I have my model trained and I want to use a labeled test set to get the real accuracy.
I’m trying to run a code like this:
data_test = ImageDataLoaders.from_df(df=df_styles_test, path=pictures_dir, valid_pct=0,
label_col='style', fn_col='new_filename',
y_block=CategoryBlock(),
item_tfms=Resize(299),
batch_tfms=Normalize.from_stats(*imagenet_stats),
bs=64
)
learner.get_preds(dl=data_test)
And I’m getting this error:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-48-c83793a06c54> in <cell line: 1>()
----> 1 learner.get_preds(dl=data_test)
3 frames
/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py in nested_reorder(t, idxs)
776 def nested_reorder(t, idxs):
777 "Reorder all tensors in `t` using `idxs`"
--> 778 if isinstance(t, (Tensor,L)): return t[idxs]
779 elif is_listy(t): return type(t)(nested_reorder(t_, idxs) for t_ in t)
780 if t is None: return t
IndexError: index 19997 is out of bounds for dimension 0 with size 19968
This is a link to the colab that I’m running: Google Colab
Thank you in advance 
Update
I solved that this way:
def test_prob(test_df, test_dir, learner_to_test):
aciertos=0
total=len(test_df);
for i in range(total):
archivo = test_df.iloc[i]['new_filename']
estilo = test_df.iloc[i]['style']
img = load_image(test_dir/archivo)
pred = learner_to_test.predict(img)
if(estilo==pred[0]):
aciertos=aciertos+1
porcentaje=aciertos/total*100
print("El pocentaje de acierto en el conjunto de test es de: ",porcentaje,"%", sep='')
It’s not a good way to solve it cause it takes a lot of time to get the test accuracy but it worked for me.