defdrop_path(x,drop_prob:float=0.,training:bool=False):ifdrop_prob==0.ornottraining:returnxkeep_prob=1-drop_probshape=(x.shape[0],)+(1,)*(x.ndim-1)random_tensor=keep_prob+torch.rand(shape,dtype=x.dtype,device=x.device)random_tensor.floor_()output=x.div(keep_prob)*random_tensor...
熟悉timm的朋友都知道,timm里面有个DropPath层,对应一个drop_path函数。里面用的也是类似的技巧,用乘以0或者1来表示选择,而不能直接用Python的if-else来选择是否调用某个模块。 总结 分布式训练的本质是多个节点协同训练,为了实现这种协同,多个节点的计算结构必须得是一致的。只要稍有差别,就很难处理,而且会带来很大...
model.patch_embed=backward_hook_wrapper(model.patch_embed)model.pos_drop=backward_hook_wrapper(model.pos_drop)model.patch_drop=backward_hook_wrapper(model.patch_drop)model.norm_pre=backward_hook_wrapper(model.norm_pre)model.blocks=backward_hook_wrapper(model.blocks)model.norm=backward_hook_wrapper(...
num_workers表示加载数据时使用的是单进程还是多进程进行加载,默认情况是0,0表示采用主进程进行加载(num_worker再windows上使用大于0时总是会出错,要注意!!)。drop_last表示当取数据取不尽时,剩下的数据是舍去还是继续取走 importtorchvisionfromtorch.utils.dataimportDataLoader# 准备测试数据集fromtorch.utils.tensorboar...
(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) class ConvBNActivation(nn.Sequential): def __init__(self, in_planes: int, out_planes: int, kernel_size: int = 3, stride: int = 1, groups: int...
drop_path(x, self.drop_prob, self.training) class ConvBNAct(nn.Module): def __init__(self, in_planes: int, out_planes: int, kernel_size: int = 3, stride: int = 1, groups: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, activation_layer: Optional[Callable[...
%matplotlib inline import torch import torch.nn as nn import numpy as np import sys sys.path.append('..') import d2lzh_pytorch as d2l def dropout(X,drop_prob): X = X.float() assert 0<=drop_prob<=1 keep_prob = 1-drop_prob if keep_prob==0: return torch.torch.zeros_like(X) ...
, qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, se=0): super().__init__() self.num_classes = num_classes depths = configs['depths'] outer_dims = configs['outer_dims'] inner_dims = configs['inner_dims'] ...
drop_path_rate(float, default = 0.0) – when > 0.0, applies stochastic depth per sample in the main path of the residual block. fuse_qkv_params(bool, default = ‘False’) – if set toTrue,TransformerLayermodule exposes a single fused parameter for query-key-value. This enables optimizati...
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) scheduler = DDPM_Scheduler(num_time_steps=num_time_steps) model = UNET().cuda() optimizer = optim.Adam(model.parameters(), lr=lr) ...