在反向传播计算梯度之前对上一次迭代时记录的梯度清零,参数set_to_none 设置为 True 时会直接将参数梯度设置为 None,从而减小内存使用, 但通常情况下不建议设置这个参数,因为梯度设置为 None 和0 在PyTorch 中处理逻辑会不一样。 def zero_grad(self, set_to_none: bool = False): r"""Sets the gradients...
weight_decay (float, optional) – weight decay coefficient (default: 1e-2) 权重衰减系数,默认值0.01 amsgrad (bool, optional) – whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond (default: False) 是否使用该算法的AMSGrad变体的收敛方法,默...
🐛 Describe the bug Hello. After upgrading from torch 2.3.0 to torch 2.4.0 torch.compile produces much less graph breaks without "optimized" code, it supports zip(), accessing modules of nn.Sequential and other stuff. However, it also pro...
🐛 Describe the bug Simple compilation of UNet model works fine, but FSDP-wrapped UNet gets recompiled on every block. In real setup cache-size limit is rapidly reached. Code: import argparse import os from contextlib import nullcontext f...
pytorch中的张量类似于numpy中的ndarray,但是张量的数据类型只能为数值型。定义张量or创建函数时都可以指定device。 1.torch.empty(size,dtype=None,device=None, requires_grad=False)--生成一个指定形状为size的非初始化张量。 #数据随机生成 torch.empty(2) ...
cuda(self.gpu_id))] Example #3Source File: loss.py From KAIR with MIT License 6 votes def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0): super(GANLoss, self).__init__() self.gan_type = gan_type.lower() self.real_label_val = real_label_val self.fake...
(self): diag = np.eye(2 * self.batch_size) l1 = np.eye((2 * self.batch_size), 2 * self.batch_size, k=-self.batch_size) l2 = np.eye((2 * self.batch_size), 2 * self.batch_size, k=self.batch_size) mask = torch.from_numpy((diag + l1 + l2)) mask = (1 - mask)...
DeviceDict, Optimizer, ParamsT, ) __all__ = ["AdamW", "adamw"] class AdamW(Optimizer): def __init__( self, params: ParamsT, lr: Union[float, Tensor] = 1e-3, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, weight_decay: float = 1e-2, amsgrad: boo...