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运行Cambricon PyTorch推理快速入门部分代码报错 已解决 liubx072021-08-31 16:21:05 回复 2 查看 技术答疑 使用求助 经验交流
运行Cambricon PyTorch推理快速入门部分代码报错
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环境:官方docker,Ubuntu 18.04


在运行官方文档代码时有如下报错

image.png


请问如何处理此处报错?


附上代码:

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_mlu
import torch_mlu.core.mlu_model as ct
import torch_mlu.core.mlu_quantize as mlu_quantize
import torchvision.models as models
ct.set_core_number(1)
ct.set_core_version("MLU270")
torch.set_grad_enabled(False)
class Net(nn.Module):
   def __init__(self):
       super(Net, self).__init__()
       self.conv1 = nn.Conv2d(1, 32, 3, 1)
       self.conv2 = nn.Conv2d(32, 64, 3, 1)
       self.dropout1 = nn.Dropout2d(0.25)
       self.dropout2 = nn.Dropout2d(0.5)
       self.fc1 = nn.Linear(9216, 128)
       self.fc2 = nn.Linear(128, 10)
   def forward(self, x):
       x = self.conv1(x)
       x = F.relu(x)
       x = self.conv2(x)
       x = F.relu(x)
       x = F.max_pool2d(x, 2)
       x = self.dropout1(x)
       x = torch.flatten(x, 1)
       x = self.fc1(x)
       x = F.relu(x)
       x = self.dropout2(x)
       x = self.fc2(x)
       output = F.log_softmax(x, dim=1)
       return output
net = Net().eval()
torch.save(net.state_dict(), 'test_weights.pth')
input_data = torch.rand((1, 1, 28, 28), dtype=torch.float)
mean = [0]
std = [1/255]
net.load_state_dict(torch.load('test_weights.pth', map_location='cpu'), False)
net_quantization = mlu_quantize.quantize_dynamic_mlu(
   net, {'mean': mean, 'std': std, 'firstconv': True}, dtype='int8', gen_quant=True)
output = net_quantization(input_data)
torch.save(net_quantization.state_dict(), 'test_quantization.pth')
net_quantization.load_state_dict(torch.load('test_quantization.pth'))
net_mlu = net_quantization.to(ct.mlu_device())
input_mlu = input_data.to(ct.mlu_device())
output = net_mlu(input_mlu)
print(output.cpu())

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