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81 lines
2.7 KiB
81 lines
2.7 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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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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import numpy as np |
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import paddle |
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import paddlers.models.ppseg as paddleseg |
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def loss_computation(logits_list, labels, losses): |
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loss_list = [] |
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for i in range(len(logits_list)): |
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logits = logits_list[i] |
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loss_i = losses['types'][i] |
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if isinstance(loss_i, paddleseg.models.MixedLoss): |
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loss_list.append(losses['coef'][i] * sum(loss_i(logits, labels))) |
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else: |
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loss_list.append(losses['coef'][i] * loss_i(logits, labels)) |
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return loss_list |
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def multitask_loss_computation(logits_list, labels_list, losses): |
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loss_list = [] |
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for i in range(len(logits_list)): |
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logits = logits_list[i] |
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labels = labels_list[i] |
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loss_i = losses['types'][i] |
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if isinstance(loss_i, paddleseg.models.MixedLoss): |
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loss_list.append(losses['coef'][i] * sum(loss_i(logits, labels))) |
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else: |
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loss_list.append(losses['coef'][i] * loss_i(logits, labels)) |
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return loss_list |
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def f1_score(intersect_area, pred_area, label_area): |
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intersect_area = intersect_area.numpy() |
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pred_area = pred_area.numpy() |
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label_area = label_area.numpy() |
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class_f1_sco = [] |
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for i in range(len(intersect_area)): |
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if pred_area[i] + label_area[i] == 0: |
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f1_sco = 0 |
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elif pred_area[i] == 0: |
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f1_sco = 0 |
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else: |
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prec = intersect_area[i] / pred_area[i] |
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rec = intersect_area[i] / label_area[i] |
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f1_sco = 2 * prec * rec / (prec + rec) |
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class_f1_sco.append(f1_sco) |
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return np.array(class_f1_sco) |
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def confusion_matrix(pred, label, num_classes, ignore_index=255): |
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label = paddle.transpose(label, (0, 2, 3, 1)) |
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pred = paddle.transpose(pred, (0, 2, 3, 1)) |
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mask = label != ignore_index |
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label = paddle.masked_select(label, mask) |
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pred = paddle.masked_select(pred, mask) |
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cat_matrix = num_classes * label + pred |
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conf_mat = paddle.histogram( |
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cat_matrix, |
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bins=num_classes * num_classes, |
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min=0, |
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max=num_classes * num_classes - 1).reshape([num_classes, num_classes]) |
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return conf_mat
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