load model in pb

mace/benchmark/BUILD

@@ -16,6 +16,7 @@ cc_library(
     srcs = ["statistics.cc"],
     hdrs = ["statistics.h"],
     linkstatic = 1,
+    copts = ["-Werror"],
     deps = [
         "//mace/kernels",
         "//mace/utils",
@@ -28,7 +29,7 @@ cc_binary(
         "benchmark_model.cc",
     ],
     copts = if_android(["-DMACE_ENABLE_OPENCL"]),
-    linkopts = if_openmp_enabled(["-fopenmp"]),
+    linkopts = ["-Werror"] + if_openmp_enabled(["-fopenmp"]),
     linkstatic = 1,
     deps = [
         ":statistics",
@@ -40,6 +41,7 @@ cc_binary(
 
 cc_library(
     name = "libmace_merged",
+    copts = ["-Werror"],
     srcs = [
         "libmace_merged.a",
     ],
@@ -51,6 +53,7 @@ cc_binary(
     srcs = ["model_throughput_test.cc"],
     linkopts = if_openmp_enabled(["-fopenmp"]),
     linkstatic = 1,
+    copts = ["-Werror"],
     deps = [
         ":libmace_merged",
         "//external:gflags_nothreads",

mace/benchmark/benchmark_model.cc

@@ -24,6 +24,7 @@
 #include "mace/public/mace.h"
 #include "mace/public/mace_runtime.h"
 #include "mace/utils/logging.h"
+#include "mace/utils/utils.h"
 #include "mace/benchmark/statistics.h"
 #include "mace/codegen/engine/mace_engine_factory.h"
 
@@ -189,6 +190,8 @@ DEFINE_string(max_time, "10.0", "length to run max");
 DEFINE_int32(warmup_runs, 1, "how many runs to initialize model");
 DEFINE_string(model_data_file, "",
               "model data file name, used when EMBED_MODEL_DATA set to 0");
+DEFINE_string(model_file, "",
+              "model file name, used when load mace model in pb");
 DEFINE_int32(gpu_perf_hint, 3, "0:DEFAULT/1:LOW/2:NORMAL/3:HIGH");
 DEFINE_int32(gpu_priority_hint, 3, "0:DEFAULT/1:LOW/2:NORMAL/3:HIGH");
 DEFINE_int32(omp_num_threads, -1, "num of openmp threads");
@@ -202,6 +205,7 @@ int Main(int argc, char **argv) {
   gflags::ParseCommandLineFlags(&argc, &argv, true);
 
   LOG(INFO) << "Model name: [" << FLAGS_model_name << "]";
+  LOG(INFO) << "Model_file: " << FLAGS_model_file;
   LOG(INFO) << "Device: [" << FLAGS_device << "]";
   LOG(INFO) << "gpu_perf_hint: [" << FLAGS_gpu_perf_hint << "]";
   LOG(INFO) << "gpu_priority_hint: [" << FLAGS_gpu_priority_hint << "]";
@@ -268,18 +272,25 @@ int Main(int argc, char **argv) {
   std::shared_ptr<mace::MaceEngine> engine;
   MaceStatus create_engine_status;
   // Create Engine
-  if (FLAGS_model_data_file.empty()) {
+  const char *model_data_file_ptr =
+    FLAGS_model_data_file.empty() ? nullptr : FLAGS_model_data_file.c_str();
+  if (FLAGS_model_file != "") {
+    std::vector<unsigned char> model_pb_data;
+    if (!mace::ReadBinaryFile(&model_pb_data, FLAGS_model_file)) {
+      LOG(FATAL) << "Failed to read file: " << FLAGS_model_file;
+    }
     create_engine_status =
         CreateMaceEngine(FLAGS_model_name.c_str(),
-                         nullptr,
+                         model_data_file_ptr,
                          input_names,
                          output_names,
                          device_type,
-                         &engine);
+                         &engine,
+                         model_pb_data);
   } else {
     create_engine_status =
         CreateMaceEngine(FLAGS_model_name.c_str(),
-                         FLAGS_model_data_file.c_str(),
+                         model_data_file_ptr,
                          input_names,
                          output_names,
                          device_type,

mace/benchmark/model_throughput_test.cc

@@ -46,7 +46,7 @@ extern const unsigned char *LoadModelData(const char *model_data_file);
 
 extern void UnloadModelData(const unsigned char *model_data);
 
-extern NetDef CreateNet(const unsigned char *model_data);
+extern NetDef CreateNet(const std::vector<unsigned char> &model_pb = {});
 
 extern const std::string ModelChecksum();
 
@@ -60,7 +60,7 @@ extern const unsigned char *LoadModelData(const char *model_data_file);
 
 extern void UnloadModelData(const unsigned char *model_data);
 
-extern NetDef CreateNet(const unsigned char *model_data);
+extern NetDef CreateNet(const std::vector<unsigned char> &model_pb = {});
 
 extern const std::string ModelChecksum();
 
@@ -74,7 +74,7 @@ extern const unsigned char *LoadModelData(const char *model_data_file);
 
 extern void UnloadModelData(const unsigned char *model_data);
 
-extern NetDef CreateNet(const unsigned char *model_data);
+extern NetDef CreateNet(const std::vector<unsigned char> &model_pb = {});
 
 extern const std::string ModelChecksum();
 
@@ -255,10 +255,10 @@ int Main(int argc, char **argv) {
   const unsigned char *cpu_model_data =
       mace::MACE_CPU_MODEL_TAG::LoadModelData(
       FLAGS_cpu_model_data_file.c_str());
-  NetDef cpu_net_def = mace::MACE_CPU_MODEL_TAG::CreateNet(cpu_model_data);
+  NetDef cpu_net_def = mace::MACE_CPU_MODEL_TAG::CreateNet();
 
   mace::MaceEngine cpu_engine(&cpu_net_def, DeviceType::CPU, input_names,
-                              output_names);
+                              output_names, cpu_model_data);
 
   LOG(INFO) << "CPU Warm up run";
   t0 = NowMicros();
@@ -273,10 +273,10 @@ int Main(int argc, char **argv) {
   const unsigned char *gpu_model_data =
       mace::MACE_GPU_MODEL_TAG::LoadModelData(
       FLAGS_gpu_model_data_file.c_str());
-  NetDef gpu_net_def = mace::MACE_GPU_MODEL_TAG::CreateNet(gpu_model_data);
+  NetDef gpu_net_def = mace::MACE_GPU_MODEL_TAG::CreateNet();
 
   mace::MaceEngine gpu_engine(&gpu_net_def, DeviceType::GPU, input_names,
-                              output_names);
+                              output_names, gpu_model_data);
   mace::MACE_GPU_MODEL_TAG::UnloadModelData(gpu_model_data);
 
   LOG(INFO) << "GPU Warm up run";
@@ -292,10 +292,10 @@ int Main(int argc, char **argv) {
   const unsigned char *dsp_model_data =
       mace::MACE_DSP_MODEL_TAG::LoadModelData(
       FLAGS_dsp_model_data_file.c_str());
-  NetDef dsp_net_def = mace::MACE_DSP_MODEL_TAG::CreateNet(dsp_model_data);
+  NetDef dsp_net_def = mace::MACE_DSP_MODEL_TAG::CreateNet();
 
   mace::MaceEngine dsp_engine(&dsp_net_def, DeviceType::HEXAGON, input_names,
-                              output_names);
+                              output_names, dsp_model_data);
   mace::MACE_DSP_MODEL_TAG::UnloadModelData(dsp_model_data);
 
   LOG(INFO) << "DSP Warm up run";

mace/benchmark/statistics.cc

@@ -17,7 +17,7 @@
 #include <set>
 
 #include "mace/kernels/conv_pool_2d_util.h"
-#include "mace/public/mace_types.h"
+#include "mace/proto/mace.pb.h"
 #include "mace/utils/logging.h"
 #include "mace/utils/string_util.h"
 
@@ -59,10 +59,10 @@ std::string ShapeToString(const std::vector<OutputShape> &output_shape) {
   std::stringstream stream;
   stream << "[";
   for (size_t i = 0; i < output_shape.size(); ++i) {
-    const std::vector<index_t> &dims = output_shape[i].dims();
-    for (size_t j = 0; j < dims.size(); ++j) {
-      stream << dims[j];
-      if (j != dims.size() - 1) {
+    size_t dims_size = output_shape[i].dims_size();
+    for (size_t j = 0; j < dims_size; ++j) {
+      stream << output_shape[i].dims(j);
+      if (j != dims_size - 1) {
         stream << ",";
       }
     }

mace/codegen/BUILD

@@ -10,6 +10,7 @@ cc_library(
     srcs = glob(["models/*/*.cc"]),
     hdrs = glob(["models/*/*.h"]),
     linkstatic = 1,
+    copts = ["-Werror"],
     deps = [
         "//mace/core",
         "//mace/ops",
@@ -19,24 +20,28 @@ cc_library(
 cc_library(
     name = "generated_opencl",
     srcs = glob(["opencl/*.cc"]),
+    copts = ["-Werror"],
     linkstatic = 1,
 )
 
 cc_library(
     name = "generated_tuning_params",
     srcs = ["tuning/tuning_params.cc"],
+    copts = ["-Werror"],
     linkstatic = 1,
 )
 
 cc_library(
     name = "generated_version",
     srcs = ["version/version.cc"],
+    copts = ["-Werror"],
     linkstatic = 1,
 )
 
 cc_library(
     name = "generated_mace_engine_factory",
     hdrs = ["engine/mace_engine_factory.h"],
+    copts = ["-Werror"],
     deps = [
         "//mace/public",
     ],

mace/core/BUILD

@@ -42,7 +42,9 @@ cc_library(
             "runtime/opencl/*.h",
         ],
     )) + if_hexagon_enabled(glob(["runtime/hexagon/*.h"])),
-    copts = if_openmp_enabled([
+    copts = [
+        "-Werror",
+    ] + if_openmp_enabled([
         "-fopenmp",
         "-DMACE_ENABLE_OPENMP",
     ]) + if_android([
@@ -53,8 +55,9 @@ cc_library(
         "-lm",
     ]),
     deps = [
-        "//mace/utils",
         "//mace/codegen:generated_version",
+        "//mace/proto:mace_cc",
+        "//mace/utils",
     ] + if_android([
         ":opencl_headers",
         "//mace/codegen:generated_opencl",
@@ -74,6 +77,7 @@ cc_library(
     hdrs = glob([
         "runtime/opencl/*.h",
     ]),
+    copts = ["-Werror"],
     deps = [
         "@opencl_clhpp//:opencl_clhpp",
         "@opencl_headers//:opencl20_headers",
@@ -90,6 +94,7 @@ cc_library(
     hdrs = [
         "testing/test_benchmark.h",
     ],
+    copts = ["-Werror"],
     deps = [
         ":core",
         "//external:gflags_nothreads",

mace/core/arg_helper.h

@@ -19,8 +19,8 @@
 #include <vector>
 #include <map>
 
+#include "mace/proto/mace.pb.h"
 #include "mace/public/mace.h"
-#include "mace/public/mace_types.h"
 
 namespace mace {
 

mace/core/mace.cc

@@ -85,7 +85,8 @@ class MaceEngine::Impl {
   explicit Impl(const NetDef *net_def,
                 DeviceType device_type,
                 const std::vector<std::string> &input_nodes,
-                const std::vector<std::string> &output_nodes);
+                const std::vector<std::string> &output_nodes,
+                const unsigned char *model_data);
   ~Impl();
 
   MaceStatus Run(const std::map<std::string, MaceTensor> &inputs,
@@ -107,7 +108,8 @@ class MaceEngine::Impl {
 MaceEngine::Impl::Impl(const NetDef *net_def,
                        DeviceType device_type,
                        const std::vector<std::string> &input_nodes,
-                       const std::vector<std::string> &output_nodes)
+                       const std::vector<std::string> &output_nodes,
+                       const unsigned char *model_data)
     : op_registry_(new OperatorRegistry()),
       device_type_(device_type),
       ws_(new Workspace()),
@@ -136,14 +138,14 @@ MaceEngine::Impl::Impl(const NetDef *net_def,
     int dsp_mode =
         ArgumentHelper::GetSingleArgument<NetDef, int>(*net_def, "dsp_mode", 0);
     hexagon_controller_->SetGraphMode(dsp_mode);
-    MACE_CHECK(hexagon_controller_->SetupGraph(*net_def),
+    MACE_CHECK(hexagon_controller_->SetupGraph(*net_def, model_data),
                "hexagon setup graph error");
     if (VLOG_IS_ON(2)) {
       hexagon_controller_->PrintGraph();
     }
   } else {
 #endif
-    ws_->LoadModelTensor(*net_def, device_type);
+    ws_->LoadModelTensor(*net_def, device_type, model_data);
 
     // Init model
     auto net = CreateNet(op_registry_, *net_def, ws_.get(), device_type,
@@ -247,9 +249,11 @@ MaceStatus MaceEngine::Impl::Run(
 MaceEngine::MaceEngine(const NetDef *net_def,
                        DeviceType device_type,
                        const std::vector<std::string> &input_nodes,
-                       const std::vector<std::string> &output_nodes) {
+                       const std::vector<std::string> &output_nodes,
+                       const unsigned char *model_data) {
   impl_ = std::unique_ptr<MaceEngine::Impl>(
-      new MaceEngine::Impl(net_def, device_type, input_nodes, output_nodes));
+      new MaceEngine::Impl(net_def, device_type, input_nodes, output_nodes,
+                           model_data));
 }
 
