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import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from itertools import ifilterfalse
except ImportError: # py3k
from itertools import filterfalse as ifilterfalse
def dice_loss(probas, labels, smooth=1):
C = probas.size(1)
losses = []
for c in list(range(C)):
fg = (labels == c).float()
if fg.sum() == 0:
continue
class_pred = probas[:, c]
p0 = class_pred
g0 = fg
numerator = 2 * torch.sum(p0 * g0) + smooth
denominator = torch.sum(p0) + torch.sum(g0) + smooth
losses.append(1 - ((numerator) / (denominator)))
return mean(losses)
def tversky_loss(probas, labels, alpha=0.5, beta=0.5, epsilon=1e-6):
'''
Tversky loss function.
probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
labels: [P] Tensor, ground truth labels (between 0 and C - 1)
Same as soft dice loss when alpha=beta=0.5.
Same as Jaccord loss when alpha=beta=1.0.
See `Tversky loss function for image segmentation using 3D fully convolutional deep networks`
https://arxiv.org/pdf/1706.05721.pdf
'''
C = probas.size(1)
losses = []
for c in list(range(C)):
fg = (labels == c).float()
if fg.sum() == 0:
continue
class_pred = probas[:, c]
p0 = class_pred
p1 = 1 - class_pred
g0 = fg
g1 = 1 - fg
numerator = torch.sum(p0 * g0)
denominator = numerator + alpha * \
torch.sum(p0*g1) + beta*torch.sum(p1*g0)
losses.append(1 - ((numerator) / (denominator + epsilon)))
return mean(losses)
def flatten_probas(probas, labels, ignore=255):
"""
Flattens predictions in the batch
"""
B, C, H, W = probas.size()
probas = probas.permute(0, 2, 3,
1).contiguous().view(-1, C) # B * H * W, C = P, C
labels = labels.view(-1)
if ignore is None:
return probas, labels
valid = (labels != ignore)
vprobas = probas[valid.view(-1, 1).expand(-1, C)].reshape(-1, C)
# vprobas = probas[torch.nonzero(valid).squeeze()]
vlabels = labels[valid]
return vprobas, vlabels
def isnan(x):
return x != x
def mean(l, ignore_nan=False, empty=0):
"""
nanmean compatible with generators.
"""
l = iter(l)
if ignore_nan:
l = ifilterfalse(isnan, l)
try:
n = 1
acc = next(l)
except StopIteration:
if empty == 'raise':
raise ValueError('Empty mean')
return empty
for n, v in enumerate(l, 2):
acc += v
if n == 1:
return acc
return acc / n
class DiceLoss(nn.Module):
def __init__(self, ignore_index=255):
super(DiceLoss, self).__init__()
self.ignore_index = ignore_index
def forward(self, tmp_dic, label_dic, step=None):
total_loss = []
for idx in range(len(tmp_dic)):
pred = tmp_dic[idx]
label = label_dic[idx]
pred = F.softmax(pred, dim=1)
label = label.view(1, 1, pred.size()[2], pred.size()[3])
loss = dice_loss(
*flatten_probas(pred, label, ignore=self.ignore_index))
total_loss.append(loss.unsqueeze(0))
total_loss = torch.cat(total_loss, dim=0)
return total_loss
class SoftJaccordLoss(nn.Module):
def __init__(self, ignore_index=255):
super(SoftJaccordLoss, self).__init__()
self.ignore_index = ignore_index
def forward(self, tmp_dic, label_dic, step=None):
total_loss = []
for idx in range(len(tmp_dic)):
pred = tmp_dic[idx]
label = label_dic[idx]
pred = F.softmax(pred, dim=1)
label = label.view(1, 1, pred.size()[2], pred.size()[3])
loss = tversky_loss(*flatten_probas(pred,
label,
ignore=self.ignore_index),
alpha=1.0,
beta=1.0)
total_loss.append(loss.unsqueeze(0))
total_loss = torch.cat(total_loss, dim=0)
return total_loss
class CrossEntropyLoss(nn.Module):
def __init__(self,
top_k_percent_pixels=None,
hard_example_mining_step=100000):
super(CrossEntropyLoss, self).__init__()
self.top_k_percent_pixels = top_k_percent_pixels
if top_k_percent_pixels is not None:
assert (top_k_percent_pixels > 0 and top_k_percent_pixels < 1)
self.hard_example_mining_step = hard_example_mining_step + 1e-5
if self.top_k_percent_pixels is None:
self.celoss = nn.CrossEntropyLoss(ignore_index=255,
reduction='mean')
else:
self.celoss = nn.CrossEntropyLoss(ignore_index=255,
reduction='none')
def forward(self, dic_tmp, y, step):
total_loss = []
for i in range(len(dic_tmp)):
pred_logits = dic_tmp[i]
gts = y[i]
if self.top_k_percent_pixels is None:
final_loss = self.celoss(pred_logits, gts)
else:
# Only compute the loss for top k percent pixels.
# First, compute the loss for all pixels. Note we do not put the loss
# to loss_collection and set reduction = None to keep the shape.
num_pixels = float(pred_logits.size(2) * pred_logits.size(3))
pred_logits = pred_logits.view(
-1, pred_logits.size(1),
pred_logits.size(2) * pred_logits.size(3))
gts = gts.view(-1, gts.size(1) * gts.size(2))
pixel_losses = self.celoss(pred_logits, gts)
if self.hard_example_mining_step == 0:
top_k_pixels = int(self.top_k_percent_pixels * num_pixels)
else:
ratio = min(1.0,
step / float(self.hard_example_mining_step))
top_k_pixels = int((ratio * self.top_k_percent_pixels +
(1.0 - ratio)) * num_pixels)
top_k_loss, top_k_indices = torch.topk(pixel_losses,
k=top_k_pixels,
dim=1)
final_loss = torch.mean(top_k_loss)
final_loss = final_loss.unsqueeze(0)
total_loss.append(final_loss)
total_loss = torch.cat(total_loss, dim=0)
return total_loss
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