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sets.py
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import warnings
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch
from torch.autograd import Variable
from torch.nn import MSELoss, SmoothL1Loss, L1Loss
class FocalLoss(nn.Module):
def __init__(self, focusing_param=2.5, balance_param=1):
super(FocalLoss, self).__init__()
self.focusing_param = focusing_param
self.balance_param = balance_param
def forward(self, output, target):
cross_entropy = F.cross_entropy(output, target)
cross_entropy_log = torch.log(cross_entropy)
logpt = - F.cross_entropy(output, target)
pt = torch.exp(logpt)
focal_loss = -((1 - pt) ** self.focusing_param) * logpt
balanced_focal_loss = self.balance_param * focal_loss
return balanced_focal_loss
class DiceLoss2D(nn.Module):
def __init__(self, smooth = 1, size_average=True):
super(DiceLoss2D, self).__init__()
self.smooth = smooth
self.size_average = size_average
def forward(self, logit, target, type='softmax'):
target = target.view(-1, 1).long()
#print('target.shape',target.shape): 460800, 1
if type=='sigmoid':
if class_weight is None:
class_weight = [1]*2 #[0.5, 0.5]
prob = F.sigmoid(logit)
prob = prob.view(-1, 1)
prob = torch.cat((1-prob, prob), 1)
select = torch.FloatTensor(len(prob), 2).zero_().cuda()
select.scatter_(1, target, 1.)
elif type=='softmax':
B,C,H,W = logit.size()
#print('logit.size()',logit.size()): 8, 5, 240, 240
logit = logit.permute(0, 2, 3, 1).contiguous().view(-1, C)
#print('logit.shape',logit.shape): 460800, 5
prob = F.softmax(logit,1)
#print('prob.shape',prob.shape): 460800, 5
#print('len(prob)',len(prob)): 460800
select = torch.FloatTensor(len(prob), C).zero_().cuda()
#print('select.shape',select.shape): 460800, 5;
#print('target.SUM',target.sum()): 51224
select.scatter_(1, target, 1.)
#print('select.shape',select.shape): 460800, 5
#intersection = prob * select
#print('intersection.shape',intersection.shape)
#loss = 2 * (intersection.sum(1) + self.smooth) / (prob.sum(1) + select.sum(1) + self.smooth)
# print('loss.shape',loss.shape)
#loss = 1 - loss.sum() / len(prob)
num = torch.sum(torch.mul(prob, select), dim=1) + self.smooth
den = torch.sum(prob.pow(2) + select.pow(2), dim=1) + self.smooth
loss = 1 - num / den
if self.size_average:
loss = loss.mean()
else:
loss = loss
#print('loss',loss)
return loss
class DiceLoss3D(nn.Module):
def __init__(self, smooth = 1, size_average=True):
super(DiceLoss3D, self).__init__()
self.smooth = smooth
self.size_average = size_average
def forward(self, logit, target, type='softmax'):
target = target.view(-1, 1).long()
#print('target.shape',target.shape): 460800, 1
if type=='sigmoid':
if class_weight is None:
class_weight = [1]*2 #[0.5, 0.5]
prob = F.sigmoid(logit)
prob = prob.view(-1, 1)
prob = torch.cat((1-prob, prob), 1)
select = torch.FloatTensor(len(prob), 2).zero_().cuda()
select.scatter_(1, target, 1.)
elif type=='softmax':
B,C,H,W,D = logit.size()
#print('logit.size()',logit.size()): 8, 5, 128, 128, 128
logit = logit.permute(0, 2, 3, 4, 1).contiguous().view(-1, C)
#print('logit.shape',logit.shape): 460800, 5
prob = F.softmax(logit,1)
#print('prob.shape',prob.shape): 460800, 5
#print('len(prob)',len(prob)): 460800
select = torch.FloatTensor(len(prob), C).zero_().cuda()
#print('select.shape',select.shape): 460800, 5;
#print('target.SUM',target.sum()): 51224
select.scatter_(1, target, 1.)
