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-rwxr-xr-xmodel-integration/src/test/models/onnx/pytorch/pytorch_one_layer.py38
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diff --git a/model-integration/src/test/models/onnx/pytorch/pytorch_one_layer.py b/model-integration/src/test/models/onnx/pytorch/pytorch_one_layer.py
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+++ b/model-integration/src/test/models/onnx/pytorch/pytorch_one_layer.py
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+# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+
+import torch
+import torch.onnx
+
+
+class MyModel(torch.nn.Module):
+ def __init__(self):
+ super(MyModel, self).__init__()
+ self.linear = torch.nn.Linear(in_features=3, out_features=1)
+ self.logistic = torch.nn.Sigmoid()
+
+ def forward(self, vec):
+ return self.logistic(self.linear(vec))
+
+
+def main():
+ model = MyModel()
+
+ # Omit training - just export randomly initialized network
+
+ data = torch.FloatTensor([[0.1, 0.2, 0.3],[0.4, 0.5, 0.6]])
+ torch.onnx.export(model,
+ data,
+ "one_layer.onnx",
+ input_names = ["input"],
+ output_names = ["output"],
+ dynamic_axes = {
+ "input": {0: "batch"},
+ "output": {0: "batch"},
+ },
+ opset_version=12)
+
+
+if __name__ == "__main__":
+ main()
+
+