My Keras yolov3-tiny.h5 model has an input tensor with dimensions ?,?,?,3 while I would expect ?,416,416,3 - tensorflow

Question

When inspecting the Keras model yolov3-tiny.h5 using netron
I see that the input node is called input_1 and has type float32[?,?,?,3]. I would expect float32[?,416,416,3]
How can I force it to be float32[?,416,416,3]?
This is needed for downstream processing. The Keras model has to be converted to a frozen_model.pb in Tensorflow and then be further processed for deployement.
The deployement tools cannot handle an input with unknow w,h size.
Here is how I generated the Keras model. I downloaded the yolov3-tiny.cfg (https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg) and yolov3-tiny.weigths (https://pjreddie.com/media/files/yolov3-tiny.weights)
And then converted the model to a keras model using the following command :
python convert.py -p yolov3-tiny.cfg yolov3-tiny.weights model_data/yolov3-tiny.h5
(this code is obtained by cloning https://github.com/qqwweee/keras-yolo3)
Making a prediction using the saved Keras model works fine :
python yolo_video.py --image --model model_data/yolov3-tiny.h5
However when inspecting the Keras model yolov3-tiny.h5 using netron
I see that the input node is called input_1 and has type float32[?,?,?,3]
I would expect float32[?,416,416,3]
How can I force it to be float32[?,416,416,3]?

Answer 1

You could do that while converting the keras model file to tflite by passing input_shapes as a dictionary with input node name as the key and shape as the value.
Check out the TFLiteConverter class here
def _main_(args):
kmodel = args.kmodel
lmodel = args.lmodel
converter = tf.lite.TFLiteConverter.from_keras_model_file(kmodel, input_shapes={"input_1":[1,416,416,3]})
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
open("lmodel", "wb").write(tflite_model)
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description='Quantize a trained yolo model')
argparser.add_argument('-k', '--kmodel', help='path to model weights file input')
argparser.add_argument('-l', '--lmodel', help='path to tensor flow lite output')
args = argparser.parse_args()
_main_(args)