model_to_quantize = mAlexNet(num_classes= 10).to(device) qconfig_mapping = get_default_qat_qconfig_mapping("qnnpack") model_to_quantize.train() # prepare model_prepared = quantize_fx.prepare_qat_fx(model_to_quantize, qconfig_mapping, example_inputs) # training loop model_trained_prepared =...
model_to_quantize = mAlexNet(num_classes= 10).to(device) qconfig_mapping = get_default_qat_qconfig_mapping("qnnpack") model_to_quantize.train() # prepare model_prepared = quantize_fx.prepare_qat_fx(model_to_quantize, qconfig_mapping, example_inputs) # training loop model_trained_prepared =...
model_to_quantize = mAlexNet(num_classes= 10).to(device) qconfig_mapping = get_default_qat_qconfig_mapping("qnnpack") model_to_quantize.train() # prepare model_prepared = quantize_fx.prepare_qat_fx(model_to_quantize, qconfig_mapping, example_inputs) # training loop model_trained_prepared =...
model_to_quantize = mAlexNet(num_classes= 10).to(device) qconfig_mapping = get_default_qat_qconfig_mapping("qnnpack") model_to_quantize.train() # prepare model_prepared = quantize_fx.prepare_qat_fx(model_to_quantize, qconfig_mapping, example_inputs) # training loop model_trained_prepared =...
get_default_qat_qconfig_mapping, QConfigMapping, )importtorch.ao.quantization.quantize_fxasquantize_fximportcopy#Loadfloatmodel model_fp = mAlexNet(num_classes=10).to(device) model_fp.load_state_dict(torch.load("./float_model.pth", map_location=device)) ...
get_default_qat_qconfig_mapping, QConfigMapping, ) import torch.ao.quantization.quantize_fx as quantize_fx import copy # Load float model model_fp = mAlexNet(num_classes= 10).to(device) model_fp.load_state_dict(torch.load("./float_model.pth", map_location=device)) ...
1,设置qconfig 在设置qconfig之前,模型首先设置为训练模式,这很容易理解,因为QAT的着力点就是T嘛: cnet = CivilNet() cnet.train() 使用get_default_qat_qconfig API来给要QAT的网络设置qconfig: cnet.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') 不过,这个qconfig和静态量化中的可不一样啊...
get_default_qat_qconfig_mapping, QConfigMapping, ) import torch.ao.quantization.quantize_fx as quantize_fx import copy 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. # VGG16模型结构 # Modified from https://github.com/kuangliu/pytorch-cifar/blob/master/models/vgg.py ...
model(model):qconfig_dict={# Global Config"":torch.ao.quantization.get_default_qat_qconfig('...
每个qconfig 是 QConfig 类的实例,封装了两个 observer; 这两个 observer 分别是 activation 的 observer 和 weight 的 observer; 但是动态量化使用的是 QConfig 子类 QConfigDynamic 的实例,该实例实际上只封装了 weight 的 observer; activate 就是 post process,就是 op forward 之后的后处理,但在动态量化中...