 MaceEngine::~MaceEngine() = default;

mace/core/mace_types.cc

-// Copyright 2018 Xiaomi, Inc.  All rights reserved.
-//
-// 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 <memory>
-#include <numeric>
-
-#include "mace/public/mace_types.h"
-#include "mace/utils/logging.h"
-
-namespace mace {
-
-ConstTensor::ConstTensor(const std::string &name,
-                         const unsigned char *data,
-                         const std::vector<int64_t> &dims,
-                         const DataType data_type,
-                         uint32_t node_id)
-    : name_(name),
-      data_(data),
-      data_size_(std::accumulate(
-          dims.begin(), dims.end(), 1, std::multiplies<int64_t>())),
-      dims_(dims.begin(), dims.end()),
-      data_type_(data_type),
-      node_id_(node_id) {}
-
-ConstTensor::ConstTensor(const std::string &name,
-                         const unsigned char *data,
-                         const std::vector<int64_t> &dims,
-                         const int data_type,
-                         uint32_t node_id)
-    : name_(name),
-      data_(data),
-      data_size_(std::accumulate(
-          dims.begin(), dims.end(), 1, std::multiplies<int64_t>())),
-      dims_(dims.begin(), dims.end()),
-      data_type_(static_cast<DataType>(data_type)),
-      node_id_(node_id) {}
-
-const std::string &ConstTensor::name() const { return name_; }
-const unsigned char *ConstTensor::data() const { return data_; }
-int64_t ConstTensor::data_size() const { return data_size_; }
-const std::vector<int64_t> &ConstTensor::dims() const { return dims_; }
-DataType ConstTensor::data_type() const { return data_type_; }
-uint32_t ConstTensor::node_id() const { return node_id_; }
-
-Argument::Argument() : has_bits_(0) {}
-
-void Argument::CopyFrom(const Argument &from) {
-  this->name_ = from.name();
-  this->f_ = from.f();
-  this->i_ = from.i();
-  this->s_ = from.s();
-  auto floats = from.floats();
-  this->floats_.resize(floats.size());
-  std::copy(floats.begin(), floats.end(), this->floats_.begin());
-  auto ints = from.ints();
-  this->ints_.resize(ints.size());
-  std::copy(ints.begin(), ints.end(), this->ints_.begin());
-  auto strings = from.floats();
-  this->strings_.resize(strings.size());
-  std::copy(floats.begin(), floats.end(), this->floats_.begin());
-
-  this->has_bits_ = from.has_bits_;
-}
-const std::string &Argument::name() const { return name_; }
-void Argument::set_name(const std::string &value) { name_ = value; }
-bool Argument::has_f() const { return (has_bits_ & 0x00000001u) != 0; }
-void Argument::set_has_f() { has_bits_ |= 0x00000001u; }
-float Argument::f() const { return f_; }
-void Argument::set_f(float value) {
-  set_has_f();
-  f_ = value;
-}
-bool Argument::has_i() const { return (has_bits_ & 0x00000002u) != 0; }
-void Argument::set_has_i() { has_bits_ |= 0x00000002u; }
-int64_t Argument::i() const { return i_; }
-void Argument::set_i(int64_t value) {
-  set_has_i();
-  i_ = value;
-}
-bool Argument::has_s() const { return (has_bits_ & 0x00000004u) != 0; }
-void Argument::set_has_s() { has_bits_ |= 0x00000004u; }
-std::string Argument::s() const { return s_; }
-void Argument::set_s(const std::string &value) {
-  set_has_s();
-  s_ = value;
-}
-const std::vector<float> &Argument::floats() const { return floats_; }
-void Argument::add_floats(float value) { floats_.push_back(value); }
-void Argument::set_floats(const std::vector<float> &value) {
-  floats_.resize(value.size());
-  std::copy(value.begin(), value.end(), floats_.begin());
-}
-const std::vector<int64_t> &Argument::ints() const { return ints_; }
-void Argument::add_ints(int64_t value) { ints_.push_back(value); }
-void Argument::set_ints(const std::vector<int64_t> &value) {
-  ints_.resize(value.size());
-  std::copy(value.begin(), value.end(), ints_.begin());
-}
-const std::vector<std::string> &Argument::strings() const { return strings_; }
-void Argument::add_strings(const ::std::string &value) {
-  strings_.push_back(value);
-}
-void Argument::set_strings(const std::vector<std::string> &value) {
-  strings_.resize(value.size());
-  std::copy(value.begin(), value.end(), strings_.begin());
-}
-
-// Node Input
-NodeInput::NodeInput(int node_id, int output_port)
-    : node_id_(node_id), output_port_(output_port) {}
-void NodeInput::CopyFrom(const NodeInput &from) {
-  node_id_ = from.node_id();
-  output_port_ = from.output_port();
-}
-int NodeInput::node_id() const { return node_id_; }
-void NodeInput::set_node_id(int node_id) { node_id_ = node_id; }
-int NodeInput::output_port() const { return output_port_; }
-void NodeInput::set_output_port(int output_port) { output_port_ = output_port; }
-
-// OutputShape
-OutputShape::OutputShape() {}
-OutputShape::OutputShape(const std::vector<int64_t> &dims)
-    : dims_(dims.begin(), dims.end()) {}
-void OutputShape::CopyFrom(const OutputShape &from) {
-  auto from_dims = from.dims();
-  dims_.resize(from_dims.size());
-  std::copy(from_dims.begin(), from_dims.end(), dims_.begin());
-}
-const std::vector<int64_t> &OutputShape::dims() const { return dims_; }
-
-// Operator Def
-void OperatorDef::CopyFrom(const OperatorDef &from) {
-  name_ = from.name();
-  type_ = from.type();
-
-  auto from_input = from.input();
-  input_.resize(from_input.size());
-  std::copy(from_input.begin(), from_input.end(), input_.begin());
-  auto from_output = from.output();
-  output_.resize(from_output.size());
-  std::copy(from_output.begin(), from_output.end(), output_.begin());
-  auto from_arg = from.arg();
-  arg_.resize(from_arg.size());
-  for (size_t i = 0; i < from_arg.size(); ++i) {
-    arg_[i].CopyFrom(from_arg[i]);
-  }
-  auto from_output_shape = from.output_shape();
-  output_shape_.resize(from_output_shape.size());
-  for (size_t i = 0; i < from_output_shape.size(); ++i) {
-    output_shape_[i].CopyFrom(from_output_shape[i]);
-  }
-  auto from_data_type = from.output_type();
-  output_type_.resize(from_data_type.size());
-  std::copy(from_data_type.begin(), from_data_type.end(), output_type_.begin());
-
-  auto mem_ids = from.mem_id();
-  mem_id_.resize(mem_ids.size());
-  std::copy(mem_ids.begin(), mem_ids.end(), mem_id_.begin());
-
-  // nnlib
-  node_id_ = from.node_id();
-  op_id_ = from.op_id();
-  padding_ = from.padding();
-  auto from_node_input = from.node_input();
-  node_input_.resize(from_node_input.size());
-  for (size_t i = 0; i < from_node_input.size(); ++i) {
-    node_input_[i].CopyFrom(from_node_input[i]);
-  }
-  auto from_out_max_byte_size = from.out_max_byte_size();
-  out_max_byte_size_.resize(from_out_max_byte_size.size());
-  std::copy(from_out_max_byte_size.begin(), from_out_max_byte_size.end(),
-            out_max_byte_size_.begin());
-
-  has_bits_ = from.has_bits_;
-}
-
-const std::string &OperatorDef::name() const { return name_; }
-void OperatorDef::set_name(const std::string &name_) {
-  set_has_name();
-  OperatorDef::name_ = name_;
-}
-bool OperatorDef::has_name() const { return (has_bits_ & 0x00000001u) != 0; }
-void OperatorDef::set_has_name() { has_bits_ |= 0x00000001u; }
-const std::string &OperatorDef::type() const { return type_; }
-void OperatorDef::set_type(const std::string &type_) {
-  set_has_type();
-  OperatorDef::type_ = type_;
-}
-bool OperatorDef::has_type() const { return (has_bits_ & 0x00000002u) != 0; }
-void OperatorDef::set_has_type() { has_bits_ |= 0x00000002u; }
-const std::vector<int> &OperatorDef::mem_id() const { return mem_id_; }
-void OperatorDef::set_mem_id(const std::vector<int> &value) {
-  mem_id_.resize(value.size());
-  std::copy(value.begin(), value.end(), mem_id_.begin());
-}
-uint32_t OperatorDef::node_id() const { return node_id_; }
-void OperatorDef::set_node_id(uint32_t node_id) { node_id_ = node_id; }
-uint32_t OperatorDef::op_id() const { return op_id_; }
-uint32_t OperatorDef::padding() const { return padding_; }
-void OperatorDef::set_padding(uint32_t padding) { padding_ = padding; }
-const std::vector<NodeInput> &OperatorDef::node_input() const {
-  return node_input_;
-}
-void OperatorDef::add_node_input(const NodeInput &value) {
-  node_input_.push_back(value);
-}
-const std::vector<int> &OperatorDef::out_max_byte_size() const {
-  return out_max_byte_size_;
-}
-void OperatorDef::add_out_max_byte_size(int value) {
-  out_max_byte_size_.push_back(value);
-}
-const std::vector<std::string> &OperatorDef::input() const { return input_; }
-const std::string &OperatorDef::input(int index) const {
-  MACE_CHECK(0 <= index && index <= static_cast<int>(input_.size()));
-  return input_[index];
-}
-std::string *OperatorDef::add_input() {
-  input_.push_back("");
-  return &input_.back();
-}
-void OperatorDef::add_input(const ::std::string &value) {
-  input_.push_back(value);
-}
-void OperatorDef::add_input(::std::string &&value) { input_.push_back(value); }
-void OperatorDef::set_input(const std::vector<std::string> &value) {
-  input_.resize(value.size());
-  std::copy(value.begin(), value.end(), input_.begin());
-}
-const std::vector<std::string> &OperatorDef::output() const { return output_; }
-const std::string &OperatorDef::output(int index) const {
-  MACE_CHECK(0 <= index && index <= static_cast<int>(output_.size()));
-  return output_[index];
-}
-std::string *OperatorDef::add_output() {
-  output_.push_back("");
-  return &output_.back();
-}
-void OperatorDef::add_output(const ::std::string &value) {
-  output_.push_back(value);
-}
-void OperatorDef::add_output(::std::string &&value) {
-  output_.push_back(value);
-}
-void OperatorDef::set_output(const std::vector<std::string> &value) {
-  output_.resize(value.size());
-  std::copy(value.begin(), value.end(), output_.begin());
-}
-const std::vector<Argument> &OperatorDef::arg() const { return arg_; }
-Argument *OperatorDef::add_arg() {
-  arg_.emplace_back(Argument());
-  return &arg_.back();
-}
-const std::vector<OutputShape> &OperatorDef::output_shape() const {
-  return output_shape_;
-}
-void OperatorDef::add_output_shape(const OutputShape &value) {
-  output_shape_.push_back(value);
-}
-const std::vector<DataType> &OperatorDef::output_type() const {
-  return output_type_;
-}
-void OperatorDef::set_output_type(const std::vector<DataType> &value) {
-  output_type_.resize(value.size());
-  std::copy(value.begin(), value.end(), output_type_.begin());
-}
-
-// MemoryBlock
-MemoryBlock::MemoryBlock(int mem_id, uint32_t x, uint32_t y)
-    : mem_id_(mem_id), x_(x), y_(y) {}
-
-int MemoryBlock::mem_id() const { return mem_id_; }
-uint32_t MemoryBlock::x() const { return x_; }
-uint32_t MemoryBlock::y() const { return y_; }
-
-// MemoryArena
-const std::vector<MemoryBlock> &MemoryArena::mem_block() const {
-  return mem_block_;
-}
-std::vector<MemoryBlock> &MemoryArena::mutable_mem_block() {
-  return mem_block_;
-}
-int MemoryArena::mem_block_size() const { return mem_block_.size(); }
-
-// InputInfo
-const std::string &InputInfo::name() const { return name_; }
-int32_t InputInfo::node_id() const { return node_id_; }
-int32_t InputInfo::max_byte_size() const { return max_byte_size_; }
-DataType InputInfo::data_type() const { return data_type_; }
-const std::vector<int32_t> &InputInfo::dims() const { return dims_; }
-
-// OutputInfo
-const std::string &OutputInfo::name() const { return name_; }
-int32_t OutputInfo::node_id() const { return node_id_; }
-int32_t OutputInfo::max_byte_size() const { return max_byte_size_; }
-DataType OutputInfo::data_type() const { return data_type_; }
-void OutputInfo::set_data_type(DataType data_type) { data_type_ = data_type; }
-const std::vector<int32_t> &OutputInfo::dims() const { return dims_; }
-void OutputInfo::set_dims(const std::vector<int32_t> &dims) { dims_ = dims; }
-
-// NetDef
-NetDef::NetDef() : has_bits_(0) {}
-
-const std::string &NetDef::name() const { return name_; }
-void NetDef::set_name(const std::string &value) {
-  set_has_name();
-  name_ = value;
-}
-bool NetDef::has_name() const { return (has_bits_ & 0x00000001u) != 0; }
-void NetDef::set_has_name() { has_bits_ |= 0x00000001u; }
-const std::string &NetDef::version() const { return version_; }
-void NetDef::set_version(const std::string &value) {
-  set_has_version();
-  version_ = value;
-}
-bool NetDef::has_version() const { return (has_bits_ & 0x00000002u) != 0; }
-void NetDef::set_has_version() { has_bits_ |= 0x00000002u; }
-const std::vector<OperatorDef> &NetDef::op() const { return op_; }
-OperatorDef *NetDef::add_op() {
-  op_.emplace_back(OperatorDef());
-  return &op_.back();
-}
-std::vector<OperatorDef> &NetDef::mutable_op() { return op_; }
-const std::vector<Argument> &NetDef::arg() const { return arg_; }
-Argument *NetDef::add_arg() {
-  arg_.emplace_back(Argument());
-  return &arg_.back();
-}
-std::vector<Argument> &NetDef::mutable_arg() { return arg_; }
-const std::vector<ConstTensor> &NetDef::tensors() const { return tensors_; }
-std::vector<ConstTensor> &NetDef::mutable_tensors() { return tensors_; }
-const MemoryArena &NetDef::mem_arena() const { return mem_arena_; }
-MemoryArena &NetDef::mutable_mem_arena() {
-  set_has_mem_arena();
-  return mem_arena_;
-}
-bool NetDef::has_mem_arena() const { return (has_bits_ & 0x00000004u) != 0; }
-void NetDef::set_has_mem_arena() { has_bits_ |= 0x00000004u; }
-const std::vector<InputInfo> &NetDef::input_info() const { return input_info_; }
-const std::vector<OutputInfo> &NetDef::output_info() const {
-  return output_info_;
-}
-std::vector<OutputInfo> &NetDef::mutable_output_info() { return output_info_; }
-
-int NetDef::op_size() const { return op_.size(); }
-
-const OperatorDef &NetDef::op(const int idx) const {
-  MACE_CHECK(0 <= idx && idx < op_size());
-  return op_[idx];
-}
-
-};  // namespace mace

