[GitHub] sandeep-krishnamurthy commented on issue #11849: gluon.SymbolBlock cannot imports resnet trained with dtype="float16"

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sandeep-krishnamurthy commented on issue #11849: gluon.SymbolBlock cannot imports resnet trained with dtype="float16"
URL: https://github.com/apache/incubator-mxnet/issues/11849#issuecomment-417124700
 
 
   Using below snippet:
   ```
   import mxnet as mx
   
   ctx = mx.cpu(0)
   data = mx.nd.zeros((1,3,224,224), ctx=ctx, dtype='float64')
   net_fp32 = mx.gluon.model_zoo.vision.resnet34_v2(pretrained=True, ctx=ctx)
   net_fp32.cast('float64')
   net_fp32.hybridize()
   pred = net_fp32.forward(data)
   net_fp32.export('resnet34_fp16', 0)
   print('export fp16 model')
   
   sm = mx.sym.load('resnet34_fp16-symbol.json')
   inputs = mx.sym.var('data', dtype='float64')
   net_fp16 = mx.gluon.SymbolBlock(sm, inputs)
   net_fp16.collect_params().load('resnet34_fp16-0000.params', ctx)
   pred = net_fp16.forward(data)
   ```
   
   Below are my findings:
   1. Casting worked fine.
   2. Saved parameters are in the correct format (fp64 in my sample code)
   3. sym.load worked fine. If I infer symbol's type (sym.infer_type(data='float64') I get correct inferred type (float64) for all params.
   
   Below is the issue:
   1. When you create mx.gluon.SymbolBlock(sm, input). It creates initializes parameters in the Block. By default, this uses Float32.
   See here - https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L1058 and the behavior is "If there is not parameter to get, it creates one and uses default type (fp32) https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/parameter.py#L688
   
   I am working on the fix.
   
   @apeforest @ThomasDelteil  - FYI

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