenceInterpreterDelegateRunner(
std::move(model_packet), std::move(op_resolver_packet),
std::move(delegate), interpreter_num_threads);
}
absl::StatusOr<TfLiteDelegatePtr>
InferenceCalculatorXnnpackImpl::CreateDelegate(CalculatorContext* cc) {
const auto& calculator_opts =
cc->Options<mediapipe::InferenceCalculatorOptions>();
auto opts_delegate = calculator_opts.delegate();
if (!kDelegate(cc).IsEmpty()) {
const mediapipe::InferenceCalculatorOptions::Delegate&
input_side_packet_delegate = kDelegate(cc).Get();
RET_CHECK(
input_side_packet_delegate.has_xnnpack() ||
input_side_packet_delegate.delegate_case() ==
mediapipe::InferenceCalculatorOptions::Delegate::DELEGATE_NOT_SET)
<< "inference_calculator_cpu only supports delegate input side packet "
<< "for TFLite, XNNPack";
opts_delegate.MergeFrom(input_side_packet_delegate);
}
const bool opts_has_delegate =
calculator_opts.has_delegate() || !kDelegate(cc).IsEmpty();
auto xnnpack_opts = TfLiteXNNPackDelegateOptionsDefault();
xnnpack_opts.num_threads =
GetXnnpackNumThreads(opts_has_delegate, opts_delegate);
return TfLiteDelegatePtr(TfLiteXNNPackDelegateCreate(&xnnpack_opts),
&TfLiteXNNPackDelegateDelete);
}
} // namespace api2
} // namespace mediapipe

181
mediapipe/calculators/tensor/inference_interpreter_delegate_runner.cc Normal file
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@ -0,0 +1,181 @@
// Copyright 2022 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "mediapipe/calculators/tensor/inference_interpreter_delegate_runner.h"
#include <memory>
#include <vector>
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "mediapipe/framework/formats/tensor.h"
#include "mediapipe/framework/port/ret_check.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/interpreter_builder.h"
namespace mediapipe {
namespace {
template <typename T>
void CopyTensorBufferToInterpreter(const Tensor& input_tensor,
tflite::Interpreter* interpreter,
int input_tensor_index) {
auto input_tensor_view = input_tensor.GetCpuReadView();
auto input_tensor_buffer = input_tensor_view.buffer<T>();
T* local_tensor_buffer =
interpreter->typed_input_tensor<T>(input_tensor_index);
std::memcpy(local_tensor_buffer, input_tensor_buffer, input_tensor.bytes());
}
template <typename T>
void CopyTensorBufferFromInterpreter(tflite::Interpreter* interpreter,
int output_tensor_index,
Tensor* output_tensor) {
auto output_tensor_view = output_tensor->GetCpuWriteView();
auto output_tensor_buffer = output_tensor_view.buffer<T>();
T* local_tensor_buffer =
interpreter->typed_output_tensor<T>(output_tensor_index);
std::memcpy(output_tensor_buffer, local_tensor_buffer,
output_tensor->bytes());
}
} // namespace
class InferenceInterpreterDelegateRunner : public InferenceRunner {
public:
InferenceInterpreterDelegateRunner(
api2::Packet<TfLiteModelPtr> model,
std::unique_ptr<tflite::Interpreter> interpreter,
TfLiteDelegatePtr delegate)
: model_(std::move(model)),
interpreter_(std::move(interpreter)),
delegate_(std::move(delegate)) {}
absl::StatusOr<std::vector<Tensor>> Run(
const std::vector<Tensor>& input_tensors) override;
private:
api2::Packet<TfLiteModelPtr> model_;
std::unique_ptr<tflite::Interpreter> interpreter_;
TfLiteDelegatePtr delegate_;
};
absl::StatusOr<std::vector<Tensor>> InferenceInterpreterDelegateRunner::Run(
const std::vector<Tensor>& input_tensors) {
// Read CPU input into tensors.
RET_CHECK_EQ(interpreter_->inputs().size(), input_tensors.size());
for (int i = 0; i < input_tensors.size(); ++i) {
const TfLiteType input_tensor_type =
interpreter_->tensor(interpreter_->inputs()[i])->type;
switch (input_tensor_type) {
case TfLiteType::kTfLiteFloat16:
case TfLiteType::kTfLiteFloat32: {
CopyTensorBufferToInterpreter<float>(input_tensors[i],
interpreter_.get(), i);
break;
}
case TfLiteType::kTfLiteUInt8: {
CopyTensorBufferToInterpreter<uint8>(input_tensors[i],
interpreter_.get(), i);
break;
}
case TfLiteType::kTfLiteInt8: {
CopyTensorBufferToInterpreter<int8>(input_tensors[i],
interpreter_.get(), i);
break;
}
case TfLiteType::kTfLiteInt32: {
CopyTensorBufferToInterpreter<int32_t>(input_tensors[i],
interpreter_.get(), i);
break;
}
default:
return absl::InvalidArgumentError(
absl::StrCat("Unsupported input tensor type:", input_tensor_type));
}
}
// Run inference.
RET_CHECK_EQ(interpreter_->Invoke(), kTfLiteOk);
// Output result tensors (CPU).
const auto& tensor_indexes = interpreter_->outputs();
std::vector<Tensor> output_tensors;
output_tensors.reserve(tensor_indexes.size());
for (int i = 0; i < tensor_indexes.size(); ++i) {
TfLiteTensor* tensor = interpreter_->tensor(tensor_indexes[i]);
Tensor::Shape shape{std::vector<int>{
tensor->dims->data, tensor->dims->data + tensor->dims->size}};
switch (tensor->type) {
case TfLiteType::kTfLiteFloat16:
case TfLiteType::kTfLiteFloat32:
output_tensors.emplace_back(Tensor::ElementType::kFloat32, shape);
CopyTensorBufferFromInterpreter<float>(interpreter_.get(), i,
&output_tensors.back());
break;
case TfLiteType::kTfLiteUInt8:
output_tensors.emplace_back(
Tensor::ElementType::kUInt8, shape,
Tensor::QuantizationParameters{tensor->params.scale,
tensor->params.zero_point});
CopyTensorBufferFromInterpreter<uint8>(interpreter_.get(), i,
&output_tensors.back());
break;
case TfLiteType::kTfLiteInt8:
output_tensors.emplace_back(
Tensor::ElementType::kInt8, shape,
Tensor::QuantizationParameters{tensor->params.scale,
tensor->params.zero_point});
CopyTensorBufferFromInterpreter<int8>(interpreter_.get(), i,
&output_tensors.back());
break;
case TfLiteType::kTfLiteInt32:
output_tensors.emplace_back(Tensor::ElementType::kInt32, shape);
CopyTensorBufferFromInterpreter<int32_t>(interpreter_.get(), i,
&output_tensors.back());
break;
default:
return absl::InvalidArgumentError(
absl::StrCat("Unsupported output tensor type:",
TfLiteTypeGetName(tensor->type)));
}
}
return output_tensors;
}
absl::StatusOr<std::unique_ptr<InferenceRunner>>
CreateInferenceInterpreterDelegateRunner(
api2::Packet<TfLiteModelPtr> model,
api2::Packet<tflite::OpResolver> op_resolver, TfLiteDelegatePtr delegate,
int interpreter_num_threads) {
tflite::InterpreterBuilder interpreter_builder(*model.Get(),
op_resolver.Get());
if (delegate) {
interpreter_builder.AddDelegate(delegate.get());
}
#if defined(__EMSCRIPTEN__)
interpreter_builder.SetNumThreads(1);
#else
interpreter_builder.SetNumThreads(interpreter_num_threads);
#endif // __EMSCRIPTEN__
std::unique_ptr<tflite::Interpreter> interpreter;
RET_CHECK_EQ(interpreter_builder(&interpreter), kTfLiteOk);
RET_CHECK(interpreter);
RET_CHECK_EQ(interpreter->AllocateTensors(), kTfLiteOk);
return std::make_unique<InferenceInterpreterDelegateRunner>(
std::move(model), std::move(interpreter), std::move(delegate));
}
} // namespace mediapipe

