Added test for parsing batchnorm ONNX

test/onnx/onnx_test.cpp

@@ -39,6 +39,29 @@ void pytorch_conv_relu_maxpool()
     EXPECT(p == prog);
 }
 
+void pytorch_conv_bn_relu_maxpool()
+{
+    migraph::program p;
+    auto l0 = p.add_parameter("0", {migraph::shape::float_type, {1, 3, 32, 32}});
+    auto l1 = p.add_parameter("1", {migraph::shape::float_type, {1, 3, 5, 5}});
+    auto l2 = p.add_parameter("2", {migraph::shape::float_type, {1}});
+
+    auto p3       = p.add_parameter("3", {migraph::shape::float_type, {1}});
+    auto p4       = p.add_parameter("4", {migraph::shape::float_type, {1}});
+    auto p5       = p.add_parameter("5", {migraph::shape::float_type, {1}});
+    auto p6       = p.add_parameter("6", {migraph::shape::float_type, {1}});
+    uint64_t axis = 1;
+    auto l3       = p.add_instruction(migraph::convolution{}, l0, l1);
+    auto l4       = p.add_instruction(migraph::broadcast{axis}, l3, l2);
+    auto l5       = p.add_instruction(migraph::add{}, l3, l4);
+    auto l6       = p.add_instruction(migraph::batch_norm_inference{}, l5, p3, p4, p5, p6);
+    auto l7       = p.add_instruction(migraph::activation{"relu"}, l6);
+    p.add_instruction(migraph::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l7);
+
+    auto prog = migraph::parse_onnx("conv_bn_relu_maxpool.onnx");
+    EXPECT(p == prog);
+}
+
 void pytorch_conv_relu_maxpoolX2()
 {
     migraph::program p;
@@ -69,5 +92,6 @@ int main()
 {
     pytorch_conv_bias_test();
     pytorch_conv_relu_maxpool();
+    pytorch_conv_bn_relu_maxpool();
     pytorch_conv_relu_maxpoolX2();
 }