Merge branch 'master' into r0.16

Author: avijit-nervana

CMakeLists.txt

@@ -234,7 +234,7 @@ if (NOT USE_PRE_BUILT_NGRAPH)
     ExternalProject_Add(
         ext_ngraph
         GIT_REPOSITORY https://github.com/NervanaSystems/ngraph
-        GIT_TAG v0.22.0
+        GIT_TAG v0.23.0-rc.2
         CMAKE_ARGS 
             -DNGRAPH_DISTRIBUTED_ENABLE=${NGRAPH_DISTRIBUTED_ENABLE}
             -DNGRAPH_INSTALL_PREFIX=${NGRAPH_ARTIFACTS_DIR}

README.md

@@ -69,7 +69,7 @@ The installation prerequisites are the same as described in the TensorFlow
 
         git clone https://github.com/tensorflow/ngraph-bridge.git
         cd ngraph-bridge
-        git checkout v0.16.0-rc0
+        git checkout v0.16.0-rc1
 
 
 2. Run the following Python script to build TensorFlow, nGraph and the bridge. Please use Python 3.5:

bazel/WORKSPACE

@@ -25,11 +25,11 @@ tf_configure(
 http_archive(
     name = "ngraph",
     build_file = "//:bazel/ngraph.BUILD",
-    sha256 = "",
-    strip_prefix = "ngraph-0.22.0",
+    sha256 = "1a1182f639506d472a22cefff53c9bdd536f2feacb633f66ad6a72fb5354ee06",
+    strip_prefix = "ngraph-0.23.0-rc.2",
     urls = [
-        "https://mirror.bazel.build/github.com/NervanaSystems/ngraph/archive/v0.22.0.tar.gz",
-        "https://github.com/NervanaSystems/ngraph/archive/v0.22.0.tar.gz"
+        "https://mirror.bazel.build/github.com/NervanaSystems/ngraph/archive/v0.23.0-rc.2.tar.gz",
+        "https://github.com/NervanaSystems/ngraph/archive/v0.23.0-rc.2.tar.gz"
     ],
 )
 
@@ -86,4 +86,4 @@ http_archive(
             "http://mirror.tensorflow.org/github.com/intel/mkl-dnn/releases/download/v0.18/mklml_lnx_2019.0.3.20190220.tgz",
             "https://github.com/intel/mkl-dnn/releases/download/v0.18/mklml_lnx_2019.0.3.20190220.tgz",
         ],
-    )
+    )

bazel/ngraph.BUILD

@@ -65,6 +65,7 @@ cc_library(
         ],
         exclude = [
         "src/ngraph/ngraph.cpp",
+        "src/ngraph/serializer_stub.cpp"
     ]),
     deps = [
         ":ngraph_headers",

bazel/tbb.BUILD

@@ -38,10 +38,6 @@ genrule(
 	    set -e
 	    WORK_DIR=$$PWD
 		DEST_DIR=$$PWD/$(@D)
-        export PATH=$$(dirname $(AR)):$$PATH
-		export CXXFLAGS=$(CC_FLAGS)
-		export NM=$(NM)
-		export AR=$(AR)
 		cd $$(dirname $(location :Makefile))
 
         #TBB's build needs some help to figure out what compiler it's using

build_ngtf.py

@@ -53,7 +53,7 @@ def main():
     '''
 
     # Component versions
-    ngraph_version = "v0.22.0"
+    ngraph_version = "v0.23.0-rc.2"
     tf_version = "v1.14.0"
 
     # Command line parser options
@@ -492,7 +492,7 @@ def main():
         link_src = os.path.join(artifacts_location,
                                 "tensorflow/tensorflow/python")
         link_dst = os.path.join(artifacts_location, "tensorflow/python")
-        command_executor(['ln', '-s', link_src, link_dst], verbose=True)
+        command_executor(['ln', '-sf', link_src, link_dst], verbose=True)
 
     # Run a quick test
     install_ngraph_tf(venv_dir, os.path.join(artifacts_location, ng_tf_whl))

build_tf.py

@@ -45,11 +45,14 @@ def main():
 
     assert not os.path.isdir(
         arguments.output_dir), arguments.output_dir + " already exists"
-    os.mkdir(arguments.output_dir)
+    os.makedirs(arguments.output_dir)
     os.chdir(arguments.output_dir)
     assert not is_venv(
     ), "Please deactivate virtual environment before running this script"
 
