Static quantization tutorial #686
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| print('\n Inverted Residual Block: Before fusion \n\n', float_model.features[1].conv) | ||
| float_model.eval() | ||
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| # Fusion is optional |
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Lets remove this comment, it is required for quantization.
| # quantization. | ||
| # - We can also simulate the accuracy of a quantized model in floating point since | ||
| # we are using fake-quantization to model the numerics of actual quantized arithmetic. | ||
| # - We can mimic post training quantization easily too. |
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We can also state that quantization aware training yields an accuracy of over 71% on the entire imagenet dataset, which is close to the floating point accuracy of 71.9%. In addition, we can also state that the simple quantization technique that we first try gives us 63% accuracy and the per-channel technique boosts it to 67%
raghuramank100
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Oct 10, 2019
dzhulgakov
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Oct 10, 2019
rodrigo-techera
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Nov 29, 2021
Static quantization tutorial
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Static quantization tutorial, covering:
Based on @raghuramank100's Bento notebook.