Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

2026-09-24 13:22:23 +07:00
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+import torch.nn.functional as F
+from torch import Tensor
+
+from .module import Module
+
+
+__all__ = ["PixelShuffle", "PixelUnshuffle"]
+
+
+class PixelShuffle(Module):
+    r"""Rearrange elements in a tensor according to an upscaling factor.
+
+    Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
+    to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.
+
+    This is useful for implementing efficient sub-pixel convolution
+    with a stride of :math:`1/r`.
+
+    See the paper:
+    `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
+    by Shi et al. (2016) for more details.
+
+    Args:
+        upscale_factor (int): factor to increase spatial resolution by
+
+    Shape:
+        - Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
+        - Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where
+
+    .. math::
+        C_{out} = C_{in} \div \text{upscale\_factor}^2
+
+    .. math::
+        H_{out} = H_{in} \times \text{upscale\_factor}
+
+    .. math::
+        W_{out} = W_{in} \times \text{upscale\_factor}
+
+    Examples::
+
+        >>> pixel_shuffle = nn.PixelShuffle(3)
+        >>> input = torch.randn(1, 9, 4, 4)
+        >>> output = pixel_shuffle(input)
+        >>> print(output.size())
+        torch.Size([1, 1, 12, 12])
+
+    .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
+        https://arxiv.org/abs/1609.05158
+    """
+
+    __constants__ = ["upscale_factor"]
+    upscale_factor: int
+
+    def __init__(self, upscale_factor: int) -> None:
+        super().__init__()
+        self.upscale_factor = upscale_factor
+
+    def forward(self, input: Tensor) -> Tensor:
+        """
+        Runs the forward pass.
+        """
+        return F.pixel_shuffle(input, self.upscale_factor)
+
+    def extra_repr(self) -> str:
+        """
+        Return the extra representation of the module.
+        """
+        return f"upscale_factor={self.upscale_factor}"
+
+
+class PixelUnshuffle(Module):
+    r"""Reverse the PixelShuffle operation.
+
+    Reverses the :class:`~torch.nn.PixelShuffle` operation by rearranging elements
+    in a tensor of shape :math:`(*, C, H \times r, W \times r)` to a tensor of shape
+    :math:`(*, C \times r^2, H, W)`, where r is a downscale factor.
+
+    See the paper:
+    `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
+    by Shi et al. (2016) for more details.
+
+    Args:
+        downscale_factor (int): factor to decrease spatial resolution by
+
+    Shape:
+        - Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
+        - Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where
+
+    .. math::
+        C_{out} = C_{in} \times \text{downscale\_factor}^2
+
+    .. math::
+        H_{out} = H_{in} \div \text{downscale\_factor}
+
+    .. math::
+        W_{out} = W_{in} \div \text{downscale\_factor}
+
+    Examples::
+
+        >>> pixel_unshuffle = nn.PixelUnshuffle(3)
+        >>> input = torch.randn(1, 1, 12, 12)
+        >>> output = pixel_unshuffle(input)
+        >>> print(output.size())
+        torch.Size([1, 9, 4, 4])
+
+    .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
+        https://arxiv.org/abs/1609.05158
+    """
+
+    __constants__ = ["downscale_factor"]
+    downscale_factor: int
+
+    def __init__(self, downscale_factor: int) -> None:
+        super().__init__()
+        self.downscale_factor = downscale_factor
+
+    def forward(self, input: Tensor) -> Tensor:
+        """
+        Runs the forward pass.
+        """
+        return F.pixel_unshuffle(input, self.downscale_factor)
+
+    def extra_repr(self) -> str:
+        """
+        Return the extra representation of the module.
+        """
+        return f"downscale_factor={self.downscale_factor}"