# 需要導入模塊: from torch.nn import functional [as 別名]# 或者: from torch.nn.functional importbinary_cross_entropy_with_logits[as 別名]defquality_focal_loss(pred, target, beta=2.0):r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning Qualified and Distributed Boun...
loss = F.binary_cross_entropy_with_logits(input=preds, target=onehot_target, weight=weights, reduction='mean') File "/home/hotaek/anaconda3/envs/pytorch1-py368/lib/python3.6/site-packages/torch/nn/functional.py", line 2077, in binary_cross_entropy_with_logits return torch.binary_cross_ent...
Source File: losses.py From EfficientDet-PyTorch with Apache License 2.0 6 votes def focal_loss(self, x, y): '''Focal loss. Args: x: (tensor) sized [N,D]. y: (tensor) sized [N,]. Return: (tensor) focal loss. ''' alpha = 0.25 gamma = 2 t = F.one_hot(y.data, 1+...
Binary Cross Entropy确实应用于二分类，但确实是对每个标签的二分类，也就是需要判断某个标签是否属于这...
Also, the gradient of the loss function in Eq. (7) can be robustly achieved via autograd of Pytorch without the need to develop gradient manually. Speckle analysis for random phase holography We show the theoretical speckle analysis to ensure the fundamental challenge for random phase holography ...
The development environment is Pytorch1.3.0+CUDA11.3. The CPU is Intel Xeon Sliver 4210 and the GPU is 4*Nvida RTX 2080ti. To train an IR-DC Net, we jointly fine-tune the backbone and train the detection header. The batch size is specified as 32. On the PASCAL VOC dataset, the ...
The development environment is Pytorch1.3.0+CUDA11.3. The CPU is Intel Xeon Sliver 4210 and the GPU is 4*Nvida RTX 2080ti. To train an IR-DC Net, we jointly fine-tune the backbone and train the detection header. The batch size is specified as 32. On the PASCAL VOC dataset, the ...