From 90dc74c450a0ec671af0e7f73d6b4a7b5396a7af Mon Sep 17 00:00:00 2001
From: Christoph Reich <34400551+ChristophReich1996@users.noreply.github.com>
Date: Sat, 19 Feb 2022 22:12:11 +0100
Subject: [PATCH] Add code from
 https://github.com/ChristophReich1996/Swin-Transformer-V2 and change
 docstring style to match timm

---
 timm/models/swin_transformer_v2.py | 927 +++++++++++++++++++++++++++++
 1 file changed, 927 insertions(+)
 create mode 100644 timm/models/swin_transformer_v2.py

diff --git a/timm/models/swin_transformer_v2.py b/timm/models/swin_transformer_v2.py
new file mode 100644
index 00000000..c146ecc8
--- /dev/null
+++ b/timm/models/swin_transformer_v2.py
@@ -0,0 +1,927 @@
+""" Swin Transformer V2
+A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
+    - https://arxiv.org/pdf/2111.09883
+
+Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
+
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
+"""
+# --------------------------------------------------------
+# Swin Transformer V2 reimplementation
+# Copyright (c) 2021 Christoph Reich
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Christoph Reich
+# --------------------------------------------------------
+from typing import Tuple, Optional, List, Union, Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+
+from .layers import DropPath, Mlp
+
+
+def bchw_to_bhwc(input: torch.Tensor) -> torch.Tensor:
+    """ Permutes a tensor from the shape (B, C, H, W) to (B, H, W, C).
+
+    Args:
+        input (torch.Tensor): Input tensor of the shape (B, C, H, W)
+
+    Returns:
+        output (torch.Tensor): Permuted tensor of the shape (B, H, W, C)
+    """
+    output: torch.Tensor = input.permute(0, 2, 3, 1)
+    return output
+
+
+def bhwc_to_bchw(input: torch.Tensor) -> torch.Tensor:
+    """ Permutes a tensor from the shape (B, H, W, C) to (B, C, H, W).
+
+    Args:
+        input (torch.Tensor): Input tensor of the shape (B, H, W, C)
+
+    Returns:
+        output (torch.Tensor): Permuted tensor of the shape (B, C, H, W)
+    """
+    output: torch.Tensor = input.permute(0, 3, 1, 2)
+    return output
+
+
+def unfold(input: torch.Tensor,
+           window_size: int) -> torch.Tensor:
+    """ Unfolds (non-overlapping) a given feature map by the given window size (stride = window size).
+
+    Args:
+        input (torch.Tensor): Input feature map of the shape (B, C, H, W)
+        window_size (int): Window size to be applied
+
+    Returns:
+        output (torch.Tensor): Unfolded tensor of the shape [B * windows, C, window size, window size]
+    """
+    # Get original shape
+    _, channels, height, width = input.shape  # type: int, int, int, int
+    # Unfold input
+    output: torch.Tensor = input.unfold(dimension=3, size=window_size, step=window_size) \
+        .unfold(dimension=2, size=window_size, step=window_size)
+    # Reshape to (B * windows, C, window size, window size)
+    output: torch.Tensor = output.permute(0, 2, 3, 1, 5, 4).reshape(-1, channels, window_size, window_size)
+    return output
+
+
+def fold(input: torch.Tensor,
+         window_size: int,
+         height: int,
+         width: int) -> torch.Tensor:
+    """ Folds a tensor of windows again to a 4D feature map.
+
+    Args:
+        input (torch.Tensor): Input feature map of the shape (B, C, H, W)
+        window_size (int): Window size of the unfold operation
+        height (int): Height of the feature map
+        width (int): Width of the feature map
+
+    Returns:
+        output (torch.Tensor): Folded output tensor of the shape (B, C, H, W)
+    """
+    # Get channels of windows
+    channels: int = input.shape[1]
+    # Get original batch size
+    batch_size: int = int(input.shape[0] // (height * width // window_size // window_size))
+    # Reshape input to (B, C, H, W)
+    output: torch.Tensor = input.view(batch_size, height // window_size, width // window_size, channels,
+                                      window_size, window_size)
+    output: torch.Tensor = output.permute(0, 3, 1, 4, 2, 5).reshape(batch_size, channels, height, width)
+    return output
+
+
+class WindowMultiHeadAttention(nn.Module):
+    r""" This class implements window-based Multi-Head-Attention with log-spaced continuous position bias.
+
+    Args:
+        in_features (int): Number of input features
+        window_size (int): Window size
+        number_of_heads (int): Number of attention heads
+        dropout_attention (float): Dropout rate of attention map
+        dropout_projection (float): Dropout rate after projection
+        meta_network_hidden_features (int): Number of hidden features in the two layer MLP meta network
+        sequential_self_attention (bool): If true sequential self-attention is performed
+    """
+
+    def __init__(self,
+                 in_features: int,
+                 window_size: int,
+                 number_of_heads: int,
+                 dropout_attention: float = 0.,
+                 dropout_projection: float = 0.,
+                 meta_network_hidden_features: int = 256,
+                 sequential_self_attention: bool = False) -> None:
+        # Call super constructor
+        super(WindowMultiHeadAttention, self).__init__()
+        # Check parameter
+        assert (in_features % number_of_heads) == 0, \
+            "The number of input features (in_features) are not divisible by the number of heads (number_of_heads)."
