ACAT2021 benchmark

.github/workflows/test.yml

@@ -24,7 +24,8 @@ jobs:
             tensorflow-probability==0.12.2 tensorflow-addons==0.13.0 \
             tqdm click tensorflow-datasets 'ray[default]' 'ray[tune]' \
             tf-models-official tensorflow-text \
-            tf2onnx onnxruntime zenodo_get seaborn scikit-optimize nevergrad
+            tf2onnx onnxruntime zenodo_get seaborn scikit-optimize nevergrad \
+            tensorflow-estimator==2.6.0 keras==2.6.0
           git submodule init
           git submodule update
       - name: Run delphes TF model
@@ -45,7 +46,8 @@ jobs:
             tensorflow-probability==0.12.2 tensorflow-addons==0.13.0 \
             tqdm click tensorflow-datasets 'ray[default]' 'ray[tune]' \
             tf-models-official tensorflow-text \
-            tf2onnx onnxruntime zenodo_get seaborn scikit-optimize nevergrad
+            tf2onnx onnxruntime zenodo_get seaborn scikit-optimize nevergrad \
+            tensorflow-estimator==2.6.0 keras==2.6.0
           git submodule init
           git submodule update
       - name: Run CMS TF model using the pipeline

mlpf/pipeline.py

@@ -87,6 +87,7 @@ def customize_pipeline_test(config):
     if "physical" in config["train_test_datasets"]:
         config["train_test_datasets"]["physical"]["datasets"] = ["cms_pf_ttbar"]
         config["train_test_datasets"] = {"physical": config["train_test_datasets"]["physical"]}
+        config["train_test_datasets"]["physical"]["batch_per_gpu"] = 5
 
     return config
 

mlpf/plotting/cms_fwlite.py

@@ -34,6 +34,32 @@ def get(self, event):
             ("energy", "[o.energy() for o in obj]"),
         ]
     ))
+    expressions.append(Expression(
+        "ak4PFJetsPuppi",
+        "vector<reco::PFJet>",
+        [
+            ("pt", "[o.pt() for o in obj]"),
+            ("eta", "[o.eta() for o in obj]"),
+            ("phi", "[o.phi() for o in obj]"),
+            ("energy", "[o.energy() for o in obj]"),
+        ]
+    ))
+    expressions.append(Expression(
+        "pfMet",
+        "vector<reco::PFMET>",
+        [
+            ("pt", "[o.pt() for o in obj]"),
+            ("phi", "[o.phi() for o in obj]"),
+        ]
+    ))
+    expressions.append(Expression(
+        "pfMetPuppi",
+        "vector<reco::PFMET>",
+        [
+            ("pt", "[o.pt() for o in obj]"),
+            ("phi", "[o.phi() for o in obj]"),
+        ]
+    ))
     expressions.append(Expression(
         "particleFlow",
         "vector<reco::PFCandidate>",

mlpf/tfmodel/timing.py

@@ -1,6 +1,7 @@
 import numpy as np
 import time
 import pynvml
+import sys
 
 #pip install only onnxruntime_gpu, not onnxruntime!
 import onnxruntime
@@ -17,7 +18,7 @@
     mem_initial = mem.used/1000/1000
     print("mem_initial", mem_initial)
 
-    onnx_sess = onnxruntime.InferenceSession("model.onnx", providers=EP_list)
+    onnx_sess = onnxruntime.InferenceSession(sys.argv[1], providers=EP_list)
     time.sleep(5)
 
     mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
@@ -32,7 +33,7 @@
         for i in range(100):
 
             #allocate array in system RAM
-            X = np.array(np.random.randn(1, num_elems, 18), np.float32)
+            X = np.array(np.random.randn(1, num_elems, 25), np.float32)
 
             #transfer data to GPU, run model, transfer data back
             t0 = time.time()