正如我在这里概述的那样，由于硬件原因，例如使用 ppc64le IBM 架构，我一直在使用旧版本的 pytorch 和 torchvision。
出于这个原因，我在不同的计算机、集群和我的个人 mac 之间发送和接收检查点时遇到问题。我想知道是否有任何方法可以避免此问题的加载模型？例如，可能在使用 1.6.x 时以旧格式和新格式保存模型。当然，对于 1.3.1 到 1.6.x 是不可能的，但至少我希望有些东西会起作用。
有什么建议吗？当然，我理想的解决方案是我不必担心它，我可以随时加载和保存我的检查点以及我通常在所有硬件上统一腌制的所有内容。
我得到的第一个错误是 zip jit 错误：
RuntimeError: /home/miranda9/data/f.pt is a zip archive (did you mean to use torch.jit.load()?)
所以我使用了它（和其他泡菜库）：
# %%
import torch
from pathlib import Path
def load(path):
import torch
import pickle
import dill
path = str(path)
try:
db = torch.load(path)
f = db['f']
except Exception as e:
db = torch.jit.load(path)
f = db['f']
#with open():
# db = pickle.load(open(path, "r+"))
# db = dill.load(open(path, "r+"))
#raise ValueError(f'FAILED: {e}')
return db, f
p = "~/data/f.pt"
path = Path(p).expanduser()
db, f = load(path)
Din, nb_examples = 1, 5
x = torch.distributions.Normal(loc=0.0, scale=1.0).sample(sample_shape=(nb_examples, Din))
y = f(x)
print(y)
print('Success!\a')
但我抱怨我不得不使用不同的 pytorch 版本：
Traceback (most recent call last):
File "hal_pg.py", line 27, in <module>
db, f = load(path)
File "hal_pg.py", line 16, in load
db = torch.jit.load(path)
File "/home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/jit/__init__.py", line 239, in load
cpp_module = torch._C.import_ir_module(cu, f, map_location, _extra_files)
RuntimeError: version_number <= kMaxSupportedFileFormatVersion INTERNAL ASSERT FAILED at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131, please report a bug to PyTorch. Attempted to read a PyTorch file with version 3, but the maximum supported version for reading is 1. Your PyTorch installation may be too old. (init at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xbc (0x7fff7b527b9c in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: caffe2::serialize::PyTorchStreamReader::init() + 0x1d98 (0x7fff1d293c78 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: caffe2::serialize::PyTorchStreamReader::PyTorchStreamReader(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x88 (0x7fff1d2950d8 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: torch::jit::import_ir_module(std::shared_ptr<torch::jit::script::CompilationUnit>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, c10::optional<c10::Device>, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > >&) + 0x64 (0x7fff1e624664 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #4: <unknown function> + 0x70e210 (0x7fff7c0ae210 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: <unknown function> + 0x28efc4 (0x7fff7bc2efc4 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #26: <unknown function> + 0x25280 (0x7fff84b35280 in /lib64/libc.so.6)
frame #27: __libc_start_main + 0xc4 (0x7fff84b35474 in /lib64/libc.so.6)
任何想法如何使集群中的所有内容保持一致？我什至无法打开泡菜文件。
也许这在我被迫使用的当前 pytorch 版本中是不可能的:(
RuntimeError: version_number <= kMaxSupportedFileFormatVersion INTERNAL ASSERT FAILED at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131, please report a bug to PyTorch. Attempted to read a PyTorch file with version 3, but the maximum supported version for reading is 1. Your PyTorch installation may be too old. (init at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xbc (0x7fff83ba7b9c in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: caffe2::serialize::PyTorchStreamReader::init() + 0x1d98 (0x7fff25993c78 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: caffe2::serialize::PyTorchStreamReader::PyTorchStreamReader(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x88 (0x7fff259950d8 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: torch::jit::import_ir_module(std::shared_ptr<torch::jit::script::CompilationUnit>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, c10::optional<c10::Device>, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > >&) + 0x64 (0x7fff26d24664 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #4: <unknown function> + 0x70e210 (0x7fff8472e210 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: <unknown function> + 0x28efc4 (0x7fff842aefc4 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #23: <unknown function> + 0x25280 (0x7fff8d335280 in /lib64/libc.so.6)
frame #24: __libc_start_main + 0xc4 (0x7fff8d335474 in /lib64/libc.so.6)
使用代码：
from pathlib import Path
import torch
path = '/home/miranda9/data/dataset/'
path = Path(path).expanduser() / 'fi_db.pt'
path = str(path)
# db = torch.load(path)
# torch.jit.load(path)
db = torch.jit.load(str(path))
print(db)
相关链接：
这不是一个理想的解决方案，但它适用于将检查点从较新版本转移到较旧版本。
我也使用 ppc64le 并面临同样的问题。可以将模型保存为任何 PyTorch 版本都可以读取的文本格式。我在 ppc64le 机器上安装了 PyTorch v1.3.0，在我的笔记本上安装了 v1.7.0（不需要显卡）。
步骤 1. 通过较新的 PyTorch 版本保存模型
def save_model_txt(model, path):
fout = open(path, 'w')
for k, v in model.state_dict().items():
fout.write(str(k) + '\n')
fout.write(str(v.tolist()) + '\n')
fout.close()
在保存之前，我像这样加载模型
checkpoint = torch.load(path, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint, strict=False)
步骤 2. 传输文本文件
步骤 3. 在旧 PyTorch 中加载文本文件
def load_model_txt(model, path):
data_dict = {}
fin = open(path, 'r')
i = 0
odd = 1
prev_key = None
while True:
s = fin.readline().strip()
if not s:
break
if odd:
prev_key = s
else:
print('Iter', i)
val = eval(s)
if type(val) != type([]):
data_dict[prev_key] = torch.FloatTensor([eval(s)])[0]
else:
data_dict[prev_key] = torch.FloatTensor(eval(s))
i += 1
odd = (odd + 1) % 2
# Replace existing values with loaded
print('Loading...')
own_state = model.state_dict()
print('Items:', len(own_state.items()))
for k, v in data_dict.items():
if not k in own_state:
print('Parameter', k, 'not found in own_state!!!')
else:
try:
own_state[k].copy_(v)
except:
print('Key:', k)
print('Old:', own_state[k])
print('New:', v)
sys.exit(0)
print('Model loaded')
模型必须在加载前初始化。空模型被传递到函数中。
局限性
如果您的模型 state_dict 包含 (str: torch.Tensor) 值以外的其他内容，则此方法将不起作用。您可以检查您的 state_dict 内容
for k, v in model.state_dict().items():
...
阅读这些内容以了解：
https://pytorch.org/tutorials/recipes/recipes/ saving_and_loading_models_for_inference.html
https://discuss.pytorch.org/t/how-to-load-part-of-pre-trained-model/1113
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