+        # Save parameters
+        self.in_features: int = in_features
+        self.window_size: int = window_size
+        self.number_of_heads: int = number_of_heads
+        self.sequential_self_attention: bool = sequential_self_attention
+        # Init query, key and value mapping as a single layer
+        self.mapping_qkv: nn.Module = nn.Linear(in_features=in_features, out_features=in_features * 3, bias=True)
+        # Init attention dropout
+        self.attention_dropout: nn.Module = nn.Dropout(dropout_attention)
+        # Init projection mapping
+        self.projection: nn.Module = nn.Linear(in_features=in_features, out_features=in_features, bias=True)
+        # Init projection dropout
+        self.projection_dropout: nn.Module = nn.Dropout(dropout_projection)
+        # Init meta network for positional encodings
+        self.meta_network: nn.Module = nn.Sequential(
+            nn.Linear(in_features=2, out_features=meta_network_hidden_features, bias=True),
+            nn.ReLU(inplace=True),
+            nn.Linear(in_features=meta_network_hidden_features, out_features=number_of_heads, bias=True))
+        # Init tau
+        self.register_parameter("tau", torch.nn.Parameter(torch.ones(1, number_of_heads, 1, 1)))
+        # Init pair-wise relative positions (log-spaced)
+        self.__make_pair_wise_relative_positions()
+
+    def __make_pair_wise_relative_positions(self) -> None:
+        """ Method initializes the pair-wise relative positions to compute the positional biases."""
+        indexes: torch.Tensor = torch.arange(self.window_size, device=self.tau.device)
+        coordinates: torch.Tensor = torch.stack(torch.meshgrid([indexes, indexes]), dim=0)
+        coordinates: torch.Tensor = torch.flatten(coordinates, start_dim=1)
+        relative_coordinates: torch.Tensor = coordinates[:, :, None] - coordinates[:, None, :]
+        relative_coordinates: torch.Tensor = relative_coordinates.permute(1, 2, 0).reshape(-1, 2).float()
+        relative_coordinates_log: torch.Tensor = torch.sign(relative_coordinates) \
+                                                 * torch.log(1. + relative_coordinates.abs())
+        self.register_buffer("relative_coordinates_log", relative_coordinates_log)
+
+    def update_resolution(self,
+                          new_window_size: int,
+                          **kwargs: Any) -> None:
+        """ Method updates the window size and so the pair-wise relative positions
+
+        Args:
+            new_window_size (int): New window size
+            kwargs (Any): Unused
+        """
+        # Set new window size
+        self.window_size: int = new_window_size
+        # Make new pair-wise relative positions
+        self.__make_pair_wise_relative_positions()
+
+    def __get_relative_positional_encodings(self) -> torch.Tensor:
+        """ Method computes the relative positional encodings
+
+        Returns:
+            relative_position_bias (torch.Tensor): Relative positional encodings
+            (1, number of heads, window size ** 2, window size ** 2)
+        """
+        relative_position_bias: torch.Tensor = self.meta_network(self.relative_coordinates_log)
+        relative_position_bias: torch.Tensor = relative_position_bias.permute(1, 0)
+        relative_position_bias: torch.Tensor = relative_position_bias.reshape(self.number_of_heads,
+                                                                              self.window_size * self.window_size,
+                                                                              self.window_size * self.window_size)
+        relative_position_bias: torch.Tensor = relative_position_bias.unsqueeze(0)
+        return relative_position_bias
+
+    def __self_attention(self,
+                         query: torch.Tensor,
+                         key: torch.Tensor,
+                         value: torch.Tensor,
+                         batch_size_windows: int,
+                         tokens: int,
+                         mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """ This function performs standard (non-sequential) scaled cosine self-attention.
+
+        Args:
+            query (torch.Tensor): Query tensor of the shape [B * windows, heads, tokens, C // heads]
+            key (torch.Tensor): Key tensor of the shape [B * windows, heads, tokens, C // heads]
+            value (torch.Tensor): Value tensor of the shape (B * windows, heads, tokens, C // heads)
+            batch_size_windows (int): Size of the first dimension of the input tensor batch size * windows
+            tokens (int): Number of tokens in the input
+            mask (Optional[torch.Tensor]): Attention mask for the shift case
+
+        Returns:
+            output (torch.Tensor): Output feature map of the shape [B * windows, tokens, C]
+        """
+        # Compute attention map with scaled cosine attention
+        attention_map: torch.Tensor = torch.einsum("bhqd, bhkd -> bhqk", query, key) \
+                                      / torch.maximum(torch.norm(query, dim=-1, keepdim=True)
+                                                      * torch.norm(key, dim=-1, keepdim=True).transpose(-2, -1),
+                                                      torch.tensor(1e-06, device=query.device, dtype=query.dtype))
+        attention_map: torch.Tensor = attention_map / self.tau.clamp(min=0.01)
+        # Apply relative positional encodings
+        attention_map: torch.Tensor = attention_map + self.__get_relative_positional_encodings()
+        # Apply mask if utilized
+        if mask is not None:
+            number_of_windows: int = mask.shape[0]
+            attention_map: torch.Tensor = attention_map.view(batch_size_windows // number_of_windows, number_of_windows,
+                                                             self.number_of_heads, tokens, tokens)
+            attention_map: torch.Tensor = attention_map + mask.unsqueeze(1).unsqueeze(0)
+            attention_map: torch.Tensor = attention_map.view(-1, self.number_of_heads, tokens, tokens)
+        attention_map: torch.Tensor = attention_map.softmax(dim=-1)
+        # Perform attention dropout
+        attention_map: torch.Tensor = self.attention_dropout(attention_map)
+        # Apply attention map and reshape
+        output: torch.Tensor = torch.einsum("bhal, bhlv -> bhav", attention_map, value)
+        output: torch.Tensor = output.permute(0, 2, 1, 3).reshape(batch_size_windows, tokens, -1)
+        return output
+
+    def __sequential_self_attention(self,
+                                    query: torch.Tensor,
+                                    key: torch.Tensor,
+                                    value: torch.Tensor,
+                                    batch_size_windows: int,
+                                    tokens: int,
+                                    mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """ This function performs sequential scaled cosine self-attention.