#print('select.shape',select.shape): 460800, 5
#intersection = prob * select
#print('intersection.shape',intersection.shape)
#loss = 2 * (intersection.sum(1) + self.smooth) / (prob.sum(1) + select.sum(1) + self.smooth)
# print('loss.shape',loss.shape)
#loss = 1 - loss.sum() / len(prob)
#print(prob.shape,select.shape)
num = torch.sum(torch.mul(prob, select), dim=1) + self.smooth
den = torch.sum(prob.pow(2) + select.pow(2), dim=1) + self.smooth
loss = 1 - num / den
if self.size_average:
loss = loss.mean()
else:
loss = loss
#print('loss',loss)
return loss
class Maskloss(nn.Module):
def __init__(self, smooth = 1, size_average=True):
super(Maskloss, self).__init__()
self.smooth = smooth
self.size_average = size_average
def forward(self, logit, target, mask, type='softmax'):
target = target.view(-1, 1).long()
#print('target.shape',target.shape): 460800, 1
if type=='sigmoid':
if class_weight is None:
class_weight = [1]*2 #[0.5, 0.5]
prob = F.sigmoid(logit)
prob = prob.view(-1, 1)
prob = torch.cat((1-prob, prob), 1)
select = torch.FloatTensor(len(prob), 2).zero_().cuda()
select.scatter_(1, target, 1.)
elif type=='softmax':
B,C,H,W,D = logit.size()
#print('logit.size()',logit.size()): 8, 5, 128, 128, 128
logit = logit.permute(0, 2, 3, 4, 1).contiguous().view(-1, C)
#print('logit.shape',logit.shape): 460800, 5
prob = F.softmax(logit,1)
#print('prob.shape',prob.shape): 460800, 5
#print('len(prob)',len(prob)): 460800
select = torch.FloatTensor(len(prob), C).zero_().cuda()
#print('select.shape',select.shape): 460800, 5;
#print('target.SUM',target.sum()): 51224
select.scatter_(1, target, 1.)
#print('select.shape',select.shape): 460800, 5
#intersection = prob * select
#print('intersection.shape',intersection.shape)
#loss = 2 * (intersection.sum(1) + self.smooth) / (prob.sum(1) + select.sum(1) + self.smooth)
# print('loss.shape',loss.shape)
#loss = 1 - loss.sum() / len(prob)
mask = mask.view(-1, 1).long()
maskk = torch.cat((mask,mask,mask,mask,mask,mask),1)
#print(maskk.shape)
#for i in range(maskk.shape[0]):
# if torch.sum(maskk[i,:]) > 0:
# print(maskk[i,:])
# break
select = select[maskk<1].view(-1, C)
prob = prob[maskk<1].view(-1, C)
#print(select[0,:])
#print(select[10,:])
#print(select[110,:])
#print(select[1110,:])
#print(select[11110,:])
#print(select[111110,:])
num = torch.sum(torch.mul(prob, select), dim=1) + self.smooth
den = torch.sum(prob.pow(2) + select.pow(2), dim=1) + self.smooth
loss = 1 - num / den
if self.size_average:
loss = loss.mean()
else:
loss = loss
#print('loss',loss)
return loss
class FocalLoss2d(nn.Module):
def __init__(self, gamma=2, size_average=True):
super(FocalLoss2d, self).__init__()
self.gamma = gamma
self.size_average = size_average
def forward(self, logit, target, class_weight=None, type='softmax'):
target = target.view(-1, 1).long()
if type=='sigmoid':
if class_weight is None:
class_weight = [1]*2 #[0.5, 0.5]
prob = F.sigmoid(logit)
prob = prob.view(-1, 1)
prob = torch.cat((1-prob, prob), 1)
select = torch.FloatTensor(len(prob), 2).zero_().cuda()
select.scatter_(1, target, 1.)
elif type=='softmax':
B,C,H,W = logit.size()
if class_weight is None:
class_weight =[1]*C #[1/C]*C
logit = logit.permute(0, 2, 3, 1).contiguous().view(-1, C)
prob = F.softmax(logit,1)
select = torch.FloatTensor(len(prob), C).zero_().cuda()
select.scatter_(1, target, 1.)
class_weight = torch.FloatTensor(class_weight).cuda().view(-1,1)
class_weight = torch.gather(class_weight, 0, target)
prob = (prob*select).sum(1).view(-1,1)
prob = torch.clamp(prob,1e-8,1-1e-8)
batch_loss = - class_weight *(torch.pow((1-prob), self.gamma))*prob.log()
if self.size_average:
loss = batch_loss.mean()
else:
loss = batch_loss
return loss
class DefaultConfig(object):
env = '552552' # visdom 鐜
vis_port =8097
model = 'unet_3d' # 浣跨敤鐨勬ā鍨嬶紝鍚嶅瓧蹇呴』涓巑odels/__init__.py涓殑鍚嶅瓧涓€鑷?
