Merge pull request #47 from JDAI-CV/update_onnx2bnn

Update onnx2bnn and convert onnx->daq in ci test

Author: daquexian

.gitmodules

@@ -13,6 +13,3 @@
 [submodule "third_party/protobuf"]
 	path = third_party/protobuf
 	url = https://github.com/protocolbuffers/protobuf
-[submodule "third_party/pybind11"]
-	path = third_party/pybind11
-	url = https://github.com/pybind/pybind11

ci/build_onnx2bnn.sh

@@ -1,9 +1,7 @@
 #! /usr/bin/env bash
 set -e
 
-nproc=$(ci/get_cores.sh)
-
 mkdir build_onnx2bnn && cd build_onnx2bnn
 cmake ..
-cmake --build . -- -j$nproc
+cmake --build .
 cd -

ci/dabnn_build_and_test.yml

@@ -32,7 +32,7 @@ jobs:
   - bash: brew install watch gnu-sed
     displayName: Install watch and gnu-sed
   - bash: ci/build_dabnn.sh
-    displayName: Build
+    displayName: Build dabnn
   - bash: ci/start_android_emulator.sh
     displayName: Start Android Emulator
   - template: template_dabnn_run_test.yml
@@ -49,7 +49,7 @@ jobs:
   - bash: brew install watch gnu-sed
     displayName: Install watch and gnu-sed
   - bash: ci/build_dabnn_v7.sh
-    displayName: Build
+    displayName: Build dabnn
   - bash: ci/start_android_emulator_v7.sh
     displayName: Start Android Emulator
   - template: template_dabnn_run_test.yml

ci/download_and_convert_models.sh

@@ -0,0 +1,8 @@
+#! /usr/bin/env bash
+
+wget "https://drive.google.com/uc?export=download&id=1Xp3HB51H6Nhl6e555ieJubVutQake5sR" -O model_imagenet.onnx
+./build_onnx2bnn/tools/onnx2bnn/onnx2bnn model_imagenet.onnx model_imagenet.dab --aggressive --verbose
+adb push model_imagenet.dab /data/local/tmp 
+wget "https://drive.google.com/uc?export=download&id=1zu48CFptAGZ91IDCBPJSPM0bxDuPm9HS" -O model_imagenet_stem.onnx
+./build_onnx2bnn/tools/onnx2bnn/onnx2bnn model_imagenet_stem.onnx model_imagenet_stem.dab --aggressive --verbose
+adb push model_imagenet_stem.dab /data/local/tmp/ 

ci/download_models.sh

@@ -51,29 +51,24 @@ jobs:
   steps:
   - checkout: self
     submodules: true
-  - template: template_onnx2bnn_build_python_all_version.yml
-  - task: CopyFiles@2
-    inputs:
-      sourceFolder: '.setuptools-cmake-build\tools\onnx2bnn\Release\'
-      contents: 'onnx2bnn.exe'
-      targetFolder: $(Build.ArtifactStagingDirectory)
+  - bash: ci/build_onnx2bnn.sh
+    displayName: Build
   - template: template_onnx2bnn_publish_artifacts.yml
   - template: template_onnx2bnn_github_release.yml
-  - template: template_onnx2bnn_upload_to_pypi.yml
 - job: macOS
   pool:
       vmImage: 'macOS-10.14'
   steps:
   - checkout: self
     submodules: true
-  - template: template_onnx2bnn_build_python_all_version.yml
-  - script: 'cp .setuptools-cmake-build/tools/onnx2bnn/onnx2bnn .setuptools-cmake-build/tools/onnx2bnn/onnx2bnn-macos'
+  - bash: ci/build_onnx2bnn.sh
+    displayName: Build
+  - script: 'cp build_onnx2bnn/tools/onnx2bnn/onnx2bnn build_onnx2bnn/tools/onnx2bnn/onnx2bnn-macos'
     displayName: 'Rename onnx2bnn'
   - task: CopyFiles@2
     inputs:
-      sourceFolder: '.setuptools-cmake-build/tools/onnx2bnn'
+      sourceFolder: 'build_onnx2bnn/tools/onnx2bnn'
       contents: 'onnx2bnn-macos'
       targetFolder: $(Build.ArtifactStagingDirectory)
   - template: template_onnx2bnn_publish_artifacts.yml
   - template: template_onnx2bnn_github_release.yml
-  - template: template_onnx2bnn_upload_to_pypi.yml

ci/onnx2bnn_build.yml

@@ -3,7 +3,9 @@ steps:
   displayName: Binary Conv Test
 - bash: ci/adb_push_and_run.sh build_dabnn/tests/bgemm_test
   displayName: Binary Gemm Test
-- bash: ci/download_models.sh
-  displayName: Download Models
+- bash: ci/build_onnx2bnn.sh
+  displayName: Build onnx2bnn
+- bash: ci/download_and_convert_models.sh
+  displayName: Download and Convert Models
 - bash: ci/adb_push_and_run.sh build_dabnn/tests/net_test
   displayName: Model Test

ci/template_dabnn_run_test.yml

@@ -172,9 +172,10 @@ std::vector<OnnxConverter::BTensor> OnnxConverter::split(
     return outputs;
 }
 