46
mediapipe/calculators/tensor/inference_interpreter_delegate_runner.h Normal file
View File

@ -0,0 +1,46 @@
// Copyright 2022 The MediaPipe Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_INTERPRETER_DELEGATE_RUNNER_H_
#define MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_INTERPRETER_DELEGATE_RUNNER_H_
#include <memory>
#include <vector>
#include "absl/status/statusor.h"
#include "mediapipe/calculators/tensor/inference_runner.h"
#include "mediapipe/framework/api2/packet.h"
#include "mediapipe/util/tflite/tflite_model_loader.h"
#include "tensorflow/lite/core/api/op_resolver.h"
#include "tensorflow/lite/interpreter.h"
namespace mediapipe {
using TfLiteDelegatePtr =
std::unique_ptr<TfLiteDelegate, std::function<void(TfLiteDelegate*)>>;
// Creates inference runner which run inference using newly initialized
// interpreter and provided `delegate`.
//
// `delegate` can be nullptr, in that case newly initialized interpreter will
// use what is available by default.
absl::StatusOr<std::unique_ptr<InferenceRunner>>
CreateInferenceInterpreterDelegateRunner(
api2::Packet<TfLiteModelPtr> model,
api2::Packet<tflite::OpResolver> op_resolver, TfLiteDelegatePtr delegate,
int interpreter_num_threads);
} // namespace mediapipe
#endif // MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_INTERPRETER_DELEGATE_RUNNER_H_

