torch.repeat_interleave是PyTorch中的一个函数,用于重复张量的元素。以下是它的用法: torch.repeat_interleave(input, repeats, dim=None, *, output_size=None) → Tensor 参数说明: input (Tensor) - 输入的张量。 repeats (Tensor或者int) - 每个元素的重复次数。这个参数会被广播以适应输入张量的维度。
a,torch.repeat_interleave(a,torch.tensor([2,3,4]),dim=0)#表示第一行重复2遍,第二行重复3遍,第三行重复4遍 输出结果如下:(tensor([[-0.79,0.54],[-0.47,-0.25],[-0.13,1.03]]),tensor([[-0.79,0.54],[-0.79,0.54],[-0.47,-0.25],[-0.47,-0.25],[-0.47,-0.25],[-0.131.03][-0.131....
x.repeat_interleave(2) tensor([1, 1, 2, 2, 3, 3]) y = torch.tensor([[1, 2], [3, 4]]) torch.repeat_interleave(y, 2) tensor([1, 1, 2, 2, 3, 3, 4, 4]) torch.repeat_interleave(y, 3, dim=1) tensor([[1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4]]) tor...
通过继承torch.autograd.Function类,我们可以轻松地实现自定义的数据增强函数,并将它们与repeat_interleave结合使用。例如,以下是一个自定义的repeat_interleave函数,用于对张量进行数据增强: importtorchfromtorch.autogradimportFunctiondefcustom_repeat_interleave(input):# 自定义数据增强逻辑output=input.repeat(2,3)# 对...
torch.repeat_interleave(input, repeats, dim=None) → Tensor 重复张量的元素 input (类型:torch.Tensor):输入张量 repeats(类型:int或torch.Tensor):每个元素的重复次数。repeats参数会被广播来适应输入张量的维度 dim(类型:int)需要重复的维度。默认情况下,将把输入张量展平(flatten)为向量,然后将每个元素重复rep...
1. repeat_interleave(self: Tensor, repeats: _int, dim: Optional[_int]=None)参数说明：self: 传⼊的数据为tensor repeats: 复制的份数 dim: 要复制的维度，可设定为0/1/2...2. 例⼦ 2.1 Code 此处定义了⼀个4维tensor，要对第2个维度复制，由原来的1变为3，即将设定dim=1。1import torch...
expansion = torch.repeat_interleave(expansion, counts) offset = torch.arange(0, counts.sum(), device=data.device) expansion = expansion - offset -1expanded = torch.repeat_interleave(data, expansion.to(data.device), dim=0) expansion_offset = counts.roll(1) ...
@ZzSean 没理解isse#33210如何通过reshape,将b_paddle (paddle.tile)转成b (torch.repeat_interleave)? 对于上述例子,需要对低维进行复制,先调用tile再调用reshape,完整代码如下: a_paddle = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) b_paddle = paddle.tile(a_paddle, repeat_times=(1, 3))...
repeat_idx = [1] * (x_encoded.dim() -1) x_enc_span = x_encoded.repeat(3, *repeat_idx) y_enc_span = torch.repeat_interleave(y_encoded, repeats=3, dim=0) bs, c, *img = x.size() c_out, c_in, *ks = y.size()
🐛 Describe the bug I encountered an error when exporting a model with a repeat_interleave op. Here is a minimal repro: import torch import torch.nn as nn class MyModel(nn.Module): def forward(self, x): return x.repeat_interleave(2) model...