mace/core/net.cc

@@ -110,7 +110,8 @@ bool SerialNet::Run(RunMetadata *run_metadata) {
       }
 
       OperatorStats op_stats = {op->debug_def().name(), op->debug_def().type(),
-                                op->debug_def().output_shape(),
+                                {op->debug_def().output_shape().begin(),
+                                op->debug_def().output_shape().end()},
                                 {strides, padding_type, paddings, dilations,
                                  kernels}, call_stats};
       run_metadata->op_stats.emplace_back(op_stats);

mace/core/operator.h

@@ -25,8 +25,8 @@
 #include "mace/core/registry.h"
 #include "mace/core/tensor.h"
 #include "mace/core/workspace.h"
+#include "mace/proto/mace.pb.h"
 #include "mace/public/mace.h"
-#include "mace/public/mace_types.h"
 
 namespace mace {
 
@@ -108,20 +108,20 @@ class Operator : public OperatorBase {
       inputs_.push_back(tensor);
     }
 
-    for (size_t i = 0; i < operator_def.output().size(); ++i) {
-      const std::string output_str = operator_def.output()[i];
+    for (size_t i = 0; i < (size_t)operator_def.output_size(); ++i) {
+      const std::string output_str = operator_def.output(i);
       if (ws->HasTensor(output_str)) {
         outputs_.push_back(ws->GetTensor(output_str));
       } else {
         MACE_CHECK(
-          operator_def.output_type().size() == 0
-          || operator_def.output().size() == operator_def.output_type().size(),
+          operator_def.output_type_size() == 0
+          || operator_def.output_size() == operator_def.output_type_size(),
           "operator output size != operator output type size",
-          operator_def.output().size(),
-          operator_def.output_type().size());
+          operator_def.output_size(),
+          operator_def.output_type_size());
         DataType output_type;
-        if (i < operator_def.output_type().size()) {
-          output_type = operator_def.output_type()[i];
+        if (i < (size_t)operator_def.output_type_size()) {
+          output_type = operator_def.output_type(i);
         } else {
           output_type = DataTypeToEnum<T>::v();
         }

mace/core/runtime/hexagon/hexagon_control_wrapper.cc

@@ -68,7 +68,8 @@ bool HexagonControlWrapper::Finalize() {
   return hexagon_controller_DeInitHexagon() == 0;
 }
 
-bool HexagonControlWrapper::SetupGraph(const NetDef &net_def) {
+bool HexagonControlWrapper::SetupGraph(const NetDef &net_def,
+                                       unsigned const char *model_data) {
   LOG(INFO) << "Hexagon setup graph";
 
   int64_t t0 = NowMicros();
@@ -96,7 +97,7 @@ bool HexagonControlWrapper::SetupGraph(const NetDef &net_def) {
         const_node.tensor.dataLen = 0;
       } else {
         const_node.tensor.data =
-            const_cast<unsigned char *>(const_tensor.data());
+            const_cast<unsigned char *>(model_data + const_tensor.offset());
         const_node.tensor.dataLen = const_tensor.data_size() *
                                     GetEnumTypeSize(const_tensor.data_type());
       }
@@ -133,12 +134,12 @@ bool HexagonControlWrapper::SetupGraph(const NetDef &net_def) {
     for (const OperatorDef &op : net_def.op()) {
       int op_id = op_map.GetOpId(op.type());
       inputs.resize(op.node_input().size());
-      for (size_t i = 0; i < op.node_input().size(); ++i) {
+      for (size_t i = 0; i < (size_t)op.node_input().size(); ++i) {
         inputs[i].src_id = node_id(op.node_input()[i].node_id());
         inputs[i].output_idx = op.node_input()[i].output_port();
       }
       outputs.resize(op.out_max_byte_size().size());
-      for (size_t i = 0; i < op.out_max_byte_size().size(); ++i) {
+      for (size_t i = 0; i < (size_t)op.out_max_byte_size().size(); ++i) {
         outputs[i].max_size = op.out_max_byte_size()[i];
       }
       cached_inputs.push_back(inputs);

mace/core/runtime/hexagon/hexagon_control_wrapper.h

@@ -31,7 +31,7 @@ class HexagonControlWrapper {
   bool Config();
   bool Init();
   bool Finalize();
-  bool SetupGraph(const NetDef &net_def);
+  bool SetupGraph(const NetDef &net_def, const unsigned char *model_data);
   bool ExecuteGraph(const Tensor &input_tensor, Tensor *output_tensor);
   bool ExecuteGraphNew(const std::vector<Tensor> &input_tensors,
                        std::vector<Tensor> *output_tensors);

mace/core/types.h

@@ -18,7 +18,7 @@
 #include <cstdint>
 #include <string>
 
-#include "mace/public/mace_types.h"
+#include "mace/proto/mace.pb.h"
 #ifdef MACE_ENABLE_OPENCL
 #include "include/half.hpp"
 #endif

mace/core/workspace.cc

@@ -60,24 +60,17 @@ std::vector<std::string> Workspace::Tensors() const {
   return names;
 }
 
-void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) {
+void Workspace::LoadModelTensor(const NetDef &net_def,
+                                DeviceType type,
+                                const unsigned char *model_data) {
   MACE_LATENCY_LOGGER(1, "Load model tensors");
   index_t model_data_size = 0;
-  unsigned char *model_data_ptr = nullptr;
-  for (auto &const_tensor : net_def.tensors()) {
-    if (model_data_ptr == nullptr ||
-        reinterpret_cast<int64_t>(const_tensor.data()) <
-            reinterpret_cast<int64_t>(model_data_ptr)) {
-      model_data_ptr = const_cast<unsigned char *>(const_tensor.data());
-    }
-  }
   for (auto &const_tensor : net_def.tensors()) {
     model_data_size = std::max(
         model_data_size,
-        static_cast<index_t>((reinterpret_cast<int64_t>(const_tensor.data()) -
-                              reinterpret_cast<int64_t>(model_data_ptr)) +
+        static_cast<index_t>(const_tensor.offset() +
                              const_tensor.data_size() *
-                                 GetEnumTypeSize(const_tensor.data_type())));
+                             GetEnumTypeSize(const_tensor.data_type())));
   }
   VLOG(3) << "Model data size: " << model_data_size;
 
@@ -85,13 +78,14 @@ void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) {
     if (type == DeviceType::CPU) {
       tensor_buffer_ = std::unique_ptr<Buffer>(
           new Buffer(GetDeviceAllocator(type),
-                     model_data_ptr,
+                     const_cast<unsigned char*>(model_data),
                      model_data_size));
     } else {
       tensor_buffer_ = std::unique_ptr<Buffer>(
           new Buffer(GetDeviceAllocator(type), model_data_size));
       tensor_buffer_->Map(nullptr);
-      tensor_buffer_->Copy(model_data_ptr, 0, model_data_size);
+      tensor_buffer_->Copy(const_cast<unsigned char*>(model_data),
+                           0, model_data_size);
       tensor_buffer_->UnMap();
     }
   }
@@ -107,10 +101,8 @@ void Workspace::LoadModelTensor(const NetDef &net_def, DeviceType type) {
       dims.push_back(d);
     }
 
-    index_t offset = reinterpret_cast<int64_t>(const_tensor.data())
-        - reinterpret_cast<int64_t>(model_data_ptr);
     std::unique_ptr<Tensor> tensor(
-        new Tensor(BufferSlice(tensor_buffer_.get(), offset,
+        new Tensor(BufferSlice(tensor_buffer_.get(), const_tensor.offset(),
                                const_tensor.data_size() *
                                    GetEnumTypeSize(const_tensor.data_type())),
                    const_tensor.data_type()));

mace/core/workspace.h

@@ -47,7 +47,9 @@ class Workspace {
 
   std::vector<std::string> Tensors() const;
 
-  void LoadModelTensor(const NetDef &net_def, DeviceType type);
+  void LoadModelTensor(const NetDef &net_def,
+                       DeviceType type,
+                       const unsigned char *model_data);
 
   ScratchBuffer *GetScratchBuffer(DeviceType device_type);
 

mace/examples/BUILD

@@ -6,6 +6,7 @@ cc_binary(
     srcs = ["example.cc"],
     linkopts = if_openmp_enabled(["-fopenmp"]),
     linkstatic = 1,
+    copts = ["-Werror"],
     deps = [
         "//external:gflags_nothreads",
         "//mace/codegen:generated_models",

mace/kernels/BUILD

@@ -38,7 +38,8 @@ cc_library(
             "opencl/*.h",
             "buffer_to_image.h",
         ])),
-    copts = if_openmp_enabled(["-fopenmp"]) +
+    copts = ["-Werror"] +
+      if_openmp_enabled(["-fopenmp"]) +
       if_neon_enabled(["-DMACE_ENABLE_NEON"]) +
       if_android_armv7(["-mfpu=neon"]) +
       if_android_armv7(["-mfloat-abi=softfp"]) +
@@ -61,7 +62,8 @@ cc_test(
             "opencl/*_test.cc",
         ],
     ),
-    copts = if_openmp_enabled(["-fopenmp"]) +
+    copts = ["-Werror"] +
+      if_openmp_enabled(["-fopenmp"]) +
       if_neon_enabled(["-DMACE_ENABLE_NEON"]) +
       if_android_armv7(["-mfpu=neon"]) +
       if_android_armv7(["-mfloat-abi=softfp"]) +

mace/kernels/image_to_buffer.h

@@ -37,6 +37,10 @@ struct ImageToBufferFunctor : ImageToBufferFunctorBase {
                   const BufferType type,
                   Tensor *output,
                   StatsFuture *future) {
+    MACE_UNUSED(input);
+    MACE_UNUSED(type);
+    MACE_UNUSED(output);
+    MACE_UNUSED(future);
     MACE_NOT_IMPLEMENTED;
   }
 };

mace/kernels/winograd_transform.h

@@ -111,6 +111,7 @@ struct WinogradInverseTransformFunctor : WinogradInverseTransformFunctorBase {
     MACE_UNUSED(input);
     MACE_UNUSED(bias);
     MACE_UNUSED(output);
+    MACE_UNUSED(future);
     MACE_NOT_IMPLEMENTED;
   }
 };

mace/ops/BUILD

@@ -15,6 +15,7 @@ cc_library(
     hdrs = [
         "ops_test_util.h",
     ],
+    copts = ["-Werror"],
     deps = [
         "//mace/core",
         "@gtest//:gtest",
@@ -40,7 +41,8 @@ cc_library(
         ["*.h"],
         exclude = ["ops_test_util.h"],
     ),
-    copts = if_openmp_enabled(["-fopenmp"]) +
+    copts = ["-Werror"] +
+      if_openmp_enabled(["-fopenmp"]) +
       if_neon_enabled(["-DMACE_ENABLE_NEON"]) +
       if_android_armv7(["-mfpu=neon"]) +
       if_android_armv7(["-mfloat-abi=softfp"]) +
@@ -58,7 +60,8 @@ cc_test(
     srcs = glob(
         ["*_test.cc"],
     ),
-    copts = if_openmp_enabled(["-fopenmp"]) +
+    copts = ["-Werror"] +
+      if_openmp_enabled(["-fopenmp"]) +
       if_neon_enabled(["-DMACE_ENABLE_NEON"]) +
       if_android_armv7(["-mfpu=neon"]) +
       if_android_armv7(["-mfloat-abi=softfp"]) +
@@ -77,7 +80,8 @@ cc_test(
     name = "ops_benchmark",
     testonly = 1,
     srcs = glob(["*_benchmark.cc"]),
-    copts = if_openmp_enabled(["-fopenmp"]) +
+    copts = ["-Werror"] +
+      if_openmp_enabled(["-fopenmp"]) +
       if_neon_enabled(["-DMACE_ENABLE_NEON"]) +
       if_android_armv7(["-mfpu=neon"]) +
       if_android_armv7(["-mfloat-abi=softfp"]) +

mace/ops/ops_test_util.h

@@ -53,7 +53,9 @@ class OpDefBuilder {
   }
 
   OpDefBuilder &OutputType(const std::vector<DataType> &output_type) {
-    op_def_.set_output_type(output_type);
+    for (auto out_t : output_type) {
+      op_def_.add_output_type(out_t);
+    }
     return *this;
   }
 

mace/proto/BUILD

@@ -8,7 +8,9 @@ package(
 
 licenses(["notice"])  # Apache 2.0
 
-load("@com_google_protobuf//:protobuf.bzl", "py_proto_library")
+load("@com_google_protobuf//:protobuf.bzl",
+     "py_proto_library",
+     "cc_proto_library")
 
 py_proto_library(
     name = "mace_py",
@@ -19,11 +21,9 @@ py_proto_library(
     deps = ["@com_google_protobuf//:protobuf_python"],
 )
 
