ghm pytorch
时间: 2023-05-03 13:04:26 浏览: 75
ghm pytorch是一种PyTorch实现的图像分类损失函数。该方法通过对样本进行加权,减少了对易分类的样本的权重,对难分类的样本的权重进行增加,提高了模型在难样本上的性能表现,同时减少了对易样本的过拟合现象。使用ghm pytorch可以有效地提高深度学习模型的准确性和泛化性能,并且可以很好地适应不同数据分布和不同难度程度的样本。此外,该方法在实现上也很简单,只需要在PyTorch框架中添加相应的代码即可实现。由于ghm pytorch的有效性和简易性,它在图像分类任务中被广泛应用。值得一提的是,在与其他图像分类损失函数进行比较时,ghm pytorch在各类任务中均取得了非常优秀的表现,成为了当前最为流行的一种图像分类损失函数之一。
相关问题
在CNN中加入GHM损失函数pytorch
好的,GHM(Gradient Harmonized Single-stage Detector Loss)是一种用于目标检测的损失函数,可以解决训练过程中类别不平衡问题。下面是一个简单的用PyTorch实现CNN并加入GHM损失函数的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torch.autograd import Variable
# 定义CNN模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 5)
self.fc1 = nn.Linear(64 * 5 * 5, 1000)
self.fc2 = nn.Linear(1000, 10)
def forward(self, x):
x = self.pool(nn.functional.relu(self.conv1(x)))
x = self.pool(nn.functional.relu(self.conv2(x)))
x = x.view(-1, 64 * 5 * 5)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义GHM损失函数
class GHMLoss(nn.Module):
def __init__(self, bins=10, alpha=0.75):
super(GHMLoss, self).__init__()
self.bins = bins
self.alpha = alpha
self.edges = torch.arange(bins + 1).float() / bins
def calc_grad(self, pred, target, weight):
# 计算梯度
bin_idx = torch.histc(target, self.bins, 0, self.bins - 1).long()
bin_weight = weight / weight.sum()
n = self.bins
g = torch.zeros(n).cuda()
for i in range(n):
in_bin = bin_idx == i
if in_bin.any():
g[i] = (1 - pred[in_bin]).sum() / (in_bin.sum() + 1e-12)
g = g.clamp(min=1e-6)
g = self.alpha * weight * (n / g.sum()) * g
return g[bin_idx]
def forward(self, pred, target, weight):
# 计算损失
loss = nn.functional.cross_entropy(pred, target, reduction='none')
g = self.calc_grad(torch.softmax(pred.detach(), dim=1)[:, 1], target, weight)
return (loss * g).mean()
# 训练CNN模型并使用GHM损失函数进行训练
def train(model, data, num_epochs=10, lr=0.001):
optimizer = optim.Adam(model.parameters(), lr=lr)
criterion = GHMLoss()
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for i, data in enumerate(data, 0):
inputs, labels, weights = data
inputs, labels, weights = Variable(inputs), Variable(labels), Variable(weights)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels, weights)
running_loss += loss.item()
loss.backward()
optimizer.step()
# 每个epoch结束后输出loss
print('Epoch %d loss: %.3f' %
(epoch + 1, running_loss / len(data)))
print('Finished Training')
```
这段代码定义了一个CNN模型,以及GHM损失函数和训练函数。在训练函数中,我们使用PyTorch的自动求导功能计算CNN模型的GHM损失,并使用Adam优化器进行模型参数的更新。注意,在训练数据中还需要提供每个样本的权重,用于计算GHM损失。
GHM loss 在pytorch中的代码实现
GHM loss是一种用于解决目标类别分布不均衡问题的损失函数。在PyTorch中可以通过以下代码实现GHM loss:
```python
import torch
class GHMLoss(torch.nn.Module):
def __init__(self, bins=10, alpha=0.75):
super(GHMLoss, self).__init__()
self.bins = bins
self.alpha = alpha
self.edges = [x / bins for x in range(bins + 1)]
self.edges[-1] += 1e-6
def forward(self, input, target):
N, C = input.size()
grad_input = input.clone().detach()
grad_input.zero_()
target = target.view(-1, 1)
edges = self.edges
inds = (torch.arange(1, self.bins + 1).float() / self.bins).to(input.device)
weights = torch.zeros((self.bins,)).to(input.device)
weights[0] = inds[0]
weights[1:] = inds[1:] - inds[:-1]
inds = (target * self.bins).long().clamp(0, self.bins - 1)
weights = weights[inds.view(-1)]
Ns = torch.zeros((self.bins,)).to(input.device)
for i in range(self.bins):
Ns[i] = ((inds == i).sum()).float()
Ns[Ns == 0] = float('inf')
weights = (weights * Ns).sqrt()
weights = (weights / weights.sum()) * self.bins
inds = torch.bucketize(input.softmax(dim=1)[:, 0], edges)
g = -(target - input.softmax(dim=1)[:, 0]).detach().abs()
grad_input[:, 0] = g / (2 * g.abs().mean() + 1e-8)
grad_input[:, 0] *= weights[inds.view(-1)].view(N, 1)
return torch.nn.functional.binary_cross_entropy_with_logits(input, target, weight=None, reduction='mean', pos_weight=None, label_smoothing=None) + grad_input.sum() * self.alpha / N
```
其中,`bins`表示将概率分布分成的区间数量,`alpha`为平衡交叉熵损失和GHM损失的权重。在`forward`函数中,首先计算每个样本的概率分布落在哪个区间,并根据该区间的样本数量和梯度权重计算出每个样本的权重。然后,根据权重计算GHM损失,并计算交叉熵损失和GHM损失的加权和。最后,将GHM损失的梯度乘以`alpha`并加入到交叉熵损失的梯度中,返回总的损失值。