我试图使用ViTT转染子。我得到以下代码错误：
from pathlib import Path
import torchvision
from typing import Callable
root = Path("~/data/").expanduser()
# root = Path(".").expanduser()
train = torchvision.datasets.CIFAR100(root=root, train=True, download=True)
test = torchvision.datasets.CIFAR100(root=root, train=False, download=True)
img2tensor: Callable = torchvision.transforms.ToTensor()
from transformers import ViTFeatureExtractor
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
x, y = train_ds[0]
print(f'{y=}')
print(f'{type(x)=}')
x = img2tensor(x)
x = x.unsqueeze(0) # add batch size 1
out_cls: ImageClassifierOutput = model(x)
print(f'{out_cls.logits=}')
错误
Files already downloaded and verified
Files already downloaded and verified
Traceback (most recent call last):
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 11, in <module>
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_bundle/pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/transformers/__init__.py", line 30, in <module>
from . import dependency_versions_check
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_bundle/pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/transformers/dependency_versions_check.py", line 36, in <module>
from .utils import is_tokenizers_available
ImportError: cannot import name 'is_tokenizers_available' from 'transformers.utils' (/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/transformers/utils/__init__.py)
我试着升级所有东西，但还是失败了。升级命令：
/Users/brandomiranda/opt/anaconda3/envs/meta_learning/bin/python -m
pip install --upgrade pip
pip install --upgrade pip
pip install --upgrade huggingface-hub
pip install --upgrade transformers
pip install --upgrade huggingface-hub
pip install --upgrade datasets
pip install --upgrade tokenizers
pip install pytorch-transformers
pip install --upgrade torch
pip install --upgrade torchvision
pip install --upgrade torchtext
pip install --upgrade torchaudio
# pip install --upgrade torchmeta
pip uninstall torchmeta
为什么以及如何修复？
Pip列表：
(meta_learning) ❯ pip list
Package Version Editable project location
------------------------------------------------- ---------- ------------------------------------------------------------------------------
absl-py 1.0.0
aiohttp 3.8.1
aiosignal 1.2.0
antlr4-python3-runtime 4.8
argcomplete 2.0.0
async-timeout 4.0.1
attrs 21.4.0
automl-meta-learning 0.1.0 /Users/brandomiranda/automl-meta-learning/automl-proj-src
bcj-cffi 0.5.1
boto 2.49.0
boto3 1.24.85
botocore 1.27.85
Bottleneck 1.3.4
Brotli 1.0.9
brotlicffi 1.0.9.2
brotlipy 0.7.0
cachetools 4.2.4
certifi 2022.9.14
cffi 1.15.1
charset-normalizer 2.0.9
cherry-rl 0.1.4
click 8.0.3
cloudpickle 2.0.0
colorama 0.4.4
configparser 5.2.0
conllu 4.4.1
crcmod 1.7
cryptography 37.0.1
cycler 0.11.0
Cython 0.29.25
dataclasses 0.6
datasets 2.5.1
dill 0.3.4
diversity-for-predictive-success-of-meta-learning 0.0.1 /Users/brandomiranda/diversity-for-predictive-success-of-meta-learning/div_src
docker-pycreds 0.4.0
editdistance 0.6.0
et-xmlfile 1.1.0
fairseq 0.10.0
fastcluster 1.2.4
fasteners 0.17.3
filelock 3.6.0
fonttools 4.28.3
frozenlist 1.2.0
fsspec 2022.7.1
gcs-oauth2-boto-plugin 3.0
gitdb 4.0.9
GitPython 3.1.24
google-apitools 0.5.32
google-auth 2.3.3
google-auth-oauthlib 0.4.6
google-reauth 0.1.1
grpcio 1.42.0
gsutil 5.6
gym 0.21.0
h5py 3.6.0
higher 0.2.1
