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- # Copyright (c) 2020 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
- import paddle.nn.functional as F
- def calculate_area(pred, label, num_classes, ignore_index=255):
- """
- Calculate intersect, prediction and label area
- Args:
- pred (Tensor): The prediction by model.
- label (Tensor): The ground truth of image.
- num_classes (int): The unique number of target classes.
- ignore_index (int): Specifies a target value that is ignored. Default: 255.
- Returns:
- Tensor: The intersection area of prediction and the ground on all class.
- Tensor: The prediction area on all class.
- Tensor: The ground truth area on all class
- """
- if len(pred.shape) == 4:
- pred = paddle.squeeze(pred, axis=1)
- if len(label.shape) == 4:
- label = paddle.squeeze(label, axis=1)
- if not pred.shape == label.shape:
- raise ValueError('Shape of `pred` and `label should be equal, '
- 'but there are {} and {}.'.format(pred.shape,
- label.shape))
- # Delete ignore_index
- mask = label != ignore_index
- pred = pred + 1
- label = label + 1
- pred = pred * mask
- label = label * mask
- pred = F.one_hot(pred, num_classes + 1)
- label = F.one_hot(label, num_classes + 1)
- pred = pred[:, :, :, 1:]
- label = label[:, :, :, 1:]
- pred_area = []
- label_area = []
- intersect_area = []
- for i in range(num_classes):
- pred_i = pred[:, :, :, i]
- label_i = label[:, :, :, i]
- pred_area_i = paddle.sum(pred_i)
- label_area_i = paddle.sum(label_i)
- intersect_area_i = paddle.sum(pred_i * label_i)
- pred_area.append(pred_area_i)
- label_area.append(label_area_i)
- intersect_area.append(intersect_area_i)
- pred_area = paddle.concat(pred_area)
- label_area = paddle.concat(label_area)
- intersect_area = paddle.concat(intersect_area)
- return intersect_area, pred_area, label_area
- def mean_iou(intersect_area, pred_area, label_area):
- """
- Calculate iou.
- Args:
- intersect_area (Tensor): The intersection area of prediction and ground truth on all classes.
- pred_area (Tensor): The prediction area on all classes.
- label_area (Tensor): The ground truth area on all classes.
- Returns:
- np.ndarray: iou on all classes.
- float: mean iou of all classes.
- """
- intersect_area = intersect_area.numpy()
- pred_area = pred_area.numpy()
- label_area = label_area.numpy()
- union = pred_area + label_area - intersect_area
- class_iou = []
- for i in range(len(intersect_area)):
- if union[i] == 0:
- iou = 0
- else:
- iou = intersect_area[i] / union[i]
- class_iou.append(iou)
- miou = np.mean(class_iou)
- return np.array(class_iou), miou
- def accuracy(intersect_area, pred_area):
- """
- Calculate accuracy
- Args:
- intersect_area (Tensor): The intersection area of prediction and ground truth on all classes..
- pred_area (Tensor): The prediction area on all classes.
- Returns:
- np.ndarray: accuracy on all classes.
- float: mean accuracy.
- """
- intersect_area = intersect_area.numpy()
- pred_area = pred_area.numpy()
- class_acc = []
- for i in range(len(intersect_area)):
- if pred_area[i] == 0:
- acc = 0
- else:
- acc = intersect_area[i] / pred_area[i]
- class_acc.append(acc)
- macc = np.sum(intersect_area) / np.sum(pred_area)
- return np.array(class_acc), macc
- def kappa(intersect_area, pred_area, label_area):
- """
- Calculate kappa coefficient
- Args:
- intersect_area (Tensor): The intersection area of prediction and ground truth on all classes..
- pred_area (Tensor): The prediction area on all classes.
- label_area (Tensor): The ground truth area on all classes.
- Returns:
- float: kappa coefficient.
- """
- intersect_area = intersect_area.numpy()
- pred_area = pred_area.numpy()
- label_area = label_area.numpy()
- total_area = np.sum(label_area)
- po = np.sum(intersect_area) / total_area
- pe = np.sum(pred_area * label_area) / (total_area * total_area)
- kappa = (po - pe) / (1 - pe)
- return kappa
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