+
+        Args:
+            query (torch.Tensor): Query tensor of the shape [B * windows, heads, tokens, C // heads]
+            key (torch.Tensor): Key tensor of the shape [B * windows, heads, tokens, C // heads]
+            value (torch.Tensor): Value tensor of the shape (B * windows, heads, tokens, C // heads)
+            batch_size_windows (int): Size of the first dimension of the input tensor batch size * windows
+            tokens (int): Number of tokens in the input
+            mask (Optional[torch.Tensor]): Attention mask for the shift case
+
+        Returns:
+            output (torch.Tensor): Output feature map of the shape [B * windows, tokens, C]
+        """
+        # Init output tensor
+        output: torch.Tensor = torch.ones_like(query)
+        # Compute relative positional encodings fist
+        relative_position_bias: torch.Tensor = self.__get_relative_positional_encodings()
+        # Iterate over query and key tokens
+        for token_index_query in range(tokens):
+            # Compute attention map with scaled cosine attention
+            attention_map: torch.Tensor = \
+                torch.einsum("bhd, bhkd -> bhk", query[:, :, token_index_query], key) \
+                / torch.maximum(torch.norm(query[:, :, token_index_query], dim=-1, keepdim=True)
+                                * torch.norm(key, dim=-1, keepdim=False),
+                                torch.tensor(1e-06, device=query.device, dtype=query.dtype))
+            attention_map: torch.Tensor = attention_map / self.tau.clamp(min=0.01)[..., 0]
+            # Apply positional encodings
+            attention_map: torch.Tensor = attention_map + relative_position_bias[..., token_index_query, :]
+            # Apply mask if utilized
+            if mask is not None:
+                number_of_windows: int = mask.shape[0]
+                attention_map: torch.Tensor = attention_map.view(batch_size_windows // number_of_windows,
+                                                                 number_of_windows, self.number_of_heads, 1,
+                                                                 tokens)
+                attention_map: torch.Tensor = attention_map \
+                                              + mask.unsqueeze(1).unsqueeze(0)[..., token_index_query, :].unsqueeze(3)
+                attention_map: torch.Tensor = attention_map.view(-1, self.number_of_heads, tokens)
+            attention_map: torch.Tensor = attention_map.softmax(dim=-1)
+            # Perform attention dropout
+            attention_map: torch.Tensor = self.attention_dropout(attention_map)
+            # Apply attention map and reshape
+            output[:, :, token_index_query] = torch.einsum("bhl, bhlv -> bhv", attention_map, value)
+        output: torch.Tensor = output.permute(0, 2, 1, 3).reshape(batch_size_windows, tokens, -1)
+        return output
+
+    def forward(self,
+                input: torch.Tensor,
+                mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """ Forward pass.
+
+        Args:
+            input (torch.Tensor): Input tensor of the shape (B * windows, C, H, W)
+            mask (Optional[torch.Tensor]): Attention mask for the shift case
+
+        Returns:
+            output (torch.Tensor): Output tensor of the shape [B * windows, C, H, W]
+        """
+        # Save original shape
+        batch_size_windows, channels, height, width = input.shape  # type: int, int, int, int
+        tokens: int = height * width
+        # Reshape input to (B * windows, tokens (height * width), C)
+        input: torch.Tensor = input.reshape(batch_size_windows, channels, tokens).permute(0, 2, 1)
+        # Perform query, key, and value mapping
+        query_key_value: torch.Tensor = self.mapping_qkv(input)
+        query_key_value: torch.Tensor = query_key_value.view(batch_size_windows, tokens, 3, self.number_of_heads,
+                                                             channels // self.number_of_heads).permute(2, 0, 3, 1, 4)
+        query, key, value = query_key_value[0], query_key_value[1], query_key_value[2]
+        # Perform attention
+        if self.sequential_self_attention:
+            output: torch.Tensor = self.__sequential_self_attention(query=query, key=key, value=value,
+                                                                    batch_size_windows=batch_size_windows,
+                                                                    tokens=tokens,
+                                                                    mask=mask)
+        else:
+            output: torch.Tensor = self.__self_attention(query=query, key=key, value=value,
+                                                         batch_size_windows=batch_size_windows, tokens=tokens,
+                                                         mask=mask)
+        # Perform linear mapping and dropout
+        output: torch.Tensor = self.projection_dropout(self.projection(output))
+        # Reshape output to original shape [B * windows, C, H, W]
+        output: torch.Tensor = output.permute(0, 2, 1).view(batch_size_windows, channels, height, width)
+        return output
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" This class implements the Swin transformer block.
