def misclassified_prop(mis_pixels: np.ndarray, prob: nd.NDArray) -> np.ndarray:
"""
get the probability distribution of misclassified pixels.
:param mis_pixels: misclassified pixels of shape HW where 0 indicates misclassified pixels
:param prob: the predicted probability with shape CHW
:return: numpy array of shape NC where N is the total number of misclassified pixels
"""
prob = prob.as_in_context(mx.cpu())
prob = nd.transpose(nd.reshape(prob, shape=(0, -1)), axes=(1, 0))
flag_bit = np.reshape(mis_pixels, newshape=(-1))
mis_index = nd.array(np.where(flag_bit == 0)).squeeze()
return prob[mis_index, :].asnumpy()
def misclassified_pixels(prob: nd.NDArray,
label: nd.NDArray,
ignore_label: int = -1) -> np.ndarray:
"""
return misclassified pixels.
:param prob: the predicted probability with shape CHW
:param label: the ground truth label with shape HW
:param ignore_label: ignored label
:return: numpy array of shape HW where 0 indicates misclassified pixels
"""
# needs to process on cpu
prob = prob.as_in_context(mx.cpu())
label = label.as_in_context(mx.cpu())
# determine equal or not to get misclassified pixels
pred = nd.squeeze(nd.argmax(prob, axis=0)).astype('int32')
mis_classify = (pred == label).asnumpy()
# deal with ignored label via numpy
label = label.asnumpy()
mis_classify[label == ignore_label] = 1
return mis_classify