voxel51/fiftyone

[BUG] Compute Uniqueness not working on Windows

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#840 建立於 2021年2月3日

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 (11 留言) (2 反應) (0 負責人)Python (400 fork)batch import
bughelp wanted

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描述

Instructions

I tried to check for finding duplicate images on local dataset. However, I am facing the following issue.

System information

  • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
  • FiftyOne installed from (pip or source): pip3 install fiftyone
  • FiftyOne version (run fiftyone --version): default
  • Python version: python 3.9

Commands to reproduce

import fiftyone as fo
dataset = fo.Dataset.from_images_dir("D:\\data\\similar\\class1", recursive=True, name="sim-images")  
session = fo.launch_app(dataset)
import fiftyone.brain as fob
fob.compute_uniqueness(dataset)

Describe the problem

It looks like there is some problem with model loading.

Loading uniqueness model...
Downloading model from Google Drive ID '1SIO9XreK0w1ja4EuhBWcR10CnWxCOsom'...
 100% |██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████|  100.6Mb/100.6Mb [1.4s elapsed, 0s remaining, 85.2Mb/s]
Uncaught exception
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<frozen fiftyone.brain>", line 146, in compute_uniqueness
  File "<frozen fiftyone.brain.internal.core.uniqueness>", line 67, in compute_uniqueness
  File "<frozen fiftyone.brain.internal.core.uniqueness>", line 88, in _load_model
  File "<frozen fiftyone.brain.internal.models>", line 183, in load_model
  File "C:\Users\soura\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\fiftyone\core\models.py", line 472, in load_model
    return config.build()
  File "C:\Users\soura\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\eta\core\learning.py", line 296, in build
    return self._model_cls(self.config)
  File "C:\Users\soura\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\fiftyone\utils\torch.py", line 535, in __init__
    self._model = self._load_model(config)
  File "C:\Users\soura\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\fiftyone\utils\torch.py", line 730, in _load_model
    self._load_state_dict(model, config)
  File "<frozen fiftyone.brain.internal.models.torch>", line 44, in _load_state_dict
  File "C:\Users\soura\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\torch\nn\modules\module.py", line 1051, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Network:
        Missing key(s) in state_dict: "prep\conv.weight", "prep\bn.weight", "prep\bn.bias", "prep\bn.running_mean", "prep\bn.running_var", "layer1\conv.weight", "layer1\bn.weight", "layer1\bn.bias", "layer1\bn.running_mean", "layer1\bn.running_var", "layer1\residual\res1\conv.weight", "layer1\residual\res1\bn.weight", "layer1\residual\res1\bn.bias", "layer1\residual\res1\bn.running_mean", "layer1\residual\res1\bn.running_var", "layer1\residual\res2\conv.weight", "layer1\residual\res2\bn.weight", "layer1\residual\res2\bn.bias", "layer1\residual\res2\bn.running_mean", "layer1\residual\res2\bn.running_var", "layer2\conv.weight", "layer2\bn.weight", "layer2\bn.bias", "layer2\bn.running_mean", "layer2\bn.running_var", "layer3\conv.weight", "layer3\bn.weight", "layer3\bn.bias", "layer3\bn.running_mean", "layer3\bn.running_var", "layer3\residual\res1\conv.weight", "layer3\residual\res1\bn.weight", "layer3\residual\res1\bn.bias", "layer3\residual\res1\bn.running_mean", "layer3\residual\res1\bn.running_var", "layer3\residual\res2\conv.weight", "layer3\residual\res2\bn.weight", "layer3\residual\res2\bn.bias", "layer3\residual\res2\bn.running_mean", "layer3\residual\res2\bn.running_var".
        Unexpected key(s) in state_dict: "prep_conv.weight", "prep_bn.weight", "prep_bn.bias", "prep_bn.running_mean", "prep_bn.running_var", "prep_bn.num_batches_tracked", "layer1_conv.weight", "layer1_bn.weight", "layer1_bn.bias", "layer1_bn.running_mean", "layer1_bn.running_var", "layer1_bn.num_batches_tracked", "layer1_residual_res1_conv.weight", "layer1_residual_res1_bn.weight", "layer1_residual_res1_bn.bias", "layer1_residual_res1_bn.running_mean", "layer1_residual_res1_bn.running_var", "layer1_residual_res1_bn.num_batches_tracked", "layer1_residual_res2_conv.weight", "layer1_residual_res2_bn.weight", "layer1_residual_res2_bn.bias", "layer1_residual_res2_bn.running_mean", "layer1_residual_res2_bn.running_var", "layer1_residual_res2_bn.num_batches_tracked", "layer2_conv.weight", "layer2_bn.weight", "layer2_bn.bias", "layer2_bn.running_mean", "layer2_bn.running_var", "layer2_bn.num_batches_tracked", "layer3_conv.weight", "layer3_bn.weight", "layer3_bn.bias", "layer3_bn.running_mean", "layer3_bn.running_var", "layer3_bn.num_batches_tracked", "layer3_residual_res1_conv.weight", "layer3_residual_res1_bn.weight", "layer3_residual_res1_bn.bias", "layer3_residual_res1_bn.running_mean", "layer3_residual_res1_bn.running_var", "layer3_residual_res1_bn.num_batches_tracked", "layer3_residual_res2_conv.weight", "layer3_residual_res2_bn.weight", "layer3_residual_res2_bn.bias", "layer3_residual_res2_bn.running_mean", "layer3_residual_res2_bn.running_var", "layer3_residual_res2_bn.num_batches_tracked".

What areas of FiftyOne does this bug affect?

  • App: FiftyOne application issue
  • Core: Core fiftyone Python library issue
  • Server: Fiftyone server issue

Willingness to contribute

The FiftyOne Community encourages bug fix contributions. Would you or another member of your organization be willing to contribute a fix for this bug to the FiftyOne codebase?

  • Yes. I can contribute a fix for this bug independently.
  • Yes. I would be willing to contribute a fix for this bug with guidance from the FiftyOne community.
  • No. I cannot contribute a bug fix at this time.

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