Fixed reading batch norm epsilon from ONNX

src/onnx/onnx.cpp

@@ -234,7 +234,7 @@ struct onnx_parser
         }
         if(contains(attributes, "momentum"))
         {
-            epsilon = parse_value(attributes.at("momentum")).at<float>();
+            momentum = parse_value(attributes.at("momentum")).at<float>();
         }
         if(contains(attributes, "is_test"))
         {

src/targets/cpu/cpu_lowering.cpp

@@ -48,10 +48,10 @@ struct cpu_batch_norm_inference
 
         double epsilon           = op.epsilon;
         auto input               = args[0];
-        auto mini_batch_mean     = args[1];
-        auto mini_batch_variance = args[2];
-        auto arg_gamma           = args[3];
-        auto arg_bias            = args[4];
+        auto arg_gamma           = args[1];
+        auto arg_bias            = args[2];
+        auto mini_batch_mean     = args[3];
+        auto mini_batch_variance = args[4];
 
         auto num_batch    = output_shape.lens()[0];
         auto num_channels = output_shape.lens()[1];

src/targets/gpu/lowering.cpp

@@ -48,10 +48,10 @@ struct miopen_batch_norm_inference
                                                  y_desc.get(),
                                                  args[5].implicit(),
                                                  bn_desc.get(),
-                                                 args[3].implicit(),
-                                                 args[4].implicit(),
                                                  args[1].implicit(),
                                                  args[2].implicit(),
+                                                 args[3].implicit(),
+                                                 args[4].implicit(),
                                                  op.epsilon);
 
         return args[5];

test/cpu_ops_test.cpp

@@ -34,7 +34,7 @@ void batch_norm_inference_test()
     auto mean     = p.add_literal(migraph::literal{vars, mean_data});
     auto variance = p.add_literal(migraph::literal{vars, variance_data});
 
-    p.add_instruction(migraph::batch_norm_inference{}, x, mean, variance, scale, bias);
+    p.add_instruction(migraph::batch_norm_inference{}, x, scale, bias, mean, variance);
     p.compile(migraph::cpu::cpu_target{});
     auto result = p.eval({});
 

test/gpu/miopen.cpp

@@ -287,11 +287,11 @@ struct test_batchnorm_inference_2
         migraph::shape s{migraph::shape::float_type, {batches, channels, height, width}};
         migraph::shape vars{migraph::shape::float_type, {channels}};
         auto x        = p.add_parameter("x", s);
-        auto mean     = p.add_parameter("mean", vars);
-        auto variance = p.add_parameter("variance", vars);
-        auto scale    = p.add_parameter("scale", vars);
-        auto bias     = p.add_parameter("bias", vars);
-        p.add_instruction(migraph::batch_norm_inference{}, x, mean, variance, scale, bias);
+        auto scale    = p.add_literal(migraph::abs(migraph::generate_literal(vars, 1)));
+        auto bias     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 2)));
+        auto mean     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 3)));
+        auto variance = p.add_literal(migraph::abs(migraph::generate_literal(vars, 4)));
+        p.add_instruction(migraph::batch_norm_inference{}, x, scale, bias, mean, variance);
         return p;
     }
 };
@@ -310,11 +310,35 @@ struct test_batchnorm_inference
         migraph::shape s{migraph::shape::float_type, {batches, channels, height, width}};
         migraph::shape vars{migraph::shape::float_type, {channels}};
         auto x        = p.add_parameter("x", s);
-        auto mean     = p.add_parameter("mean", vars);
-        auto variance = p.add_parameter("variance", vars);
-        auto scale    = p.add_parameter("scale", vars);
-        auto bias     = p.add_parameter("bias", vars);
-        p.add_instruction(migraph::batch_norm_inference{}, x, mean, variance, scale, bias);
+        auto scale    = p.add_literal(migraph::abs(migraph::generate_literal(vars, 1)));
+        auto bias     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 2)));
+        auto mean     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 3)));
+        auto variance = p.add_literal(migraph::abs(migraph::generate_literal(vars, 4)));
+        p.add_instruction(migraph::batch_norm_inference{}, x, scale, bias, mean, variance);
+        return p;
+    }
+};
+
+struct test_conv_bn_relu_pooling
+{
+    migraph::program create_program() const
+    {
+        migraph::program p;
+
+        migraph::shape xs{migraph::shape::float_type, {1, 3, 224, 224}};
+        migraph::shape ws{migraph::shape::float_type, {64, 3, 7, 7}};
+        migraph::shape vars{migraph::shape::float_type, {64}};
+        auto x    = p.add_parameter("x", xs);
+        auto w    = p.add_parameter("w", ws);
+        auto conv = p.add_instruction(migraph::convolution{{3, 3}, {2, 2}, {1, 1}}, x, w);
+        auto scale    = p.add_literal(migraph::abs(migraph::generate_literal(vars, 1)));
+        auto bias     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 2)));
+        auto mean     = p.add_literal(migraph::abs(migraph::generate_literal(vars, 3)));
+        auto variance = p.add_literal(migraph::abs(migraph::generate_literal(vars, 4)));
+        auto bn =
+            p.add_instruction(migraph::batch_norm_inference{}, conv, scale, bias, mean, variance);
+        auto relu = p.add_instruction(migraph::activation{"relu"}, bn);
+        p.add_instruction(migraph::pooling{"average", {1, 1}, {2, 2}, {3, 3}}, relu);
         return p;
     }
 };