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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
import sklearn.metrics as skmetrics
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))
pred_area = []
label_area = []
intersect_area = []
mask = label != ignore_index
for i in range(num_classes):
pred_i = paddle.logical_and(pred == i, mask)
label_i = label == i
intersect_i = paddle.logical_and(pred_i, label_i)
pred_area.append(paddle.sum(paddle.cast(pred_i, "int32")))
label_area.append(paddle.sum(paddle.cast(label_i, "int32")))
intersect_area.append(paddle.sum(paddle.cast(intersect_i, "int32")))
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 auc_roc(logits, label, num_classes, ignore_index=None):
"""
Calculate area under the roc curve
Args:
logits (Tensor): The prediction by model on testset, of shape (N,C,H,W) .
label (Tensor): The ground truth of image. (N,1,H,W)
num_classes (int): The unique number of target classes.
ignore_index (int): Specifies a target value that is ignored. Default: 255.
Returns:
auc_roc(float): The area under roc curve
"""
if ignore_index or len(np.unique(label)) > num_classes:
raise RuntimeError('labels with ignore_index is not supported yet.')
if len(label.shape) != 4:
raise ValueError(
'The shape of label is not 4 dimension as (N, C, H, W), it is {}'.
format(label.shape))
if len(logits.shape) != 4:
raise ValueError(
'The shape of logits is not 4 dimension as (N, C, H, W), it is {}'.
format(logits.shape))
N, C, H, W = logits.shape
logits = np.transpose(logits, (1, 0, 2, 3))
logits = logits.reshape([C, N * H * W]).transpose([1, 0])
label = np.transpose(label, (1, 0, 2, 3))
label = label.reshape([1, N * H * W]).squeeze()
if not logits.shape[0] == label.shape[0]:
raise ValueError('length of `logit` and `label` should be equal, '
'but they are {} and {}.'.format(logits.shape[0],
label.shape[0]))
if num_classes == 2:
auc = skmetrics.roc_auc_score(label, logits[:, 1])
else:
auc = skmetrics.roc_auc_score(label, logits, multi_class='ovr')
return auc
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 dice(intersect_area, pred_area, label_area):
"""
Calculate DICE.
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: DICE on all classes.
float: mean DICE of all classes.
"""
intersect_area = intersect_area.numpy()
pred_area = pred_area.numpy()
label_area = label_area.numpy()
union = pred_area + label_area
class_dice = []
for i in range(len(intersect_area)):
if union[i] == 0:
dice = 0
else:
dice = (2 * intersect_area[i]) / union[i]
class_dice.append(dice)
mdice = np.mean(class_dice)
return np.array(class_dice), mdice
# This is a deprecated function, please use class_measurement function.
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 class_measurement(intersect_area, pred_area, label_area):
"""
Calculate accuracy, calss precision and class recall.
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: The mean accuracy.
np.ndarray: The precision of all classes.
np.ndarray: The recall of all classes.
"""
intersect_area = intersect_area.numpy()
pred_area = pred_area.numpy()
label_area = label_area.numpy()
mean_acc = np.sum(intersect_area) / np.sum(pred_area)
class_precision = []
class_recall = []
for i in range(len(intersect_area)):
precision = 0 if pred_area[i] == 0 \
else intersect_area[i] / pred_area[i]
recall = 0 if label_area[i] == 0 \
else intersect_area[i] / label_area[i]
class_precision.append(precision)
class_recall.append(recall)
return mean_acc, np.array(class_precision), np.array(class_recall)
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().astype(np.float64)
pred_area = pred_area.numpy().astype(np.float64)
label_area = label_area.numpy().astype(np.float64)
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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