optimization pytorch ssim loss-functions iqa image-quality-assessment vif steerable-filters lpips dists fsim Updated Aug 18, 2023 Python stdlib-js / stats-base-dists-invgamma-variance Sponsor Star 3 Code Issues Pull requests Inverse gamma distribution variance....
在以往的自监督单目深度估计网络中,使用的重投影loss除了包含SSIM项外,还包含有L1项,但是论文中发现引入L1 loss计算逐像素差异时会导致训练不稳定,这是因为采用的教师监督信号(视差空间)本身就存在误差,而从视差空间向深度空间转换时需要执行导数操作进一步放大了预测结果的不确定性,并在任意范围内产生更大的异常值。...
pyro dist.gama分布bug loss=-inf UserWarning: UserWarning: Encountered +inf: log_prob_sum pyro bug , dist.gama分布 似乎不支持 cuda,否则会报错,数值太小的问题 文档 glaringlee.github.io/_modules/torch/distributions/gamma.html 官方文档 pytorch.org/docs/stable/genindex.html pytorch.org/docs/stable/d...
loss1=paddle.nn.functional.smooth_l1_loss(predict_action_Q, target) #得到smooth L1损失 [batch_size,n_num] distance=predict_action_Q-target #获取公式中的L1距离 loss2=paddle.abs(tau-paddle.sign(-distance)) #获取分位数损失 [batch_size,n_num] cost = paddle.mean(loss1*loss2) cost.backwar...
Spatial refinement loss (5) (6) (7) 上述DistDepth框架中,结构蒸馏分枝可以使student model具有更高的泛化能力,从而可以更好的分离出底层线索;而自监督分枝则可以帮助DPT学习不同室内场景的室内深度范围。直观上看,有一种更简单的方法是直接基于DPT的结果预测缩放因子和平移因子,从而使得DPT结果能够与度量深度对齐...
loss = positive_distance - negative_distance if margin == 'maxplus': loss = K.maximum(0.0, 1 + loss) elif margin == 'softplus': loss = K.log(1 + K.exp(loss)) return K.mean(loss) def get_model(): input_shape = (image_size, image_size, 3) ...
Investments in below investment grade or unrated debt securities, may be subject to higher liquidity risks and credit risks comparing with investment grade bonds, with an increased risk of loss of investment. Duration is a measure of the sensitivity of the price (the value of the prin...
3、overall loss function L=\lambda_1 L_{dep} + \lambda_2 L_{edd} \lambda_1=0.4,\lambda_2=0.6 Fine-tuning with DictBERT Downstream task fine-tuning with DictBERT 本文微调阶段将 DictBERT 作为 bert 的插件。 具体来说,从 input 中识别字典词条,将 DictBERT 作为知识库从中检索词条 embedding...
[line:71] - INFO: Loss at epoch 1 step 0: [1.0205], acc: [0.671875] 2019-10-24 07:46:03,340 - train.py[line:71] - INFO: Loss at epoch 1 step 10: [1.0369928], acc: [0.578125] 2019-10-24 07:46:11,504 - train.py[line:71] - INFO: Loss at epoch 1 step 20: [...
多模态分离框架主要通过对异构模型配置不同的计算资源和并行配置,减少冗余的静态资源和异构模型间的气泡,使能异构模型之间的运行速度达到最优匹配。 需要与MindSpeed-MM配套运行 精度验证结果: InternVL2-8B在910B3环境单机验证 开关特性,5000 steps,loss逐渐降低,趋于收敛,平均相对误差≤1%。 InternVL2-8B精度验证...