<filename>timm/models/swin_transformer_v2_cr.py
""" 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

This implementation is experimental and subject to change in manners that will break weight compat:
* Size of the pos embed MLP are not spelled out in paper in terms of dim, fixed for all models? vary with num_heads?
  * currently dim is fixed, I feel it may make sense to scale with num_heads (dim per head)
* The specifics of the memory saving 'sequential attention' are not detailed, Christoph Reich has an impl at
  GitHub link above. It needs further investigation as throughput vs mem tradeoff doesn't appear beneficial.
* num_heads per stage is not detailed for Huge and Giant model variants
* 'Giant' is 3B params in paper but ~2.6B here despite matching paper dim + block counts
* experiments are ongoing wrt to 'main branch' norm layer use and weight init scheme

Noteworthy additions over official Swin v1:
* MLP relative position embedding is looking promising and adapts to different image/window sizes
* This impl has been designed to allow easy change of image size with matching window size changes
* Non-square image size and window size are supported

Modifications and additions for timm hacked together by / Copyright 2022, <NAME>
"""
# --------------------------------------------------------
# Swin Transformer V2 reimplementation
# Copyright (c) 2021 <NAME>
# Licensed under The MIT License [see LICENSE for details]
# Written by <NAME>
# --------------------------------------------------------
import logging
import math
from copy import deepcopy
from typing import Tuple, Optional, List, Union, Any, Type

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .fx_features import register_notrace_function
from .helpers import build_model_with_cfg, named_apply
from .layers import DropPath, Mlp, to_2tuple, _assert
from .registry import register_model
from .vision_transformer import checkpoint_filter_fn

_logger = logging.getLogger(__name__)


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000,
        'input_size': (3, 224, 224),
        'pool_size': None,
        'crop_pct': 0.9,
        'interpolation': 'bicubic',
        'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN,
        'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.proj',
        'classifier': 'head',
        **kwargs,
    }


default_cfgs = {
    # patch models (my experiments)
    'swin_v2_cr_tiny_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_tiny_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_tiny_ns_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_small_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_small_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_base_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_base_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_large_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_large_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_huge_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_huge_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_giant_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_giant_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
}


def bchw_to_bhwc(x: torch.Tensor) -> torch.Tensor:
    """Permutes a tensor from the shape (B, C, H, W) to (B, H, W, C). """
    return x.permute(0, 2, 3, 1)


def bhwc_to_bchw(x: torch.Tensor) -> torch.Tensor:
    """Permutes a tensor from the shape (B, H, W, C) to (B, C, H, W). """
    return x.permute(0, 3, 1, 2)


def window_partition(x, window_size: Tuple[int, int]):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
    return windows


@register_notrace_function  # reason: int argument is a Proxy
def window_reverse(windows, window_size: Tuple[int, int], img_size: Tuple[int, int]):
    """
    Args:
        windows: (num_windows * B, window_size[0], window_size[1], C)
        window_size (Tuple[int, int]): Window size
        img_size (Tuple[int, int]): Image size

    Returns:
        x: (B, H, W, C)
    """
    H, W = img_size
    B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))
    x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowMultiHeadAttention(nn.Module):
    r"""This class implements window-based Multi-Head-Attention with log-spaced continuous position bias.

    Args:
        dim (int): Number of input features
        window_size (int): Window size
        num_heads (int): Number of attention heads
        drop_attn (float): Dropout rate of attention map
        drop_proj (float): Dropout rate after projection
        meta_hidden_dim (int): Number of hidden features in the two layer MLP meta network
        sequential_attn (bool): If true sequential self-attention is performed
    """

    def __init__(
        self,
        dim: int,
        num_heads: int,
        window_size: Tuple[int, int],
        drop_attn: float = 0.0,
        drop_proj: float = 0.0,
        meta_hidden_dim: int = 384,  # FIXME what's the optimal value?
        sequential_attn: bool = False,
    ) -> None:
        super(WindowMultiHeadAttention, self).__init__()
        assert dim % num_heads == 0, \
            "The number of input features (in_features) are not divisible by the number of heads (num_heads)."
        self.in_features: int = dim
        self.window_size: Tuple[int, int] = window_size
        self.num_heads: int = num_heads
        self.sequential_attn: bool = sequential_attn

        self.qkv = nn.Linear(in_features=dim, out_features=dim * 3, bias=True)
        self.attn_drop = nn.Dropout(drop_attn)
        self.proj = nn.Linear(in_features=dim, out_features=dim, bias=True)
        self.proj_drop = nn.Dropout(drop_proj)
        # meta network for positional encodings
        self.meta_mlp = Mlp(
            2,  # x, y
            hidden_features=meta_hidden_dim,
            out_features=num_heads,
            act_layer=nn.ReLU,
            drop=0.1  # FIXME should there be stochasticity, appears to 'overfit' without?
        )
        self.register_parameter("tau", torch.nn.Parameter(torch.ones(num_heads)))
        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."""
        device = self.tau.device
        coordinates = torch.stack(torch.meshgrid([
            torch.arange(self.window_size[0], device=device),
            torch.arange(self.window_size[1], device=device)]), dim=0).flatten(1)
        relative_coordinates = coordinates[:, :, None] - coordinates[:, None, :]
        relative_coordinates = relative_coordinates.permute(1, 2, 0).reshape(-1, 2).float()
        relative_coordinates_log = torch.sign(relative_coordinates) * torch.log(
            1.0 + relative_coordinates.abs())
        self.register_buffer("relative_coordinates_log", relative_coordinates_log, persistent=False)

    def update_input_size(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 and new pair-wise relative positions
        self.window_size: int = new_window_size
        self._make_pair_wise_relative_positions()