+
+    Args:
+        in_channels (int): Number of input channels
+        input_resolution (Tuple[int, int]): Input resolution
+        number_of_heads (int): Number of attention heads to be utilized
+        window_size (int): Window size to be utilized
+        shift_size (int): Shifting size to be used
+        ff_feature_ratio (int): Ratio of the hidden dimension in the FFN to the input channels
+        dropout (float): Dropout in input mapping
+        dropout_attention (float): Dropout rate of attention map
+        dropout_path (float): Dropout in main path
+        sequential_self_attention (bool): If true sequential self-attention is performed
+    """
+
+    def __init__(self,
+                 in_channels: int,
+                 input_resolution: Tuple[int, int],
+                 number_of_heads: int,
+                 window_size: int = 7,
+                 shift_size: int = 0,
+                 ff_feature_ratio: int = 4,
+                 dropout: float = 0.0,
+                 dropout_attention: float = 0.0,
+                 dropout_path: float = 0.0,
+                 sequential_self_attention: bool = False) -> None:
+        # Call super constructor
+        super(SwinTransformerBlock, self).__init__()
+        # Save parameters
+        self.in_channels: int = in_channels
+        self.input_resolution: Tuple[int, int] = input_resolution
+        # Catch case if resolution is smaller than the window size
+        if min(self.input_resolution) <= window_size:
+            self.window_size: int = min(self.input_resolution)
+            self.shift_size: int = 0
+            self.make_windows: bool = False
+        else:
+            self.window_size: int = window_size
+            self.shift_size: int = shift_size
+            self.make_windows: bool = True
+        # Init normalization layers
+        self.normalization_1: nn.Module = nn.LayerNorm(normalized_shape=in_channels)
+        self.normalization_2: nn.Module = nn.LayerNorm(normalized_shape=in_channels)
+        # Init window attention module
+        self.window_attention: WindowMultiHeadAttention = WindowMultiHeadAttention(
+            in_features=in_channels,
+            window_size=self.window_size,
+            number_of_heads=number_of_heads,
+            dropout_attention=dropout_attention,
+            dropout_projection=dropout,
+            sequential_self_attention=sequential_self_attention)
+        # Init dropout layer
+        self.dropout: nn.Module = DropPath(drop_prob=dropout_path) if dropout_path > 0. else nn.Identity()
+        # Init feed-forward network
+        self.feed_forward_network: nn.Module = Mlp(in_features=in_channels,
+                                                   hidden_features=int(in_channels * ff_feature_ratio),
+                                                   drop=dropout,
+                                                   out_features=in_channels)
+        # Make attention mask
+        self.__make_attention_mask()
+
+    def __make_attention_mask(self) -> None:
+        """ Method generates the attention mask used in shift case. """
+        # Make masks for shift case
+        if self.shift_size > 0:
+            height, width = self.input_resolution  # type: int, int
+            mask: torch.Tensor = torch.zeros(height, width, device=self.window_attention.tau.device)
+            height_slices: Tuple = (slice(0, -self.window_size),
+                                    slice(-self.window_size, -self.shift_size),
+                                    slice(-self.shift_size, None))
+            width_slices: Tuple = (slice(0, -self.window_size),
+                                   slice(-self.window_size, -self.shift_size),
+                                   slice(-self.shift_size, None))
+            counter: int = 0
+            for height_slice in height_slices:
+                for width_slice in width_slices:
+                    mask[height_slice, width_slice] = counter
+                    counter += 1
+            mask_windows: torch.Tensor = unfold(mask[None, None], self.window_size)
+            mask_windows: torch.Tensor = mask_windows.reshape(-1, self.window_size * self.window_size)
+            attention_mask: Optional[torch.Tensor] = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attention_mask: Optional[torch.Tensor] = attention_mask.masked_fill(attention_mask != 0, float(-100.0))
+            attention_mask: Optional[torch.Tensor] = attention_mask.masked_fill(attention_mask == 0, float(0.0))
+        else:
+            attention_mask: Optional[torch.Tensor] = None
+        # Save mask
+        self.register_buffer("attention_mask", attention_mask)
+
+    def update_resolution(self,
+                          new_window_size: int,
+                          new_input_resolution: Tuple[int, int]) -> None:
+        """ Method updates the image resolution to be processed and window size and so the pair-wise relative positions.
+
+        Args:
+            new_window_size (int): New window size
+            new_input_resolution (Tuple[int, int]): New input resolution
+        """
+        # Update input resolution
+        self.input_resolution: Tuple[int, int] = new_input_resolution
+        # Catch case if resolution is smaller than the window size
+        if min(self.input_resolution) <= new_window_size:
+            self.window_size: int = min(self.input_resolution)
+            self.shift_size: int = 0
+            self.make_windows: bool = False
+        else:
+            self.window_size: int = new_window_size
+            self.shift_size: int = self.shift_size
+            self.make_windows: bool = True
+        # Update attention mask
+        self.__make_attention_mask()
+        # Update attention module
+        self.window_attention.update_resolution(new_window_size=new_window_size)
+
+    def forward(self,
+                input: torch.Tensor) -> torch.Tensor:
+        """ Forward pass.
