KineticCookie commented on issue #9361: infer_shape error for 'resnet-152' URL: https://github.com/apache/incubator-mxnet/issues/9361#issuecomment-357217016 I executed your snippet, and I can't find information about shapes model was trained with. ``` ResNetV2( (features): HybridSequential( /* layer attributes */) (output): Dense(2048 -> 1000, linear) ) ``` Layer attribute doesn't contain information to get a model input and output shape. Moreover I can't find a data types for each layer. I made an additional search and found this issue #7641 with @jeremiedb explaining shapes implementation: > There's typically no need to specify shape of data input when building the symbolic network. This will typically will be set at training time when the model is bind and the shapes infered from what the iterator provides as input data. This allows the same network to be trained with different batch sizes. Seems like mxnet infers information about shapes and data types at training, but doesn't store it in model files. `infer_[shape, type]` methods are the only way to get this info, but they require hardcoded variables. I try to implement Tensorflow Serving-like server that could handle any exported mxnet model, and serve it via HTTP api. But to do this I need to know: 1. What data model is waiting for? (name of input, shape of input, data type of input) So server can prepare user data to be passed to inference method. 2. What model will return after inference? (name of output, shape of output, data type of ouput) So clients of my server expect some specified values to return after they send a request. If I put away my serving case: I got a mxnet model from datascientist to use in my app. Model ships with no documentation. I can't contact datascientist either. Is there any way to use this mxnet model, considering I have no clue what data was used to train this model?
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