Formatting

src/onnx/onnx.cpp

@@ -24,10 +24,7 @@ struct unknown
         else
             return input.front();
     }
-    argument compute(shape, std::vector<argument>) const
-    {
-        RTG_THROW("not computable");
-    }
+    argument compute(shape, std::vector<argument>) const { RTG_THROW("not computable"); }
     friend std::ostream& operator<<(std::ostream& os, const unknown& x)
     {
         os << x.name();
@@ -51,8 +48,7 @@ struct onnx_parser
 {
     using attribute_map = std::unordered_map<std::string, onnx::AttributeProto>;
     using node_map      = std::unordered_map<std::string, onnx::NodeProto>;
-    using op_func =
-        std::function<instruction_ref(attribute_map, std::vector<instruction_ref>)>;
+    using op_func = std::function<instruction_ref(attribute_map, std::vector<instruction_ref>)>;
     node_map nodes;
     std::unordered_map<std::string, instruction_ref> instructions;
     program prog = program();
@@ -245,8 +241,7 @@ struct onnx_parser
         case onnx::AttributeProto::STRING: return {};
         case onnx::AttributeProto::TENSOR: return parse_tensor(attr.t());
         case onnx::AttributeProto::GRAPH: return {};
-        case onnx::AttributeProto::FLOATS:
-            return from_repeated(shape::float_type, attr.floats());
+        case onnx::AttributeProto::FLOATS: return from_repeated(shape::float_type, attr.floats());
         case onnx::AttributeProto::INTS: return from_repeated(shape::int64_type, attr.ints());
         case onnx::AttributeProto::STRINGS: return {};
         case onnx::AttributeProto::TENSORS: return {};
@@ -262,28 +257,21 @@ struct onnx_parser
         {
         case onnx::TensorProto::UNDEFINED: throw std::runtime_error("");
         case onnx::TensorProto::FLOAT:
-            return literal{
-                {shape::float_type, dims}, t.float_data().begin(), t.float_data().end()};
+            return literal{{shape::float_type, dims}, t.float_data().begin(), t.float_data().end()};
         case onnx::TensorProto::UINT8: throw std::runtime_error("");
         case onnx::TensorProto::INT8:
-            return literal{
-                {shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
+            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
         case onnx::TensorProto::UINT16:
-            return literal{
-                {shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
+            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
         case onnx::TensorProto::INT16:
-            return literal{
-                {shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
+            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
         case onnx::TensorProto::INT32:
-            return literal{
-                {shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
+            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
         case onnx::TensorProto::INT64:
-            return literal{
-                {shape::int64_type, dims}, t.int64_data().begin(), t.int64_data().end()};
+            return literal{{shape::int64_type, dims}, t.int64_data().begin(), t.int64_data().end()};
         case onnx::TensorProto::STRING: throw std::runtime_error("");
         case onnx::TensorProto::BOOL:
-            return literal{
-                {shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
+            return literal{{shape::int32_type, dims}, t.int32_data().begin(), t.int32_data().end()};
         case onnx::TensorProto::FLOAT16: throw std::runtime_error("");
         case onnx::TensorProto::DOUBLE:
             return literal{