def task1_tta_predict(model, img_arr): img_arr_tta = cyclic_stacking(img_arr) mask_arr_tta = [] for _img_crops in img_arr_tta: _mask_crops = model.predict(_img_crops) mask_arr_tta.append(_mask_crops) mask_crops_pred = cyclic_pooling(*mask_arr_tta) return mask_crops_pred
def task3_tta_predict(model, img_arr): img_arr_tta = cyclic_stacking(img_arr) pred_logits = np.zeros(shape=(img_arr.shape[0], 7)) for _img_crops in img_arr_tta: pred_logits += model.predict(_img_crops) pred_logits = pred_logits/len(img_arr_tta) return pred_logits