# 需要导入模块: import torch [as 别名]# 或者: from torch importset_grad_enabled[as 别名]defdo_one_epoch(self, epoch, episodes):mode ="train"ifself.naff.trainingelse"val"epoch_loss, accuracy, steps =0.,0.,0data_generator = self.generate_batch(episodes)forx_t, x_tnindata_generator:w...
在with torch.set_grad_enabled中创建的张量requires_grad不受其影响(默认为False),如果经过运算会影响新张量的requires_grad. with torch.set_grad_enabled(True): print(torch.zeros((1, 1), requires_grad=True).requires_grad) True with torch.set_grad_enabled(True): print(torch.zeros((1, 1), requ...
如果通过no_grad或set_grad_enabled禁用梯度计算,则启用梯度计算。这个上下文管理器是线程本地的;它不会影响其他线程中的计算。还可以用作装饰器。 例: >>> x = torch.tensor([1], requires_grad=True) >>> with torch.no_grad(): ... with torch.enable_grad(): ... y = x * 2 >>> y.requir...
>>>x=torch.tensor([1],requires_grad=True)>>>is_train=False>>>withtorch.set_grad_enabled(is_train):...y=x*2>>>y.requires_grad False>>>torch.set_grad_enabled(True)>>>y=x*2>>>y.requires_grad True>>>torch.set_grad_enabled(False)>>>y=x*2>>>y.requires_grad False In-place ...
torch.cuda.set_rng_state(ctx.fwd_cuda_rng_state) detached_inputs = detach_variable(inputs)withtorch.enable_grad(): outputs = ctx.run_function(*detached_inputs)ifisinstance(outputs, torch.Tensor): outputs = (outputs,) torch.autograd.backward(outputs, args)return(None,) + tuple(inp.gradfor...
不需要创建计算图（默认PyTorch会给张量计算创建计算图），所以关掉这个功能，用no_grad这个上下文管理器...
t().mm(grad) 调用时再 autocast mymm = MyMM.apply with autocast(): output = mymm(input1, input2) 2.1.6 源码分析 autocast主要实现接口有: A. __enter__ def __enter__(self): self.prev = torch.is_autocast_enabled() torch.set_autocast_enabled(self._enabled) torch.autocast_increment...
(s): 1 Vendor ID: AuthenticAMD CPU family: 25 Model: 33 Model name: AMD Ryzen 5 5600X 6-Core Processor Stepping: 2 Frequency boost: enabled CPU MHz: 2200.000 CPU max MHz: 3700,0000 CPU min MHz: 2200,0000 BogoMIPS: 7400.61 Virtualization: AMD-V L1d cache: 192 KiB L1i cache: ...
(optimizer), config.TRAIN.CLIP_GRAD)else: grad_norm = get_grad_norm(amp.master_params(optimizer))else: loss.backward()ifconfig.TRAIN.CLIP_GRAD: grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)else: grad_norm = get_grad_norm(model.parameters()...
Issue: [torchbench] Training benchmarks failing with: tensor does not require grad #6084 Issue: [torchbench] nvidia_deeprecommender fails to run. #6006 PyTorch/XLA PR: Re-land: Fix model initialization. #6296 PyTorch/XLA PR: Fix model initialization. #6076 pytorch_CycleGAN_and_pix2pix ...