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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from isegm.utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2, max_mult=-1, eps=1e-12,
from_sigmoid=False, detach_delimeter=True,
batch_axis=0, weight=None, size_average=True,
ignore_label=-1):
super(NormalizedFocalLossSigmoid, self).__init__()
self._axis = axis
self._alpha = alpha
self._gamma = gamma
self._ignore_label = ignore_label
self._weight = weight if weight is not None else 1.0
self._batch_axis = batch_axis
self._from_logits = from_sigmoid
self._eps = eps
self._size_average = size_average
self._detach_delimeter = detach_delimeter
self._max_mult = max_mult
self._k_sum = 0
self._m_max = 0
def forward(self, pred, label):
one_hot = label > 0.5
sample_weight = label != self._ignore_label
if not self._from_logits:
pred = torch.sigmoid(pred)
alpha = torch.where(one_hot, self._alpha * sample_weight, (1 - self._alpha) * sample_weight)
pt = torch.where(sample_weight, 1.0 - torch.abs(label - pred), torch.ones_like(pred))
beta = (1 - pt) ** self._gamma
sw_sum = torch.sum(sample_weight, dim=(-2, -1), keepdim=True)
beta_sum = torch.sum(beta, dim=(-2, -1), keepdim=True)
mult = sw_sum / (beta_sum + self._eps)
if self._detach_delimeter:
mult = mult.detach()
beta = beta * mult
if self._max_mult > 0:
beta = torch.clamp_max(beta, self._max_mult)
with torch.no_grad():
ignore_area = torch.sum(label == self._ignore_label, dim=tuple(range(1, label.dim()))).cpu().numpy()
sample_mult = torch.mean(mult, dim=tuple(range(1, mult.dim()))).cpu().numpy()
if np.any(ignore_area == 0):
self._k_sum = 0.9 * self._k_sum + 0.1 * sample_mult[ignore_area == 0].mean()
beta_pmax, _ = torch.flatten(beta, start_dim=1).max(dim=1)
beta_pmax = beta_pmax.mean().item()
self._m_max = 0.8 * self._m_max + 0.2 * beta_pmax
loss = -alpha * beta * torch.log(torch.min(pt + self._eps, torch.ones(1, dtype=torch.float).to(pt.device)))
loss = self._weight * (loss * sample_weight)
if self._size_average:
bsum = torch.sum(sample_weight, dim=misc.get_dims_with_exclusion(sample_weight.dim(), self._batch_axis))
loss = torch.sum(loss, dim=misc.get_dims_with_exclusion(loss.dim(), self._batch_axis)) / (bsum + self._eps)
else:
loss = torch.sum(loss, dim=misc.get_dims_with_exclusion(loss.dim(), self._batch_axis))
return loss
def log_states(self, sw, name, global_step):
sw.add_scalar(tag=name + '_k', value=self._k_sum, global_step=global_step)
sw.add_scalar(tag=name + '_m', value=self._m_max, global_step=global_step)
class FocalLoss(nn.Module):
def __init__(self, axis=-1, alpha=0.25, gamma=2,
from_logits=False, batch_axis=0,
weight=None, num_class=None,
eps=1e-9, size_average=True, scale=1.0,
ignore_label=-1):
super(FocalLoss, self).__init__()
self._axis = axis
self._alpha = alpha
self._gamma = gamma
self._ignore_label = ignore_label
self._weight = weight if weight is not None else 1.0
self._batch_axis = batch_axis
self._scale = scale
self._num_class = num_class
self._from_logits = from_logits
self._eps = eps
self._size_average = size_average
def forward(self, pred, label, sample_weight=None):
one_hot = label > 0.5
sample_weight = label != self._ignore_label
if not self._from_logits:
pred = torch.sigmoid(pred)
alpha = torch.where(one_hot, self._alpha * sample_weight, (1 - self._alpha) * sample_weight)
pt = torch.where(sample_weight, 1.0 - torch.abs(label - pred), torch.ones_like(pred))
beta = (1 - pt) ** self._gamma
loss = -alpha * beta * torch.log(torch.min(pt + self._eps, torch.ones(1, dtype=torch.float).to(pt.device)))
loss = self._weight * (loss * sample_weight)
if self._size_average:
tsum = torch.sum(sample_weight, dim=misc.get_dims_with_exclusion(label.dim(), self._batch_axis))
loss = torch.sum(loss, dim=misc.get_dims_with_exclusion(loss.dim(), self._batch_axis)) / (tsum + self._eps)
else:
loss = torch.sum(loss, dim=misc.get_dims_with_exclusion(loss.dim(), self._batch_axis))
return self._scale * loss
class SoftIoU(nn.Module):
def __init__(self, from_sigmoid=False, ignore_label=-1):
super().__init__()
self._from_sigmoid = from_sigmoid
self._ignore_label = ignore_label
def forward(self, pred, label):
label = label.view(pred.size())
sample_weight = label != self._ignore_label
if not self._from_sigmoid:
pred = torch.sigmoid(pred)
loss = 1.0 - torch.sum(pred * label * sample_weight, dim=(1, 2, 3)) \
/ (torch.sum(torch.max(pred, label) * sample_weight, dim=(1, 2, 3)) + 1e-8)
return loss
class SigmoidBinaryCrossEntropyLoss(nn.Module):
def __init__(self, from_sigmoid=False, weight=None, batch_axis=0, ignore_label=-1):
super(SigmoidBinaryCrossEntropyLoss, self).__init__()
self._from_sigmoid = from_sigmoid
self._ignore_label = ignore_label
self._weight = weight if weight is not None else 1.0
self._batch_axis = batch_axis
def forward(self, pred, label):
label = label.view(pred.size())
sample_weight = label != self._ignore_label
label = torch.where(sample_weight, label, torch.zeros_like(label))
if not self._from_sigmoid:
loss = torch.relu(pred) - pred * label + F.softplus(-torch.abs(pred))
else:
eps = 1e-12
loss = -(torch.log(pred + eps) * label
+ torch.log(1. - pred + eps) * (1. - label))
loss = self._weight * (loss * sample_weight)
return torch.mean(loss, dim=misc.get_dims_with_exclusion(loss.dim(), self._batch_axis))
class BinaryDiceLoss(nn.Module):
""" Dice Loss for binary segmentation
"""
def forward(self, pred, label):
batchsize = pred.size(0)
# convert probability to binary label using maximum probability
input_pred, input_label = pred.max(1)
input_pred *= input_label.float()
# convert to floats
input_pred = input_pred.float()
target_label = label.float()
# convert to 1D
input_pred = input_pred.view(batchsize, -1)
target_label = target_label.view(batchsize, -1)
# compute dice score
intersect = torch.sum(input_pred * target_label, 1)
input_area = torch.sum(input_pred * input_pred, 1)
target_area = torch.sum(target_label * target_label, 1)
sum = input_area + target_area
epsilon = torch.tensor(1e-6)
# batch dice loss and ignore dice loss where target area = 0
batch_loss = torch.tensor(1.0) - (torch.tensor(2.0) * intersect + epsilon) / (sum + epsilon)
loss = batch_loss.mean()
return loss |