+
+        Args:
+            input (torch.Tensor): Input tensor of the shape [B, C, H, W]
+
+        Returns:
+            output (torch.Tensor): Output tensor of the shape [B, C, H, W]
+        """
+        # Save shape
+        batch_size, channels, height, width = input.shape  # type: int, int, int, int
+        # Shift input if utilized
+        if self.shift_size > 0:
+            output_shift: torch.Tensor = torch.roll(input=input, shifts=(-self.shift_size, -self.shift_size),
+                                                    dims=(-1, -2))
+        else:
+            output_shift: torch.Tensor = input
+        # Make patches
+        output_patches: torch.Tensor = unfold(input=output_shift, window_size=self.window_size) \
+            if self.make_windows else output_shift
+        # Perform window attention
+        output_attention: torch.Tensor = self.window_attention(output_patches, mask=self.attention_mask)
+        # Merge patches
+        output_merge: torch.Tensor = fold(input=output_attention, window_size=self.window_size, height=height,
+                                          width=width) if self.make_windows else output_attention
+        # Reverse shift if utilized
+        if self.shift_size > 0:
+            output_shift: torch.Tensor = torch.roll(input=output_merge, shifts=(self.shift_size, self.shift_size),
+                                                    dims=(-1, -2))
+        else:
+            output_shift: torch.Tensor = output_merge
+        # Perform normalization
+        output_normalize: torch.Tensor = self.normalization_1(output_shift.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        # Skip connection
+        output_skip: torch.Tensor = self.dropout(output_normalize) + input
+        # Feed forward network, normalization and skip connection
+        output_feed_forward: torch.Tensor = self.feed_forward_network(
+            output_skip.view(batch_size, channels, -1).permute(0, 2, 1)).permute(0, 2, 1)
+        output_feed_forward: torch.Tensor = output_feed_forward.view(batch_size, channels, height, width)
+        output_normalize: torch.Tensor = bhwc_to_bchw(self.normalization_2(bchw_to_bhwc(output_feed_forward)))
+        output: torch.Tensor = output_skip + self.dropout(output_normalize)
+        return output
+
+
+class DeformableSwinTransformerBlock(SwinTransformerBlock):
+    r""" This class implements a deformable version of the Swin Transformer block.
+        Inspired by: https://arxiv.org/pdf/2201.00520
+
+    Args:
+        in_channels (int): Number of input channels
+        input_resolution (Tuple[int, int]): Input resolution
+        number_of_heads (int): Number of attention heads to be utilized
+        window_size (int): Window size to be utilized
+        shift_size (int): Shifting size to be used
+        ff_feature_ratio (int): Ratio of the hidden dimension in the FFN to the input channels
+        dropout (float): Dropout in input mapping
+        dropout_attention (float): Dropout rate of attention map
+        dropout_path (float): Dropout in main path
+        sequential_self_attention (bool): If true sequential self-attention is performed
+        offset_downscale_factor (int): Downscale factor of offset network
+    """
+
+    def __init__(self,
+                 in_channels: int,
+                 input_resolution: Tuple[int, int],
+                 number_of_heads: int,
+                 window_size: int = 7,
+                 shift_size: int = 0,
+                 ff_feature_ratio: int = 4,
+                 dropout: float = 0.0,
+                 dropout_attention: float = 0.0,
+                 dropout_path: float = 0.0,
+                 sequential_self_attention: bool = False,
+                 offset_downscale_factor: int = 2) -> None:
+        # Call super constructor
+        super(DeformableSwinTransformerBlock, self).__init__(
+            in_channels=in_channels,
+            input_resolution=input_resolution,
+            number_of_heads=number_of_heads,
+            window_size=window_size,
+            shift_size=shift_size,
+            ff_feature_ratio=ff_feature_ratio,
+            dropout=dropout,
+            dropout_attention=dropout_attention,
+            dropout_path=dropout_path,
+            sequential_self_attention=sequential_self_attention
+        )
+        # Save parameter
+        self.offset_downscale_factor: int = offset_downscale_factor
+        self.number_of_heads: int = number_of_heads
+        # Make default offsets
+        self.__make_default_offsets()
+        # Init offset network
+        self.offset_network: nn.Module = nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=5, stride=offset_downscale_factor,
+                      padding=3, groups=in_channels, bias=True),
+            nn.GELU(),
+            nn.Conv2d(in_channels=in_channels, out_channels=2 * self.number_of_heads, kernel_size=1, stride=1,
+                      padding=0, bias=True)
+        )
+
+    def __make_default_offsets(self) -> None:
+        """ Method generates the default sampling grid (inspired by kornia) """
+        # Init x and y coordinates
+        x: torch.Tensor = torch.linspace(0, self.input_resolution[1] - 1, self.input_resolution[1],
+                                         device=self.window_attention.tau.device)
+        y: torch.Tensor = torch.linspace(0, self.input_resolution[0] - 1, self.input_resolution[0],
+                                         device=self.window_attention.tau.device)
+        # Normalize coordinates to a range of [-1, 1]
+        x: torch.Tensor = (x / (self.input_resolution[1] - 1) - 0.5) * 2
+        y: torch.Tensor = (y / (self.input_resolution[0] - 1) - 0.5) * 2
+        # Make grid [2, height, width]
+        grid: torch.Tensor = torch.stack(torch.meshgrid([x, y])).transpose(1, 2)
+        # Reshape grid to [1, height, width, 2]
+        grid: torch.Tensor = grid.unsqueeze(dim=0).permute(0, 2, 3, 1)
+        # Register in module
+        self.register_buffer("default_grid", grid)
+
+    def update_resolution(self,
+                          new_window_size: int,
+                          new_input_resolution: Tuple[int, int]) -> None:
+        """ Method updates the window size and so the pair-wise relative positions.