19
mediapipe/calculators/tensor/inference_runner.h Normal file
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@ -0,0 +1,19 @@
#ifndef MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_RUNNER_H_
#define MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_RUNNER_H_
#include "absl/status/statusor.h"
#include "mediapipe/framework/formats/tensor.h"
namespace mediapipe {
// Common interface to implement inference runners in MediaPipe.
class InferenceRunner {
public:
virtual ~InferenceRunner() = default;
virtual absl::StatusOr<std::vector<Tensor>> Run(
const std::vector<Tensor>& inputs) = 0;
};
} // namespace mediapipe
#endif // MEDIAPIPE_CALCULATORS_TENSOR_INFERENCE_RUNNER_H_

9
mediapipe/objc/BUILD
View File

@ -69,7 +69,8 @@ objc_library(
"-Wno-shorten-64-to-32",
],
sdk_frameworks = ["Accelerate"],
visibility = ["//mediapipe/framework:mediapipe_internal"],
# This build rule is public to allow external customers to build their own iOS apps.
visibility = ["//visibility:public"],
deps = [
":CFHolder",
":util",
@ -124,7 +125,8 @@ objc_library(
"CoreVideo",
"Foundation",
],
visibility = ["//mediapipe/framework:mediapipe_internal"],
# This build rule is public to allow external customers to build their own iOS apps.
visibility = ["//visibility:public"],
)
objc_library(
@ -166,7 +168,8 @@ objc_library(
"Foundation",
"GLKit",
],
visibility = ["//mediapipe/framework:mediapipe_internal"],
# This build rule is public to allow external customers to build their own iOS apps.
visibility = ["//visibility:public"],
deps = [
":mediapipe_framework_ios",
":mediapipe_gl_view_renderer",

8
mediapipe/python/solutions/download_utils.py
View File

@ -17,21 +17,21 @@ import os
import shutil
import urllib.request
_OSS_URL_PREFIX = 'https://github.com/google/mediapipe/raw/master/'
_GCS_URL_PREFIX = 'https://storage.googleapis.com/mediapipe-assets/'
def download_oss_model(model_path: str):
"""Downloads the oss model from the MediaPipe GitHub repo if it doesn't exist in the package."""
"""Downloads the oss model from Google Cloud Storage if it doesn't exist in the package."""
mp_root_path = os.sep.join(os.path.abspath(__file__).split(os.sep)[:-4])
model_abspath = os.path.join(mp_root_path, model_path)
if os.path.exists(model_abspath):
return
model_url = _OSS_URL_PREFIX + model_path
model_url = _GCS_URL_PREFIX + model_path.split('/')[-1]
print('Downloading model to ' + model_abspath)
with urllib.request.urlopen(model_url) as response, open(model_abspath,
'wb') as out_file:
if response.code != 200:
raise ConnectionError('Cannot download ' + model_path +
' from the MediaPipe Github repo.')
' from Google Cloud Storage.')
shutil.copyfileobj(response, out_file)

2
mediapipe/tasks/cc/vision/image_classification/image_classifier_test.cc
View File

@ -142,7 +142,7 @@ TEST_F(CreateTest, FailsWithSelectiveOpResolverMissingOps) {
// interpreter errors (e.g., "Encountered unresolved custom op").
EXPECT_EQ(image_classifier_or.status().code(), absl::StatusCode::kInternal);
EXPECT_THAT(image_classifier_or.status().message(),
HasSubstr("interpreter_builder(&interpreter_) == kTfLiteOk"));
HasSubstr("interpreter_builder(&interpreter) == kTfLiteOk"));
}
TEST_F(CreateTest, FailsWithMissingModel) {
auto image_classifier_or =

2
mediapipe/tasks/cc/vision/object_detector/object_detector_test.cc
View File

@ -194,7 +194,7 @@ TEST_F(CreateFromOptionsTest, FailsWithSelectiveOpResolverMissingOps) {
// interpreter errors (e.g., "Encountered unresolved custom op").
EXPECT_EQ(object_detector.status().code(), absl::StatusCode::kInternal);
EXPECT_THAT(object_detector.status().message(),
HasSubstr("interpreter_->AllocateTensors() == kTfLiteOk"));
HasSubstr("interpreter->AllocateTensors() == kTfLiteOk"));
}
TEST_F(CreateFromOptionsTest, FailsWithMissingModel) {

2
mediapipe/tasks/cc/vision/segmentation/image_segmenter_test.cc
View File

@ -185,7 +185,7 @@ TEST_F(CreateFromOptionsTest, FailsWithSelectiveOpResolverMissingOps) {
EXPECT_EQ(segmenter_or.status().code(), absl::StatusCode::kInternal);
EXPECT_THAT(
segmenter_or.status().message(),
testing::HasSubstr("interpreter_builder(&interpreter_) == kTfLiteOk"));
testing::HasSubstr("interpreter_builder(&interpreter) == kTfLiteOk"));
}
TEST_F(CreateFromOptionsTest, FailsWithMissingModel) {