名称错误“_C”未定义
问题描述 投票：0回答：1
我正在 jupyter 笔记本中编写代码。尝试了很多工作人员，但没有成功。这是我的完整设置和错误：
设置：Windows 10、python 3.9、venv
pip list:
Package Version
------------------------- ------------
addict 2.4.0
annotated-types 0.6.0
anyio 4.3.0
argon2-cffi 23.1.0
argon2-cffi-bindings 21.2.0
asttokens 2.4.1
async-lru 2.0.4
attrs 23.2.0
Babel 2.14.0
beautifulsoup4 4.12.3
bleach 6.1.0
blis 0.7.11
catalogue 2.0.10
certifi 2024.2.2
cffi 1.16.0
charset-normalizer 3.3.2
click 8.1.7
cloudpathlib 0.16.0
colorama 0.4.6
comm 0.2.1
confection 0.1.4
contourpy 1.1.1
cycler 0.12.1
cymem 2.0.8
Cython 3.0.8
dataclasses-json 0.6.4
debugpy 1.8.1
decorator 5.1.1
defusedxml 0.7.1
exceptiongroup 1.2.0
executing 2.0.1
fastjsonschema 2.19.1
filelock 3.13.1
fonttools 4.49.0
fsspec 2024.2.0
h11 0.14.0
httpcore 1.0.4
httpx 0.27.0
huggingface-hub 0.20.3
idna 3.6
importlib-metadata 7.0.1
importlib-resources 6.1.1
ipykernel 6.29.2
ipython 8.18.1
ipywidgets 8.1.2
jedi 0.19.1
Jinja2 3.1.3
json5 0.9.17
jsonschema 4.21.1
jsonschema-specifications 2023.12.1
jupyter 1.0.0
jupyter_client 8.6.0
jupyter-console 6.6.3
jupyter_core 5.7.1
jupyter-events 0.9.0
jupyter-lsp 2.2.2
jupyter_server 2.12.5
jupyter_server_terminals 0.5.2
jupyterlab 4.1.2
jupyterlab_pygments 0.3.0
jupyterlab_server 2.25.3
jupyterlab_widgets 3.0.10
kiwisolver 1.4.5
langcodes 3.3.0
MarkupSafe 2.1.5
marshmallow 3.20.2
matplotlib 3.8.3
matplotlib-inline 0.1.6
mistune 3.0.2
mpmath 1.3.0
murmurhash 1.0.10
mypy-extensions 1.0.0
nbclient 0.9.0
nbconvert 7.16.1
nbformat 5.9.2
nest-asyncio 1.6.0
notebook 7.1.0
notebook_shim 0.2.4
numpy 1.23.5
opencv-python 4.9.0.80
opencv-python-headless 4.9.0.80
overrides 7.7.0
packaging 23.2
pandocfilters 1.5.1
parso 0.8.3
pickleshare 0.7.5
pillow 10.2.0
pip 24.0
platformdirs 4.2.0
preshed 3.0.9
prometheus_client 0.20.0
prompt-toolkit 3.0.43
psutil 5.9.8
pure-eval 0.2.2
pycocotools 2.0.7
pycparser 2.21
pydantic 2.6.2
pydantic_core 2.16.3
Pygments 2.17.2
pyparsing 3.1.1
python-dateutil 2.8.2
python-json-logger 2.0.7
pywin32 306
pywinpty 2.0.12
PyYAML 6.0.1
pyzmq 25.1.2
qtconsole 5.5.1
QtPy 2.4.1
referencing 0.33.0
regex 2023.12.25
requests 2.31.0
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rpds-py 0.18.0
safetensors 0.3.0
scipy 1.12.0
Send2Trash 1.8.2
setuptools 69.1.1
six 1.16.0
smart-open 6.4.0
sniffio 1.3.0
soupsieve 2.5
spacy 3.7.4
spacy-legacy 3.0.12
spacy-loggers 1.0.5
srsly 2.4.8
stack-data 0.6.3
supervision 0.4.0
sympy 1.12
terminado 0.18.0
thinc 8.2.3
timm 0.9.16
tinycss2 1.2.1
tokenizers 0.13.3
tomli 2.0.1
torch 1.9.1+cu111
torchaudio 0.9.1
torchvision 0.10.1+cu111
tornado 6.4
tqdm 4.66.2
traitlets 5.14.1
transformers 4.29.2
typer 0.9.0
typing_extensions 4.9.0
typing-inspect 0.9.0
urllib3 2.2.1
wasabi 1.1.2
wcwidth 0.2.13
weasel 0.3.4
webencodings 0.5.1
websocket-client 1.7.0
wheel 0.42.0
widgetsnbextension 4.0.10
yapf 0.40.2
zipp 3.17.0
所有测试：
import torch
!nvcc --version
TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
CUDA_VERSION = torch.__version__.split("+")[-1]
print("torch: ", TORCH_VERSION, "; cuda: ", CUDA_VERSION)
print(torch.cuda.is_available())
print(torch.cuda.device_count())
print(torch.cuda.current_device())
print(torch.cuda.device(0))
print(torch.cuda.get_device_name(0))
