# Problems with a custom model using a transformer base model in the evaluation phase (eval\_strategy)

**URL:** <https://discuss.huggingface.co/t/problems-with-a-custom-model-using-a-transformer-base-model-in-the-evaluation-phase-eval-strategy/23812>\
**Category:** 🤗Transformers\
**Created:** [September 30, 2022, 5:08am UTC](https://discuss.huggingface.co/t/problems-with-a-custom-model-using-a-transformer-base-model-in-the-evaluation-phase-eval-strategy/23812 "2022-09-30T05:08:42Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![alkzar90](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/alkzar90/32/30144_2.png) [@alkzar90](https://discuss.huggingface.co/u/alkzar90)\
**Post date:** [September 30, 2022, 5:08am UTC](https://discuss.huggingface.co/t/problems-with-a-custom-model-using-a-transformer-base-model-in-the-evaluation-phase-eval-strategy/23812/1 "2022-09-30T05:08:42Z")

</div>

Hi 😄

I am trying to create a custom model using the `transforms` library, specifically the ViT model.

So to have some benchmark, my goal is to replicate the [`ViTForImageClassification`](https://github.com/huggingface/transformers/blob/v4.22.1/src/transformers/models/vit/modeling_vit.py) class, only adding a linear layer on top of the `ViTModel` output.

But I experience some problems during the evaluation phase when I used my `ViTCustom` model. If I set the argument `evaluation_stage="no"` inside the function `TrainingArguments(),` the model update the parameters, and the training loss starts to decrease and reach similar levels that comparing with the `ViTForImageClassification` (good, sanity check). But, when I set `evaluation_strategy="steps"` the model returns the following error 💣 :

```python
/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  FutureWarning,
*****Running training*****
  Num examples = 1451
  Num Epochs = 4
  Instantaneous batch size per device = 16
  Total train batch size (w. parallel, distributed & accumulation) = 16
  Gradient Accumulation steps = 1
  Total optimization steps = 364
 [101/364 00:30 < 01:20, 3.28 it/s, Epoch 1.10/4]
Step	Training Loss	Validation Loss
*****Running Evaluation*****
  Num examples = 170
  Batch size = 8
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-59-5bdd51e9c139> in <module>
----> 1 train_results = trainer.train()
      2 trainer.save_model()
      3 trainer.log_metrics("train", train_results.metrics)
      4 trainer.save_metrics("train", train_results.metrics)
      5 trainer.save_state()

8 frames
/usr/local/lib/python3.7/dist-packages/transformers/trainer_pt_utils.py in nested_detach(tensors)
    158 if isinstance(tensors, (list, tuple)):
    159 return type(tensors)(nested_detach(t) for t in tensors)
--> 160 return tensors.detach()
    161 
    162 

AttributeError: 'NoneType' object has no attribute 'detach' 

```

Does anyone that faced some similar issue have an idea why the `ViTCustom` model returns a ‘NoneType’ on evaluation mode that raises an error trying to detach the tensors? 🤔

best,  
Cristóbal