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