Comments (5)
YeahTech
commented on May 18, 2024
coex-share.zip
命令：python post_train_quantization.py --netconfig configs/stereo/cfg_coex-crestereo-input640-disp64-costAA2-GW-shenzhenbatch12.yaml
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peterjc123
commented on May 18, 2024
好吧，直接原因是因为在converter中没有把量化的bn映射成BATCH_NORM算子，所以没有做fusion，其实根本原因是我们不能在converter中直接完成这一步因为这样会导致精度损失。好在1.11的PyTorch已经支持了ConvTranspose和BatchNorm在后量化时的融合，所以可以在量化那边把那些pattern加上，这样可以完成你的需求了。
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peterjc123
commented on May 18, 2024
相关pr: #80
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peterjc123
commented on May 18, 2024
@yaoxinghua 相关改动已合入，升级到PyTorch 1.11应该就可以了
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YeahTech
commented on May 18, 2024
@peterjc123 收到，感谢大佬
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Related Issues (20)
- 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
- aten::repeat_interleave is considered an unsupported Tensor and causing an error when trying to convert to TFLite HOT 2
- convert model error HOT 5
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