Simple Submitting Kernel to Kaggle Competition

blog_predict-plant.py

preds = []
# move model to GPU for faster inference and set evaluation
model.cuda().eval()
for imgs, names in dm.test_dataloader():
    # for the prediction we do not need gradients
    with torch.no_grad():
        onehots = model(imgs.cuda()).cpu()
    # aggregate particular preditions
    for oh, name in zip(onehots, names):
        lbs = dm.onehot_to_labels(oh)
        preds.append(dict(image=name, labels=" ".join(lbs[::-1])))

df_preds = pd.DataFrame(preds)
# show sample predictions
print(df_preds.head())