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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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.
- import numpy as np
- import paddle
- def loss_computation(logits_list, labels, losses):
- loss_list = []
- for i in range(len(logits_list)):
- logits = logits_list[i]
- loss_i = losses['types'][i]
- loss_list.append(losses['coef'][i] * loss_i(logits, labels))
- return loss_list
- def f1_score(intersect_area, pred_area, label_area):
- intersect_area = intersect_area.numpy()
- pred_area = pred_area.numpy()
- label_area = label_area.numpy()
- class_f1_sco = []
- for i in range(len(intersect_area)):
- if pred_area[i] + label_area[i] == 0:
- f1_sco = 0
- elif pred_area[i] == 0:
- f1_sco = 0
- else:
- prec = intersect_area[i] / pred_area[i]
- rec = intersect_area[i] / label_area[i]
- f1_sco = 2 * prec * rec / (prec + rec)
- class_f1_sco.append(f1_sco)
- return np.array(class_f1_sco)
- def confusion_matrix(pred, label, num_classes, ignore_index=255):
- label = paddle.transpose(label, (0, 2, 3, 1))
- pred = paddle.transpose(pred, (0, 2, 3, 1))
- mask = label != ignore_index
- label = paddle.masked_select(label, mask)
- pred = paddle.masked_select(pred, mask)
- cat_matrix = num_classes * label + pred
- conf_mat = paddle.histogram(
- cat_matrix,
- bins=num_classes * num_classes,
- min=0,
- max=num_classes * num_classes - 1).reshape([num_classes, num_classes])
- return conf_mat
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