|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
|
import FWCore.ParameterSet.Config as cms
hltTiclTrackstersCLUE3DHighL1Seeded = cms.EDProducer("TrackstersProducer",
detector = cms.string('HGCAL'),
filtered_mask = cms.InputTag("hltFilteredLayerClustersCLUE3DHighL1Seeded","CLUE3DHigh"),
itername = cms.string('CLUE3DHigh'),
layer_clusters = cms.InputTag("hltHgcalMergeLayerClustersL1Seeded"),
layer_clusters_hfnose_tiles = cms.InputTag("ticlLayerTileHFNose"),
layer_clusters_tiles = cms.InputTag("hltTiclLayerTileProducerL1Seeded"),
mightGet = cms.optional.untracked.vstring,
original_mask = cms.InputTag("hltHgcalMergeLayerClustersL1Seeded","InitialLayerClustersMask"),
patternRecognitionBy = cms.string('CLUE3D'),
inferenceAlgo = cms.string('TracksterInferenceByCNNv4'),
pluginPatternRecognitionByCA = cms.PSet(
algo_verbosity = cms.int32(0),
energy_em_over_total_threshold = cms.double(-1),
etaLimitIncreaseWindow = cms.double(2.1),
filter_on_categories = cms.vint32(0),
max_delta_time = cms.double(3),
max_longitudinal_sigmaPCA = cms.double(9999),
max_missing_layers_in_trackster = cms.int32(9999),
max_out_in_hops = cms.int32(10),
min_cos_pointing = cms.double(-1),
min_cos_theta = cms.double(0.915),
min_layers_per_trackster = cms.int32(10),
oneTracksterPerTrackSeed = cms.bool(False),
out_in_dfs = cms.bool(True),
pid_threshold = cms.double(0),
promoteEmptyRegionToTrackster = cms.bool(False),
root_doublet_max_distance_from_seed_squared = cms.double(9999),
shower_start_max_layer = cms.int32(9999),
siblings_maxRSquared = cms.vdouble(0.0006, 0.0006, 0.0006),
skip_layers = cms.int32(0),
type = cms.string('CA')
),
pluginPatternRecognitionByCLUE3D = cms.PSet(
algo_verbosity = cms.int32(0),
criticalDensity = cms.vdouble(
0.6,
0.6,
0.6
),
criticalSelfDensity = cms.vdouble(
0.15,
0.15,
0.15
),
densitySiblingLayers = cms.vint32(
3,
3,
3
),
densityEtaPhiDistanceSqr = cms.vdouble(
0.0008,
0.0008,
0.0008
),
densityXYDistanceSqr = cms.vdouble(
3.24,
3.24,
3.24
),
kernelDensityFactor = cms.vdouble(
0.2,
0.2,
0.2
),
densityOnSameLayer = cms.bool(False),
nearestHigherOnSameLayer = cms.bool(False),
useAbsoluteProjectiveScale = cms.bool(True),
useClusterDimensionXY = cms.bool(False),
rescaleDensityByZ = cms.bool(False),
criticalEtaPhiDistance = cms.vdouble(
0.025,
0.025,
0.025
),
criticalXYDistance = cms.vdouble(
1.8,
1.8,
1.8
),
criticalZDistanceLyr = cms.vint32(
5,
5,
5
),
outlierMultiplier = cms.vdouble(
2,
2,
2
),
minNumLayerCluster = cms.vint32(
2,
2,
2
),
computeLocalTime = cms.bool(False),
doPidCut = cms.bool(True),
cutHadProb = cms.double(999.),
type = cms.string('CLUE3D')
),
pluginPatternRecognitionByFastJet = cms.PSet(
algo_verbosity = cms.int32(0),
antikt_radius = cms.double(0.09),
minNumLayerCluster = cms.int32(5),
type = cms.string('FastJet')
),
pluginInferenceAlgoTracksterInferenceByCNNv4 = cms.PSet(
algo_verbosity = cms.int32(0),
onnxModelPath = cms.FileInPath('RecoHGCal/TICL/data/ticlv4/onnx_models/energy_id_v0.onnx'),
inputNames = cms.vstring('input:0'),
outputNames = cms.vstring("output/regressed_energy:0", "output/id_probabilities:0"),
eid_min_cluster_energy = cms.double(1),
eid_n_layers = cms.int32(50),
eid_n_clusters = cms.int32(10),
doPID = cms.int32(1),
doRegression = cms.int32(0),
type = cms.string('TracksterInferenceByCNNv4')
),
pluginInferenceAlgoTracksterInferenceByDNN = cms.PSet(
algo_verbosity = cms.int32(0),
onnxPIDModelPath = cms.FileInPath('RecoHGCal/TICL/data/ticlv5/onnx_models/DNN/patternrecognition/id_v0.onnx'),
onnxEnergyModelPath = cms.FileInPath('RecoHGCal/TICL/data/ticlv5/onnx_models/DNN/patternrecognition/energy_v0.onnx'),
inputNames = cms.vstring('input'),
output_en = cms.vstring('enreg_output'),
output_id = cms.vstring('pid_output'),
eid_n_layers = cms.int32(50),
eid_n_clusters = cms.int32(10),
doPID = cms.int32(1),
doRegression = cms.int32(0),
type = cms.string('TracksterInferenceByDNN')
),
pluginInferenceAlgoTracksterInferenceByPFN = cms.PSet(
algo_verbosity = cms.int32(0),
onnxPIDModelPath = cms.FileInPath('RecoHGCal/TICL/data/ticlv5/onnx_models/PFN/patternrecognition/id_v0.onnx'),
onnxEnergyModelPath = cms.FileInPath('RecoHGCal/TICL/data/ticlv5/onnx_models/PFN/patternrecognition/energy_v0.onnx'),
inputNames = cms.vstring('input','input_tr_features'),
output_en = cms.vstring('enreg_output'),
output_id = cms.vstring('pid_output'),
eid_n_layers = cms.int32(50),
eid_n_clusters = cms.int32(10),
doPID = cms.int32(1),
doRegression = cms.int32(0),
type = cms.string('TracksterInferenceByPFN')
),
pluginInferenceAlgoTracksterInferenceByANN = cms.PSet(
algo_verbosity = cms.int32(0),
type = cms.string('TracksterInferenceByANN')
),
seeding_regions = cms.InputTag("hltTiclSeedingL1"),
time_layerclusters = cms.InputTag("hltHgcalMergeLayerClustersL1Seeded","timeLayerCluster"),
)
from Configuration.ProcessModifiers.ticl_v5_cff import ticl_v5
ticl_v5.toModify(hltTiclTrackstersCLUE3DHighL1Seeded.pluginPatternRecognitionByCLUE3D, computeLocalTime = cms.bool(True))
ticl_v5.toModify(hltTiclTrackstersCLUE3DHighL1Seeded, inferenceAlgo = cms.string('TracksterInferenceByPFN'))
|