+    assert not is_venv(
+    ), "Please deactivate virtual environment before running this script"
+
     venv_dir = './venv3/'
 
     install_virtual_env(venv_dir)
@@ -63,11 +66,21 @@ def main():
     # Build TensorFlow
     build_tensorflow(venv_dir, "tensorflow", 'artifacts', arguments.target_arch,
                      False)
-    shutil.copytree('./tensorflow/tensorflow/python',
-                    './artifacts/tensorflow/python')
-    print('To build ngraph-bridge using this prebuilt tensorflow, use:')
-    print('./build_ngtf.py --use_tensorflow_from_location ' +
-          os.path.abspath(arguments.output_dir))
+
+    # Build TensorFlow C++ Library
+    build_tensorflow_cc("tensorflow", 'artifacts', arguments.target_arch, False)
+
+    pwd = os.getcwd()
+    artifacts_dir = os.path.join(pwd, 'artifacts/tensorflow')
+    os.chdir("tensorflow")
+
+    copy_tf_to_artifacts(artifacts_dir, None)
+
+    print('\033[1;35mTensorFlow Build finished\033[0m')
+
+    print('When building ngraph-bridge using this prebuilt tensorflow, use:')
+    print('\033[3;34mpython3 build_ngtf.py --use_tensorflow_from_location ' +
+          os.path.abspath(arguments.output_dir) + '\033[1;0m')
 
 
 if __name__ == '__main__':

examples/cpp/infer_multiple_networks/inference_engine.cc

@@ -40,28 +40,30 @@ extern tf::Status LoadGraph(const string& graph_file_name,
                             std::unique_ptr<tf::Session>* session,
                             const tf::SessionOptions& options);
 
-extern tf::Status ReadTensorFromImageFile(const string& file_name,
+extern tf::Status ReadTensorFromImageFile(const std::vector<string>& file_names,
                                           const int input_height,
                                           const int input_width,
                                           const float input_mean,
                                           const float input_std, bool use_NCHW,
+                                          const int input_channels,
                                           std::vector<tf::Tensor>* out_tensors);
 
 namespace infer_multiple_networks {
 
 InferenceEngine::InferenceEngine(const string& name, const string& backend)
     : m_name(name) {}
-Status InferenceEngine::Load(const string& network, const string& image_file,
+Status InferenceEngine::Load(const string& network,
+                             const std::vector<string>& image_files,
                              int input_width, int input_height,
                              float input_mean, float input_std,
                              const string& input_layer,
                              const string& output_layer, bool use_NCHW,
-                             bool preload_images) {
+                             bool preload_images, int input_channels) {
   // Load the network
   TF_CHECK_OK(CreateSession(network, m_session));
 
   // Save the input related information
-  m_image_file = image_file;
+  m_image_files = image_files;
   m_input_width = input_width;
   m_input_height = input_height;
   m_input_mean = input_mean;
@@ -70,6 +72,7 @@ Status InferenceEngine::Load(const string& network, const string& image_file,
   m_output_layer = output_layer;
   m_use_NCHW = use_NCHW;
   m_preload_images = preload_images;
+  m_input_channels = input_channels;
 
   // Preload the image is requested
   if (m_preload_images) {
@@ -79,8 +82,8 @@ Status InferenceEngine::Load(const string& network, const string& image_file,
     tf::ngraph_bridge::BackendManager::SetBackendName("CPU");
     std::vector<tf::Tensor> resized_tensors;
     TF_CHECK_OK(ReadTensorFromImageFile(
-        m_image_file, m_input_height, m_input_width, m_input_mean, m_input_std,
-        m_use_NCHW, &resized_tensors));
+        m_image_files, m_input_height, m_input_width, m_input_mean, m_input_std,
+        m_use_NCHW, m_input_channels, &resized_tensors));
     m_image_to_repeat = resized_tensors[0];
     tf::ngraph_bridge::BackendManager::SetBackendName(current_backend);
   }
@@ -131,8 +134,8 @@ void InferenceEngine::ThreadMain() {
 
       std::vector<tf::Tensor> resized_tensors;
       TF_CHECK_OK(ReadTensorFromImageFile(
-          m_image_file, m_input_height, m_input_width, m_input_mean,
-          m_input_std, m_use_NCHW, &resized_tensors));
+          m_image_files, m_input_height, m_input_width, m_input_mean,
+          m_input_std, m_use_NCHW, m_input_channels, &resized_tensors));
 
       m_image_to_repeat = resized_tensors[0];
       tf::ngraph_bridge::BackendManager::SetBackendName(current_backend);
@@ -150,7 +153,6 @@ void InferenceEngine::ThreadMain() {
     std::vector<Tensor> outputs;
     TF_CHECK_OK(m_session->Run({{m_input_layer, resized_tensor}},
                                {m_output_layer}, {}, &outputs));
-
     infer_event.Stop();
 
     ngraph::Event::write_trace(infer_event);

examples/cpp/infer_multiple_networks/inference_engine.h

@@ -23,6 +23,7 @@
 #include <memory>
 #include <string>
 #include <thread>
+#include <vector>
 