-py_proto_library(
-    name = "caffe_py",
-    srcs = ["caffe.proto"],
-    default_runtime = "@com_google_protobuf//:protobuf_python",
+cc_proto_library(
+    name = "mace_cc",
+    srcs = ["mace.proto"],
+    default_runtime = "@com_google_protobuf//:protobuf_lite",
     protoc = "@com_google_protobuf//:protoc",
-    srcs_version = "PY2AND3",
-    deps = ["@com_google_protobuf//:protobuf_python"],
 )

mace/proto/mace.proto

@@ -2,14 +2,16 @@ syntax = "proto2";
 
 package mace;
 
+option optimize_for = LITE_RUNTIME;
+
 enum NetMode {
   INIT   = 0;
   NORMAL = 1;
 }
 
 enum DeviceType {
-  CPU    = 0;                    // In default, we will use CPU.
-  GPU    = 2;
+  CPU     = 0;                    // In default, we will use CPU.
+  GPU     = 2;
   HEXAGON = 3;
 }
 
@@ -32,7 +34,7 @@ enum DataType {
   DT_UINT32 = 22;
 }
 
-message TensorProto {
+message ConstTensor {
   // The dimensions in the tensor.
   repeated int64 dims = 1;
   optional DataType data_type = 2 [default = DT_FLOAT];
@@ -52,6 +54,8 @@ message TensorProto {
   repeated int64 int64_data = 10 [packed = true];
   // Optionally, a name for the tensor.
   optional string name = 7;
+  optional int64 offset = 11;
+  optional int64 data_size = 12;
 
   optional uint32 node_id = 100;
 }
@@ -126,7 +130,7 @@ message NetDef {
   repeated OperatorDef op = 2;
   optional string version = 3;
   repeated Argument arg = 4;
-  repeated TensorProto tensors = 5;
+  repeated ConstTensor tensors = 5;
 
   // for mem optimization
   optional MemoryArena mem_arena = 10;

mace/public/BUILD

@@ -12,6 +12,9 @@ cc_library(
     hdrs = [
         "mace.h",
         "mace_runtime.h",
-        "mace_types.h",
+    ],
+    copts = ["-Werror"],
+    deps = [
+        "//mace/proto:mace_cc",
     ],
 )

mace/public/mace.h

@@ -24,11 +24,39 @@
 #include <string>
 #include <vector>
 
+#include "mace/proto/mace.pb.h"
+
 namespace mace {
 
+struct CallStats {
+  int64_t start_micros;
+  int64_t end_micros;
+};
+
+struct ConvPoolArgs {
+  std::vector<int> strides;
+  int padding_type;
+  std::vector<int> paddings;
+  std::vector<int> dilations;
+  std::vector<int64_t> kernels;
+};
+
+struct OperatorStats {
+  std::string operator_name;
+  std::string type;
+  std::vector<OutputShape> output_shape;
+  ConvPoolArgs args;
+  CallStats stats;
+};
+
+class RunMetadata {
+ public:
+  std::vector<OperatorStats> op_stats;
+};
+
 const char *MaceVersion();
 
-enum DeviceType { CPU = 0, GPU = 2, HEXAGON = 3 };
+// enum DeviceType { CPU = 0, GPU = 2, HEXAGON = 3 };
 
 enum MaceStatus { MACE_SUCCESS = 0, MACE_INVALID_ARGS = 1 };
 
@@ -56,15 +84,13 @@ class MaceTensor {
   std::unique_ptr<Impl> impl_;
 };
 
-class NetDef;
-class RunMetadata;
-
 class MaceEngine {
  public:
   explicit MaceEngine(const NetDef *net_def,
                       DeviceType device_type,
                       const std::vector<std::string> &input_nodes,
-                      const std::vector<std::string> &output_nodes);
+                      const std::vector<std::string> &output_nodes,
+                      const unsigned char *model_data);
   ~MaceEngine();
 
   MaceStatus Run(const std::map<std::string, MaceTensor> &inputs,

mace/public/mace_types.h

-// Copyright 2018 Xiaomi, Inc.  All rights reserved.
-//
-// 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.
-
-// This file defines data types used by net creation and benchmark tools.
-// These APIs are not stable and should only be used by advanced users.
-
-#ifndef MACE_PUBLIC_MACE_TYPES_H_
-#define MACE_PUBLIC_MACE_TYPES_H_
-
-#include <string>
-#include <vector>
-
-namespace mace {
-
-// Disable the copy and assignment operator for a class.
-#ifndef DISABLE_COPY_AND_ASSIGN
-#define DISABLE_COPY_AND_ASSIGN(classname) \
- private:                                  \
-  classname(const classname &) = delete;   \
-  classname &operator=(const classname &) = delete
-#endif
-
-enum NetMode { INIT = 0, NORMAL = 1 };
-
-enum DataType {
-  DT_INVALID = 0,
-  DT_FLOAT = 1,
-  DT_DOUBLE = 2,
-  DT_INT32 = 3,
-  DT_UINT8 = 4,
-  DT_INT16 = 5,
-  DT_INT8 = 6,
-  DT_STRING = 7,
-  DT_INT64 = 8,
-  DT_UINT16 = 9,
-  DT_BOOL = 10,
-  DT_HALF = 19,
-  DT_UINT32 = 22
-};
-
-class ConstTensor {
- public:
-  ConstTensor(const std::string &name,
-              const unsigned char *data,
-              const std::vector<int64_t> &dims,
-              const DataType data_type = DT_FLOAT,
-              uint32_t node_id = 0);
-  ConstTensor(const std::string &name,
-              const unsigned char *data,
-              const std::vector<int64_t> &dims,
-              const int data_type,
-              uint32_t node_id = 0);
-
-  const std::string &name() const;
-  const unsigned char *data() const;
-  int64_t data_size() const;
-  const std::vector<int64_t> &dims() const;
-  DataType data_type() const;
-  uint32_t node_id() const;
-
- private:
-  const std::string name_;
-  const unsigned char *data_;
-  const int64_t data_size_;
-  const std::vector<int64_t> dims_;
-  const DataType data_type_;
-  const uint32_t node_id_;
-};
-
-class Argument {
- public:
-  Argument();
-  void CopyFrom(const Argument &from);
-
- public:
-  const std::string &name() const;
-  void set_name(const std::string &value);
-  bool has_f() const;
-  float f() const;
-  void set_f(float value);
-  bool has_i() const;
-  int64_t i() const;
-  void set_i(int64_t value);
-  bool has_s() const;
-  std::string s() const;
-  void set_s(const std::string &value);
-  const std::vector<float> &floats() const;
-  void add_floats(float value);
-  void set_floats(const std::vector<float> &value);
-  const std::vector<int64_t> &ints() const;
-  void add_ints(int64_t value);
-  void set_ints(const std::vector<int64_t> &value);
-  const std::vector<std::string> &strings() const;
-  void add_strings(const ::std::string &value);
-  void set_strings(const std::vector<std::string> &value);
-
- private:
-  void set_has_f();
-  void set_has_i();
-  void set_has_s();
-
- private:
-  std::string name_;
-  float f_;
-  int64_t i_;
-  std::string s_;
-  std::vector<float> floats_;
-  std::vector<int64_t> ints_;
-  std::vector<std::string> strings_;
-  uint32_t has_bits_;
-};
-
-class NodeInput {
- public:
-  NodeInput() {}
-  NodeInput(int node_id, int output_port);
-  void CopyFrom(const NodeInput &from);
-
- public:
-  int node_id() const;
-  void set_node_id(int node_id);
-  int output_port() const;
-  void set_output_port(int output_port);
-
- private:
-  int node_id_;
-  int output_port_;
-};
-
-class OutputShape {
- public:
-  OutputShape();
-  OutputShape(const std::vector<int64_t> &dims);  // NOLINT(runtime/explicit)
-  void CopyFrom(const OutputShape &from);
-
- public:
-  const std::vector<int64_t> &dims() const;
-
- private:
-  std::vector<int64_t> dims_;
-};
-
-class OperatorDef {
- public:
-  void CopyFrom(const OperatorDef &from);
-
- public:
-  const std::string &name() const;
-  void set_name(const std::string &name_);
-  bool has_name() const;
-  const std::string &type() const;
-  void set_type(const std::string &type_);
-  bool has_type() const;
-  const std::vector<int> &mem_id() const;
-  void set_mem_id(const std::vector<int> &value);
-  uint32_t node_id() const;
-  void set_node_id(uint32_t node_id);
-  uint32_t op_id() const;
-  uint32_t padding() const;
-  void set_padding(uint32_t padding);
-  const std::vector<NodeInput> &node_input() const;
-  void add_node_input(const NodeInput &value);
-  const std::vector<int> &out_max_byte_size() const;
-  void add_out_max_byte_size(int value);
-  const std::vector<std::string> &input() const;
-  const std::string &input(int index) const;
-  std::string *add_input();
-  void add_input(const ::std::string &value);
-  void add_input(::std::string &&value);
-  void set_input(const std::vector<std::string> &value);
-  const std::vector<std::string> &output() const;
-  const std::string &output(int index) const;
-  std::string *add_output();
-  void add_output(const ::std::string &value);
-  void add_output(::std::string &&value);
-  void set_output(const std::vector<std::string> &value);
-  const std::vector<Argument> &arg() const;
-  Argument *add_arg();
-  const std::vector<OutputShape> &output_shape() const;
-  void add_output_shape(const OutputShape &value);
-  const std::vector<DataType> &output_type() const;
-  void set_output_type(const std::vector<DataType> &value);
-
- private:
-  void set_has_name();
-  void set_has_type();
-  void set_has_mem_id();
-
- private:
-  std::string name_;
-  std::string type_;
-
-  std::vector<std::string> input_;
-  std::vector<std::string> output_;
-  std::vector<Argument> arg_;
-  std::vector<OutputShape> output_shape_;
-  std::vector<DataType> output_type_;
-
-  std::vector<int> mem_id_;
-
-  // nnlib
-  uint32_t node_id_;
-  uint32_t op_id_;
-  uint32_t padding_;
-  std::vector<NodeInput> node_input_;
-  std::vector<int> out_max_byte_size_;
-
-  uint32_t has_bits_;
-};
-
-class MemoryBlock {
- public:
-  MemoryBlock(int mem_id, uint32_t x, uint32_t y);
-
- public:
-  int mem_id() const;
-  uint32_t x() const;
-  uint32_t y() const;
-
- private:
-  int mem_id_;
-  uint32_t x_;
-  uint32_t y_;
-};
-
-class MemoryArena {
- public:
-  const std::vector<MemoryBlock> &mem_block() const;
-  std::vector<MemoryBlock> &mutable_mem_block();
-  int mem_block_size() const;
-
- private:
-  std::vector<MemoryBlock> mem_block_;
-};
-
-// for hexagon mace-nnlib
-class InputInfo {
- public:
-  const std::string &name() const;
-  int32_t node_id() const;
-  int32_t max_byte_size() const;
-  DataType data_type() const;
-  const std::vector<int32_t> &dims() const;
-
- private:
-  std::string name_;
-  int32_t node_id_;
-  int32_t max_byte_size_;  // only support 32-bit len
-  DataType data_type_;
-  std::vector<int32_t> dims_;
-};
-
-class OutputInfo {
- public:
-  const std::string &name() const;
-  int32_t node_id() const;
-  int32_t max_byte_size() const;
-  DataType data_type() const;
-  void set_data_type(DataType data_type);
-  const std::vector<int32_t> &dims() const;
-  void set_dims(const std::vector<int32_t> &dims);
-
- private:
-  std::string name_;
-  int32_t node_id_;
-  int32_t max_byte_size_;  // only support 32-bit len
-  DataType data_type_;
-  std::vector<int32_t> dims_;
-};
-
-class NetDef {
- public:
-  NetDef();
-  int op_size() const;
-
-  const OperatorDef &op(const int idx) const;
-
- public:
-  const std::string &name() const;
-  bool has_name() const;
-  void set_name(const std::string &value);
-  const std::string &version() const;
-  bool has_version() const;
-  void set_version(const std::string &value);
-
-  const std::vector<OperatorDef> &op() const;
-  OperatorDef *add_op();
-  std::vector<OperatorDef> &mutable_op();
-  const std::vector<Argument> &arg() const;
-  Argument *add_arg();
-  std::vector<Argument> &mutable_arg();
-  const std::vector<ConstTensor> &tensors() const;
-  std::vector<ConstTensor> &mutable_tensors();
-  const MemoryArena &mem_arena() const;
-  bool has_mem_arena() const;
-  MemoryArena &mutable_mem_arena();
-  const std::vector<InputInfo> &input_info() const;
-  const std::vector<OutputInfo> &output_info() const;
-  std::vector<OutputInfo> &mutable_output_info();
-
- private:
-  void set_has_name();
-  void set_has_version();
-  void set_has_mem_arena();
-
- private:
-  std::string name_;
-  std::string version_;
-  std::vector<OperatorDef> op_;
-  std::vector<Argument> arg_;
-  std::vector<ConstTensor> tensors_;
-
-  // for mem optimization
-  MemoryArena mem_arena_;
-
-  // for hexagon mace-nnlib
-  std::vector<InputInfo> input_info_;
-  std::vector<OutputInfo> output_info_;
-
-  uint32_t has_bits_;
-};
-
-struct CallStats {
-  int64_t start_micros;
-  int64_t end_micros;
-};
-
-struct ConvPoolArgs {
-  std::vector<int> strides;
-  int padding_type;
-  std::vector<int> paddings;
-  std::vector<int> dilations;
-  std::vector<int64_t> kernels;
-};
-
-struct OperatorStats {
-  std::string operator_name;
-  std::string type;
-  std::vector<OutputShape> output_shape;
-  ConvPoolArgs args;
-  CallStats stats;
-};
-
-class RunMetadata {
- public:
-  std::vector<OperatorStats> op_stats;
-};
-
-}  // namespace mace
-
-#endif  // MACE_PUBLIC_MACE_TYPES_H_

mace/python/tools/converter.py

@@ -173,15 +173,14 @@ def main(unused_args):
 
             print "Memory optimization done."
 
