Description

Cannot load fine-tuned resnet101 (incubator-mxnet/example/image-classification/symbols/resnet.py) with dtype="float16" with "gluon.SymbolBlock.imports" method.

Error Message:

AssertionError: Failed loading Parameter 'stage3_unit2_conv2_weight' from saved params: dtype incompatible expected <type 'numpy.float32'> vs saved <type 'numpy.float16'>

Minimum reproducible example

net = gluon.SymbolBlock.imports('resnet-101-symbol.json',['data','softmax_label'],'resnet-101-0007.params')  # This line gives the error message.
net = gluon.SymbolBlock.imports('resnet-101-symbol.json',['data','softmax_label']) 
print(net.collect_params())

My Questions

In incubator-mxnet/example/image-classification/symbols/resnet.py,
there is mx.sym.Cast for type conversion.
I fine-tuned resnet101 with dtype="float16", and I need to load this model as HybridBlock, However, the method gluon.SymbolBlock.imports makes every params' type in the network as float32. Therefore, the trained model cannot be updated.

Here, resnet-101-0007.params are trained with argument dtype='float16'
In resnet-101-symbol.json file, there is the Cast op.
{
"op": "Cast",
"name": "cast0",
"attrs": {"dtype": "float16"},
"inputs": [[7, 0, 0]]
},
It seems that gluon.SymbolBlock.imports does not consider the type conversion operator.

For now, I think I need to load all parameter manualy, and change types then save.
Is there any other solution to solve this problem?