#1QiuJiang回复你好,导出离线模型的代码看上去不完整,请重新粘贴一下谢谢。另外,模型量化过程可以参考示例(请重点参考 模型量化、在线推理 这两部分,其他部分是大多是针对yolov5模型的一些修改): https://developer.cambricon.com/index/curriculum/expdetails/id/10/classid/8.html
展开 最小复现代码如下:
import torch
import torch.nn as nn
model = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=1, kernel_size=64,stride=1, padding=0,groups=0,bias=False))
model.eval()
import torch_mlu
import torch_mlu.core.mlu_model as ct
net = model.float()
example_mlu = torch.randn(1, 3, 180, 180, dtype=torch.float)
randn_mlu = torch.randn(1, 3, 180, 180, dtype=torch.float)
ct.set_core_number(1)
ct.set_core_version('MLU220')
ct.set_device(-1)
ct.save_as_cambricon('test')
net_traced = torch.jit.trace(net.to(ct.mlu_device()), randn_mlu.to(ct.mlu_device()), check_trace=False)
net_traced(example_mlu.to(ct.mlu_device()))
ct.save_as_cambricon("")