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# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/whai362/PSENet/blob/python3/models/loss/iou.py
"""
import paddle
EPS = 1e-6
def iou_single(a, b, mask, n_class):
valid = mask == 1
a = a.masked_select(valid)
b = b.masked_select(valid)
miou = []
for i in range(n_class):
if a.shape == [0] and a.shape == b.shape:
inter = paddle.to_tensor(0.0)
union = paddle.to_tensor(0.0)
else:
inter = ((a == i).logical_and(b == i)).astype('float32')
union = ((a == i).logical_or(b == i)).astype('float32')
miou.append(paddle.sum(inter) / (paddle.sum(union) + EPS))
miou = sum(miou) / len(miou)
return miou
def iou(a, b, mask, n_class=2, reduce=True):
batch_size = a.shape[0]
a = a.reshape([batch_size, -1])
b = b.reshape([batch_size, -1])
mask = mask.reshape([batch_size, -1])
iou = paddle.zeros((batch_size, ), dtype='float32')
for i in range(batch_size):
iou[i] = iou_single(a[i], b[i], mask[i], n_class)
if reduce:
iou = paddle.mean(iou)
return iou
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