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