-    if FLAGS.output_type == 'source':
-        source_converter_lib.convert_to_source(
-            output_graph_def, model_checksum, weight_checksum, FLAGS.template,
-            FLAGS.obfuscate, FLAGS.model_tag, FLAGS.output, FLAGS.runtime,
-            FLAGS.embed_model_data, FLAGS.winograd)
-    else:
-        with open(FLAGS.output, "wb") as f:
+    source_converter_lib.convert_to_source(
+        output_graph_def, model_checksum, weight_checksum, FLAGS.template,
+        FLAGS.obfuscate, FLAGS.model_tag, FLAGS.codegen_output, FLAGS.runtime,
+        FLAGS.embed_model_data, FLAGS.winograd, FLAGS.model_load_type)
+    if FLAGS.model_load_type == 'pb':
+        with open(FLAGS.pb_output, "wb") as f:
             f.write(output_graph_def.SerializeToString())
-        with open(FLAGS.output + '_txt', "wb") as f:
+        with open(FLAGS.pb_output + '_txt', "wb") as f:
             # output_graph_def.ClearField('tensors')
             f.write(str(output_graph_def))
     print("Model conversion is completed.")
@@ -219,10 +218,15 @@ def parse_args():
         default="",
         help="Weight file sha256 checksum")
     parser.add_argument(
-        "--output",
+        "--codegen_output",
         type=str,
         default="",
         help="File to save the output graph to.")
+    parser.add_argument(
+        "--pb_output",
+        type=str,
+        default="",
+        help="File to save the mace model to.")
     parser.add_argument(
         "--runtime", type=str, default="", help="Runtime: cpu/gpu/dsp")
     parser.add_argument(
@@ -266,6 +270,12 @@ def parse_args():
         type=str2bool,
         default=True,
         help="embed model data.")
+    parser.add_argument(
+        "--model_load_type",
+        type=str,
+        default="source",
+        help="[source|pb] Load models in generated `source` code" +
+                "or `pb` file.")
     return parser.parse_known_args()
 
 

mace/python/tools/mace_engine_factory.h.jinja2

@@ -23,6 +23,7 @@
 #include "mace/public/mace_runtime.h"
 
 namespace mace {
+
 {% for tag in model_tags %}
 namespace {{tag}} {
 
@@ -30,7 +31,7 @@ extern const unsigned char *LoadModelData(const char *model_data_file);
 
 extern void UnloadModelData(const unsigned char *model_data);
 
-extern NetDef CreateNet(const unsigned char *model_data);
+extern NetDef CreateNet(const std::vector<unsigned char> &model_pb = {});
 
 extern const std::string ModelName();
 extern const std::string ModelChecksum();
@@ -54,7 +55,8 @@ MaceStatus CreateMaceEngine(
     const std::vector<std::string> &input_nodes,
     const std::vector<std::string> &output_nodes,
     const DeviceType device_type,
-    std::shared_ptr<MaceEngine> *engine) {
+    std::shared_ptr<MaceEngine> *engine,
+    const std::vector<unsigned char> model_pb = {}) {
   // load model
   if (engine == nullptr) {
     return MaceStatus::MACE_INVALID_ARGS;
@@ -66,9 +68,11 @@ MaceStatus CreateMaceEngine(
    case {{ i }}:
     model_data =
         mace::{{model_tags[i]}}::LoadModelData(model_data_file);
-    net_def = mace::{{model_tags[i]}}::CreateNet(model_data);
+    net_def = mace::{{model_tags[i]}}::CreateNet(model_pb);
+
     engine->reset(
-        new mace::MaceEngine(&net_def, device_type, input_nodes, output_nodes));
+        new mace::MaceEngine(&net_def, device_type, input_nodes, output_nodes,
+                             model_data));
     if (device_type == DeviceType::GPU || device_type == DeviceType::HEXAGON) {
       mace::{{model_tags[i]}}::UnloadModelData(model_data);
     }

mace/python/tools/mace_engine_factory_codegen.py

@@ -45,7 +45,7 @@ def parse_args():
     parser.add_argument(
         "--template_dir", type=str, default="", help="template path")
     parser.add_argument(
-        "--output_dir", type=str, default="", help="template path")
+        "--output_dir", type=str, default="", help="output path")
     return parser.parse_known_args()
 
 

mace/python/tools/model.jinja2

@@ -17,16 +17,17 @@
 #include <vector>
 #include <string>
 
+#include "mace/core/macros.h"
 #include "mace/public/mace.h"
 #include "mace/utils/env_time.h"
 #include "mace/utils/logging.h"
 
 namespace mace {
+{% if model_type == 'source' %}
 namespace {{tag}} {
 
 {% for tensor in tensors %}
-extern void CreateTensor{{ tensor.id }}(std::vector<mace::ConstTensor> *tensors,
-                                        const unsigned char *model_data);
+extern void CreateTensor{{ tensor.id }}(mace::ConstTensor *tensor);
 {% endfor %}
 
 
@@ -40,7 +41,6 @@ namespace {
 
 {% if net.arg|length != 0 %}
 void CreateNetArg(mace::NetDef &net_def) {
-  net_def.mutable_arg().reserve({{ net.arg|length }});
   mace::Argument *arg = nullptr;
   {% for arg in net.arg %}
 
@@ -59,15 +59,15 @@ void CreateNetArg(mace::NetDef &net_def) {
   arg->set_s({{ arg.s|tojson }});
   {% endif %}
 
-  {% if arg.floats|length != 0 %}
-  arg->set_floats({ {{ arg.floats|join(', ') }} });
-  {% endif %}
-  {% if arg.ints|length != 0 %}
-  arg->set_ints({ {{ arg.ints|join(', ') }} });
-  {% endif %}
-  {% if arg.strings|length != 0 %}
-  arg->set_strings({ {{ arg.strings|stringfy() }} });
-  {% endif %}
+  {% for float_value in arg.floats %}
+  arg->add_floats({ {{ float_value }} });
+  {% endfor %}
+  {% for int_value in arg.ints %}
+  arg->add_ints({ {{ int_value }} });
+  {% endfor %}
+  {% for str_value in arg.strings %}
+  arg->add_strings({ {{ str_value }} });
+  {% endfor %}
 
   {% endfor %}
 }
@@ -75,82 +75,86 @@ void CreateNetArg(mace::NetDef &net_def) {
 
 {% if net.output_info | length > 0 %}
 void CreateOutputInfo(mace::NetDef &net_def) {
-	std::vector<std::vector<int>> dims { {{net.output_info | map(attribute='dims') | join(', ') | replace('[', '{') | replace(']', '}') }} };
+  std::vector<std::vector<int>> dims { {{net.output_info | map(attribute='dims') | join(', ') | replace('[', '{') | replace(']', '}') }} };
 
   std::vector<int> data_types_int { {{ net.output_info | map(attribute='data_type') | join(', ') }} };
   std::vector<mace::DataType> data_types({{ net.output_info | length }});
   for (int k = 0; k < {{ net.output_info | length }}; ++k) {
     data_types[k] = static_cast<mace::DataType>(data_types_int[k]);
   }
-  net_def.mutable_output_info().resize({{ net.output_info | length }});
   for (int i = 0; i < {{ net.output_info | length }}; ++i) {
-    net_def.mutable_output_info()[i].set_data_type(data_types[i]);
-    net_def.mutable_output_info()[i].set_dims(dims[i]);
+    net_def.add_output_info();
+    net_def.mutable_output_info(i)->set_data_type(data_types[i]);
+    for (int j = 0; j < (int)dims[i].size(); ++j) {
+      net_def.mutable_output_info(i)->add_dims(dims[i][j]);
+    }
   }
 }
 {% endif %}
 
-void CreateOperators(std::vector<mace::OperatorDef> *ops) {
+void CreateOperators(mace::NetDef *net_def) {
   MACE_LATENCY_LOGGER(1, "Create operators");
 
-  ops->resize({{ net.op|length }});
-
   {% for i in range(net.op|length) %}
-  mace::{{tag}}::CreateOperator{{i}}(&ops->at({{i}}));
+  mace::{{tag}}::CreateOperator{{i}}(net_def->add_op());
   {% endfor %}
 }
 
-void CreateTensors(std::vector<mace::ConstTensor> *tensors,
-                   const unsigned char *model_data) {
+void CreateTensors(mace::NetDef *net_def) {
   MACE_LATENCY_LOGGER(1, "Create tensors");
-  tensors->reserve({{ net.tensors|length }});
 
   {% for tensor in tensors %}
-  mace::{{tag}}::CreateTensor{{tensor.id}}(tensors, model_data);
+  mace::{{tag}}::CreateTensor{{tensor.id}}(net_def->add_tensors());
   {% endfor %}
 }
 
-
 {% if net.mem_arena.mem_block|length != 0 %}
 void CreateMemoryArena(mace::MemoryArena *mem_arena) {
-  std::vector<mace::MemoryBlock> &mem_block = mem_arena->mutable_mem_block();
-  mem_block.reserve({{ net.mem_arena.mem_block|length }});
+  {% for i in range(net.mem_arena.mem_block|length) %}
 
-  {% for mem_blk in net.mem_arena.mem_block %}
-  mem_block.emplace_back(mace::MemoryBlock({{ mem_blk.mem_id }},
-                                           {{mem_blk.x}},
-                                           {{mem_blk.y}}));
-  {% endfor %}
+  mace::MemoryBlock* mem_block{{i}} = mem_arena->add_mem_block();
+  mem_block{{i}}->set_mem_id({{net.mem_arena.mem_block[i].mem_id}});
+  mem_block{{i}}->set_x({{net.mem_arena.mem_block[i].x}});
+  mem_block{{i}}->set_y({{net.mem_arena.mem_block[i].y}});
 
+  {% endfor %}
 }
 {% endif %}
 
+
 }  // namespace
+{% endif %}
 
 namespace {{tag}} {
 
-NetDef CreateNet(const unsigned char *model_data) {
+NetDef CreateNet(const std::vector<unsigned char> &model_pb = {}) {
   MACE_LATENCY_LOGGER(1, "Create net {{ net.name }}");
+
   NetDef net_def;
+
+  {% if model_type == 'source' %}
+  MACE_UNUSED(model_pb);
   net_def.set_name("{{ net.name}}");
   net_def.set_version("{{ net.version }}");
 
+  CreateOperators(&net_def);
+  CreateTensors(&net_def);
+
   {% if net.arg|length != 0 %}
   CreateNetArg(net_def);
   {% endif %}
-
-  CreateOperators(&net_def.mutable_op());
-
-  CreateTensors(&net_def.mutable_tensors(), model_data);
-
   {% if net.mem_arena.mem_block|length != 0 %}
-  CreateMemoryArena(&net_def.mutable_mem_arena());
+  CreateMemoryArena(net_def.mutable_mem_arena());
   {% endif %}
-
   {% if net.output_info | length > 0 %}
   CreateOutputInfo(net_def);
   {% endif %}
 
+  {% else %}
+
+  net_def.ParseFromArray(&model_pb[0], model_pb.size());
+  {% endif %}
+
   return net_def;
 }
 

mace/python/tools/operator.jinja2

@@ -34,11 +34,20 @@ void UpdateOp(mace::OperatorDef *op,
               const std::vector<int> &mem_ids) {
   op->set_name(name);
   op->set_type(type);
-  op->set_input(inputs);
-  op->set_output(outputs);
-  op->set_output_type(output_types);
   op->set_node_id(node_id);
-  op->set_mem_id(mem_ids);
+
+  for (auto input : inputs) {
+    op->add_input(input);
+  }
+  for (auto output : outputs) {
+    op->add_output(output);
+  }
+  for (auto output_type : output_types) {
+    op->add_output_type(output_type);
+  }
+  for (auto mem_id : mem_ids) {
+    op->add_mem_id(mem_id);
+  }
 }
 