-void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
-                            const std::string &filepath,
-                            const OnnxConverter::Level level) {
+std::vector<std::string> OnnxConverter::Convert(
+    const ONNX_NAMESPACE::ModelProto &model_proto, const std::string &filepath,
+    const OnnxConverter::Level level,
+    const std::vector<std::string> &expected_binary_conv_outputs) {
     GOOGLE_PROTOBUF_VERIFY_VERSION;
 
     // We recognize binary convolutions in our custom ONNX optimizers.
@@ -236,6 +237,7 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
         inputs.push_back(flat_input);
     }
 
+    vector<string> binary_conv_outputs;
     vector<string> skipped_act;
     bool has_reshape = false;
     for (const auto &node : model_proto_.graph().node()) {
@@ -270,7 +272,15 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
             }
 
             auto ori_weight_name = m(node.input(1));
-            const bool binary_conv = (node.domain() == "dabnn");
+            const bool binary_conv =
+                (node.domain() == "dabnn") ||
+                (std::find(expected_binary_conv_outputs.begin(),
+                           expected_binary_conv_outputs.end(),
+                           node.output(0)) !=
+                 expected_binary_conv_outputs.end());
+            if (binary_conv) {
+                binary_conv_outputs.push_back(node.output(0));
+            }
             AddConv(m(node.input(0)), strides, pads, dilations, group,
                     ori_weight_name, bias_name, m(node.output(0)), binary_conv);
             VLOG(5) << "Converting Conv completed";
@@ -472,6 +482,17 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
             throw std::invalid_argument("Unsupported operator " + op);
         }
     }
+
+    for (const auto &expected : expected_binary_conv_outputs) {
+        if (std::find(binary_conv_outputs.begin(), binary_conv_outputs.end(),
+                      expected) == binary_conv_outputs.end()) {
+            throw std::invalid_argument(
+                expected +
+                " is in the list file but not in the ONNX model, please check "
+                "your list file");
+        }
+    }
+
     auto flat_layers = builder_.CreateVector(layers_);
     auto flat_inputs = builder_.CreateVector(inputs);
     auto flat_tensors = builder_.CreateVector(tensors_);
@@ -487,6 +508,8 @@ void OnnxConverter::Convert(const ONNX_NAMESPACE::ModelProto &model_proto,
     ofs.write(reinterpret_cast<char *>(builder_.GetBufferPointer()),
               builder_.GetSize());
     ofs.close();
+
+    return binary_conv_outputs;
 }
 
 void OnnxConverter::CalculateCoeff(const ONNX_NAMESPACE::NodeProto &node,

ci/template_onnx2bnn_build_python.yml

@@ -153,9 +153,9 @@ class OnnxConverter {
         kModerate,
         kAggressive,
     };
-    void Convert(const ONNX_NAMESPACE::ModelProto &model,
+    std::vector<std::string> Convert(const ONNX_NAMESPACE::ModelProto &model,
                  const std::string &filepath,
-                 const Level level=Level::kModerate);
+                 const Level level, const std::vector<std::string> &expected_binary_conv_outputs);
 };
 
 template <>

ci/template_onnx2bnn_build_python_all_version.yml

@@ -16,52 +16,91 @@ using std::string;
 using std::vector;
 