+
+        Args:
+            new_window_size (int): New window size
+            new_input_resolution (Tuple[int, int]): New input resolution
+        """
+        # Update resolution and window size
+        super(DeformableSwinTransformerBlock, self).update_resolution(new_window_size=new_window_size,
+                                                                      new_input_resolution=new_input_resolution)
+        # Update default sampling grid
+        self.__make_default_offsets()
+
+    def forward(self,
+                input: torch.Tensor) -> torch.Tensor:
+        """ Forward pass
+        Args:
+            input (torch.Tensor): Input tensor of the shape [B, C, H, W]
+
+        Returns:
+            output (torch.Tensor): Output tensor of the shape [B, C, H, W]
+        """
+        # Get input shape
+        batch_size, channels, height, width = input.shape
+        # Compute offsets of the shape [batch size, 2, height / r, width / r]
+        offsets: torch.Tensor = self.offset_network(input)
+        # Upscale offsets to the shape [batch size, 2 * number of heads, height, width]
+        offsets: torch.Tensor = F.interpolate(input=offsets,
+                                              size=(height, width), mode="bilinear", align_corners=True)
+        # Reshape offsets to [batch size, number of heads, height, width, 2]
+        offsets: torch.Tensor = offsets.reshape(batch_size, -1, 2, height, width).permute(0, 1, 3, 4, 2)
+        # Flatten batch size and number of heads and apply tanh
+        offsets: torch.Tensor = offsets.view(-1, height, width, 2).tanh()
+        # Cast offset grid to input data type
+        if input.dtype != self.default_grid.dtype:
+            self.default_grid = self.default_grid.type(input.dtype)
+        # Construct offset grid
+        offset_grid: torch.Tensor = self.default_grid.repeat_interleave(repeats=offsets.shape[0], dim=0) + offsets
+        # Reshape input to [batch size * number of heads, channels / number of heads, height, width]
+        input: torch.Tensor = input.view(batch_size, self.number_of_heads, channels // self.number_of_heads, height,
+                                         width).flatten(start_dim=0, end_dim=1)
+        # Apply sampling grid
+        input_resampled: torch.Tensor = F.grid_sample(input=input, grid=offset_grid.clip(min=-1, max=1),
+                                                      mode="bilinear", align_corners=True, padding_mode="reflection")
+        # Reshape resampled tensor again to [batch size, channels, height, width]
+        input_resampled: torch.Tensor = input_resampled.view(batch_size, channels, height, width)
+        output: torch.Tensor = super(DeformableSwinTransformerBlock, self).forward(input=input_resampled)
+        return output
+
+
+class PatchMerging(nn.Module):
+    """ This class implements the patch merging as a strided convolution with a normalization before.
+
+    Args:
+        in_channels (int): Number of input channels
+    """
+
+    def __init__(self,
+                 in_channels: int) -> None:
+        # Call super constructor
+        super(PatchMerging, self).__init__()
+        # Init normalization
+        self.normalization: nn.Module = nn.LayerNorm(normalized_shape=4 * in_channels)
+        # Init linear mapping
+        self.linear_mapping: nn.Module = nn.Linear(in_features=4 * in_channels, out_features=2 * in_channels,
+                                                   bias=False)
+
+    def forward(self,
+                input: torch.Tensor) -> torch.Tensor:
+        """ Forward pass.
+
+        Args:
+            input (torch.Tensor): Input tensor of the shape [B, C, H, W]
+
+        Returns:
+            output (torch.Tensor): Output tensor of the shape [B, 2 * C, H // 2, W // 2]
+        """
+        # Get original shape
+        batch_size, channels, height, width = input.shape  # type: int, int, int, int
+        # Reshape input to [batch size, in channels, height, width]
+        input: torch.Tensor = bchw_to_bhwc(input)
+        # Unfold input
+        input: torch.Tensor = input.unfold(dimension=1, size=2, step=2).unfold(dimension=2, size=2, step=2)
+        input: torch.Tensor = input.reshape(batch_size, input.shape[1], input.shape[2], -1)
+        # Normalize input
+        input: torch.Tensor = self.normalization(input)
+        # Perform linear mapping
+        output: torch.Tensor = bhwc_to_bchw(self.linear_mapping(input))
+        return output
+
+
+class PatchEmbedding(nn.Module):
+    """ Module embeds a given image into patch embeddings.
+
+    Args:
+        in_channels (int): Number of input channels
+        out_channels (int): Number of output channels
+        patch_size (int): Patch size to be utilized
+        image_size (int): Image size to be used
+    """
+
+    def __init__(self,
+                 in_channels: int = 3,
+                 out_channels: int = 96,
+                 patch_size: int = 4) -> None:
+        # Call super constructor
+        super(PatchEmbedding, self).__init__()
+        # Save parameters
+        self.out_channels: int = out_channels
+        # Init linear embedding as a convolution
+        self.linear_embedding: nn.Module = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
+                                                     kernel_size=(patch_size, patch_size),
+                                                     stride=(patch_size, patch_size))
+        # Init layer normalization
+        self.normalization: nn.Module = nn.LayerNorm(normalized_shape=out_channels)
+
+    def forward(self,
+                input: torch.Tensor) -> torch.Tensor:
+        """ Forward pass.
