Bad Performance with default_observer for quantization

[zacurr]

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I tried to quantize mobilenet v2 from float model file.
I found defaut_observer is used for activation in QConfig.
https://github.com/pytorch/vision/blob/master/torchvision/models/quantization/utils.py#L27

I got bad imagenet classification accuracy with this configuration.

https://pytorch.org/docs/stable/quantization.html
Here, the following configuration is recommended.
qconfig = torch.quantization.get_default_qconfig('qnnpack')

this uses HistogramObserver.
qconfig = QConfig(activation=HistogramObserver.with_args(reduce_range=False),
weight=default_weight_observer)

When I changed default observer to HistogramObserver, I got much better accuracy.

I think the following configuration should be changed as recommended in official docs.
https://github.com/pytorch/vision/blob/master/torchvision/models/quantization/utils.py#L27