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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
- #
- # 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 paddle
- def compute_mean_covariance(img):
- batch_size = img.shape[0]
- channel_num = img.shape[1]
- height = img.shape[2]
- width = img.shape[3]
- num_pixels = height * width
- # batch_size * channel_num * 1 * 1
- mu = img.mean(2, keepdim=True).mean(3, keepdim=True)
- # batch_size * channel_num * num_pixels
- img_hat = img - mu.expand_as(img)
- img_hat = img_hat.reshape([batch_size, channel_num, num_pixels])
- # batch_size * num_pixels * channel_num
- img_hat_transpose = img_hat.transpose([0, 2, 1])
- # batch_size * channel_num * channel_num
- covariance = paddle.bmm(img_hat, img_hat_transpose)
- covariance = covariance / num_pixels
- return mu, covariance
- def dice_coefficient(y_true_cls, y_pred_cls, training_mask):
- eps = 1e-5
- intersection = paddle.sum(y_true_cls * y_pred_cls * training_mask)
- union = paddle.sum(y_true_cls * training_mask) + paddle.sum(
- y_pred_cls * training_mask) + eps
- loss = 1. - (2 * intersection / union)
- return loss
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