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lmoneta merged 2 commits into from
Oct 20, 2025
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[tmva][sofie] Fix testSofieModels for new torch version #20149

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b48ab8a
[tmva][sofie] Fix testSofieModels for new torch version
lmoneta Oct 20, 2025
de83a5b
Revert "[CMake] Disable TestSofieModels if PyTorch >= 2.9.0"
lmoneta Oct 20, 2025
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26 changes: 8 additions & 18 deletions tmva/sofie/test/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -116,24 +116,14 @@ if (ROOT_TORCH_FOUND)
configure_file(RecurrentModelGenerator.py RecurrentModelGenerator.py COPYONLY)

if (BLAS_FOUND)
execute_process(COMMAND "${Python3_EXECUTABLE}" "-c"
"import torch; print(getattr(torch, '__version__', 'unknown'))"
RESULT_VARIABLE status
OUTPUT_VARIABLE module_version
OUTPUT_STRIP_TRAILING_WHITESPACE
ERROR_QUIET)
if(${status} EQUAL 0 AND ${module_version} VERSION_LESS 2.9.0)
# Disable TestSofieModels which is failing on alma8, alma10, ubuntu2204, and ubuntu2404
# with PyTorch 2.9.0
ROOT_ADD_GTEST(TestSofieModels TestSofieModels.cxx
LIBRARIES
ROOTTMVASofie
ROOTTMVASofieParser
BLAS::BLAS
INCLUDE_DIRS
${CMAKE_CURRENT_BINARY_DIR}
)
endif()
ROOT_ADD_GTEST(TestSofieModels TestSofieModels.cxx
LIBRARIES
ROOTTMVASofie
ROOTTMVASofieParser
BLAS::BLAS
INCLUDE_DIRS
${CMAKE_CURRENT_BINARY_DIR}
)
endif()
endif()

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48 changes: 25 additions & 23 deletions tmva/sofie/test/Conv1dModelGenerator.py
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Expand Up @@ -2,18 +2,18 @@

### generate COnv2d model using Pytorch

import numpy as np

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tmva/sofie/test/Conv1dModelGenerator.py:5:17: F401 `numpy` imported but unused
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F

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tmva/sofie/test/Conv1dModelGenerator.py:5:1: I001 Import block is un-sorted or un-formatted


result = []


class Net(nn.Module):

def __init__(self, nc = 1, ng = 1, nl = 4, use_bn = False, use_maxpool = False, use_avgpool = False):
super(Net, self).__init__()

Expand All @@ -23,11 +23,11 @@
self.use_bn = use_bn
self.use_maxpool = use_maxpool
self.use_avgpool = use_avgpool

self.conv0 = nn.Conv1d(in_channels=self.nc, out_channels=4, kernel_size=2, groups=1, stride=1, padding=1)
if (self.use_bn): self.bn1 = nn.BatchNorm2d(4)

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tmva/sofie/test/Conv1dModelGenerator.py:28:25: E701 Multiple statements on one line (colon)
if (self.use_maxpool): self.pool1 = nn.MaxPool2d(2)

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tmva/sofie/test/Conv1dModelGenerator.py:29:30: E701 Multiple statements on one line (colon)
if (self.use_avgpool): self.pool1 = nn.AvgPool2d(2)

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tmva/sofie/test/Conv1dModelGenerator.py:30:30: E701 Multiple statements on one line (colon)
if (self.nl > 1):
# output is 4x4 with optionally using group convolution
self.conv1 = nn.Conv1d(in_channels=4, out_channels=8, groups = self.ng, kernel_size=3, stride=1, padding=1)
Expand All @@ -42,7 +42,7 @@
x = self.bn1(x)
if (self.use_maxpool or self.use_avgpool):
x = self.pool1(x)
if (self.nl == 1) : return x

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tmva/sofie/test/Conv1dModelGenerator.py:45:25: E701 Multiple statements on one line (colon)
x = self.conv1(x)
x = F.relu(x)

Expand All @@ -55,7 +55,7 @@
parser = argparse.ArgumentParser(description='PyTorch model generator')
parser.add_argument('params', type=int, nargs='+',
help='parameters for the Conv network : batchSize , inputChannels, inputImageSize, nGroups, nLayers ')

parser.add_argument('--bn', action='store_true', default=False,
help='For using batch norm layer')
parser.add_argument('--maxpool', action='store_true', default=False,
Expand All @@ -69,13 +69,13 @@


args = parser.parse_args()

#args.params = (4,2,4,1,4)

np = len(args.params)

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tmva/sofie/test/Conv1dModelGenerator.py:75:4: F811 Redefinition of unused `np` from line 5
if (np < 5) : exit()

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tmva/sofie/test/Conv1dModelGenerator.py:76:16: E701 Multiple statements on one line (colon)
bsize = args.params[0]
nc = args.params[1]
nc = args.params[1]
d = args.params[2]
ngroups = args.params[3]
nlayers = args.params[4]
Expand All @@ -85,31 +85,31 @@
# use_1d = args.oneD

print ("using batch-size =",bsize,"nchannels =",nc,"dim =",d,"ngroups =",ngroups,"nlayers =",nlayers)
if (use_bn): print("using batch normalization layer")

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tmva/sofie/test/Conv1dModelGenerator.py:88:15: E701 Multiple statements on one line (colon)
if (use_maxpool): print("using maxpool layer")

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tmva/sofie/test/Conv1dModelGenerator.py:89:20: E701 Multiple statements on one line (colon)

