第一步:制作数据集
在数据集制作完后，定义损失函数，源码链接:https://github.com/vandit15/Class-balanced-loss-pytorch
要注意的是，源码需要修改，不然无法调用gpu.
修改好的代码为:
class_balanced_loss.py
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import lr_scheduler
import torch.optim as optim
def focal_loss(labels, logits, alpha, gamma):
"""Compute the focal loss between `logits` and the ground truth `labels`.
Focal loss = -alpha_t * (1-pt)^gamma * log(pt)
where pt is the probability of being classified to the true class.
pt = p (if true class), otherwise pt = 1 - p. p = sigmoid(logit).
Args:
labels: A float tensor of size [batch, num_classes].
logits: A float tensor of size [batch, num_classes].
alpha: A float tensor of size [batch_size]
specifying per-example weight for balanced cross entropy.
gamma: A float scalar modulating loss from hard and easy examples.
Returns:
focal_loss: A float32 scalar representing normalized total loss.
"""
BCLoss = F.binary_cross_entropy_with_logits(input = logits, target = labels,reduction = "none")
if gamma == 0.0:
modulator = 1.0
else:
modulator = torch.exp(-gamma * labels * logits - gamma * torch.log(1 +
torch.exp(-1.0 * logits)))
loss = modulator * BCLoss
weighted_loss = alpha * loss
focal_loss = torch.sum(weighted_loss)
focal_loss /= torch.sum(labels)
return focal_loss
def CB_loss(labels, logits, samples_per_cls, no_of_classes, loss_type, beta, gamma):
"""Compute the Class Balanced Loss between `logits` and the ground truth `labels`.
Class Balanced Loss: ((1-beta)/(1-beta^n))*Loss(labels, logits)
where Loss is one of the standard losses used for Neural Networks.
Args:
labels: A int tensor of size [batch].
logits: A float tensor of size [batch, no_of_classes].
samples_per_cls: A python list of size [no_of_classes].
no_of_classes: total number of classes. int
loss_type: string. One of "sigmoid", "focal", "softmax".
beta: float. Hyperparameter for Class balanced loss.
gamma: float. Hyperparameter for Focal loss.
Returns:
cb_loss: A float tensor representing class balanced loss
"""
effective_num = 1.0 - np.power(beta, samples_per_cls)
weights = (1.0 - beta) / np.array(effective_num)
weights = weights / np.sum(weights) * no_of_classes
# print(weights.shape)
labels_one_hot = F.one_hot(labels, no_of_classes).float().cuda()
# print(labels_one_hot.shape)
weights = torch.tensor(weights).float()
# 增加维度
weights = weights.unsqueeze(0).cuda()
# print(weights)
# labels_one_hot.shape[0] -- batch_size
weights = weights.repeat(labels_one_hot.shape[0],1) * labels_one_hot
weights = weights.sum(1)
weights = weights.unsqueeze(1)
weights = weights.repeat(1,no_of_classes)
if loss_type == "focal":
cb_loss = focal_loss(labels_one_hot, logits, weights, gamma)
elif loss_type == "sigmoid":
cb_loss = F.binary_cross_entropy_with_logits(input = logits,target = labels_one_hot, weight = weights)
elif loss_type == "softmax":
pred = logits.softmax(dim = 1)
cb_loss = F.binary_cross_entropy(input = pred, target = labels_one_hot, weight = weights)
return cb_loss