import torch
import torch.nn as nn
import torch.utils.data as data
import torchvision.transforms as TF
import torchvision.utils as vutils
import torch.nn.functional as F
from torch.autograd import Function
class CenterLoss(nn.Module):
"""
paper: http://ydwen.github.io/papers/WenECCV16.pdf
code: https://github.com/pangyupo/mxnet_center_loss
pytorch code: https://blog.csdn.net/sinat_37787331/article/details/80296964
"""
def __init__(self, features_dim, num_class=10, alpha=0.01, scale=1.0, batch_size=64):
"""
初始化
:param features_dim: 特征维度 = c*h*w
:param num_class: 类别数量
:param alpha: centerloss的权重系数 [0,1]
"""
assert 0 <= alpha <= 1
super(CenterLoss, self).__init__()
self.alpha = alpha
self.num_class = num_class
self.scale = scale
self.batch_size = batch_size
self.feat_dim = features_dim
# store the center of each class , should be ( num_class, features_dim)
self.feature_centers = nn.Parameter(torch.randn([num_class, features_dim]))
self.lossfunc = CenterLossFunc.apply
init_weight(self, 'normal')
def forward(self, output_features, y_truth):
"""
损失计算
:param output_features: conv层输出的特征, [b,c,h,w]
:param y_truth: 标签值 [b,]
:return:
"""
batch_size = y_truth.size(0)
output_features = output_features.view(batch_size, -1)
assert output_features.size(-1) == self.feat_dim
loss = self.lossfunc(output_features, y_truth, self.feature_centers)
loss /= batch_size
# centers_pred = self.feature_centers.index_select(0, y_truth.long()) # [b,features_dim]
# diff = output_features - centers_pred
# loss = self.alpha * 1 / 2.0 * (diff.pow(2).sum()) / self.batch_size
return loss
class CenterLossFunc(Function):
# https://blog.csdn.net/xiewenbo/article/details/89286462
@staticmethod
def forward(ctx, feat, labels, centers):
ctx.save_for_backward(feat, labels, centers)
centers_batch = centers.index_select(0, labels.long())
return (feat - centers_batch).pow(2).sum() / 2.0
@staticmethod
def backward(ctx, grad_output):
feature, label, centers = ctx.saved_tensors
centers_batch = centers.index_select(0, label.long())
diff = centers_batch - feature
# init every iteration
counts = centers.new(centers.size(0)).fill_(1)
ones = centers.new(label.size(0)).fill_(1)
grad_centers = centers.new(centers.size()).fill_(0)
counts = counts.scatter_add_(0, label.long(), ones)
grad_centers.scatter_add_(0, label.unsqueeze(1).expand(feature.size()).long(), diff)
grad_centers = grad_centers / counts.view(-1, 1)
return - grad_output * diff, None, grad_centers
if __name__ == '__main__':
ct = CenterLoss(2, 10, 0.1).cuda()
y = torch.Tensor([0, 0, 2, 1]).cuda()
feat = torch.zeros(4, 2).cuda().requires_grad_()
print(list(ct.parameters()))
print(ct.feature_centers.grad)
out = ct(feat, y)
print(out.item())
out.backward()
print(ct.feature_centers.grad)
print(feat.grad)
