From 92c6b636a34ecaad4291e15c638ecbecba712d23 Mon Sep 17 00:00:00 2001
From: Ashwin Nair <ash1995@gmail.com>
Date: Sat, 28 Dec 2024 01:18:38 +0400
Subject: [PATCH] Remove _ExtractPatches

---
 torchgeo/transforms/transforms.py | 76 -------------------------------
 1 file changed, 76 deletions(-)

diff --git a/torchgeo/transforms/transforms.py b/torchgeo/transforms/transforms.py
index 97a1fc1757..a98fe99529 100644
--- a/torchgeo/transforms/transforms.py
+++ b/torchgeo/transforms/transforms.py
@@ -7,8 +7,6 @@
 
 import kornia.augmentation as K
 import torch
-from einops import rearrange
-from kornia.contrib import extract_tensor_patches
 from kornia.geometry import crop_by_indices
 from torch import Tensor
 
@@ -103,80 +101,6 @@ def forward(
         }
 
 
-class _ExtractPatches(K.GeometricAugmentationBase2D):
-    """Extract patches from an image or mask."""
-
-    def __init__(
-        self,
-        window_size: int | tuple[int, int],
-        stride: int | tuple[int, int] | None = None,
-        padding: int | tuple[int, int] | None = 0,
-        keepdim: bool = True,
-    ) -> None:
-        """Initialize a new _ExtractPatches instance.
-
-        Args:
-            window_size: desired output size (out_h, out_w) of the crop
-            stride: stride of window to extract patches. Defaults to non-overlapping
-                patches (stride=window_size)
-            padding: zero padding added to the height and width dimensions
-            keepdim: Combine the patch dimension into the batch dimension
-        """
-        super().__init__(p=1)
-        self.flags = {
-            'window_size': window_size,
-            'stride': stride if stride is not None else window_size,
-            'padding': padding,
-            'keepdim': keepdim,
-        }
-
-    def compute_transformation(
-        self, input: Tensor, params: dict[str, Tensor], flags: dict[str, Any]
-    ) -> Tensor:
-        """Compute the transformation.
-
-        Args:
-            input: the input tensor
-            params: generated parameters
-            flags: static parameters
-
-        Returns:
-            the transformation
-        """
-        out: Tensor = self.identity_matrix(input)
-        return out
-
-    def apply_transform(
-        self,
-        input: Tensor,
-        params: dict[str, Tensor],
-        flags: dict[str, Any],
-        transform: Tensor | None = None,
-    ) -> Tensor:
-        """Apply the transform.
-
-        Args:
-            input: the input tensor
-            params: generated parameters
-            flags: static parameters
-            transform: the geometric transformation tensor
-
-        Returns:
-            the augmented input
-        """
-        out = extract_tensor_patches(
-            input,
-            window_size=flags['window_size'],
-            stride=flags['stride'],
-            padding=flags['padding'],
-        )
-
-        if flags['keepdim']:
-            out = rearrange(out, 'b t c h w -> (b t) c h w')
-
-        return out
-
-
 class _Clamp(K.IntensityAugmentationBase2D):
     """Clamp images to a specific range."""