 }  // namespace
@@ -68,20 +77,27 @@ void CreateOperator{{i}}(mace::OperatorDef *op) {
   arg->set_s({{ arg.s|tojson }});
   {%- endif %}
 
-  {% if arg.floats|length != 0 %}
-  arg->set_floats({ {{ arg.floats|join(', ') }} });
-  {% endif %}
-  {% if arg.ints|length != 0 %}
-  arg->set_ints({ {{ arg.ints|join(', ') }} });
-  {% endif %}
-  {% if arg.strings|length != 0 %}
-  arg->set_strings({ {{ arg.strings|stringfy() }} });
-  {% endif %}
+  {% for float_value in arg.floats %}
+  arg->add_floats({{ float_value }});
+  {% endfor %}
+  {% for int_value in arg.ints %}
+  arg->add_ints({{ int_value }});
+  {% endfor %}
+  {% for str_value in arg.strings %}
+  arg->add_strings({{ str_value }});
+  {% endfor %}
+
   {% endfor %}
 
   {% for shape in net.op[i].output_shape %}
 	{% if shape.dims | length > 0 %}
-  op->add_output_shape(mace::OutputShape({ {{ shape.dims|join(', ') }} }));
+  mace::OutputShape * output_shape = nullptr;
+  output_shape = op->add_output_shape();
+
+  {% for dim in shape.dims %}
+  output_shape->add_dims({{ dim }});
+  {% endfor %}
+
 	{% endif %}
   {% endfor %}
 
@@ -103,11 +119,14 @@ void CreateOperator{{i}}(mace::OperatorDef *op) {
     std::vector<int> input_node_ids({ {{ net.op[i].node_input | map(attribute='node_id') | join(', ') }} });
     std::vector<int> input_output_ports({ {{ net.op[i].node_input | map(attribute='output_port') | join(', ')}} });
 
+    mace::NodeInput *node_input = nullptr;
     for (size_t i = 0; i < {{ net.op[i].node_input | length }}; ++i) {
-      mace::NodeInput input(input_node_ids[i], input_output_ports[i]);
-      op->add_node_input(input);
+      node_input = op->add_node_input();
+      node_input->set_node_id(input_node_ids[i]);
+      node_input->set_output_port(input_output_ports[i]);
     }
     {% endif %}
+
     {% if net.op[i].out_max_byte_size | length > 0 %}
     std::vector<int> out_max_byte_sizes {{ net.op[i].out_max_byte_size | replace('[', '{') | replace(']', '}') }};
     for (size_t i = 0; i < {{ net.op[i].out_max_byte_size | length }}; ++i) {

mace/python/tools/source_converter_lib.py

@@ -137,7 +137,7 @@ def stringfy(value):
 
 def convert_to_source(net_def, model_checksum, weight_checksum, template_dir,
                       obfuscate, model_tag, output, runtime, embed_model_data,
-                      winograd_conv):
+                      winograd_conv, model_load_type):
     if obfuscate:
         obfuscate_name(net_def)
     else:
@@ -163,17 +163,26 @@ def convert_to_source(net_def, model_checksum, weight_checksum, template_dir,
             padding = 4 - offset % 4
             model_data.extend(bytearray([0] * padding))
             offset += padding
-        source = j2_env.get_template(template_name).render(
-            tensor_info=tensor_info,
-            tensor=t,
-            tag=model_tag,
-            offset=offset,
-        )
+
+        if t.data_type == mace_pb2.DT_FLOAT:
+            t.data_size = len(t.float_data)
+        elif t.data_type == mace_pb2.DT_INT32:
+            t.data_size = len(t.int32_data)
+        elif t.data_type == mace_pb2.DT_UINT8:
+            t.data_size = len(t.int32_data)
+        t.offset = offset
+
+        if model_load_type == 'source':
+            source = j2_env.get_template(template_name).render(
+                tensor_info=tensor_info,
+                tensor=t,
+                tag=model_tag,
+            )
+            with open(output_dir + 'tensor' + str(counter) + '.cc', "wb") as f:
+                f.write(source)
+        counter += 1
         model_data.extend(tensor_info.data)
         offset += len(tensor_info.data)
-        with open(output_dir + 'tensor' + str(counter) + '.cc', "wb") as f:
-            f.write(source)
-        counter += 1
 
     # generate tensor data
     template_name = 'tensor_data.jinja2'
@@ -188,21 +197,22 @@ def convert_to_source(net_def, model_checksum, weight_checksum, template_dir,
         with open(output_dir + model_tag + '.data', "wb") as f:
             f.write(bytearray(model_data))
 
-    # generate op source files
-    template_name = 'operator.jinja2'
-    counter = 0
-    op_size = len(net_def.op)
-    for start in range(0, op_size, 10):
-        source = j2_env.get_template(template_name).render(
-            start=start,
-            end=min(start + 10, op_size),
-            net=net_def,
-            tag=model_tag,
-            runtime=runtime,
-        )
-        with open(output_dir + 'op' + str(counter) + '.cc', "wb") as f:
-            f.write(source)
-        counter += 1
+    if model_load_type == 'source':
+        # generate op source files
+        template_name = 'operator.jinja2'
+        counter = 0
+        op_size = len(net_def.op)
+        for start in range(0, op_size, 10):
+            source = j2_env.get_template(template_name).render(
+                start=start,
+                end=min(start + 10, op_size),
+                net=net_def,
+                tag=model_tag,
+                runtime=runtime,
+            )
+            with open(output_dir + 'op' + str(counter) + '.cc', "wb") as f:
+                f.write(source)
+            counter += 1
 
     # generate model source files
     build_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
@@ -223,7 +233,8 @@ def convert_to_source(net_def, model_checksum, weight_checksum, template_dir,
         embed_model_data=embed_model_data,
         winograd_conv=winograd_conv,
         checksum=checksum,
-        build_time=build_time)
+        build_time=build_time,
+        model_type=model_load_type)
     with open(output, "wb") as f:
         f.write(source)
 
@@ -232,3 +243,15 @@ def convert_to_source(net_def, model_checksum, weight_checksum, template_dir,
     source = j2_env.get_template(template_name).render(tag=model_tag, )
     with open(output_dir + model_tag + '.h', "wb") as f:
         f.write(source)
+
+    for t in net_def.tensors:
+        if t.data_type == mace_pb2.DT_FLOAT:
+            del t.float_data[:]
+            if runtime == 'gpu':
+                t.data_type = mace_pb2.DT_HALF
+            else:
+                t.data_type = mace_pb2.DT_FLOAT
+        elif t.data_type == mace_pb2.DT_INT32:
+            del t.int32_data[:]
+        elif t.data_type == mace_pb2.DT_UINT8:
+            del t.int32_data[:]

mace/python/tools/tensor_source.jinja2

@@ -24,12 +24,16 @@
 namespace mace {
 namespace {{tag}} {
 
-void CreateTensor{{tensor_info.id}}(std::vector<mace::ConstTensor> *tensors,
-                               const unsigned char *model_data) {
+void CreateTensor{{tensor_info.id}}(mace::ConstTensor *const_tensor) {
   MACE_LATENCY_LOGGER(2, "Create tensor {{ tensor.name }}");
-  tensors->emplace_back(mace::ConstTensor(
-      {{ tensor.name|tojson }}, model_data + {{ offset }},
-      { {{ tensor.dims|join(', ') }} }, {{ tensor_info.data_type }}, {{ tensor.node_id }}));
+  const_tensor->set_name({{ tensor.name|tojson }});
+  const_tensor->set_offset({{ tensor.offset }});
+  const_tensor->set_data_size({{ tensor.data_size }});
+  {% for dim in tensor.dims %}
+  const_tensor->add_dims({{ dim }});
+  {% endfor %}
+  const_tensor->set_data_type(static_cast<DataType>({{ tensor_info.data_type }}));
+  const_tensor->set_node_id({{ tensor.node_id }});
 }
 
 }  // namespace {{tag}}

mace/test/BUILD

@@ -13,7 +13,8 @@ cc_test(
     name = "mace_api_test",
     testonly = 1,
     srcs = ["mace_api_test.cc"],
-    copts = if_openmp_enabled(["-fopenmp"]) +
+    copts = ["-Werror"] +
+      if_openmp_enabled(["-fopenmp"]) +
       if_neon_enabled(["-DMACE_ENABLE_NEON"]) +
       if_android_armv7(["-mfpu=neon"]) +
       if_android_armv7(["-mfloat-abi=softfp"]) +
@@ -33,7 +34,8 @@ cc_test(
     name = "mace_api_mt_test",
     testonly = 1,
     srcs = ["mace_api_mt_test.cc"],
-    copts = if_openmp_enabled(["-fopenmp"]) +
+    copts = ["-Werror"] +
+      if_openmp_enabled(["-fopenmp"]) +
       if_neon_enabled(["-DMACE_ENABLE_NEON"]) +
       if_android_armv7(["-mfpu=neon"]) +
       if_android_armv7(["-mfloat-abi=softfp"]) +

mace/test/mace_api_mt_test.cc

@@ -69,8 +69,9 @@ void BufferToImage(const std::string &input_name,
       .AddIntArg("mode", mode)
       .Finalize(&operator_def);
 
-  operator_def.set_mem_id(mem_ids);
-
+  for (auto mem_id : mem_ids) {
+    operator_def.add_mem_id(mem_id);
+  }
   net_def->add_op()->CopyFrom(operator_def);
 }
 
@@ -112,7 +113,9 @@ void Conv3x3(const std::string &input_name,
       .AddIntArg("device", static_cast<int>(device_type))
       .Finalize(&operator_def);
 
-  operator_def.set_mem_id(mem_ids);
+  for (auto mem_id : mem_ids) {
+    operator_def.add_mem_id(mem_id);
+  }
   net_def->add_op()->CopyFrom(operator_def);
 }
 
@@ -136,20 +139,24 @@ void Relu(const std::string &input_name,
 template <typename T>
 void AddTensor(const std::string &name,
                const std::vector<int64_t> &shape,
-               T *data,
+               const int offset,
+               const int data_size,
                NetDef *net_def) {
-  ConstTensor tensor(name,
-                     reinterpret_cast<unsigned char *>(data),
-                     shape,
-                     DataTypeToEnum<T>::value);
-
-  net_def->mutable_tensors().push_back(tensor);
+  ConstTensor *tensor_ptr = net_def->add_tensors();
+  tensor_ptr->set_name(name);
+  for (auto dim : shape) {
+    tensor_ptr->add_dims(dim);
+  }
+  tensor_ptr->set_offset(offset);
+  tensor_ptr->set_data_size(data_size);
+  tensor_ptr->set_data_type(DataTypeToEnum<T>::value);
 }
 
 template <DeviceType D, typename T>
 void CheckOutputs(const NetDef &net_def,
                   const std::map<std::string, mace::MaceTensor> &inputs,
-                  const std::map<std::string, mace::MaceTensor> &outputs) {
+                  const std::map<std::string, mace::MaceTensor> &outputs,
+                  const std::vector<T> &tensor_data) {
   ops::test::OpsTestNet net;
   for (auto input : inputs) {
     auto input_shape = input.second.shape();
@@ -166,13 +173,14 @@ void CheckOutputs(const NetDef &net_def,
   }
   auto tensors = net_def.tensors();
   for (auto tensor : tensors) {
-    auto shape = tensor.dims();
+    std::vector<index_t> shape = {tensor.dims().begin(), tensor.dims().end()};
     const int64_t data_size = std::accumulate(shape.begin(),
                                               shape.end(), 1,
                                               std::multiplies<int64_t>());
     std::vector<T> data(data_size);
-    memcpy(data.data(), reinterpret_cast<const T *>(tensor.data()),
-           data_size * sizeof(T));
+    memcpy(data.data(),
+           reinterpret_cast<const T *>(tensor_data.data()) + tensor.offset(),
+           tensor.data_size() * sizeof(T));
     net.AddInputFromArray<D, T>(tensor.name(), shape, data);
   }
   net.RunNet(net_def, D);
@@ -218,8 +226,11 @@ std::map<std::string, int> AddMemoryOptimization(
   }
   size_t input_size = input_names.size();
   for (size_t i = 0; i < input_size; ++i) {
-    net_def->mutable_mem_arena().mutable_mem_block().push_back(
-        MemoryBlock(mem_id, in_mem_block_x, in_mem_block_y));
+    MemoryArena *mem_arena_ptr = net_def->mutable_mem_arena();
+    MemoryBlock *mem_blk_ptr = mem_arena_ptr->add_mem_block();
+    mem_blk_ptr->set_mem_id(mem_id);
+    mem_blk_ptr->set_x(in_mem_block_x);
+    mem_blk_ptr->set_y(in_mem_block_y);
     res[input_names[i]] = mem_id;
     mem_id++;
   }
@@ -236,8 +247,11 @@ std::map<std::string, int> AddMemoryOptimization(
   }
   size_t output_size = output_names.size();
   for (size_t i = 0; i < output_size; ++i) {
-    net_def->mutable_mem_arena().mutable_mem_block().push_back(
-        MemoryBlock(mem_id, out_mem_block_x, out_mem_block_y));
+    MemoryArena *mem_arena_ptr = net_def->mutable_mem_arena();
+    MemoryBlock *mem_blk_ptr = mem_arena_ptr->add_mem_block();
+    mem_blk_ptr->set_mem_id(mem_id);
+    mem_blk_ptr->set_x(out_mem_block_x);
+    mem_blk_ptr->set_y(out_mem_block_y);
     res[output_names[i]] = mem_id;
     mem_id++;
   }
@@ -270,7 +284,7 @@ void MaceRunFunc(const int in_out_size) {
 
   std::vector<half> data;
   ops::test::GenerateRandomRealTypeData<half>(filter_shape, &data);
-  AddTensor<half>(filter_tensor_name, filter_shape, data.data(), &net_def);
+  AddTensor<half>(filter_tensor_name, filter_shape, 0, data.size(), &net_def);
 
   for (size_t i = 0; i < input_names.size(); ++i) {
     std::string input_name = MakeString("mace_input_node_",
@@ -304,7 +318,8 @@ void MaceRunFunc(const int in_out_size) {
       new FileStorageFactory(file_path));
   mace::SetKVStorageFactory(storage_factory);
 
