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

2026-09-24 13:22:23 +07:00
commit 642cc11a9f
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+import torch.nn.functional as F
+from torch import Tensor
+
+from .module import Module
+
+
+__all__ = ["ChannelShuffle"]
+
+
+class ChannelShuffle(Module):
+    r"""Divides and rearranges the channels in a tensor.
+
+    This operation divides the channels in a tensor of shape :math:`(N, C, *)`
+    into g groups as :math:`(N, \frac{C}{g}, g, *)` and shuffles them,
+    while retaining the original tensor shape in the final output.
+
+    Args:
+        groups (int): number of groups to divide channels in.
+
+    Examples::
+
+        >>> channel_shuffle = nn.ChannelShuffle(2)
+        >>> input = torch.arange(1, 17, dtype=torch.float32).view(1, 4, 2, 2)
+        >>> input
+        tensor([[[[ 1.,  2.],
+                  [ 3.,  4.]],
+                 [[ 5.,  6.],
+                  [ 7.,  8.]],
+                 [[ 9., 10.],
+                  [11., 12.]],
+                 [[13., 14.],
+                  [15., 16.]]]])
+        >>> output = channel_shuffle(input)
+        >>> output
+        tensor([[[[ 1.,  2.],
+                  [ 3.,  4.]],
+                 [[ 9., 10.],
+                  [11., 12.]],
+                 [[ 5.,  6.],
+                  [ 7.,  8.]],
+                 [[13., 14.],
+                  [15., 16.]]]])
+    """
+
+    __constants__ = ["groups"]
+    groups: int
+
+    def __init__(self, groups: int) -> None:
+        super().__init__()
+        self.groups = groups
+
+    def forward(self, input: Tensor) -> Tensor:
+        """
+        Runs the forward pass.
+        """
+        return F.channel_shuffle(input, self.groups)
+
+    def extra_repr(self) -> str:
+        """
+        Return the extra representation of the module.
+        """
+        return f"groups={self.groups}"