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| # 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 | |
| from paddle import nn | |
| import paddle.nn.functional as F | |
| from paddleseg.cvlibs import manager | |
| class DiceLoss(nn.Layer): | |
| """ | |
| The implements of the dice loss. | |
| Args: | |
| weight (list[float], optional): The weight for each class. Default: None. | |
| ignore_index (int64): ignore_index (int64, optional): Specifies a target value that | |
| is ignored and does not contribute to the input gradient. Default ``255``. | |
| smooth (float32): Laplace smoothing to smooth dice loss and accelerate convergence. | |
| Default: 1.0 | |
| """ | |
| def __init__(self, weight=None, ignore_index=255, smooth=1.0): | |
| super().__init__() | |
| self.weight = weight | |
| self.ignore_index = ignore_index | |
| self.smooth = smooth | |
| self.eps = 1e-8 | |
| def forward(self, logits, labels): | |
| num_class = logits.shape[1] | |
| if self.weight is not None: | |
| assert num_class == len(self.weight), \ | |
| "The lenght of weight should be euqal to the num class" | |
| mask = labels != self.ignore_index | |
| mask = paddle.cast(paddle.unsqueeze(mask, 1), 'float32') | |
| labels[labels == self.ignore_index] = 0 | |
| labels_one_hot = F.one_hot(labels, num_class) | |
| labels_one_hot = paddle.transpose(labels_one_hot, [0, 3, 1, 2]) | |
| logits = F.softmax(logits, axis=1) | |
| dice_loss = 0.0 | |
| for i in range(num_class): | |
| dice_loss_i = dice_loss_helper(logits[:, i], labels_one_hot[:, i], | |
| mask, self.smooth, self.eps) | |
| if self.weight is not None: | |
| dice_loss_i *= self.weight[i] | |
| dice_loss += dice_loss_i | |
| dice_loss = dice_loss / num_class | |
| return dice_loss | |
| def dice_loss_helper(logit, label, mask, smooth, eps): | |
| assert logit.shape == label.shape, \ | |
| "The shape of logit and label should be the same" | |
| logit = paddle.reshape(logit, [0, -1]) | |
| label = paddle.reshape(label, [0, -1]) | |
| mask = paddle.reshape(mask, [0, -1]) | |
| logit *= mask | |
| label *= mask | |
| intersection = paddle.sum(logit * label, axis=1) | |
| cardinality = paddle.sum(logit + label, axis=1) | |
| dice_loss = 1 - (2 * intersection + smooth) / (cardinality + smooth + eps) | |
| dice_loss = dice_loss.mean() | |
| return dice_loss | |