-  MaceEngine engine(&net_def, device, input_names, output_names);
+  MaceEngine engine(&net_def, device, input_names, output_names,
+                    reinterpret_cast<unsigned char *>(data.data()));
 
   std::map<std::string, mace::MaceTensor> inputs;
   std::map<std::string, mace::MaceTensor> outputs;
@@ -320,7 +335,7 @@ void MaceRunFunc(const int in_out_size) {
     }
   }
 
-  CheckOutputs<DeviceType::GPU, half>(net_def, inputs, outputs);
+  CheckOutputs<DeviceType::GPU, half>(net_def, inputs, outputs, data);
 }
 
 }  // namespace

mace/test/mace_api_test.cc

@@ -79,7 +79,9 @@ void BufferToImage(const std::string &input_name,
       .AddIntArg("mode", mode)
       .Finalize(&operator_def);
 
-  operator_def.set_mem_id(mem_ids);
+  for (auto mem_id : mem_ids) {
+    operator_def.add_mem_id(mem_id);
+  }
 
   net_def->add_op()->CopyFrom(operator_def);
 }
@@ -122,7 +124,9 @@ void Conv3x3(const std::string &input_name,
       .AddIntArg("device", static_cast<int>(device_type))
       .Finalize(&operator_def);
 
-  operator_def.set_mem_id(mem_ids);
+  for (auto mem_id : mem_ids) {
+    operator_def.add_mem_id(mem_id);
+  }
   net_def->add_op()->CopyFrom(operator_def);
 }
 
@@ -146,20 +150,24 @@ void Relu(const std::string &input_name,
 template <typename T>
 void AddTensor(const std::string &name,
                const std::vector<int64_t> &shape,
-               T *data,
+               const int offset,
+               const int data_size,
                NetDef *net_def) {
-  ConstTensor tensor(name,
-                     reinterpret_cast<unsigned char *>(data),
-                     shape,
-                     DataTypeToEnum<T>::value);
-
-  net_def->mutable_tensors().push_back(tensor);
+  ConstTensor *tensor_ptr = net_def->add_tensors();
+  tensor_ptr->set_name(name);
+  for (auto dim : shape) {
+    tensor_ptr->add_dims(dim);
+  }
+  tensor_ptr->set_offset(offset);
+  tensor_ptr->set_data_size(data_size);
+  tensor_ptr->set_data_type(DataTypeToEnum<T>::value);
 }
 
 template <DeviceType D, typename T>
 void CheckOutputs(const NetDef &net_def,
                   const std::map<std::string, mace::MaceTensor> &inputs,
-                  const std::map<std::string, mace::MaceTensor> &outputs) {
+                  const std::map<std::string, mace::MaceTensor> &outputs,
+                  const std::vector<T> &tensor_data) {
   ops::test::OpsTestNet net;
   for (auto input : inputs) {
     auto input_shape = input.second.shape();
@@ -176,13 +184,14 @@ void CheckOutputs(const NetDef &net_def,
   }
   auto tensors = net_def.tensors();
   for (auto tensor : tensors) {
-    auto shape = tensor.dims();
+    std::vector<index_t> shape = {tensor.dims().begin(), tensor.dims().end()};
     const int64_t data_size = std::accumulate(shape.begin(),
                                               shape.end(), 1,
                                               std::multiplies<int64_t>());
     std::vector<T> data(data_size);
-    memcpy(data.data(), reinterpret_cast<const T *>(tensor.data()),
-           data_size * sizeof(T));
+    memcpy(data.data(),
+           reinterpret_cast<const T *>(tensor_data.data()) + tensor.offset(),
+           tensor.data_size() * sizeof(T));
     net.AddInputFromArray<D, T>(tensor.name(), shape, data);
   }
   net.RunNet(net_def, D);
@@ -228,8 +237,11 @@ std::map<std::string, int> AddMemoryOptimization(
   }
   size_t input_size = input_names.size();
   for (size_t i = 0; i < input_size; ++i) {
-    net_def->mutable_mem_arena().mutable_mem_block().push_back(
-        MemoryBlock(mem_id, in_mem_block_x, in_mem_block_y));
+    MemoryArena *mem_arena_ptr = net_def->mutable_mem_arena();
+    MemoryBlock *mem_blk_ptr = mem_arena_ptr->add_mem_block();
+    mem_blk_ptr->set_mem_id(mem_id);
+    mem_blk_ptr->set_x(in_mem_block_x);
+    mem_blk_ptr->set_y(in_mem_block_y);
     res[input_names[i]] = mem_id;
     mem_id++;
   }
@@ -246,8 +258,11 @@ std::map<std::string, int> AddMemoryOptimization(
   }
   size_t output_size = output_names.size();
   for (size_t i = 0; i < output_size; ++i) {
-    net_def->mutable_mem_arena().mutable_mem_block().push_back(
-        MemoryBlock(mem_id, out_mem_block_x, out_mem_block_y));
+    MemoryArena *mem_arena_ptr = net_def->mutable_mem_arena();
+    MemoryBlock *mem_blk_ptr = mem_arena_ptr->add_mem_block();
+    mem_blk_ptr->set_mem_id(mem_id);
+    mem_blk_ptr->set_x(out_mem_block_x);
+    mem_blk_ptr->set_y(out_mem_block_y);
     res[output_names[i]] = mem_id;
     mem_id++;
   }
@@ -280,7 +295,7 @@ void MaceRun(const int in_out_size,
 
   std::vector<T> data;
   ops::test::GenerateRandomRealTypeData<T>(filter_shape, &data);
-  AddTensor<T>(filter_tensor_name, filter_shape, data.data(), &net_def);
+  AddTensor<T>(filter_tensor_name, filter_shape, 0, data.size(), &net_def);
 
   for (size_t i = 0; i < input_names.size(); ++i) {
     std::string input_name = MakeString("mace_input_node_",
@@ -308,7 +323,8 @@ void MaceRun(const int in_out_size,
                          &net_def);
   }
 
-  MaceEngine engine(&net_def, device, input_names, output_names);
+  MaceEngine engine(&net_def, device, input_names, output_names,
+                    reinterpret_cast<unsigned char *>(data.data()));
 
   std::map<std::string, mace::MaceTensor> inputs;
   std::map<std::string, mace::MaceTensor> outputs;
@@ -324,7 +340,7 @@ void MaceRun(const int in_out_size,
     }
   }
 
-  CheckOutputs<DeviceType::GPU, T>(net_def, inputs, outputs);
+  CheckOutputs<DeviceType::GPU, T>(net_def, inputs, outputs, data);
 }
 
 }  // namespace

mace/tools/validation/mace_run.cc

@@ -28,7 +28,6 @@
 #include <stdint.h>
 #include <cstdio>
 #include <cstdlib>
-#include <fstream>
 #include <iostream>
 #include <numeric>
 
@@ -37,6 +36,7 @@
 #include "mace/public/mace_runtime.h"
 #include "mace/utils/env_time.h"
 #include "mace/utils/logging.h"
+#include "mace/utils/utils.h"
 
 #ifdef MACE_ENABLE_OPENCL
 #include "mace/core/runtime/opencl/opencl_runtime.h"
@@ -186,7 +186,10 @@ DEFINE_string(output_file,
               "output file name | output file prefix for multiple outputs");
 DEFINE_string(model_data_file,
               "",
-              "model data file name, used when EMBED_MODEL_DATA set to 0");
+              "model data file name, used when EMBED_MODEL_DATA set to 0 or 2");
+DEFINE_string(model_file,
+              "",
+              "model file name, used when load mace model in pb");
 DEFINE_string(device, "GPU", "CPU/GPU/HEXAGON");
 DEFINE_int32(round, 1, "round");
 DEFINE_int32(restart_round, 1, "restart round");
@@ -228,18 +231,25 @@ bool RunModel(const std::string &model_name,
   MaceStatus create_engine_status;
   // Create Engine
   int64_t t0 = NowMicros();
-  if (FLAGS_model_data_file.empty()) {
+  const char *model_data_file_ptr =
+    FLAGS_model_data_file.empty() ? nullptr : FLAGS_model_data_file.c_str();
+  if (FLAGS_model_file != "") {
+    std::vector<unsigned char> model_pb_data;
+    if (!mace::ReadBinaryFile(&model_pb_data, FLAGS_model_file)) {
+      LOG(FATAL) << "Failed to read file: " << FLAGS_model_file;
+    }
     create_engine_status =
         CreateMaceEngine(model_name.c_str(),
-                         nullptr,
+                         model_data_file_ptr,
                          input_names,
                          output_names,
                          device_type,
-                         &engine);
+                         &engine,
+                         model_pb_data);
   } else {
     create_engine_status =
         CreateMaceEngine(model_name.c_str(),
-                         FLAGS_model_data_file.c_str(),
+                         model_data_file_ptr,
                          input_names,
                          output_names,
                          device_type,
@@ -358,6 +368,7 @@ int Main(int argc, char **argv) {
   LOG(INFO) << "input_file: " << FLAGS_input_file;
   LOG(INFO) << "output_file: " << FLAGS_output_file;
   LOG(INFO) << "model_data_file: " << FLAGS_model_data_file;
+  LOG(INFO) << "model_file: " << FLAGS_model_file;
   LOG(INFO) << "device: " << FLAGS_device;
   LOG(INFO) << "round: " << FLAGS_round;
   LOG(INFO) << "restart_round: " << FLAGS_restart_round;

mace/utils/BUILD

@@ -28,8 +28,10 @@ cc_library(
     linkopts = if_android([
         "-llog",
     ]),
+    copts = ["-Werror"],
     deps = [
         "//mace/public",
+        "//mace/proto:mace_cc",
     ],
 )
 
@@ -38,6 +40,7 @@ cc_library(
     srcs = [
         "tuner_development.cc",
     ],
+    copts = ["-Werror"],
     deps = [
         ":utils",
     ],
@@ -48,6 +51,7 @@ cc_library(
     srcs = [
         "tuner_production.cc",
     ],
+    copts = ["-Werror"],
     deps = [
         ":utils",
         "//mace/codegen:generated_tuning_params",
@@ -60,6 +64,7 @@ cc_test(
     srcs = [
         "tuner_test.cc",
     ],
+    copts = ["-Werror"],
     linkopts = if_android([
         "-pie",
         "-lm",  # Required by unordered_map

mace/utils/logging.h

@@ -21,10 +21,11 @@
 #include <vector>
 #include <utility>
 
+#include "mace/proto/mace.pb.h"
 #include "mace/public/mace.h"
-#include "mace/public/mace_types.h"
 #include "mace/utils/env_time.h"
 #include "mace/utils/string_util.h"
+#include "mace/utils/utils.h"
 
 #undef ERROR
 

mace/utils/utils.h

@@ -15,12 +15,22 @@
 #ifndef MACE_UTILS_UTILS_H_
 #define MACE_UTILS_UTILS_H_
 
+#include <fstream>
 #include <sstream>
 #include <string>
 #include <utility>
 #include <vector>
 
 namespace mace {
+
+// Disable the copy and assignment operator for a class.
+#ifndef DISABLE_COPY_AND_ASSIGN
+#define DISABLE_COPY_AND_ASSIGN(classname) \
+ private:                                  \
+  classname(const classname &) = delete;   \
+  classname &operator=(const classname &) = delete
+#endif
+
 template <typename Integer>
 Integer RoundUp(Integer i, Integer factor) {
   return (i + factor - 1) / factor * factor;
@@ -121,5 +131,26 @@ inline std::vector<std::string> Split(const std::string &str, char delims) {
   return result;
 }
 