#sample = torch.zeros([2,1,5,5])
input = torch.zeros([])
for ib in range(0,bsize):
xa = torch.ones([1, 1, d]) * (ib+1)
if (nc > 1) :
if (nc > 1) :
xb = xa.neg()
xc = torch.cat((xa,xb),1) # concatenate tensors
if (nc > 2) :
xd = torch.zeros([1,nc-2,d])
xc = torch.cat((xa,xb,xd),1)
else:
xc = xa
#concatenate tensors
if (ib == 0) :

#concatenate tensors
if (ib == 0) :
xinput = xc
else :
xinput = torch.cat((xinput,xc),0)
xinput = torch.cat((xinput,xc),0)

print("input data",xinput.shape)
print(xinput)

name = "Conv1dModel"
if (use_bn): name += "_BN"
if (use_maxpool): name += "_MAXP"
Expand All @@ -120,24 +120,26 @@
loadModel=False
savePtModel = False


model = Net(nc,ngroups,nlayers, use_bn, use_maxpool, use_avgpool)
print(model)

model(xinput)

model.forward(xinput)

if savePtModel :
torch.save({'model_state_dict':model.state_dict()}, name + ".pt")

if saveOnnx:
torch.onnx.export(
model,
xinput,
name + ".onnx",
export_params=True
)
torch.onnx.export(
model,
xinput,
name + ".onnx",
export_params=True,
dynamo=True,
external_data=False
)

if loadModel :
print('Loading model from file....')
Expand All @@ -158,10 +160,10 @@

f = open(name + ".out", "w")
for i in range(0,outSize):
f.write(str(float(yvec[i]))+" ")
f.write(str(float(yvec[i].detach()))+" ")




if __name__ == '__main__':
main()
57 changes: 33 additions & 24 deletions tmva/sofie/test/Conv2dModelGenerator.py
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Expand Up @@ -12,7 +12,7 @@
result = []

class Net(nn.Module):

def __init__(self, nc = 1, ng = 1, nl = 4, use_bn = False, use_maxpool = False, use_avgpool = False):
super(Net, self).__init__()

Expand All @@ -22,7 +22,7 @@ def __init__(self, nc = 1, ng = 1, nl = 4, use_bn = False, use_maxpool = False,
self.use_bn = use_bn
self.use_maxpool = use_maxpool
self.use_avgpool = use_avgpool

self.conv0 = nn.Conv2d(in_channels=self.nc, out_channels=4, kernel_size=2, groups=1, stride=1, padding=1)
if (self.use_bn): self.bn1 = nn.BatchNorm2d(4)
if (self.use_maxpool): self.pool1 = nn.MaxPool2d(2)
Expand All @@ -32,7 +32,7 @@ def __init__(self, nc = 1, ng = 1, nl = 4, use_bn = False, use_maxpool = False,
self.conv1 = nn.Conv2d(in_channels=4, out_channels=8, groups = self.ng, kernel_size=3, stride=1, padding=1)
#output is same 4x4
self.conv2 = nn.Conv2d(in_channels=8, out_channels=4, kernel_size=3, stride=1, padding=1)
#use stride last layer
#use stride last layer
self.conv3 = nn.Conv2d(in_channels=4, out_channels=1, kernel_size=2, stride=2, padding=0)


Expand Down Expand Up @@ -60,7 +60,7 @@ def main():
parser = argparse.ArgumentParser(description='PyTorch model generator')
parser.add_argument('params', type=int, nargs='+',
help='parameters for the Conv network : batchSize , inputChannels, inputImageSize, nGroups, nLayers ')

parser.add_argument('--bn', action='store_true', default=False,
help='For using batch norm layer')
parser.add_argument('--maxpool', action='store_true', default=False,
Expand All @@ -72,13 +72,13 @@ def main():


args = parser.parse_args()

#args.params = (4,2,4,1,4)

np = len(args.params)
if (np < 5) : exit()
bsize = args.params[0]
nc = args.params[1]
nc = args.params[1]
d = args.params[2]
ngroups = args.params[3]
nlayers = args.params[4]
Expand All @@ -94,24 +94,24 @@ def main():
input = torch.zeros([])
for ib in range(0,bsize):
xa = torch.ones([1, 1, d, d]) * (ib+1)
if (nc > 1) :
if (nc > 1) :
xb = xa.neg()
xc = torch.cat((xa,xb),1) # concatenate tensors
if (nc > 2) :
xd = torch.zeros([1,nc-2,d,d])
xc = torch.cat((xa,xb,xd),1)
else:
xc = xa
#concatenate tensors
if (ib == 0) :

#concatenate tensors
if (ib == 0) :
xinput = xc
else :
xinput = torch.cat((xinput,xc),0)
xinput = torch.cat((xinput,xc),0)

print("input data",xinput.shape)
print(xinput)

name = "Conv2dModel"
if (use_bn): name += "_BN"
if (use_maxpool): name += "_MAXP"
Expand All @@ -122,24 +122,33 @@ def main():
loadModel=False
savePtModel = False


model = Net(nc,ngroups,nlayers, use_bn, use_maxpool, use_avgpool)
print(model)

model(xinput)

model.forward(xinput)

if savePtModel :
torch.save({'model_state_dict':model.state_dict()}, name + ".pt")


if saveOnnx:
torch.onnx.export(
model,
xinput,
name + ".onnx",
export_params=True
)

#new ONNX exporter does not work for batchmorm
dynamo_export=True
if (use_bn): dynamo_export=False

torch.onnx.export(
model,
xinput,
name + ".onnx",
export_params=True,
dynamo=dynamo_export,
external_data=False
)


if loadModel :
print('Loading model from file....')
Expand All @@ -160,10 +169,10 @@ def main():

f = open(name + ".out", "w")
for i in range(0,outSize):
f.write(str(float(yvec[i]))+" ")
f.write(str(float(yvec[i].detach()))+" ")




if __name__ == '__main__':
main()
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