torch.utils.checkpoint.checkpoint_sequential(functions,segments,input,**kwargs)[source] 用于检查点顺序模型的辅助函数。顺序模型按顺序(顺序)执行一列模块/功能。因此,我们可以将该模型划分为各个分段和每个分段的检查点。除最后一个段外,所有段都将以torch.no_grad()方式运行,
**args上运行函数的输出。 torch.utils.checkpoint.checkpoint_sequential(functions,segments,input,**kwargs)[source] 用于检查点顺序模型的辅助函数。顺序模型按顺序(顺序)执行一列模块/功能。因此,我们可以将该模型划分为各个分段和每个分段的检查点。除最后一个段外,所有段都将以torch.no_grad()方式运行,而不存...
fromtorch.utils.dataimportDataset,DataLoader importtorch.utils.checkpointascheckpoint fromtqdmimporttqdm importshutil fromtorch.utils.checkpointimportcheckpoint_sequential device="cuda"iftorch.cuda.is_available() else"cpu" %matplotlibinline importrandom nvidia_smi.nvmlInit() 1. 2. 3. 4. 5. 6. 7. 8...
Using torch.utils.checkpoint.checkpoint_sequential and torch.autograd.grad breaks when used in combination with DistributedDataParallel resulting in the following stacktrace Traceback (most recent call last): File "minimal_buggy_2.py", line 198, in <module> train(hps) File "minimal_buggy_2.py",...
(x)) from torch.utils.checkpoint import checkpoint class BottleneckCSP2L(nn.Module): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(...
torch.utils.checkpoint.checkpoint_sequential(functions, segments, *inputs) 用于checkpointsequential模型的辅助函数。 sequential模型按顺序执行一系列模块/函数(按顺序)。因此,我们可以将这种模型分为不同的部分和checkpoint。除最后一个段以外的所有段都将以某种torch.no_grad()方式运行 ,即不存储中间活动。将保存每...
例如，如果你想让原来的线性层 torch.nn. linear 是并行的，只需将 torch 变成 ts，并调用带有 dim 参数的子类 nn.ParallelLinear，如下所示：import torchshard as tsts.init_process_group(group_size=2) # init parallel groupsm = torch.nn.Sequential( torch.nn.Linear(20, 30, bias=True), ...
import torch.utils.data.distributed from torchvision import transforms import torch.nn as nn from datetime import datetime class ConvNet(nn.Module): def __init__(self, num_classes=10): super(ConvNet, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(1, 16, kernel_size=5, stri...
import torch import torch.nn as nn import torch.optim as optim import torchvision from torch4keras.model import BaseModel from torch4keras.snippets import seed_everything, Checkpoint, Evaluator, EarlyStopping from torch.utils.data import TensorDataset, DataLoader from tqdm import tqdm seed_everything...
utils.data.DataLoader(dataset,batch_size=256) model = AlexNet(NUM_CLASSES) checkpoint = torch.load(save_path+'modelparams.pth') model.load_state_dict(checkpoint['net']) model.to(DEVICE) train_acc_list=checkpoint['train_acc_list'] val_acc_list=checkpoint['val_acc_list'] cost_list=...