I have a tiny yolo v3 model trained with https://github.com/AlexeyAB/darknet and would like to convert it to Tensorflow and then to Openvino IR format, as it is mentioned on their documentation.
My model has an input shape of 352x288x1 and there is no option to set width and height separately with the convert_weights_pb.py script. When still try to run it I get the following error:
ValueError: Dimension 1 in both shapes must be equal, but are 18 and 22. Shapes are [?,18,22] and [?,22,18]. for 'detector/yolo-v3-tiny/concat_3' (op: 'ConcatV2') with input shapes: [?,18,22,128], [?,22,18,256], [] and with computed input tensors: input[2] = <3>.
Does anyone know a workaround or is it planned to support rectangular input shapes?
Help would be appreciated since I can not change the input shape.
I have a tiny yolo v3 model trained with https://github.com/AlexeyAB/darknet and would like to convert it to Tensorflow and then to Openvino IR format, as it is mentioned on their documentation. My model has an input shape of 352x288x1 and there is no option to set width and height separately with the convert_weights_pb.py script. When still try to run it I get the following error:
ValueError: Dimension 1 in both shapes must be equal, but are 18 and 22. Shapes are [?,18,22] and [?,22,18]. for 'detector/yolo-v3-tiny/concat_3' (op: 'ConcatV2') with input shapes: [?,18,22,128], [?,22,18,256], [] and with computed input tensors: input[2] = <3>.Does anyone know a workaround or is it planned to support rectangular input shapes? Help would be appreciated since I can not change the input shape.