然而跟踪历史信息并且更新的这个行为是基于track_running_stats为true并且training=true的情况同时成立的时候,才会进行的,当在track_running_stats=true, training=false时(在默认的model.eval()情况下,即是之前谈到的四种组合的第三个,既满足这种情况),将不涉及到统计参数的指数滑动更新了。[12,13] 这里引用一个不...
training=False, track_running_stats=True, 这是我们期待的test时候的行为，即使用training阶段估计的runn...
bn = nn.BatchNorm2d(32, track_running_stats=False) out = bn(c1(img)).relu() check_schedule(out, 1, [c1.weight, c1.bias]) check_schedule(out, 4, [c1.weight, c1.bias]) Collaborator geohot Aug 20, 2024 Can you add the test that keeps this 1? geohot closed this Aug...
import torch import torch.nn as nn bn = nn.BatchNorm1d(4) bn.track_running_stats = False # Store initial values num_batches = bn.num_batches_tracked.clone() running_mean = bn.running_mean.clone() running_var = bn.running_var.clone() # Forward random tensor _ = bn(torch.rand(32,...
相反的,如果track_running_stats=False那么就只是计算当前输入的batch的统计特性中的均值和方差了。当在推理阶段的时候,如果track_running_stats=False,此时如果batch_size比较小,那么其统计特性就会和全局统计特性有着较大偏差,可能导致糟糕的效果。 一般来说,trainning和track_running_stats有四种组合[7]...
接近，但bn不只是有mean和inv_var，你这么做把w和b也冻住了。看代码最清楚了：torch.nn.modules....
// we alias running_mean and running_var because they are const but we want to modify their data if (running_mean.defined()) { at::alias(running_mean).copy_(running_mean_.view({ b, c }).mean(0, false)); } if (running_var.defined()) { at::alias(running_var).copy_(running_...
Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: 15.0.0 (git@github.com:llvm/llvm-project.git 4ba6a9c9f65bbc8bd06e3652cb20fd4dfc846137) ...