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...
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repeats次,并返回重复后的张量。 >>> x = torch.tensor([1, 2, 3])>>> x.repeat_interleave(2) tensor([1, 1, 2, 2, 3, 3])#传入多维张量,默认`展平`>>> y = torch.tensor([[1, 2], [3, 4]])>>> torch.repeat_inter...
首先,我们来了解一下repeat_interleave的基本原理。repeat_interleave接受一个可迭代对象作为输入,如列表、元组或Numpy数组等。它会将这些元素重复指定的次数,并在每次重复后将其插入到一个新的列表中。同时,对于每个元素,它还会将其沿着指定维度进行重复,从而实现不同维度的数据增强。
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...
repeat_interleave res reshape resize_as_ result_type rnn_relu rnn_relu_cell rnn_tanh rnn_tanh_cell roll rot90 round round_ row_stack rrelu rrelu_ rsqrt rsqrt_ rsub saddmm save scalar_tensor scatter scatter_add searchsorted seed select selu selu_ serialization set_anomaly_enabled set_autocast...