Comments (4)
As of today, the QNNPACK can be used only from Caffe2 backend of PyTorch 1.0. We are working on converters and API to expose QNNPACK directly from PyTorch frontend and graph representation.
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Thank for your answer. How can I get the QNNPACK model for caffe2. Before I used onnx to convert the model to caffe2 model and the model can run using nnpack. But now how can I convert the model to caffe2 model using qnnpack?
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We haven't released the tooling for converting floating-point models to quantized (8-bit) models yet. You may use QNNPACK with the two pre-trained models that we released:
- ResNet-50: https://github.com/caffe2/models/tree/master/resnet50_quantized
- MobileNet v2: https://github.com/caffe2/models/tree/master/mobilenet_v2_quantized
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Thanks. I've tested the model. The results seem good.
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Related Issues (20)
- Per channel quantization for the weights HOT 1
- Implementation detail things... HOT 9
- Deconvolutional Layer is slow for large kernel size HOT 2
- No performance difference between ARMv7 (32-bit) Android and ARM64 (64-bit) Android HOT 1
- Request to add Conv1d related operation HOT 1
- What's the relationship between the ASM and C version micro kernel? HOT 5
- Request to add a Concat layer HOT 1
- Crashed when running conv3x3 test
- Typo in the announcement blog? HOT 1
- 3d convolution support HOT 1
- Is the code multithreaded for arm64? HOT 7
- build error
- is there any example to use fp16 gemm?
- COMPAIR ERROR HOT 2
- How to build dependencies separately
- Is QNNPACK still being developed? HOT 1
- Understanding XZP ukernel HOT 1
- Build failed on rasp pi 4 (Error: selected processor does not support `yield' in ARM mode) HOT 1
- Support for nn.ConvTranspose2d
- Qnnpack accuracy very poor on unet model HOT 3
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