fromtorch.quantizationimportget_default_qconfig fromtorch.quantization.quantize_fximportprepare_fx, convert_fx float_model.eval()# 因为是PTQ,所以就推理模式就够了 qconfig = get_default_qconfig("fbgemm")# 指定量化细节配置 qconfig_dict = {"": qconfig}# 指定量化选项 defcalibrate(model, data_loader...
from torch.quantization.quantize_fx import prepare_fx, convert_fx from torch.ao.quantization.fx.graph_module import ObservedGraphModule from torch.quantization import ( get_default_qconfig, ) from torch import optim import os import time def train_model(model, train_loader, test_loader, device): ...
from torch.ao.quantization.fx.graph_module import ObservedGraphModule from torch.quantization import ( get_default_qconfig, ) from torch import optim import os import time def train_model(model, train_loader, test_loader, device): # The training configurations were not carefully selected. learning_...
resnet18fromtorch.quantization.quantize_fximportprepare_fx,convert_fxfromtorch.ao.quantization.fx.graph_moduleimportObservedGraphModulefromtorch.quantizationimportget_default_qconfigfromtorchimportoptimimportosimportonnximportonnxruntimeimportnumpyasnp
model_fp32.qconfig = torch.quantization.get_default_qconfig('fbgemm') 3、定义模型融合 # Fuse the activations to preceding layers, where applicable. # This needs to be done manually depending on the model architecture. # Common fusions include `conv + relu` and `conv + batchnorm + relu` ...
from torch.ao.quantization.fx.graph_module import ObservedGraphModule from torch.quantization import ( get_default_qconfig, ) from torch import optim import os import time def train_model(model, train_loader, test_loader, device): # The training configurations were not carefully selected. ...