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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
@manager.LOSSES.add_component
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
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