Comments (6)
dinghuanghao
commented on May 2, 2024
Tensorflow Lite currently does not support the instance_norm.
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steven0129
commented on May 2, 2024
Thanks for fast reply.
Any chances to convert instance_norm to other supported op?
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peterjc123
commented on May 2, 2024
We may convert the float variant of instance_norm to a bunch of TFLite ops if you want, in which case we will make quantizer skip this op for QAT prepration. FYI, there is no way to translate the quantized one to TFLite.
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steven0129
commented on May 2, 2024
Converting the float variant of instance_norm to a bunch of TFLite ops looks good to me.
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peterjc123
commented on May 2, 2024
@steven0129 Implemented, would you please give it a try?
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steven0129
commented on May 2, 2024
@peterjc123 This patch works. This issue can be closed.
Thanks
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Related Issues (20)
- Float model failed to convert to TFLite
- [converter] map gather(+reshape) ops with seperate consecutive indices to split(unpack) ops
- tinynn.converter module not found! HOT 2
- [CI] several tests for modifier failed
- Whether to support pytorch to keras HOT 1
- TransposeConv wrong shape? HOT 15
- change input to INT8 after converting to tflite HOT 2
- [converter] implement torch's `aten::scaled_dot_product_attention` operator HOT 2
- Request: clamp would be more efficient to go to Bounded Relu than Maximum + Minimum HOT 3
- Do not support PReLU module? HOT 5
- torch.max not working HOT 2
- OneShotChannelPruner results in the miss of some operators HOT 4
- KeyError when executing quantization HOT 5
- PyTorch 转 TFLite 使用 int8 量化 HOT 4
- Does tinynn support following int16 quantization? HOT 1
- jit.trace succeed but tinynn tracer failed HOT 1
- It became larger after converting to tflite model HOT 4
- how to do Post-training integer quantization with int16 activation HOT 4
- unnecessary float() variables cause quantization to fail. HOT 7
- aten::index nodes take multiple indices in PyTorch model but cause an error when trying to convert to TFLite HOT 1
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