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[Bug]: Training Error: 'FigureCanvasAgg' object has no attribute 'tostring_rgb' #2469

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VivizSun opened this issue Dec 19, 2024 · 2 comments · May be fixed by #2471
Closed
1 task done

[Bug]: Training Error: 'FigureCanvasAgg' object has no attribute 'tostring_rgb' #2469

VivizSun opened this issue Dec 19, 2024 · 2 comments · May be fixed by #2471

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@VivizSun
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Describe the bug

Thank you for your contributions.

I encountered a training error (as shown in the image below) while training.
Could you please advise me on how to resolve this issue?

error

Dataset

MVTec

Model

N/A

Steps to reproduce the behavior

from anomalib.data import MVTec
from anomalib.models import Patchcore
from anomalib.engine import Engine

def main():
datamodule = MVTec()
model = Patchcore()
engine = Engine()

datamodule = MVTec(root='../datas')
engine.train(datamodule=datamodule, model=model)

if name == "main":
main()

OS information

OS information:

  • OS: Windows 11
  • Python version: 3.10.4
  • Anomalib version: 2.0.0
  • PyTorch version: 2.5.1+cu124
  • CUDA/cuDNN version: 12.4.1/8.9.7
  • GPU models and configuration: GeForce RTX 3090 Laptop

Python environment:
aiohappyeyeballs 2.4.4
aiohttp 3.11.10
aiosignal 1.3.2
anomalib 1.2.0
antlr4-python3-runtime 4.9.3
async-timeout 5.0.1
attrs 24.3.0
certifi 2024.12.14
charset-normalizer 3.4.0
colorama 0.4.6
contourpy 1.3.1
cycler 0.12.1
docstring_parser 0.16
dotenv 0.0.5
einops 0.8.0
filelock 3.16.1
fonttools 4.55.3
FrEIA 0.2
frozenlist 1.5.0
fsspec 2024.10.0
ftfy 6.3.1
huggingface-hub 0.27.0
idna 3.10
imageio 2.36.1
imgaug 0.4.0
importlib_resources 6.4.5
Jinja2 3.1.4
joblib 1.4.2
jsonargparse 4.35.0
kiwisolver 1.4.7
kornia 0.7.4
kornia_rs 0.1.7
lazy_loader 0.4
lightning 2.4.0
lightning-utilities 0.11.9
markdown-it-py 3.0.0
MarkupSafe 3.0.2
matplotlib 3.5.0
mdurl 0.1.2
mpmath 1.3.0
multidict 6.1.0
networkx 3.4.2
numpy 2.2.0
omegaconf 2.3.0
open_clip_torch 2.29.0
opencv-python 4.10.0.84
packaging 24.2
pandas 2.2.3
pillow 10.2.0
pip 24.3.1
propcache 0.2.1
Pygments 2.18.0
pyparsing 3.2.0
python-dateutil 2.9.0.post0
python-dotenv 1.0.1
pytorch-lightning 2.4.0
pytz 2024.2
PyYAML 6.0.2
regex 2024.11.6
requests 2.32.3
rich 13.9.4
rich-argparse 1.6.0
safetensors 0.4.5
scikit-image 0.25.0
scikit-learn 1.6.0
scipy 1.14.1
setuptools 58.1.0
setuptools-scm 8.1.0
shapely 2.0.6
six 1.17.0
sympy 1.13.1
threadpoolctl 3.5.0
tifffile 2024.12.12
timm 1.0.12
tomli 2.2.1
torch 2.5.1+cu124
torchaudio 2.5.1+cu124
torchmetrics 1.6.0
torchvision 0.20.1+cu124
tqdm 4.67.1
typeshed_client 2.7.0
typing_extensions 4.12.2
tzdata 2024.2
urllib3 2.2.3
wcwidth 0.2.13
yarl 1.18.3

Expected behavior

An error occurred during the training process.

Screenshots

No response

Pip/GitHub

GitHub

What version/branch did you use?

