map_location=device)model.eval()input_names=['input']output_names=['output']x=torch.randn(1,3,224,224,device=device)#与实际输入数据的shape一致即可,取值没有影响,所以用了随机数torch.onnx.export(model,x,'name.onnx',input_names=input_
1.2.1、训练后动态量化(Post Training Dynamic Quantization) 官方地址:torch.quantization.quantize_dynamic() <1>顾名思义,就是在训练完成后进行量化。 <2>编译器中书写方法 Class torch.quantization.quantize_dynamic(model, qconfig_spec=None, dtype=torch.qint8, mapping=None, inplace=False) <3>将float型...
(https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html) 张量量化 张量量化是将一个张量进行统一的量化,简单但是对模型精度影响大。 1. 加载模型 myModel = load_model(saved_model_dir + float_model_file).to('cpu') myModel.eval() 2. 模型融合 myModel.fuse_model() 3. 配置量化...
num_calibration_batches = 10 myModel = load_model(saved_model_dir + float_model_file).to('cpu') myModel.eval() # Fuse Conv, bn and relu myModel.fuse_model() # Specify quantization configuration # Start with simple min/max range estimation and per-tensor quantization of weights myModel....
The user has a lot of control and can choose different quantization and calibration functions for different parts of the model and can apply quantization to the whole model or just parts. The tutorial should work with top of tree. It is currently CPU focused. ...
文本多标签分类是常见的NLP任务,文本介绍了如何使用Bert模型完成文本多标签分类,并给出了各自的步骤。 参考官方教程:https://pytorch.org/tutorials/intermediate/dynamic_quantization_bert_tutorial.html 复旦大学邱锡鹏老师课题组的研究论文《How to Fine-Tune BERT for Text Classification?》。
model.weight现在是一个属性 它有一个新的module.parametrizations属性 未参数化的权重已移动到module.parametrizations.weight.original 在对weight进行参数化之后,layer.weight被转换为Python 属性。每当我们请求layer.weight时,此属性会计算parametrization(weight),就像我们在上面的LinearSymmetric实现中所做的那样。
with torch.inference_mode(): for _ in range(10): x = torch.rand(1, 2, 28, 28) model_prepared(x) # quantize model_quantized = quantize_fx.convert_fx(model_prepared) PS:直观对比EAGER模式和FX模式的代码量,可以看出FX模式真香! 感知量化[Quantization-aware Training (QAT)] PTQ方法适用于大型...
(beta) Dynamic Quantization on an LSTM Word Language Model Tutorial:这一篇我已经计划要撰写学习笔记博文 Quantization API Documentaion (beta) Dynamic Quantization on BERT:这一篇我已经计划要撰写学习笔记博文 Introduction to Quantization on PyTorch | PyTorch...
Quantization We also provide a simple demo to quantize these models to specified bit-width with several methods, including linear method, minmax method and non-linear method. quantize --type cifar10 --quant_method linear --param_bits 8 --fwd_bits 8 --bn_bits 8 --ngpu 1 Top1 Accuracy ...