httplib2 0.20.4
huggingface-hub 0.10.0
hydra-core 1.1.1
idna 3.3
importlib-metadata 4.11.3
jmespath 1.0.1
joblib 1.1.0
kiwisolver 1.3.2
lark-parser 0.12.0
learn2learn 0.1.7
lxml 4.8.0
Markdown 3.3.6
matplotlib 3.5.1
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
monotonic 1.6
multidict 5.2.0
multiprocess 0.70.12.2
multivolumefile 0.2.3
munkres 1.1.4
networkx 2.6.3
numexpr 2.8.1
numpy 1.21.5
oauth2client 4.1.3
oauthlib 3.1.1
omegaconf 2.1.1
openpyxl 3.0.10
ordered-set 4.0.2
packaging 21.3
pandas 1.4.2
pathtools 0.1.2
Pillow 9.0.1
pip 22.2.2
plotly 5.4.0
portalocker 2.3.2
progressbar2 3.55.0
promise 2.3
protobuf 3.19.1
psutil 5.8.0
py7zr 0.16.1
pyarrow 9.0.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycparser 2.21
pycryptodomex 3.15.0
pyOpenSSL 22.0.0
pyparsing 3.0.6
pyppmd 0.16.1
PySocks 1.7.1
python-dateutil 2.8.2
python-utils 2.5.6
pytorch-transformers 1.2.0
pytz 2021.3
pyu2f 0.1.5
PyYAML 6.0
pyzstd 0.14.4
qpth 0.0.15
regex 2021.11.10
requests 2.28.1
requests-oauthlib 1.3.0
responses 0.18.0
retry-decorator 1.1.1
rsa 4.7.2
s3transfer 0.6.0
sacrebleu 2.0.0
sacremoses 0.0.46
scikit-learn 1.0.1
scipy 1.7.3
seaborn 0.11.2
sentencepiece 0.1.97
sentry-sdk 1.5.1
setproctitle 1.2.2
setuptools 58.0.4
shortuuid 1.0.8
six 1.16.0
sklearn 0.0
smmap 5.0.0
subprocess32 3.5.4
tabulate 0.8.9
tenacity 8.0.1
tensorboard 2.7.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.0
termcolor 1.1.0
texttable 1.6.4
threadpoolctl 3.0.0
tokenizers 0.13.0
torch 1.12.1
torchaudio 0.12.1
torchtext 0.13.1
torchvision 0.13.1
tornado 6.1
tqdm 4.62.3
transformers 4.22.2
typing_extensions 4.3.0
ultimate-anatome 0.1.1 /Users/brandomiranda/ultimate-anatome
ultimate-aws-cv-task2vec 0.0.1 /Users/brandomiranda/ultimate-aws-cv-task2vec
ultimate-utils 0.6.1 /Users/brandomiranda/ultimate-utils/ultimate-utils-proj-src
urllib3 1.26.11
wandb 0.13.3
Werkzeug 2.0.2
wheel 0.37.0
xxhash 2.0.2
yarl 1.8.1
yaspin 2.1.0
zipp 3.8.0
拥抱脸(HF(相关gitissues:
- https://github.com/huggingface/transformers/issues/15062
- https://github.com/huggingface/tokenizers/issues/120
- https://github.com/huggingface/tokenizers/issues/585
- https://github.com/huggingface/transformers/issues/11262
- SO相关：转换器：导入包时出错"ImportError:无法导入名称'；SAVE_STATE_WARNING'；来自'；torch.optim.lr_scheduler'">
- HF讨论：https://discuss.huggingface.co/t/how-to-resolve-the-hugging-face-error-importerror-cannot-import-name-is-tokenizers-available-from-transformers-utils/23957
- reddit：https://www.reddit.com/r/pytorch/comments/xusuuy/how_to_resolve_the_hugging_face_error_importerror/
- https://github.com/huggingface/tokenizers/issues/1080
您没有在问题中给出模型。
使用谷歌colab，我可以很容易地运行openai/clip-vit-base-patch32模型
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"],
images=image,
return_tensors="pt",
padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
还有google/vit-base-patch16-224-in21k
from transformers import ViTFeatureExtractor, ViTModel
import torch
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
model = ViTModel.from_pretrained("google/vit-base-patch16-224-in21k")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)
如果您想在自己的任务中使用它，最后一个示例是一个很好的起点，因为您可以将last_hidden_state输出传递给将要训练的自定义模型。
然而，如果您直接尝试这些模型cifar-100数据集，您将遇到形状不匹配的问题。模型需要244 x 244个图像，但cifar数据集是32 x 32个图像。
如果定义了模型，我会认为它看起来像是一个蟒蛇问题，我建议使用不同的虚拟环境来测试它，例如pipenv。另一种选择是使用timm库，该库也具有用于图像分类的模型。