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