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- # Copyright (c) 2021 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.
- """Lovasz-Softmax and Jaccard hinge loss in PaddlePaddle"""
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import numpy as np
- import paddle
- from paddle import nn
- import paddle.nn.functional as F
-
- from paddleseg.cvlibs import manager
-
-
- @manager.LOSSES.add_component
- class LovaszSoftmaxLoss(nn.Layer):
- """
- Multi-class Lovasz-Softmax loss.
-
- Args:
- ignore_index (int64): Specifies a target value that is ignored and does not contribute to the input gradient. Default ``255``.
- classes (str|list): 'all' for all, 'present' for classes present in labels, or a list of classes to average.
- """
-
- def __init__(self, ignore_index=255, classes='present'):
- super(LovaszSoftmaxLoss, self).__init__()
- self.ignore_index = ignore_index
- self.classes = classes
-
- def forward(self, logits, labels):
- r"""
- Forward computation.
-
- Args:
- logits (Tensor): Shape is [N, C, H, W], logits at each prediction (between -\infty and +\infty).
- labels (Tensor): Shape is [N, 1, H, W] or [N, H, W], ground truth labels (between 0 and C - 1).
- """
- probas = F.softmax(logits, axis=1)
- vprobas, vlabels = flatten_probas(probas, labels, self.ignore_index)
- loss = lovasz_softmax_flat(vprobas, vlabels, classes=self.classes)
- return loss
-
-
- @manager.LOSSES.add_component
- class LovaszHingeLoss(nn.Layer):
- """
- Binary Lovasz hinge loss.
-
- Args:
- ignore_index (int64): Specifies a target value that is ignored and does not contribute to the input gradient. Default ``255``.
- """
-
- def __init__(self, ignore_index=255):
- super(LovaszHingeLoss, self).__init__()
- self.ignore_index = ignore_index
-
- def forward(self, logits, labels):
- r"""
- Forward computation.
-
- Args:
- logits (Tensor): Shape is [N, 1, H, W] or [N, 2, H, W], logits at each pixel (between -\infty and +\infty).
- labels (Tensor): Shape is [N, 1, H, W] or [N, H, W], binary ground truth masks (0 or 1).
- """
- if logits.shape[1] == 2:
- logits = binary_channel_to_unary(logits)
- loss = lovasz_hinge_flat(
- *flatten_binary_scores(logits, labels, self.ignore_index))
- return loss
-
-
- def lovasz_grad(gt_sorted):
- """
- Computes gradient of the Lovasz extension w.r.t sorted errors.
- See Alg. 1 in paper.
- """
- gts = paddle.sum(gt_sorted)
- p = len(gt_sorted)
-
- intersection = gts - paddle.cumsum(gt_sorted, axis=0)
- union = gts + paddle.cumsum(1 - gt_sorted, axis=0)
- jaccard = 1.0 - intersection.cast('float32') / union.cast('float32')
-
- if p > 1: # cover 1-pixel case
- jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
- return jaccard
-
-
- def binary_channel_to_unary(logits, eps=1e-9):
- """
- Converts binary channel logits to unary channel logits for lovasz hinge loss.
- """
- probas = F.softmax(logits, axis=1)
- probas = probas[:, 1, :, :]
- logits = paddle.log(probas + eps / (1 - probas + eps))
- logits = logits.unsqueeze(1)
- return logits
-
-
- def lovasz_hinge_flat(logits, labels):
- r"""
- Binary Lovasz hinge loss.
-
- Args:
- logits (Tensor): Shape is [P], logits at each prediction (between -\infty and +\infty).
- labels (Tensor): Shape is [P], binary ground truth labels (0 or 1).
- """
- if len(labels) == 0:
- # only void pixels, the gradients should be 0
- return logits.sum() * 0.
- signs = 2. * labels - 1.
