focal_frequency = torch.pow(focal_frequency, 2) # torch.Size([75]) focal_frequency = focal_frequency.repeat(c, 1) ''' 进行repeat操作后,focal_frequency shape [num_classes,num_samples] ''' focal_frequency = focal_frequency.transpose(1, 0) loss = F.nll_loss(focal_frequency * (torch.lo...
进行repeat操作后,focal_frequency shape [num_classes,num_samples] '''focal_frequency = focal_frequency.transpose(1,0) loss = F.nll_loss(focal_frequency * (torch.log(F.softmax(input, dim=1))), target, weight=None, reduction='elementwise_mean')returnlossdeffocal_loss_zhihu(input, target):...
然后与前⾯那⼀项（即F.nll_loss输⼊的第⼀项）进⾏ element-wise production 相当于取出了 log(p_gt)即当前样本点被分类为正确类别的概率 现在去掉取log的操作，相当于 focal_frequency shape [num_samples]即取出ground truth类别的概率数值，并取了负号 '''focal_frequency += 1.0#shape [num_...
Clone this repo, install PyTorch 1.4.0 (torch>=1.1.0may also work) and other dependencies: git clone https://github.com/EndlessSora/focal-frequency-loss.gitcdfocal-frequency-loss pip install -r VanillaAE/requirements.txt In this example, please downloadimg_align_celeba.zipof the CelebA datase...
loss = -alpha_t * loss Versions Collecting environment information... PyTorch version: 2.1.0+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang versi...
To implement the HSI classification methods, we utilized Python in the Pytorch platform; specifically, Pytorch 1.12.0, CUDA 11.7, cuDNN 8.0, and Python 3.8 were the specific research environments used. 3.3. Experimental Results 3.3.1. Experiment 1 Table 4 presents the classification results with ...
Focal Loss是在论文[Focal Loss for Dense Object Detection](http://arxiv.org/abs/1708.02002)中提到,主要是为了解决one-stage目标检测中样本不均衡的问题。因为最近工作中也遇到了样本不均衡的问题,但是因为是多分类问题,Focal loss和网上提供的实现大都是针对二分类的,所以阅读论文。本文我将解释论文中的内容以及...
After that, we reduce the frequency of change in 𝛽β to train our model effectively, and the threshold for change is set to 0.05: 𝛽𝑖+1={𝛽𝑖𝛽𝑖+1if𝛽𝑖−𝛽𝑖+1<0.05if𝛽𝑖−𝛽𝑖+1≥0.05βi+1=βiifβi−βi+1<0.05βi+1ifβi−βi+1≥0.05 (...
To implement the HSI classification methods, we utilized Python in the Pytorch platform; specifically, Pytorch 1.12.0, CUDA 11.7, cuDNN 8.0, and Python 3.8 were the specific research environments used. 3.3. Experimental Results 3.3.1. Experiment 1 Table 4 presents the classification results with ...