'OrderedDict' object has no attribute 'module' 多卡训练测试

加载并跑起来多卡训练的模型来用来检测

device_ids=[0,1]

use_gpu = torch.cuda.is_available()
#创建模型
model = se_resnet20_v2()  
model = model.cuda(device_ids[0])
model = nn.DataParallel(model, device_ids=device_ids)

#加载模型

pretained_model = torch.load('/home/zj/senetial/save_models/SENet/SEnet_model_33.pth')
model.load_state_dict(pretained_model)

#加载test数据
test_file = h5py.File('/home/zj/senetial/data/round1_test_a_20181109.h5', 'r')
test_sen1 = test_file['sen1']
test_sen2 = test_file['sen2']
count=test_sen1.shape[0]

f=open('/home/zj/senetial/data/test_33_meanstd_test.csv','w')
for i in tqdm(range(int(math.ceil(count/16.0)))):###不用管这里到底能不能整除，你进行向上取整就好
    onehot = np.zeros((16,17),dtype=int)
    images=mean_std(AddFeatures(np.concatenate((test_sen1[i*16:(i+1)*16,:,:,:],test_sen2[i*16:(i+1)*16,:,:,:]),axis=3)))#列表：可以自动识别是否到了最后
    inputs = Variable((torch.from_numpy(images)).float().permute(0, 3, 1, 2).cuda(device_ids[0]))#一定要加device_ids不能默认（）
    outputs = model(inputs)
    scores, preds = torch.max(outputs.data, 1)#preds
    for i in range(inputs.shape[0]):#注意这里不能用batchsize，只能用列表[:]后的shape
        onehot[i,preds[i]]=1
        f.writelines(','.join(map(str,onehot[i]))+'\n')

 

单GPU：

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

多GPU：

device_ids = [0,1,2,3]

model = model.cuda(device_ids[0])
model = nn.DataParallel(model, device_ids=device_ids)

optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.001)
optimizer = nn.DataParallel(optimizer, device_ids=device_ids)

optimizer.module.step()
for param_lr in optimizer.module.param_groups:  # 同样是要加module
        #     param_lr['lr'] = param_lr['lr'] * 0.999

加载多GPU预训练模型

model = ft_net()
pretained_model = torch.load('./model/all/8_model.pkl')
pretained_dict = pretained_model.module.state_dict()
model = ft_net()
model.load_state_dict(pretained_dict)