- signs.stop_gradient = True
- errors = 1. - logits * signs
- if hasattr(paddle, "_legacy_C_ops"):
- errors_sorted, perm = paddle._legacy_C_ops.argsort(errors, 'axis', 0,
- 'descending', True)
- else:
- errors_sorted, perm = paddle._C_ops.argsort(errors, 'axis', 0,
- 'descending', True)
- errors_sorted.stop_gradient = False
- gt_sorted = paddle.gather(labels, perm)
- grad = lovasz_grad(gt_sorted)
- grad.stop_gradient = True
- loss = paddle.sum(F.relu(errors_sorted) * grad)
- return loss
-
-
- def flatten_binary_scores(scores, labels, ignore=None):
- """
- Flattens predictions in the batch (binary case).
- Remove labels according to 'ignore'.
- """
- scores = paddle.reshape(scores, [-1])
- labels = paddle.reshape(labels, [-1])
- labels.stop_gradient = True
- if ignore is None:
- return scores, labels
- valid = labels != ignore
- valid_mask = paddle.reshape(valid, (-1, 1))
- indexs = paddle.nonzero(valid_mask)
- indexs.stop_gradient = True
- vscores = paddle.gather(scores, indexs[:, 0])
- vlabels = paddle.gather(labels, indexs[:, 0])
- return vscores, vlabels
-
-
- def lovasz_softmax_flat(probas, labels, classes='present'):
- """
- Multi-class Lovasz-Softmax loss.
-
- Args:
- probas (Tensor): Shape is [P, C], class probabilities at each prediction (between 0 and 1).
- labels (Tensor): Shape is [P], ground truth labels (between 0 and C - 1).
- classes (str|list): 'all' for all, 'present' for classes present in labels, or a list of classes to average.
- """
- if probas.numel() == 0:
- # only void pixels, the gradients should be 0
- return probas * 0.
- C = probas.shape[1]
- losses = []
- classes_to_sum = list(range(C)) if classes in ['all', 'present'
- ] else classes
- for c in classes_to_sum:
- fg = paddle.cast(labels == c, probas.dtype) # foreground for class c
- if classes == 'present' and fg.sum() == 0:
- continue
- fg.stop_gradient = True
- if C == 1:
- if len(classes_to_sum) > 1:
- raise ValueError('Sigmoid output possible only with 1 class')
- class_pred = probas[:, 0]
- else:
- class_pred = probas[:, c]
- errors = paddle.abs(fg - class_pred)
- if hasattr(paddle, "_legacy_C_ops"):
- errors_sorted, perm = paddle._legacy_C_ops.argsort(
- errors, 'axis', 0, 'descending', True)
- else:
- errors_sorted, perm = paddle._C_ops.argsort(errors, 'axis', 0,
- 'descending', True)
- errors_sorted.stop_gradient = False
-
- fg_sorted = paddle.gather(fg, perm)
- fg_sorted.stop_gradient = True
-
- grad = lovasz_grad(fg_sorted)
- grad.stop_gradient = True
- loss = paddle.sum(errors_sorted * grad)
- losses.append(loss)
-
- if len(classes_to_sum) == 1:
- return losses[0]
-
- losses_tensor = paddle.stack(losses)
- mean_loss = paddle.mean(losses_tensor)
- return mean_loss
-
-
- def flatten_probas(probas, labels, ignore=None):
- """
- Flattens predictions in the batch.
- """
- if len(probas.shape) == 3:
- probas = paddle.unsqueeze(probas, axis=1)
- C = probas.shape[1]
- probas = paddle.transpose(probas, [0, 2, 3, 1])
- probas = paddle.reshape(probas, [-1, C])
- labels = paddle.reshape(labels, [-1])
- if ignore is None:
- return probas, labels
- valid = labels != ignore
- valid_mask = paddle.reshape(valid, [-1, 1])
- indexs = paddle.nonzero(valid_mask)
- indexs.stop_gradient = True
- vprobas = paddle.gather(probas, indexs[:, 0])
- vlabels = paddle.gather(labels, indexs[:, 0])
- return vprobas, vlabels
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