# Model.eval() gives incorrect loss for model with batchnorm layers

**URL:** <https://discuss.pytorch.org/t/model-eval-gives-incorrect-loss-for-model-with-batchnorm-layers/7561>\
**Category:** Uncategorized\
**Created:** [September 19, 2017, 4:25am UTC](https://discuss.pytorch.org/t/model-eval-gives-incorrect-loss-for-model-with-batchnorm-layers/7561 "2017-09-19T04:25:03Z")\
**Posts on this page:** 1\
**Showing post:** 2

<div class="post-metadata">

**Author:** ![zhangboknight](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/zhangboknight/32/15722_2.png) [@zhangboknight](https://discuss.pytorch.org/u/zhangboknight)\
**Post date:** [September 19, 2017, 4:27am UTC](https://discuss.pytorch.org/t/model-eval-gives-incorrect-loss-for-model-with-batchnorm-layers/7561/2 "2017-09-19T04:27:39Z")

</div>

The testing code which is called every 100 training iters is as following:

```
def test_model(model):
    psnr_test_avg = 0
    loss_test_avg = 0
    model.eval()
    
    for iteration, test_data in enumerate(dataloader_test, 0):
        label_test = test_data['label_patch']
        residue_test = test_data['residue_patch']
        stacked_test = test_data['stacked_patch']
        microshift_test = test_data['train_patch']
        inputs_test, residues_test, labels_test, microshifts_test = Variable(stacked_test.type(dtype), requires_grad=False), Variable(residue_test.type(dtype), requires_grad=False), Variable(label_test.type(dtype), requires_grad=False), Variable(microshift_test.type(dtype), requires_grad=False)
        outputs_test = model(inputs_test)
        loss_mse_test = criterion_mse(outputs_test + microshifts_test, labels_test).data.cpu().numpy()
        loss_l1_test = criterion(outputs_test, residues_test).data.cpu().numpy()
        psnr_test = 10 * np.log10(255 * 255 / loss_mse_test)
        loss_test_avg += loss_l1_test
        psnr_test_avg += psnr_test

    loss_test_avg /= (iteration + 1)
    psnr_test_avg /= (iteration + 1)

    return loss_test_avg, psnr_test_avg
```

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