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/public/session.h"
@@ -44,10 +45,11 @@ class InferenceEngine {
   InferenceEngine(const string& name, const string& backend);
   ~InferenceEngine();
 
-  Status Load(const string& network, const string& image_file, int input_width,
-              int input_height, float input_mean, float input_std,
-              const string& input_layer, const string& output_layer,
-              bool use_NCHW, bool preload_images);
+  Status Load(const string& network, const std::vector<string>& image_files,
+              int input_width, int input_height, float input_mean,
+              float input_std, const string& input_layer,
+              const string& output_layer, bool use_NCHW, bool preload_images,
+              int input_channels);
 
   Status Start();
   Status Start(const std::function<void(int)>& step_callback);
@@ -64,7 +66,7 @@ class InferenceEngine {
   std::function<void(int)> m_step_callback{nullptr};
 
   // Image related info
-  string m_image_file;
+  std::vector<string> m_image_files;
   int m_input_width;
   int m_input_height;
   float m_input_mean;
@@ -73,6 +75,7 @@ class InferenceEngine {
   string m_output_layer;
   bool m_use_NCHW;
   bool m_preload_images;
+  int m_input_channels;
   Tensor m_image_to_repeat;
 };
 

examples/cpp/infer_multiple_networks/main.cc

@@ -89,7 +89,11 @@ void PrintVersion() {
 }
 
 int main(int argc, char** argv) {
+  // parameters below need to modified as per model
   string image = "grace_hopper.jpg";
+  int batch_size = 1;
+  // Vector size is same as the batch size, populating with single image
+  vector<string> images(batch_size, image);
   string graph = "inception_v3_2016_08_28_frozen.pb";
   string labels = "";
   int input_width = 299;
@@ -100,7 +104,7 @@ int main(int argc, char** argv) {
   string output_layer = "InceptionV3/Predictions/Reshape_1";
   bool use_NCHW = false;
   bool preload_images = true;
-
+  int input_channels = 3;
   int iteration_count = 10;
 
   std::vector<tf::Flag> flag_list = {
@@ -137,27 +141,27 @@ int main(int argc, char** argv) {
     return -1;
   }
 
-  // const char* backend = "CPU";
-  // if (SetNGraphBackend(backend) != tf::Status::OK()) {
-  //   std::cout << "Error: Cannot set the backend: " << backend << std::endl;
-  //   return -1;
-  // }
+  const char* backend = "CPU";
+  if (SetNGraphBackend(backend) != tf::Status::OK()) {
+    std::cout << "Error: Cannot set the backend: " << backend << std::endl;
+    return -1;
+  }
 
   std::cout << "Component versions\n";
   PrintVersion();
 
   infer_multiple_networks::InferenceEngine infer_engine_1("engine_1", "CPU:0");
-  TF_CHECK_OK(infer_engine_1.Load(graph, image, input_width, input_height,
-                                  input_mean, input_std, input_layer,
-                                  output_layer, use_NCHW, preload_images));
+  TF_CHECK_OK(infer_engine_1.Load(
+      graph, images, input_width, input_height, input_mean, input_std,
+      input_layer, output_layer, use_NCHW, preload_images, input_channels));
   infer_multiple_networks::InferenceEngine infer_engine_2("engine_2", "CPU:0");
-  TF_CHECK_OK(infer_engine_2.Load(graph, image, input_width, input_height,
-                                  input_mean, input_std, input_layer,
-                                  output_layer, use_NCHW, preload_images));
+  TF_CHECK_OK(infer_engine_2.Load(
+      graph, images, input_width, input_height, input_mean, input_std,
+      input_layer, output_layer, use_NCHW, preload_images, input_channels));
   infer_multiple_networks::InferenceEngine infer_engine_3("engine_3", "CPU:0");
-  TF_CHECK_OK(infer_engine_3.Load(graph, image, input_width, input_height,
-                                  input_mean, input_std, input_layer,
-                                  output_layer, use_NCHW, preload_images));
+  TF_CHECK_OK(infer_engine_3.Load(
+      graph, images, input_width, input_height, input_mean, input_std,
+      input_layer, output_layer, use_NCHW, preload_images, input_channels));
 
   bool engine_1_running = true;
   infer_engine_1.Start([&](int step_count) {