「torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False):这个是创建与 input 同形状的全 0 张量」 代码语言:javascript 代码运行次数:0 运行 AI代码解释 t=torch.zeros_like(out_t)# 这里的input要是个张量print(t)tensor([[0,0,0],[0,0,0],[0,0,0]]) 除了全 ...
optimizer.zero_grad() output =model(data) train_loss =criterion(output, target) train_loss.backward() # 以上均和单机版代码相同。接下来遍历本机模型的parameters,并采集其他节点的grad梯度,计算平均值并发送到其他节点 for p in model.parameters(): # 采集其他节点的grad梯度 grad_1 = torch.zeros_lik...
torch.from_numpy(ndarray) 创建特殊值组成的tensor: torch.zeros(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False)
self.normalize = normalize_to_neg_one_to_one if auto_normalize else identityself.unnormalize = unnormalize_to_zero_to_one if auto_normalize else identity @torch.inference_mode()def p_sample(self, x: torch.Tensor, timestamp: int) -> torch.Tensor:b...
[ 0, : ] 第一行数据;[ : ,-1] 最后一列数据;nonzero 获取非零向量的下标 2.4.5 广播机制 torch.from_numpy(A) 把ndarray转换为Tensor;A1与B1维数不同,相加自动实现广播,见下图 C=A+B,自动广播 2.4.6 逐元素操作 常见逐元素操作 addcdiv( t, t1, t2) 等价于 t+(t1/t2);clamp( t, 0, 1)...
import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 第一个卷积层,输入通道为3,输出通道为6,卷积核大小为5x5 self.conv1 = nn.Conv2d(3, 6, 5)
1.1 torch.cat() (cat()不会拓展张量的维度) 功能:将张量按维度dim进行拼接 tensors:张量序列 din:要拼接的维度 AI检测代码解析 torch.cat(tensors, dim=0, out=None) 1. 2. 3. 代码实际操作如下: AI检测代码解析 import numpy as np import torch ...
With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such astorch-autograd,auto...
6.1.1 torch.arange:相当于python中的range函数 6.1.2 torch.linspace:将[start, end]拆分成 step 个 6.2 索引和数据筛选(非常重要) 6.2.1 索引选取 6.2.3 torch.nonzero:返回非零元素的索引位置 6.2.5 条件选择:torch.where 6.2.6 index_select vs gather 6.2.6.1 index_select 6.2.6.2 gather 6.2.6.2...
import torch # --- 特殊张量 --- # 创建指定形状的全零张量 zeroTensor = torch.zeros((2, 3)) # 创建指定形状的全一张量 oneTensor = torch.ones((2, 3)) # 创建指定形状的单位矩阵 eyeTensor = torch.eye(3) # 创建未初始化的张量 uninitializedTensor = torch.empty((2, 3)) # --- ...