[RSDK-10284]: MLModelService: don't check for name if there is only 1 expected tensor, return error if expected tensors not present

src/viam/sdk/services/private/mlmodel_server.cpp

@@ -11,7 +11,6 @@
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
-
 #include <viam/sdk/services/private/mlmodel_server.hpp>
 
 #include <viam/sdk/common/private/service_helper.hpp>
@@ -38,17 +37,13 @@ ::grpc::Status MLModelServiceServer::Infer(
 
         const auto md = mlms->metadata({});
         MLModelService::named_tensor_views inputs;
-        for (const auto& input : md.inputs) {
-            const auto where = request->input_tensors().tensors().find(input.name);
-            if (where == request->input_tensors().tensors().end()) {
-                // Ignore any inputs for which we don't have metadata, since
-                // we can't validate the type info.
-                //
-                // TODO: Should this be an error? For now we just don't decode
-                // those tensors.
-                continue;
-            }
-            auto tensor = mlmodel::make_sdk_tensor_from_api_tensor(where->second);
+
+        // Check if there's only one input tensor and metadata only expects one, too
+        if (request->input_tensors().tensors().size() == 1 && md.inputs.size() == 1) {
+            // Special case: just one tensor, add it without name check
+            const MLModelService::tensor_info input = md.inputs[0];
+            const auto& tensor_pair = *request->input_tensors().tensors().begin();
+            auto tensor = mlmodel::make_sdk_tensor_from_api_tensor(tensor_pair.second);
             const auto tensor_type = MLModelService::tensor_info::tensor_views_to_data_type(tensor);
             if (tensor_type != input.data_type) {
                 std::ostringstream message;
@@ -58,7 +53,35 @@ ::grpc::Status MLModelServiceServer::Infer(
                         << static_cast<ut>(tensor_type);
                 return helper.fail(::grpc::INVALID_ARGUMENT, message.str().c_str());
             }
-            inputs.emplace(std::move(input.name), std::move(tensor));
+            inputs.emplace(tensor_pair.first, std::move(tensor));
+        } else {
+            // Normal case: multiple tensors, do metadata checks
+	    // If there are extra tensors in the inputs that not found in the metadata,
+	    // they will not be passed on to the implementation.
+            for (const auto& input : md.inputs) {
+                const auto where = request->input_tensors().tensors().find(input.name);
+                if (where == request->input_tensors().tensors().end()) {
+                    // if the input vector of the expected name is not found, return an error
+                    std::ostringstream message;
+                    message << "Expected tensor input `" << input.name
+                            << "` was not found; if you believe you have this tensor under a "
+                               "different name, rename it to the expected tensor name";
+                    return helper.fail(::grpc::INVALID_ARGUMENT, message.str().c_str());
+                }
+                auto tensor = mlmodel::make_sdk_tensor_from_api_tensor(where->second);
+                const auto tensor_type =
+                    MLModelService::tensor_info::tensor_views_to_data_type(tensor);
+                if (tensor_type != input.data_type) {
+                    std::ostringstream message;
+                    using ut = std::underlying_type<MLModelService::tensor_info::data_types>::type;
+                    message << "Tensor input `" << input.name
+                            << "` was the wrong type; expected type "
+                            << static_cast<ut>(input.data_type) << " but got type "
+                            << static_cast<ut>(tensor_type);
+                    return helper.fail(::grpc::INVALID_ARGUMENT, message.str().c_str());
+                }
+                inputs.emplace(std::move(input.name), std::move(tensor));
+            }
         }
 
         const auto outputs = mlms->infer(inputs, helper.getExtra());