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openvinotoolkit / anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
https://anomalib.readthedocs.io/en/latest/
Apache License 2.0
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[Bug]: Can't download efficientAD pretrained weights #2145

Open papago2355 opened 2 weeks ago

papago2355 commented 2 weeks ago

Describe the bug

Hi. I currently reinstalled anomalib and used '001_getting_started.ipynb' code. When I use EfficientAD, the downloading process does not work at all. I tested it to another local set pc and it also got the same errors.

Dataset

MVTec

Model

Other (please specify in the field below)

Steps to reproduce the behavior

  1. Use '001_getting_started'
  2. use Colab with A100
  3. change model to 'EfficientAD()' and set train_batch = 1, eval_batch = 16
  4. can't download pretrained weights

OS information

anomalib = 1.2.0 dev google colab python = 3.10

Expected behavior

Expected to download pretrained weights but failled.

Screenshots

image

Pip/GitHub

GitHub

What version/branch did you use?

No response

Configuration YAML

Just use default settings

Logs

Trainable params: 8.1 M                                                                                            
Non-trainable params: 0                                                                                            
Total params: 8.1 M                                                                                                
Total estimated model params size (MB): 32                                                                         
/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.
/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=11` in the `DataLoader` to improve performance.
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
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efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
efficientad_pretrained_weights.zip: 0.00B [00:00, ?B/s]
---------------------------------------------------------------------------
RecursionError                            Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/rich/console.py in print(self, sep, end, style, justify, overflow, no_wrap, emoji, markup, highlight, width, height, crop, soft_wrap, new_line_start, *objects)
   1673         with self:
-> 1674             renderables = self._collect_renderables(
   1675                 objects,

37 frames
RecursionError: maximum recursion depth exceeded while calling a Python object

During handling of the above exception, another exception occurred:

RecursionError                            Traceback (most recent call last)
... last 10 frames repeated, from the frame below ...

/usr/local/lib/python3.10/dist-packages/rich/file_proxy.py in flush(self)
     51         output = "".join(self.__buffer)
     52         if output:
---> 53             self.__console.print(output)
     54         del self.__buffer[:]
     55 

RecursionError: maximum recursion depth exceeded while calling a Python object

Code of Conduct

papago2355 commented 2 weeks ago

temporary fixes is to delete rich progress bar callbacks at 'engine.py'. _callbacks: list[Callback] = [RichProgressBar(), RichModelSummary()]

to

_callbacks: list[Callback] = []

kaswall commented 1 week ago

I'm facing the same problem :(

the temporary fix seems not to work for me sadly