+inline bool ReadBinaryFile(std::vector<unsigned char> *data,
+                    const std::string &filename) {
+  std::ifstream ifs(filename, std::ios::in | std::ios::binary);
+  if (!ifs.is_open()) {
+    return false;
+  }
+  ifs.seekg(0, ifs.end);
+  size_t length = ifs.tellg();
+  ifs.seekg(0, ifs.beg);
+
+  data->reserve(length);
+  data->insert(data->begin(), std::istreambuf_iterator<char>(ifs),
+               std::istreambuf_iterator<char>());
+  if (ifs.fail()) {
+    return false;
+  }
+  ifs.close();
+
+  return true;
+}
+
 }  // namespace mace
 #endif  // MACE_UTILS_UTILS_H_

tools/mace_tools.py

@@ -159,6 +159,7 @@ def tuning_run(target_abi,
                output_nodes,
                input_shapes,
                output_shapes,
+               mace_model_dir,
                model_name,
                device_type,
                running_round,
@@ -181,6 +182,7 @@ def tuning_run(target_abi,
         output_nodes,
         input_shapes,
         output_shapes,
+        mace_model_dir,
         model_name,
         device_type,
         running_round,
@@ -206,9 +208,9 @@ def tuning_run(target_abi,
 def build_mace_run_prod(hexagon_mode, runtime, target_abi,
                         serialno, vlog_level, embed_model_data,
                         model_output_dir, input_nodes, output_nodes,
-                        input_shapes, output_shapes, model_name, device_type,
-                        running_round, restart_round, tuning,
-                        limit_opencl_kernel_time, phone_data_dir,
+                        input_shapes, output_shapes, mace_model_dir,
+                        model_name, device_type, running_round, restart_round,
+                        tuning, limit_opencl_kernel_time, phone_data_dir,
                         enable_openmp):
     mace_run_target = "//mace/tools/validation:mace_run"
     strip = "always"
@@ -232,8 +234,8 @@ def build_mace_run_prod(hexagon_mode, runtime, target_abi,
         device_type = parse_device_type("gpu")
         tuning_run(target_abi, serialno, vlog_level, embed_model_data,
                    model_output_dir, input_nodes, output_nodes, input_shapes,
-                   output_shapes, model_name, device_type, running_round=0,
-                   restart_round=1, out_of_range_check=False,
+                   output_shapes, mace_model_dir, model_name, device_type,
+                   running_round=0, restart_round=1, out_of_range_check=False,
                    phone_data_dir=phone_data_dir, tuning=tuning,
                    limit_opencl_kernel_time=limit_opencl_kernel_time)
 
@@ -271,6 +273,7 @@ def merge_libs_and_tuning_results(target_soc,
                                   project_name,
                                   output_dir,
                                   model_output_dirs,
+                                  mace_model_dirs_kv,
                                   hexagon_mode,
                                   embed_model_data):
     gen_opencl_and_tuning_code(
@@ -282,6 +285,7 @@ def merge_libs_and_tuning_results(target_soc,
                            project_name,
                            output_dir,
                            model_output_dirs,
+                           mace_model_dirs_kv,
                            hexagon_mode,
                            embed_model_data)
 
@@ -523,13 +527,21 @@ def parse_args():
         type=str,
         default="cpu",
         help="validation runtime.")
+    parser.add_argument(
+        "--model_load_type",
+        type=str,
+        default="source",
+        help="[source|pb] Load models in generated `source` code" +
+                "or `pb` file.")
     return parser.parse_known_args()
 
 
 def process_models(project_name, configs, embed_model_data, vlog_level,
-                   target_abi, phone_data_dir, target_soc="", serialno=""):
+                   target_abi, phone_data_dir, model_load_type,
+                   target_soc="", serialno=""):
     hexagon_mode = get_hexagon_mode(configs)
     model_output_dirs = []
+    mace_model_dirs_kv = {}
 
     for model_name in configs["models"]:
         print '===================', model_name, '==================='
@@ -545,6 +557,11 @@ def process_models(project_name, configs, embed_model_data, vlog_level,
         model_output_base_dir = "%s/%s/%s/%s/%s" % (
             FLAGS.output_dir, project_name, "build",
             model_name, model_path_digest)
+        if model_load_type == "pb":
+            mace_model_dir = model_output_base_dir
+            mace_model_dirs_kv[model_name] = mace_model_dir
+        else:
+            mace_model_dir = ""
 
         if target_abi == "host":
             model_output_dir = "%s/%s" % (model_output_base_dir, target_abi)
@@ -587,6 +604,7 @@ def process_models(project_name, configs, embed_model_data, vlog_level,
                                 model_config["output_nodes"],
                                 model_config["input_shapes"],
                                 model_config["output_shapes"],
+                                mace_model_dir,
                                 model_name,
                                 model_device_type,
                                 FLAGS.round,
@@ -612,6 +630,7 @@ def process_models(project_name, configs, embed_model_data, vlog_level,
                        model_config["output_nodes"],
                        model_config["input_shapes"],
                        model_config["output_shapes"],
+                       mace_model_dir,
                        model_name,
                        run_device_type,
                        FLAGS.round,
@@ -631,6 +650,7 @@ def process_models(project_name, configs, embed_model_data, vlog_level,
                                         vlog_level,
                                         embed_model_data,
                                         model_output_dir,
+                                        mace_model_dir,
                                         model_config["input_nodes"],
                                         model_config["output_nodes"],
                                         model_config["input_shapes"],
@@ -667,6 +687,7 @@ def process_models(project_name, configs, embed_model_data, vlog_level,
             project_name,
             FLAGS.output_dir,
             model_output_dirs,
+            mace_model_dirs_kv,
             hexagon_mode,
             embed_model_data)
 
@@ -778,7 +799,9 @@ def main(unused_args):
                 model_config["dsp_mode"],
                 embed_model_data,
                 model_config["fast_conv"],
-                model_config["obfuscate"])
+                model_config["obfuscate"],
+                model_output_base_dir,
+                FLAGS.model_load_type)
 
     for target_abi in configs["target_abis"]:
         for target_soc in target_socs:
@@ -796,12 +819,14 @@ def main(unused_args):
                               props["ro.product.model"]))
                         process_models(project_name, configs, embed_model_data,
                                        vlog_level, target_abi, phone_data_dir,
-                                       target_soc, serialno)
+                                       FLAGS.model_load_type, target_soc,
+                                       serialno)
             else:
                 print("====================================================")
                 print("Run on host")
                 process_models(project_name, configs, embed_model_data,
-                               vlog_level, target_abi, phone_data_dir)
+                               vlog_level, target_abi, phone_data_dir,
+                               FLAGS.model_load_type)
 
     if FLAGS.mode == "build" or FLAGS.mode == "all":
         sh_commands.packaging_lib(FLAGS.output_dir, project_name)

tools/sh_commands.py

@@ -287,7 +287,6 @@ def bazel_build(target,
             target,
             "--copt=-std=c++11",
             "--copt=-D_GLIBCXX_USE_C99_MATH_TR1",
-            "--copt=-Werror",
             "--copt=-Wextra",
             "--copt=-Wno-missing-field-initializers",
             "--copt=-O3",
@@ -316,7 +315,6 @@ def bazel_build(target,
             "--cpu=%s" % abi,
             "--copt=-std=c++11",
             "--copt=-D_GLIBCXX_USE_C99_MATH_TR1",
-            "--copt=-Werror",
             "--copt=-Wextra",
             "--copt=-Wno-missing-field-initializers",
             "--copt=-DMACE_OBFUSCATE_LITERALS",
@@ -375,7 +373,8 @@ def gen_encrypted_opencl_source(codegen_path="mace/codegen"):
                            "mace/codegen/opencl/opencl_encrypt_program.cc")
 
 
-def gen_mace_engine_factory_source(model_tags, codegen_path="mace/codegen"):
+def gen_mace_engine_factory_source(model_tags,
+                                   codegen_path="mace/codegen"):
     print("* Genearte mace engine creator source")
     codegen_tools_dir = "%s/engine" % codegen_path
     sh.rm("-rf", codegen_tools_dir)
@@ -471,12 +470,16 @@ def gen_model_code(model_codegen_dir,
                    dsp_mode,
                    embed_model_data,
                    fast_conv,
-                   obfuscate):
+                   obfuscate,
+                   model_output_dir,
+                   model_load_type):
     print("* Genearte model code")
     bazel_build_common("//mace/python/tools:converter")
+
     if os.path.exists(model_codegen_dir):
         sh.rm("-rf", model_codegen_dir)
     sh.mkdir("-p", model_codegen_dir)
+
     stdout_buff = []
     process_output = make_output_processor(stdout_buff)
     p = sh.python("bazel-bin/mace/python/tools/converter",
@@ -485,11 +488,9 @@ def gen_model_code(model_codegen_dir,
                   "--model_file=%s" % model_file_path,
                   "--weight_file=%s" % weight_file_path,
                   "--model_checksum=%s" % model_sha256_checksum,
-                  "--output=%s" % model_codegen_dir + "/model.cc",
                   "--input_node=%s" % input_nodes,
                   "--output_node=%s" % output_nodes,
                   "--runtime=%s" % runtime,
-                  "--output_type=source",
                   "--template=%s" % "mace/python/tools",
                   "--model_tag=%s" % model_tag,
                   "--input_shape=%s" % input_shapes,
@@ -497,6 +498,9 @@ def gen_model_code(model_codegen_dir,
                   "--embed_model_data=%s" % embed_model_data,
                   "--winograd=%s" % fast_conv,
                   "--obfuscate=%s" % obfuscate,
+                  "--codegen_output=%s/model.cc" % model_codegen_dir,
+                  "--pb_output=%s/%s.pb" % (model_output_dir, model_tag),
+                  "--model_load_type=%s" % model_load_type,
                   _out=process_output,
                   _bg=True,
                   _err_to_out=True)
@@ -577,6 +581,7 @@ def tuning_run(abi,
                output_nodes,
                input_shapes,
                output_shapes,
+               mace_model_dir,
                model_tag,
                device_type,
                running_round,
@@ -601,6 +606,10 @@ def tuning_run(abi,
            str(out_of_range_check), omp_num_threads, cpu_affinity_policy,
            gpu_perf_hint, gpu_priority_hint))
     if abi == "host":
+        if mace_model_dir:
+            mace_model_path = "%s/%s.pb" % (mace_model_dir, model_tag)
+        else:
+            mace_model_path = ""
         p = subprocess.Popen(
             [
                 "env",
@@ -621,6 +630,7 @@ def tuning_run(abi,
                 "--cpu_affinity_policy=%s" % cpu_affinity_policy,
                 "--gpu_perf_hint=%s" % gpu_perf_hint,
                 "--gpu_priority_hint=%s" % gpu_priority_hint,
+                "--model_file=%s" % mace_model_path,
             ],
             stderr=subprocess.PIPE,
             stdout=subprocess.PIPE)
@@ -647,6 +657,14 @@ def tuning_run(abi,
         adb_push("mace/third_party/nnlib/libhexagon_controller.so",
                  phone_data_dir, serialno)
 
+        if mace_model_dir:
+            mace_model_path = "%s/%s.pb" % (phone_data_dir, model_tag)
+            adb_push("%s/%s.pb" % (mace_model_dir, model_tag),
+                     mace_model_path,
+                     serialno)
+        else:
+            mace_model_path = ""
+
         stdout_buff = []
         process_output = make_output_processor(stdout_buff)
         adb_cmd = [
@@ -681,6 +699,7 @@ def tuning_run(abi,
             "--cpu_affinity_policy=%s" % cpu_affinity_policy,
             "--gpu_perf_hint=%s" % gpu_perf_hint,
             "--gpu_priority_hint=%s" % gpu_priority_hint,
+            "--model_file=%s" % mace_model_path,
         ])
         adb_cmd = ' '.join(adb_cmd)
         p = sh.adb(
@@ -832,6 +851,7 @@ def merge_libs(target_soc,
                project_name,
                libmace_output_dir,
                model_output_dirs,
+               mace_model_dirs_kv,
                hexagon_mode,
                embed_model_data):
     print("* Merge mace lib")
@@ -903,6 +923,10 @@ def merge_libs(target_soc,
                   model_data_dir)
         sh.cp("-f", glob.glob("%s/*.h" % model_output_dir), model_header_dir)
 
+    for model_name in mace_model_dirs_kv:
+        sh.cp("-f", "%s/%s.pb" % (mace_model_dirs_kv[model_name], model_name),
+              model_data_dir)
+
     mri_stream += "save\n"
     mri_stream += "end\n"
 
@@ -969,6 +993,7 @@ def benchmark_model(abi,
                     vlog_level,
                     embed_model_data,
                     model_output_dir,
+                    mace_model_dir,
                     input_nodes,
                     output_nodes,
                     input_shapes,
@@ -986,6 +1011,10 @@ def benchmark_model(abi,
     stdout_buff = []
     process_output = make_output_processor(stdout_buff)
     if abi == "host":
+        if mace_model_dir:
+            mace_model_path = "%s/%s.pb" % (mace_model_dir, model_tag)
+        else:
+            mace_model_path = ""
         p = subprocess.Popen(
             [
                 "env",
@@ -1003,6 +1032,7 @@ def benchmark_model(abi,
                 "--cpu_affinity_policy=%s" % cpu_affinity_policy,
                 "--gpu_perf_hint=%s" % gpu_perf_hint,
                 "--gpu_priority_hint=%s" % gpu_priority_hint,
+                "--model_file=%s" % mace_model_path,
             ])
         p.wait()
     else:
@@ -1020,6 +1050,14 @@ def benchmark_model(abi,
         if not embed_model_data:
             adb_push("%s/%s.data" % (model_output_dir, model_tag),
                      phone_data_dir, serialno)
+        if mace_model_dir:
+            mace_model_path = "%s/%s.pb" % (phone_data_dir, model_tag)
+            adb_push("%s/%s.pb" % (mace_model_dir, model_tag),
+                     mace_model_path,
+                     serialno)
+        else:
+            mace_model_path = ""
+
         p = sh.adb(
             "-s",
             serialno,
@@ -1043,6 +1081,7 @@ def benchmark_model(abi,
             "--cpu_affinity_policy=%s" % cpu_affinity_policy,
             "--gpu_perf_hint=%s" % gpu_perf_hint,
             "--gpu_priority_hint=%s" % gpu_priority_hint,
+            "--model_file=%s" % mace_model_path,
             _out=process_output,
             _bg=True,
             _err_to_out=True)