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Mon_Nov_30_19:15:10_Pacific_Standard_Time_2020
Cuda compilation tools, release 11.2, V11.2.67
Build cuda_11.2.r11.2/compiler.29373293_0
torch: 1.9 ; cuda: cu111
True
1
0
<torch.cuda.device object at 0x000002454179D7C0>
NVIDIA GeForce GTX 1650 Ti
导致 nameError 的问题：
C:\Users\nikit\Всякое\MyProjects\gpu gd\GroundingDINO
C:\Users\nikit\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\ms_deform_attn.py:31: UserWarning: Failed to load custom C++ ops. Running on CPU mode Only!
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
final text_encoder_type: bert-base-uncased
来自：
%cd {HOME}
%cd {HOME}/GroundingDINO
from groundingdino.util.inference import Model
model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)
主题一：
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[6], line 7
4 image = cv2.imread(SOURCE_IMAGE_PATH)
5 height, width, depth = image.shape
----> 7 detections = model.predict_with_classes(
8 image=image,
9 classes=enhance_class_name(class_names=CLASSES_NAME),
10 box_threshold=BOX_TRESHOLD,
11 text_threshold=TEXT_TRESHOLD
12 )
14 detections = detections[detections.class_id != None]
15 #detections = detections[detections.class_id != 'both hands']
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\util\inference.py:219, in Model.predict_with_classes(self, image, classes, box_threshold, text_threshold)
217 caption = ". ".join(classes)
218 processed_image = Model.preprocess_image(image_bgr=image).to(self.device)
--> 219 boxes, logits, phrases = predict(
220 model=self.model,
221 image=processed_image,
222 caption=caption,
223 box_threshold=box_threshold,
224 text_threshold=text_threshold,
225 device=self.device)
226 source_h, source_w, _ = image.shape
227 detections = Model.post_process_result(
228 source_h=source_h,
229 source_w=source_w,
230 boxes=boxes,
231 logits=logits)
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\util\inference.py:68, in predict(model, image, caption, box_threshold, text_threshold, device, remove_combined)
65 image = image.to(device)
67 with torch.no_grad():
---> 68 outputs = model(image[None], captions=[caption])
70 prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256)
71 prediction_boxes = outputs["pred_boxes"].cpu()[0] # prediction_boxes.shape = (nq, 4)
File c:\users\nikit\всякое\myprojects\gpu gd\gpu\lib\site-packages\torch\nn\modules\module.py:1051, in Module._call_impl(self, *input, **kwargs)
1047 # If we don't have any hooks, we want to skip the rest of the logic in
1048 # this function, and just call forward.
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\groundingdino.py:327, in GroundingDINO.forward(self, samples, targets, **kw)
324 self.poss.append(pos_l)
326 input_query_bbox = input_query_label = attn_mask = dn_meta = None
--> 327 hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
328 srcs, masks, input_query_bbox, self.poss, input_query_label, attn_mask, text_dict
329 )
331 # deformable-detr-like anchor update
332 outputs_coord_list = []
File c:\users\nikit\всякое\myprojects\gpu gd\gpu\lib\site-packages\torch\nn\modules\module.py:1051, in Module._call_impl(self, *input, **kwargs)
1047 # If we don't have any hooks, we want to skip the rest of the logic in
1048 # this function, and just call forward.