 void usage(const std::string &filename) {
+    std::cout << "Usage:" << std::endl;
+    std::cout << "  " << filename
+              << " onnx_model output_filename [ --strict | --moderate | "
+                 "--aggressive ] [--binary-list] [--verbose]"
+              << std::endl;
+    std::cout << std::endl;
+    std::cout << "Options:" << std::endl;
+    std::cout
+        << "  --aggressive    The default optimization level. In this level, "
+           "onnx2bnn will mark all convolutions with binary (+1/-1) weights as "
+           "binary convolutions. It is for the existing BNN models, which may "
+           "not use the correct padding value. Note: The output of the "
+           "generated dabnn model is different from that of the ONNX model "
+           "since the padding value is 0 instead of -1."
+        << std::endl;
+    std::cout << "  --moderate      This level is for our \"standard\" "
+                 "implementation -- A Conv operator with binary weight and "
+                 "following a -1 Pad operator."
+              << std::endl;
     std::cout
-        << "Usage: " << filename
-        << " onnx_model output_filename [--optimize strict|moderate|aggressive]"
+        << "  --strict        In this level, onnx2bnn only recognizes the "
+           "following natural and correct \"pattern\" of binary convolutions: "
+           "A Conv operator, whose input is got from a Sign op and a Pad op "
+           "(the order doesn't matter), and weight is got from a Sign op."
         << std::endl;
-    std::cout << "Example: " << filename
+    std::cout
+        << "  --binary-list   A text file containing the **output "
+           "names** of some convolutions, which will be treated as binary "
+           "convlutions unconditionally. It is mainly for benchmark purpose."
+        << std::endl;
+    std::cout << std::endl;
+    std::cout << "Example:" << std::endl;
+    std::cout << "  " << filename
               << " model.onnx model.dab (The optimization leval will be "
                  "\"aggressive\")"
               << std::endl;
-    std::cout << "Example: " << filename
-              << " model.onnx model.dab --optimize strict (The optimization "
+    std::cout << "  " << filename
+              << " model.onnx model.dab --strict (The optimization "
                  "level will be \"strict\")"
               << std::endl;
 }
 
 int main(int argc, char **argv) {
     argh::parser cmdl;
-    cmdl.add_param("optimize");
+    cmdl.add_param("--binary-list");
     cmdl.parse(argc, argv);
     google::InitGoogleLogging(cmdl[0].c_str());
     FLAGS_alsologtostderr = true;
     if (!cmdl(2)) {
         usage(cmdl[0]);
         return -1;
     }
-    // flags like 'onnx2bnn --strict' is not supported now
     for (const auto flag : cmdl.flags()) {
-        std::cout << "Invalid flag: " << flag << std::endl;
-        usage(cmdl[0]);
-        return -2;
+        if (flag != "strict" && flag != "moderate" && flag != "aggressive" &&
+            flag != "verbose") {
+            std::cout << "Invalid flag: " << flag << std::endl;
+            usage(cmdl[0]);
+            return -2;
+        }
     }
 
-    const std::string opt_level_str =
-        cmdl("optimize").str().empty() ? "aggressive" : cmdl("optimize").str();
-
-    bnn::OnnxConverter::Level opt_level;
-    if (opt_level_str == "strict") {
+    bnn::OnnxConverter::Level opt_level =
+        bnn::OnnxConverter::Level::kAggressive;
+    if (cmdl["strict"]) {
         opt_level = bnn::OnnxConverter::Level::kStrict;
-    } else if (opt_level_str == "moderate") {
+    } else if (cmdl["moderate"]) {
         opt_level = bnn::OnnxConverter::Level::kModerate;
-    } else if (opt_level_str == "aggressive") {
+    } else if (cmdl["aggressive"]) {
         opt_level = bnn::OnnxConverter::Level::kAggressive;
-    } else {
-        std::cout << "Invalid optimization level: " << opt_level_str
-                  << std::endl;
-        usage(cmdl[0]);
-        return -3;
+    }
+
+    if (cmdl["verbose"]) {
+        FLAGS_v = 5;
+    }
+
+    const auto binary_list_filepath = cmdl("binary-list").str();
+    vector<string> expected_binary_conv_outputs;
+    if (!binary_list_filepath.empty()) {
+        std::ifstream ifs(binary_list_filepath);
+        if (ifs.is_open()) {
+            string binary_conv_output;
+            while (ifs >> binary_conv_output) {
+                expected_binary_conv_outputs.push_back(binary_conv_output);
+            }
+        }
     }
 
     ONNX_NAMESPACE::ModelProto model_proto;
@@ -72,7 +111,15 @@ int main(int argc, char **argv) {
     }
 
     bnn::OnnxConverter converter;
-    converter.Convert(model_proto, cmdl[2], opt_level);
+    const auto binary_conv_outputs = converter.Convert(
+        model_proto, cmdl[2], opt_level, expected_binary_conv_outputs);
+
+    LOG(INFO) << "Conversion completed! Found " << binary_conv_outputs.size()
+              << " binary convolutions. Add --verbose to get what they are.";
+    VLOG(5) << "The outputs name of binary convolutions are: ";
+    for (const auto &x : binary_conv_outputs) {
+        VLOG(5) << x;
+    }
 
     google::protobuf::ShutdownProtobufLibrary();
     return 0;