我有这个 pandas 数据框，它实际上是一个 Excel 电子表格：
Unnamed: 0 Date Num Company Link ID
0 NaN 1990-11-15 131231 apple... http://www.example.com/201611141492/xellia... 290834
1 NaN 1990-10-22 1231 microsoft http://www.example.com/news/arnsno... NaN
2 NaN 2011-10-20 123 apple http://www.example.com/ator... 209384
3 NaN 2013-10-27 123 apple... http://example.com/sections/th-shots/2016/... 098
4 NaN 1990-10-26 123 google http://www.example.net/business/Drugmak... 098098
5 NaN 1990-10-18 1231 google... http://example.com/news/va-rece... NaN
6 NaN 2011-04-26 546 amazon... http://www.example.com/news/home/20160425... 9809
我想删除列NaN中的所有行ID并重新索引“索引假想列”：
Unnamed: 0 Date Num Company Link ID
0 NaN 1990-11-15 131231 apple... http://www.example.com/201611141492/xellia... 290834 …我有一个用 cuda 训练的张量，我想将其部署在 CPU 上。我让模型在Google Colab GPU 运行时上运行，切换到 CPU 运行时并尝试将其移植。
很抱歉没有包含可重现的示例，如果数据集位于我的谷歌驱动器上，我真的不知道最佳实践是什么。
model = mymodel()
device = torch.device("cpu")
state_dict = torch.load(loadckpt,map_location=device)
model.load_state_dict(state_dict['model'])
model.eval()
result = model(sample)
当我运行这个时，我收到以下回溯错误
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-25-5336d222ce8f> in <module>()
8 # right_pad_np = sample["right_pad"]
9 # disp_est_uint = np.round(disp_est_np * 256).astype(np.uint16)
---> 10 test_sample(sample)
8 frames
/content/CFNet/utils/experiment.py in wrapper(*f_args, **f_kwargs)
28 def wrapper(*f_args, **f_kwargs):
29 with torch.no_grad():
---> 30 ret = func(*f_args, **f_kwargs)
31 return ret
32
<ipython-input-25-5336d222ce8f> in test_sample(sample)
2 def …