5 examples of 'torch repeat' in Python

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def repeat(*args, **kwargs):
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    return torch.repeat(*args, **kwargs)

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def repeat(tensor, K):
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    """
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    [B, ...] => [B*K, ...]
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    #-- Important --#
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    Used unsqueeze and transpose to avoid [K*B] when using torch.Tensor.repeat
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    """
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    if isinstance(tensor, torch.Tensor):
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        B, *size = tensor.size()
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        repeat_size = [1] + [K] + [1] * (tensor.dim() - 1)
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        tensor = tensor.unsqueeze(1).repeat(*repeat_size).view(B * K, *size)
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        return tensor
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    elif isinstance(tensor, list):
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        out = []
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        for x in tensor:
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            for _ in range(K):
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                out.append(x.copy())
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        return out

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@staticmethod
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def forward(ctx, input, repeats):
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    ctx.repeats = repeats
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    ctx.input_dims = input.dim()
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    return input.repeat(repeats)

822
def repeat(self, *sizes):
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    """
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    Repeats :attr:`self` along the specified dimensions.
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    Unlike expand(), this function copies the tensor’s data.
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    Args:
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        sizes (torch.Size or int...): The number of times to repeat this tensor along each
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            dimension
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    """
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    raise NotImplementedError("repeat is not implemented")

1296
def repeat(g, self, repeats):
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    if not sym_help._is_value(repeats):
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        repeats = g.op("Constant", value_t=torch.LongTensor(repeats))
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    const_repeats = sym_help._maybe_get_const(repeats, 'is')
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    if self.isCompleteTensor() and not sym_help._is_value(const_repeats):
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        sizes = self.type().sizes()
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        diff_dims = len(const_repeats) - len(sizes)
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        if diff_dims > 0:
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            self = view(g, self, [1] * diff_dims + sizes)
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    return g.op("Tile", self, repeats)