add test for prelu negative slope access pattern

testdata/dnn/onnx/generate_onnx_models.py

@@ -8,6 +8,7 @@ import paddle # version 2.1.1
 import numpy as np
 import os.path
 import onnx
+import onnxsim
 import google.protobuf.text_format
 import io
 
@@ -72,6 +73,14 @@ def save_onnx_data_and_model(input, output, name, operation, *args, **kwargs):
     model = onnx.helper.make_model(graph, producer_name=name)
     onnx.save(model, models_files)
 
+def simplify(name, rename=False, **kwargs):
+    model, check = onnxsim.simplify(name, **kwargs)
+    assert check, "couldn't valide"
+    name = name[:-5]
+    if rename:
+        name += '_optimized'
+    onnx.save(model, name + '.onnx')
+
 torch.manual_seed(0)
 np.random.seed(0)
 
@@ -127,6 +136,18 @@ input = Variable(torch.randn(1, 2, 10, 10))
 relu = nn.ReLU(inplace=True)
 save_data_and_model("ReLU", input, relu)
 
+class PReLU_slope(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(PReLU_slope, self).__init__()
+
+    def forward(self, x):
+        return nn.PReLU()(x)
+
+model = PReLU_slope()
+input_ = Variable(torch.randn(1, 1, 5, 5, dtype=torch.float32))
+save_data_and_model("PReLU_slope", input_, model, export_params=True)
+simplify('models/PReLU_slope.onnx', False)
+
 
 input = Variable(torch.randn(2, 3))
 dropout = nn.Dropout()