train_data_root = r"/media/hitlab/GuoXuTao/FCN01/MICCAI_BraTS17_Data_Training" # 璁粌闆嗗瓨鏀捐矾寰? test_data_root = r'/media/hitlab/GuoXuTao/FCN01/MICCAI_BraTS17_Data_Training' # 娴嬭瘯闆嗗瓨鏀捐矾寰? load_model_path = False # 鍔犺浇棰勮缁冪殑妯″瀷鐨勮矾寰勶紝涓篘one浠ｈ〃涓嶅姞杞?
batch_size = 4 # batch size
use_gpu = True # user GPU or not
num_workers = 8 # how many workers for loading data
print_freq = 20 # print info every N batch
debug_file = '/tmp/debug' # if os.path.exists(debug_file): enter ipdb
result_file = 'result.csv'
max_epoch = 100000
lr = 0.0001 # initial learning rate
lr_decay = 1 # when val_loss increase, lr = lr*lr_decay
weight_decay = 1e-8 # 鎹熷け鍑芥暟
class AverageMeter(object):
# Computes and stores the average and current value
def __init__(self):
self.reset() # __init__():reset parameters
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def _fast_hist(label_true, label_pred, n_class):
mask = (label_true >= 0) & (label_true < n_class)
hist = np.bincount(
n_class * label_true[mask].astype(int) +
label_pred[mask], minlength=n_class**2).reshape(n_class, n_class)
return hist
def calc_dice(label_true,label_pred):
B_index=label_true.shape[0]*label_true.shape[1]*label_true.shape[2]
A_index=label_pred.shape[0]*label_pred.shape[1]*label_pred.shape[2]
count=0.0
for i in range(label_true.shape[0]):
for j in range(label_true.shape[1]):
for k in range(label_true.shape[2]):
if label_true[i][j][k]==label_pred[i][j][k]:
count=count+1
#print count,B_index,A_index
return float(2*count/(B_index+A_index)),sum(sum(label_true!=label_pred))
def scores(label_trues, label_preds, n_class):
"""Returns accuracy score evaluation result.
- overall accuracy
- mean accuracy
- mean IU
- fwavacc
"""
hist = np.zeros((n_class, n_class))
for lt, lp in zip(label_trues, label_preds):
hist += _fast_hist(lt.flatten(), lp.flatten(), n_class)
acc = np.diag(hist).sum() / hist.sum()
acc_cls = np.diag(hist) / hist.sum(axis=1)
acc_cls = np.nanmean(acc_cls)
iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))
mean_iu = np.nanmean(iu)
freq = hist.sum(axis=1) / hist.sum()
fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
cls_iu = dict(zip(range(n_class), iu))
return {'Overall Acc: \t': acc,
'Mean Acc : \t': acc_cls,
'FreqW Acc : \t': fwavacc,
'Mean IoU : \t': mean_iu,}, cls_iu,acc
def cross_entropy2d(input, target, weight=None, size_average=False):
n, c, h, w = input.size()
log_p = F.log_softmax(input)
log_p = log_p.transpose(1, 2).transpose(2, 3).contiguous().view(-1,c)
print(log_p)
log_p = log_p[target.view(n, h, w, 1).repeat(1, 1, 1, c) >= 0]
log_p = log_p.view(-1,c)
print(log_p)
mask = target >= 0
target = target[mask]
loss = F.nll_loss(log_p, target, weight=weight, size_average=False)
if size_average:
loss /= mask.data.sum()
return loss
def parse(self,kwargs):
'''
鏍规嵁瀛楀吀kwargs 鏇存柊 config鍙傛暟
'''
for k,v in kwargs.iteritems():
if not hasattr(self,k):
warnings.warn("Warning: opt has not attribut %s" %k)
setattr(self,k,v)
print('user config:')
for k,v in self.__class__.__dict__.iteritems():
if not k.startswith('__'):
print(k,getattr(self,k))
DefaultConfig.parse = parse
opt =DefaultConfig()
# opt.parse = parse