+
+        Args:
+            input (torch.Tensor): Input image of the shape (B, C_in, H, W)
+
+        Returns:
+            embedding (torch.Tensor): Embedding of the shape (B, C_out, H / patch size, W / patch size)
+        """
+        # Perform linear embedding
+        embedding: torch.Tensor = self.linear_embedding(input)
+        # Perform normalization
+        embedding: torch.Tensor = bhwc_to_bchw(self.normalization(bchw_to_bhwc(embedding)))
+        return embedding
+
+
+class SwinTransformerStage(nn.Module):
+    r""" This class implements a stage of the Swin transformer including multiple layers.
+
+    Args:
+        in_channels (int): Number of input channels
+        depth (int): Depth of the stage (number of layers)
+        downscale (bool): If true input is downsampled (see Fig. 3 or V1 paper)
+        input_resolution (Tuple[int, int]): Input resolution
+        number_of_heads (int): Number of attention heads to be utilized
+        window_size (int): Window size to be utilized
+        shift_size (int): Shifting size to be used
+        ff_feature_ratio (int): Ratio of the hidden dimension in the FFN to the input channels
+        dropout (float): Dropout in input mapping
+        dropout_attention (float): Dropout rate of attention map
+        dropout_path (float): Dropout in main path
+        use_checkpoint (bool): If true checkpointing is utilized
+        sequential_self_attention (bool): If true sequential self-attention is performed
+        use_deformable_block (bool): If true deformable block is used
+    """
+
+    def __init__(self,
+                 in_channels: int,
+                 depth: int,
+                 downscale: bool,
+                 input_resolution: Tuple[int, int],
+                 number_of_heads: int,
+                 window_size: int = 7,
+                 ff_feature_ratio: int = 4,
+                 dropout: float = 0.0,
+                 dropout_attention: float = 0.0,
+                 dropout_path: Union[List[float], float] = 0.0,
+                 use_checkpoint: bool = False,
+                 sequential_self_attention: bool = False,
+                 use_deformable_block: bool = False) -> None:
+        # Call super constructor
+        super(SwinTransformerStage, self).__init__()
+        # Save parameters
+        self.use_checkpoint: bool = use_checkpoint
+        self.downscale: bool = downscale
+        # Init downsampling
+        self.downsample: nn.Module = PatchMerging(in_channels=in_channels) if downscale else nn.Identity()
+        # Update resolution and channels
+        self.input_resolution: Tuple[int, int] = (input_resolution[0] // 2, input_resolution[1] // 2) \
+            if downscale else input_resolution
+        in_channels = in_channels * 2 if downscale else in_channels
+        # Get block
+        block = DeformableSwinTransformerBlock if use_deformable_block else SwinTransformerBlock
+        # Init blocks
+        self.blocks: nn.ModuleList = nn.ModuleList([
+            block(in_channels=in_channels,
+                  input_resolution=self.input_resolution,
+                  number_of_heads=number_of_heads,
+                  window_size=window_size,
+                  shift_size=0 if ((index % 2) == 0) else window_size // 2,
+                  ff_feature_ratio=ff_feature_ratio,
+                  dropout=dropout,
+                  dropout_attention=dropout_attention,
+                  dropout_path=dropout_path[index] if isinstance(dropout_path, list) else dropout_path,
+                  sequential_self_attention=sequential_self_attention)
+            for index in range(depth)])
+
+    def update_resolution(self,
+                          new_window_size: int,
+                          new_input_resolution: Tuple[int, int]) -> None:
+        """ Method updates the resolution to utilize and the window size and so the pair-wise relative positions.
+
+        Args:
+            new_window_size (int): New window size
+            new_input_resolution (Tuple[int, int]): New input resolution
+        """
+        # Update resolution
+        self.input_resolution: Tuple[int, int] = (new_input_resolution[0] // 2, new_input_resolution[1] // 2) \
+            if self.downscale else new_input_resolution
+        # Update resolution of each block
+        for block in self.blocks:  # type: SwinTransformerBlock
+            block.update_resolution(new_window_size=new_window_size, new_input_resolution=self.input_resolution)
+
+    def forward(self,
+                input: torch.Tensor) -> torch.Tensor:
+        """ Forward pass.
