Use ONNXRuntime for ParticleNet inference

PhysicsTools/ONNXRuntime/interface/ONNXRuntime.h

@@ -32,12 +32,14 @@ namespace cms::Ort {
     // Run inference and get outputs
     // input_names: list of the names of the input nodes.
     // input_values: list of input arrays for each input node. The order of `input_values` must match `input_names`.
+    // input_shapes: list of `int64_t` arrays specifying the shape of each input node. Can leave empty if the model does not have dynamic axes.
     // output_names: names of the output nodes to get outputs from. Empty list means all output nodes.
     // batch_size: number of samples in the batch. Each array in `input_values` must have a shape layout of (batch_size, ...).
     // Returns: a std::vector<std::vector<float>>, with the order matched to `output_names`.
     // When `output_names` is empty, will return all outputs ordered as in `getOutputNames()`.
     FloatArrays run(const std::vector<std::string>& input_names,
                     FloatArrays& input_values,
+                    const std::vector<std::vector<int64_t>>& input_shapes = {},
                     const std::vector<std::string>& output_names = {},
                     int64_t batch_size = 1) const;
 

PhysicsTools/ONNXRuntime/src/ONNXRuntime.cc

@@ -50,9 +50,6 @@ namespace cms::Ort {
       size_t num_dims = tensor_info.GetDimensionsCount();
       input_node_dims_[input_name].resize(num_dims);
       tensor_info.GetDimensions(input_node_dims_[input_name].data(), num_dims);
-
-      // set the batch size to 1 by default
-      input_node_dims_[input_name].at(0) = 1;
     }
 
     size_t num_output_nodes = session_->GetOutputCount();
@@ -82,9 +79,11 @@ namespace cms::Ort {
 
   FloatArrays ONNXRuntime::run(const std::vector<std::string>& input_names,
                                FloatArrays& input_values,
+                               const std::vector<std::vector<int64_t>>& input_shapes,
                                const std::vector<std::string>& output_names,
                                int64_t batch_size) const {
     assert(input_names.size() == input_values.size());
+    assert(input_shapes.empty() || input_names.size() == input_shapes.size());
     assert(batch_size > 0);
 
     // create input tensor objects from data values
@@ -95,9 +94,16 @@ namespace cms::Ort {
       if (iter == input_names.end()) {
         throw cms::Exception("RuntimeError") << "Input " << name << " is not provided!";
       }
-      auto value = input_values.begin() + (iter - input_names.begin());
-      auto input_dims = input_node_dims_.at(name);
-      input_dims[0] = batch_size;
+      auto input_pos = iter - input_names.begin();
+      auto value = input_values.begin() + input_pos;
+      std::vector<int64_t> input_dims;
+      if (input_shapes.empty()) {
+        input_dims = input_node_dims_.at(name);
+        input_dims[0] = batch_size;
+      } else {
+        input_dims = input_shapes[input_pos];
+        // rely on the given input_shapes to set the batch size
+      }
       auto expected_len = std::accumulate(input_dims.begin(), input_dims.end(), 1, std::multiplies<int64_t>());
       if (expected_len != (int64_t)value->size()) {
         throw cms::Exception("RuntimeError")

PhysicsTools/ONNXRuntime/test/testONNXRuntime.cc

@@ -27,7 +27,7 @@ void testONNXRuntime::checkAll() {
         std::vector<float>(batch_size * 2, 1),
     };
     FloatArrays outputs;
-    CPPUNIT_ASSERT_NO_THROW(outputs = rt.run({"X"}, input_values, {"Y"}, batch_size));
+    CPPUNIT_ASSERT_NO_THROW(outputs = rt.run({"X"}, input_values, {}, {"Y"}, batch_size));
     CPPUNIT_ASSERT(outputs.size() == 1);
     CPPUNIT_ASSERT(outputs[0].size() == batch_size);
     for (const auto &v : outputs[0]) {

PhysicsTools/PatAlgos/python/recoLayer0/bTagging_cff.py

@@ -244,7 +244,7 @@
 
 # -----------------------------------
 # setup ParticleNet
-from RecoBTag.MXNet.pfParticleNet_cff import _pfParticleNetJetTagsProbs, _pfParticleNetJetTagsMetaDiscrs, \
+from RecoBTag.ONNXRuntime.pfParticleNet_cff import _pfParticleNetJetTagsProbs, _pfParticleNetJetTagsMetaDiscrs, \
     _pfMassDecorrelatedParticleNetJetTagsProbs, _pfMassDecorrelatedParticleNetJetTagsMetaDiscrs
 # update supportedBtagDiscr
 for disc in _pfParticleNetJetTagsProbs + _pfMassDecorrelatedParticleNetJetTagsProbs:

PhysicsTools/PatAlgos/python/slimming/applyDeepBtagging_cff.py

@@ -44,7 +44,7 @@ def applyDeepBtagging( process, postfix="" ) :
 
     from RecoBTag.ONNXRuntime.pfDeepBoostedJet_cff import _pfDeepBoostedJetTagsAll as pfDeepBoostedJetTagsAll
     from RecoBTag.ONNXRuntime.pfHiggsInteractionNet_cff import _pfHiggsInteractionNetTagsProbs as pfHiggsInteractionNetTagsProbs
-    from RecoBTag.MXNet.pfParticleNet_cff import _pfParticleNetJetTagsAll as pfParticleNetJetTagsAll
+    from RecoBTag.ONNXRuntime.pfParticleNet_cff import _pfParticleNetJetTagsAll as pfParticleNetJetTagsAll
 
     # update slimmed jets to include particle-based deep taggers (keep same name)
     # make clone for DeepTags-less slimmed AK8 jets, so output name is preserved

RecoBTag/Configuration/python/RecoBTag_cff.py

@@ -11,7 +11,7 @@
 from RecoBTag.ONNXRuntime.pfDeepDoubleX_cff import *
 from RecoBTag.ONNXRuntime.pfDeepBoostedJet_cff import *
 from RecoBTag.ONNXRuntime.pfHiggsInteractionNet_cff import *
-from RecoBTag.MXNet.pfParticleNet_cff import *
+from RecoBTag.ONNXRuntime.pfParticleNet_cff import *
 from RecoVertex.AdaptiveVertexFinder.inclusiveVertexing_cff import *
 from RecoBTag.PixelCluster.pixelClusterTagInfos_cfi import *
 

RecoBTag/FeatureTools/interface/deep_helpers.h

@@ -79,21 +79,23 @@ namespace btagbtvdeep {
   struct PreprocessParams {
     struct VarInfo {
       VarInfo() {}
-      VarInfo(float median, float norm_factor, float replace_inf_value, float lower_bound, float upper_bound)
+      VarInfo(float median, float norm_factor, float replace_inf_value, float lower_bound, float upper_bound, float pad)
           : center(median),
             norm_factor(norm_factor),
             replace_inf_value(replace_inf_value),
             lower_bound(lower_bound),
-            upper_bound(upper_bound) {}
+            upper_bound(upper_bound),
+            pad(pad) {}
       float center = 0;
       float norm_factor = 1;
       float replace_inf_value = 0;
       float lower_bound = -5;
       float upper_bound = 5;
+      float pad = 0;
     };
 
-    unsigned var_length = 0;
-    float pad = 0;
+    unsigned min_length = 0;
+    unsigned max_length = 0;
     std::vector<std::string> var_names;
     std::unordered_map<std::string, VarInfo> var_info_map;