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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)) | |