ModelZoo ONNX backend for inference: support multi-input or -output

src/eynollah/model_zoo/model_zoo.py

@@ -331,14 +331,25 @@ class EynollahModelZoo:
             model_path,
             providers=providers)
         # FIXME: notify about selected provider/device
-        input_name = model.get_inputs()[0].name
-        output_name = model.get_outputs()[0].name
+        model_inputs = [model_input.name
+                        for model_input in model.get_inputs()]
+        model_outputs = [model_output.name
+                         for model_output in model.get_outputs()]
         def predict_onnx(inputs):
-            # models expect data_type() == 'tensor(float)', but np.float16 is 'tensor(float16)'
-            # FIXME: do this dynamically (but how to convert .type to np.dtype?)
-            inputs = inputs.astype(np.float32)
-            return model.run(
-                [output_name], {input_name: inputs})[0]
+            if len(model_inputs) == 1:
+                inputs = [inputs]
+            outputs = model.run(model_outputs, {
+                model_input:
+                input_data.astype(
+                    # models expect data_type() == 'tensor(float)', but np.float16 is 'tensor(float16)'
+                    # FIXME: do this dynamically (but how to convert .type to np.dtype?)
+                    np.float32 if input_data.dtype in [np.float16, np.float64] else
+                    input_data.dtype)
+                for model_input, input_data in zip(model_inputs, inputs)
+            })
+            if len(model_outputs) == 1:
+                outputs = outputs[0]
+            return outputs
         model.predict_on_batch = predict_onnx
         model.input_shape = model.get_inputs()[0].shape