+
+        Args:
+            input (torch.Tensor): Input tensor of the shape [B, C, H, W]
+
+        Returns:
+            output (torch.Tensor): Output tensor of the shape [B, 2 * C, H // 2, W // 2]
+        """
+        # Downscale input tensor
+        output: torch.Tensor = self.downsample(input)
+        # Forward pass of each block
+        for block in self.blocks:  # type: nn.Module
+            # Perform checkpointing if utilized
+            if self.use_checkpoint:
+                output: torch.Tensor = checkpoint.checkpoint(block, output)
+            else:
+                output: torch.Tensor = block(output)
+        return output
+
+
+class SwinTransformerV2(nn.Module):
+    r""" Swin Transformer V2
+        A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`  -
+          https://arxiv.org/pdf/2111.09883
+
+    Args:
+        in_channels (int): Number of input channels
+        depth (int): Depth of the stage (number of layers)
+        downscale (bool): If true input is downsampled (see Fig. 3 or V1 paper)
+        input_resolution (Tuple[int, int]): Input resolution
+        number_of_heads (int): Number of attention heads to be utilized
+        num_classes (int): Number of output classes
+        window_size (int): Window size to be utilized
+        shift_size (int): Shifting size to be used
+        ff_feature_ratio (int):  Ratio of the hidden dimension in the FFN to the input channels
+        dropout (float): Dropout in input mapping
+        dropout_attention (float): Dropout rate of attention map
+        dropout_path (float): Dropout in main path
+        use_checkpoint (bool): If true checkpointing is utilized
+        sequential_self_attention (bool): If true sequential self-attention is performed
+        use_deformable_block (bool): If true deformable block is used
+    """
+
+    def __init__(self,
+                 in_channels: int,
+                 embedding_channels: int,
+                 depths: Tuple[int, ...],
+                 input_resolution: Tuple[int, int],
+                 number_of_heads: Tuple[int, ...],
+                 num_classes: int = 1000,
+                 window_size: int = 7,
+                 patch_size: int = 4,
+                 ff_feature_ratio: int = 4,
+                 dropout: float = 0.0,
+                 dropout_attention: float = 0.0,
+                 dropout_path: float = 0.2,
+                 use_checkpoint: bool = False,
+                 sequential_self_attention: bool = False,
+                 use_deformable_block: bool = False) -> None:
+        # Call super constructor
+        super(SwinTransformerV2, self).__init__()
+        # Save parameters
+        self.patch_size: int = patch_size
+        self.input_resolution: Tuple[int, int] = input_resolution
+        self.window_size: int = window_size
+        # Init patch embedding
+        self.patch_embedding: nn.Module = PatchEmbedding(in_channels=in_channels, out_channels=embedding_channels,
+                                                         patch_size=patch_size)
+        # Compute patch resolution
+        patch_resolution: Tuple[int, int] = (input_resolution[0] // patch_size, input_resolution[1] // patch_size)
+        # Path dropout dependent on depth
+        dropout_path = torch.linspace(0., dropout_path, sum(depths)).tolist()
+        # Init stages
+        self.stages: nn.ModuleList = nn.ModuleList()
+        for index, (depth, number_of_head) in enumerate(zip(depths, number_of_heads)):
+            self.stages.append(
+                SwinTransformerStage(
+                    in_channels=embedding_channels * (2 ** max(index - 1, 0)),
+                    depth=depth,
+                    downscale=index != 0,
+                    input_resolution=(patch_resolution[0] // (2 ** max(index - 1, 0)),
+                                      patch_resolution[1] // (2 ** max(index - 1, 0))),
+                    number_of_heads=number_of_head,
+                    window_size=window_size,
+                    ff_feature_ratio=ff_feature_ratio,
+                    dropout=dropout,
+                    dropout_attention=dropout_attention,
+                    dropout_path=dropout_path[sum(depths[:index]):sum(depths[:index + 1])],
+                    use_checkpoint=use_checkpoint,
+                    sequential_self_attention=sequential_self_attention,
+                    use_deformable_block=use_deformable_block and (index > 0)
+                ))
+        # Init final adaptive average pooling, and classification head
+        self.average_pool: nn.Module = nn.AdaptiveAvgPool2d(1)
+        self.head: nn.Module = nn.Linear(in_features=embedding_channels * (2 ** len(depths) - 1),
+                                         out_features=num_classes)
+
+    def update_resolution(self,
+                          new_input_resolution: Optional[Tuple[int, int]] = None,
+                          new_window_size: Optional[int] = None) -> None:
+        """ Method updates the image resolution to be processed and window size and so the pair-wise relative positions.
+
+        Args:
+            new_window_size (Optional[int]): New window size if None current window size is used
+            new_input_resolution (Optional[Tuple[int, int]]): New input resolution if None current resolution is used
+        """
+        # Check parameters
+        if new_input_resolution is None:
+            new_input_resolution = self.input_resolution
+        if new_window_size is None:
+            new_window_size = self.window_size
+        # Compute new patch resolution
+        new_patch_resolution: Tuple[int, int] = (new_input_resolution[0] // self.patch_size,
+                                                 new_input_resolution[1] // self.patch_size)
+        # Update resolution of each stage
+        for index, stage in enumerate(self.stages):  # type: int, SwinTransformerStage
+            stage.update_resolution(new_window_size=new_window_size,
+                                    new_input_resolution=(new_patch_resolution[0] // (2 ** max(index - 1, 0)),
+                                                          new_patch_resolution[1] // (2 ** max(index - 1, 0))))
+
+    def forward_features(self,
+                         input: torch.Tensor) -> List[torch.Tensor]:
+        """ Forward pass to extract feature maps of each stage.
+
+        Args:
+            input (torch.Tensor): Input images of the shape (B, C, H, W)
+
+        Returns:
+            features (List[torch.Tensor]): List of feature maps from each stage
+        """
+        # Perform patch embedding
+        output: torch.Tensor = self.patch_embedding(input)
+        # Init list to store feature
+        features: List[torch.Tensor] = []
+        # Forward pass of each stage
+        for stage in self.stages:
+            output: torch.Tensor = stage(output)
+            features.append(output)
+        return features
+
+    def forward(self,
+                input: torch.Tensor) -> torch.Tensor:
+        """ Forward pass.
+
+        Args:
+            input (torch.Tensor): Input images of the shape (B, C, H, W)
+
+        Returns:
+            classification (torch.Tensor): Classification of the shape (B, num_classes)
+        """
+        # Perform patch embedding
+        output: torch.Tensor = self.patch_embedding(input)
+        # Forward pass of each stage
+        for stage in self.stages:
+            output: torch.Tensor = stage(output)
+        # Perform average pooling
+        output: torch.Tensor = self.average_pool(output)
+        # Predict classification
+        classification: torch.Tensor = self.head(output)
+        return classification