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\transformer.py:258, in Transformer.forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask, text_dict)
253 enc_topk_proposals = enc_refpoint_embed = None
255 #########################################################
256 # Begin Encoder
257 #########################################################
--> 258 memory, memory_text = self.encoder(
259 src_flatten,
260 pos=lvl_pos_embed_flatten,
261 level_start_index=level_start_index,
262 spatial_shapes=spatial_shapes,
263 valid_ratios=valid_ratios,
264 key_padding_mask=mask_flatten,
265 memory_text=text_dict["encoded_text"],
266 text_attention_mask=~text_dict["text_token_mask"],
267 # we ~ the mask . False means use the token; True means pad the token
268 position_ids=text_dict["position_ids"],
269 text_self_attention_masks=text_dict["text_self_attention_masks"],
270 )
271 #########################################################
272 # End Encoder
273 # - memory: bs, \sum{hw}, c
(...)
277 # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
278 #########################################################
279 text_dict["encoded_text"] = memory_text
File c:\users\nikit\всякое\myprojects\gpu gd\gpu\lib\site-packages\torch\nn\modules\module.py:1051, in Module._call_impl(self, *input, **kwargs)
1047 # If we don't have any hooks, we want to skip the rest of the logic in
1048 # this function, and just call forward.
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\transformer.py:576, in TransformerEncoder.forward(self, src, pos, spatial_shapes, level_start_index, valid_ratios, key_padding_mask, memory_text, text_attention_mask, pos_text, text_self_attention_masks, position_ids)
574 # main process
575 if self.use_transformer_ckpt:
--> 576 output = checkpoint.checkpoint(
577 layer,
578 output,
579 pos,
580 reference_points,
581 spatial_shapes,
582 level_start_index,
583 key_padding_mask,
584 )
585 else:
586 output = layer(
587 src=output,
588 pos=pos,
(...)
592 key_padding_mask=key_padding_mask,
593 )
File c:\users\nikit\всякое\myprojects\gpu gd\gpu\lib\site-packages\torch\utils\checkpoint.py:211, in checkpoint(function, *args, **kwargs)
208 if kwargs:
209 raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs))
--> 211 return CheckpointFunction.apply(function, preserve, *args)
File c:\users\nikit\всякое\myprojects\gpu gd\gpu\lib\site-packages\torch\utils\checkpoint.py:90, in CheckpointFunction.forward(ctx, run_function, preserve_rng_state, *args)
87 ctx.save_for_backward(*tensor_inputs)
89 with torch.no_grad():
---> 90 outputs = run_function(*args)
91 return outputs
File c:\users\nikit\всякое\myprojects\gpu gd\gpu\lib\site-packages\torch\nn\modules\module.py:1051, in Module._call_impl(self, *input, **kwargs)
1047 # If we don't have any hooks, we want to skip the rest of the logic in
1048 # this function, and just call forward.
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\transformer.py:785, in DeformableTransformerEncoderLayer.forward(self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask)
780 def forward(
781 self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
782 ):
783 # self attention
784 # import ipdb; ipdb.set_trace()
--> 785 src2 = self.self_attn(
786 query=self.with_pos_embed(src, pos),
787 reference_points=reference_points,
788 value=src,
789 spatial_shapes=spatial_shapes,
790 level_start_index=level_start_index,
791 key_padding_mask=key_padding_mask,
792 )
793 src = src + self.dropout1(src2)
794 src = self.norm1(src)
File c:\users\nikit\всякое\myprojects\gpu gd\gpu\lib\site-packages\torch\nn\modules\module.py:1051, in Module._call_impl(self, *input, **kwargs)
1047 # If we don't have any hooks, we want to skip the rest of the logic in
1048 # this function, and just call forward.