No response

Configuration YAML

NA

Logs

INFO:anomalib.models.components.base.anomaly_module:Initializing Patchcore model.
INFO:timm.models._builder:Loading pretrained weights from Hugging Face hub (timm/wide_resnet50_2.racm_in1k)
INFO:timm.models._hub:[timm/wide_resnet50_2.racm_in1k] Safe alternative available for 'pytorch_model.bin' (as 'model.safetensors'). Loading weights using safetensors.
INFO:timm.models._builder:Missing keys (fc.weight, fc.bias) discovered while loading pretrained weights. This is expected if model is being adapted.
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
You are using a CUDA device ('NVIDIA GeForce RTX 3080 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
INFO:anomalib.data.image.mvtec:Found the dataset.
WARNING:anomalib.metrics.f1_score:F1Score class exists for backwards compatibility. It will be removed in v1.1. Please use BinaryF1Score from torchmetrics instead
WARNING:anomalib.metrics.f1_score:F1Score class exists for backwards compatibility. It will be removed in v1.1. Please use BinaryF1Score from torchmetrics instead
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
c:\OpenPy_Recipe\AI_Weights\Envs\anomalib_env\lib\site-packages\lightning\pytorch\core\optimizer.py:182: `LightningModule.configure_optimizers` returned `None`, this fit will run with no optimizer

  | Name                  | Type                     | Params | Mode 
---------------------------------------------------------------------------
0 | model                 | PatchcoreModel           | 24.9 M | train
1 | _transform            | Compose                  | 0      | train
2 | normalization_metrics | MetricCollection         | 0      | train
3 | image_threshold       | F1AdaptiveThreshold      | 0      | train
4 | pixel_threshold       | F1AdaptiveThreshold      | 0      | train
5 | image_metrics         | AnomalibMetricCollection | 0      | train
6 | pixel_metrics         | AnomalibMetricCollection | 0      | train
---------------------------------------------------------------------------
24.9 M    Trainable params
0         Non-trainable params
24.9 M    Total params
99.450    Total estimated model params size (MB)
17        Modules in train mode
174       Modules in eval mode
c:\OpenPy_Recipe\AI_Weights\Envs\anomalib_env\lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:419: Consider setting `persistent_workers=True` in 'train_dataloader' to speed up the dataloader worker initialization.
c:\OpenPy_Recipe\AI_Weights\Envs\anomalib_env\lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:419: Consider setting `persistent_workers=True` in 'val_dataloader' to speed up the dataloader worker initialization.
Epoch 0:   0%|                                                                                                                                                                                                                                                                       | 0/7 [00:00<?, ?it/s]c:\OpenPy_Recipe\AI_Weights\Envs\anomalib_env\lib\site-packages\lightning\pytorch\loops\optimization\automatic.py:132: `training_step` returned `None`. If this was on purpose, ignore this warning...
Epoch 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:02<00:00,  2.63it/s]INFO:anomalib.models.image.patchcore.lightning_model:Aggregating the embedding extracted from the training set.                                                                                                                                                                       | 0/? [00:00<?, ?it/s] 
INFO:anomalib.models.image.patchcore.lightning_model:Applying core-set subsampling to get the embedding.
Selecting Coreset Indices.: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16385/16385 [00:41<00:00, 393.28it/s]
Epoch 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [02:01<00:00,  0.06it/s, pixel_AUROC=0.686, pixel_F1Score=0.140]`Trainer.fit` stopped: `max_epochs=1` reached.                                                                                                                                                                                                                                                               
Epoch 0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [02:02<00:00,  0.06it/s, pixel_AUROC=0.686, pixel_F1Score=0.140] 
INFO:anomalib.callbacks.timer:Training took 202.21 seconds
INFO:anomalib.data.image.mvtec:Found the dataset.
WARNING:anomalib.metrics.f1_score:F1Score class exists for backwards compatibility. It will be removed in v1.1. Please use BinaryF1Score from torchmetrics instead
WARNING:anomalib.metrics.f1_score:F1Score class exists for backwards compatibility. It will be removed in v1.1. Please use BinaryF1Score from torchmetrics instead
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
c:\OpenPy_Recipe\AI_Weights\Envs\anomalib_env\lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:419: Consider setting `persistent_workers=True` in 'test_dataloader' to speed up the dataloader worker initialization.
Testing DataLoader 0:   0%|                                                                                                                                                                                                                                                          | 0/3 [00:00<?, ?it/s]

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@samet-akcay samet-akcay mentioned this issue Dec 19, 2024
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@samet-akcay
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@VivizSun it is because of a deprecated function in Matplotlib. This should fix it. Alternatively you could use a Matplotlib version that is less than 3.10
#2471

@VivizSun
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Thank you very much, it has been fixed.

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🐞Fix visualization tests due to a deprecated function in matplotlib samet-akcay/anomalib
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@samet-akcay @VivizSun