KineticCookie commented on issue #9361: infer_shape error for 'resnet-152' URL: https://github.com/apache/incubator-mxnet/issues/9361#issuecomment-362204536 @kevinthesun > If you don't know exactly what a model does and what kind of inputs it accepts, it might not be a good idea to directly use it in your app. I understand your concerns, but the sole purpose of my app is to provide a simple way to expose models as web service. If I train model with dataset of int32 and 300x300 shape, I think that is obvious that I intend to use it with the same shape and datatype. The point is, why mxnet doesn't provide information about datatypes and shapes, which are already inferred and known in the training process, when I export model? For instance, in TensorFlow: 1. I create Signature for a model with Tensor information (as described in https://www.tensorflow.org/serving/serving_basic). 2. I specify which tensors are inputs and which ones are outputs. Shapes and types, defined statically or inferred, are written to the signature. 3. Signature information is exported with model itself. When I import a model, I know about model inputs/outputs, with their types and shapes at a runtime. This helps me to validate data and pass it to the model. This also gives me an opportunity to create a documentation or an interface, so external users are able to use this model. In conclusion, mxnet doesn't have a Signature mechanism and it infers shape and data at the training. So, why doesn't it save this information along with model?
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