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\ms_deform_attn.py:338, in MultiScaleDeformableAttention.forward(self, query, key, value, query_pos, key_padding_mask, reference_points, spatial_shapes, level_start_index, **kwargs)
335 sampling_locations = sampling_locations.float()
336 attention_weights = attention_weights.float()
--> 338 output = MultiScaleDeformableAttnFunction.apply(
339 value,
340 spatial_shapes,
341 level_start_index,
342 sampling_locations,
343 attention_weights,
344 self.im2col_step,
345 )
347 if halffloat:
348 output = output.half()
File ~\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\ms_deform_attn.py:53, in MultiScaleDeformableAttnFunction.forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step)
42 @staticmethod
43 def forward(
44 ctx,
(...)
50 im2col_step,
51 ):
52 ctx.im2col_step = im2col_step
---> 53 output = _C.ms_deform_attn_forward(
54 value,
55 value_spatial_shapes,
56 value_level_start_index,
57 sampling_locations,
58 attention_weights,
59 ctx.im2col_step,
60 )
61 ctx.save_for_backward(
62 value,
63 value_spatial_shapes,
(...)
66 attention_weights,
67 )
68 return output
名称错误：名称“_C”未定义
来自：
import cv2
import supervision as sv
image = cv2.imread(SOURCE_IMAGE_PATH)
height, width, depth = image.shape
detections = model.predict_with_classes(
image=image,
classes=enhance_class_name(class_names=CLASSES_NAME),
box_threshold=BOX_TRESHOLD,
text_threshold=TEXT_TRESHOLD
)
detections = detections[detections.class_id != None]
#detections = detections[detections.class_id != 'both hands']
detections = detections[(detections.area / (height * width)) < 0.5]
#detections = detections[(detections.area / (height * width)) >= 0.2]
box_annotator = sv.BoxAnnotator()
labels = [
f"{CLASSES_NAME[class_id]} {confidence:0.2f}"
for _, confidence, class_id, _
in detections]
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)
%matplotlib inline
sv.plot_image(annotated_frame, (16, 16))
求救！
我尝试了以下方法：
添加环境参数：
os.environ['CUDA_HOME'] = r'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2'
!echo %CUDA_HOME%
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin
Cuda 和 torch+cuxxx 不需要蜜蜂匹配，但是 cuxxx <= CUDAxxx. I tried matched ones, nothing.
尝试了我在其他主题中可以找到的所有软件包：
pip install sympy spacy Cyton
pip install numpy==1.23.5
导入火炬到空文件夹：
%cd {HOME}/empty_dir
import torch
每次操作后我都会重新启动我的内核，甚至 venv
所有代码都使用 cpu 完美运行。
主要问题：我也找不到直接的解决方案：
C:\Users\nikit\Всякое\MyProjects\gpu gd\GroundingDINO\groundingdino\models\GroundingDINO\ms_deform_attn.py:31: UserWarning: Failed to load custom C++ ops. Running on CPU mode Only!
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
也不适合这个： 名称错误：名称“_C”未定义
但是在 .py 文件中，其中 _C 是导入，它会抛出以下错误： 用户警告：无法加载自定义 C++ 操作。仅在 CPU 模式下运行！ 因为 _C 没有导入，所以就是这样。如果我能解决任何问题，GPU 将被设置。
附注Google Collab 工作得非常好。他们是如何实现这一目标的？谁知道呢。
1个回答
0
投票
投票
我解决了！
调查
所以基本上我去协作并做了 print(_C) 来找到它。 然后我转到我的 venv 并从 torch 模块中获取 _C 文件夹。 我将它粘贴到grindingDINO github 文件夹的每个文件夹中。 然后它解决了问题，我开始清除文件夹，因此 GrindingDINO/grindingdino 只包含 _C 文件夹。然后我又得到了另一个错误： AttributeError：模块“torch._C”没有属性“ms_deform_attn_forward” 之后我用谷歌搜索并发现，你需要在 GroundingDINO 根文件夹中构建 setup.py 。 我转到 cmd，进入这个文件夹，打印 py setup.py build。 构建后，我在 GroundingDINO 文件夹中得到了构建文件夹。从那里我找到了 groundingdino 文件夹并将其剪切到 GroundingDINO 中，一切都开始工作得非常好。
说明
open cmd
enter venv
cd path\to\your\GroundingDINO local github rep
py setup.py build
cut path\to\your\GroundingDINO\build\lib.win-amd64-cpython-39(in my case)\groundingdino
paste path\to\your\GroundingDINO folder
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