    def _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)
        """
        window_area = self.window_size[0] * self.window_size[1]
        relative_position_bias = self.meta_mlp(self.relative_coordinates_log)
        relative_position_bias = relative_position_bias.transpose(1, 0).reshape(
            self.num_heads, window_area, window_area
        )
        relative_position_bias = relative_position_bias.unsqueeze(0)
        return relative_position_bias

    def _forward_sequential(
        self,
        x: torch.Tensor,
        mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """
        """
        # FIXME TODO figure out 'sequential' attention mentioned in paper (should reduce GPU memory)
        assert False, "not implemented"

    def _forward_batch(
        self,
        x: torch.Tensor,
        mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """This function performs standard (non-sequential) scaled cosine self-attention.
        """
        Bw, L, C = x.shape

        qkv = self.qkv(x).view(Bw, L, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        query, key, value = qkv.unbind(0)

        # compute attention map with scaled cosine attention
        denom = torch.norm(query, dim=-1, keepdim=True) @ torch.norm(key, dim=-1, keepdim=True).transpose(-2, -1)
        attn = query @ key.transpose(-2, -1) / denom.clamp(min=1e-6)
        attn = attn / self.tau.clamp(min=0.01).reshape(1, self.num_heads, 1, 1)
        attn = attn + self._relative_positional_encodings()
        if mask is not None:
            # Apply mask if utilized
            num_win: int = mask.shape[0]
            attn = attn.view(Bw // num_win, num_win, self.num_heads, L, L)
            attn = attn + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, L, L)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ value).transpose(1, 2).reshape(Bw, L, -1)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        """ Forward pass.
        Args:
            x (torch.Tensor): Input tensor of the shape (B * windows, N, C)
            mask (Optional[torch.Tensor]): Attention mask for the shift case

        Returns:
            Output tensor of the shape [B * windows, N, C]
        """
        if self.sequential_attn:
            return self._forward_sequential(x, mask)
        else:
            return self._forward_batch(x, mask)


class SwinTransformerBlock(nn.Module):
    r"""This class implements the Swin transformer block.

    Args:
        dim (int): Number of input channels
        num_heads (int): Number of attention heads to be utilized
        feat_size (Tuple[int, int]): Input resolution
        window_size (Tuple[int, int]): Window size to be utilized
        shift_size (int): Shifting size to be used
        mlp_ratio (int): Ratio of the hidden dimension in the FFN to the input channels
        drop (float): Dropout in input mapping
        drop_attn (float): Dropout rate of attention map
        drop_path (float): Dropout in main path
        extra_norm (bool): Insert extra norm on 'main' branch if True
        sequential_attn (bool): If true sequential self-attention is performed
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized
    """

    def __init__(
        self,
        dim: int,
        num_heads: int,
        feat_size: Tuple[int, int],
        window_size: Tuple[int, int],
        shift_size: Tuple[int, int] = (0, 0),
        mlp_ratio: float = 4.0,
        drop: float = 0.0,
        drop_attn: float = 0.0,
        drop_path: float = 0.0,
        extra_norm: bool = False,
        sequential_attn: bool = False,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
    ) -> None:
        super(SwinTransformerBlock, self).__init__()
        self.dim: int = dim
        self.feat_size: Tuple[int, int] = feat_size
        self.target_shift_size: Tuple[int, int] = to_2tuple(shift_size)
        self.window_size, self.shift_size = self._calc_window_shift(to_2tuple(window_size))
        self.window_area = self.window_size[0] * self.window_size[1]

        # attn branch
        self.attn = WindowMultiHeadAttention(
            dim=dim,
            num_heads=num_heads,
            window_size=self.window_size,
            drop_attn=drop_attn,
            drop_proj=drop,
            sequential_attn=sequential_attn,
        )
        self.norm1 = norm_layer(dim)
        self.drop_path1 = DropPath(drop_prob=drop_path) if drop_path > 0.0 else nn.Identity()

        # mlp branch
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=int(dim * mlp_ratio),
            drop=drop,
            out_features=dim,
        )
        self.norm2 = norm_layer(dim)
        self.drop_path2 = DropPath(drop_prob=drop_path) if drop_path > 0.0 else nn.Identity()

        # Extra main branch norm layer mentioned for Huge/Giant models in V2 paper.
        # Also being used as final network norm and optional stage ending norm while still in a C-last format.
        self.norm3 = norm_layer(dim) if extra_norm else nn.Identity()

        self._make_attention_mask()

    def _calc_window_shift(self, target_window_size):
        window_size = [f if f <= w else w for f, w in zip(self.feat_size, target_window_size)]
        shift_size = [0 if f <= w else s for f, w, s in zip(self.feat_size, window_size, self.target_shift_size)]
        return tuple(window_size), tuple(shift_size)

    def _make_attention_mask(self) -> None:
        """Method generates the attention mask used in shift case."""
        # Make masks for shift case
        if any(self.shift_size):
            # calculate attention mask for SW-MSA
            H, W = self.feat_size
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            cnt = 0
            for h in (
                    slice(0, -self.window_size[0]),
                    slice(-self.window_size[0], -self.shift_size[0]),
                    slice(-self.shift_size[0], None)):
                for w in (
                        slice(0, -self.window_size[1]),
                        slice(-self.window_size[1], -self.shift_size[1]),
                        slice(-self.shift_size[1], None)):
                    img_mask[:, h, w, :] = cnt
                    cnt += 1
            mask_windows = window_partition(img_mask, self.window_size)  # num_windows, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_area)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None
        self.register_buffer("attn_mask", attn_mask, persistent=False)

    def update_input_size(self, new_window_size: Tuple[int, int], new_feat_size: 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_feat_size (Tuple[int, int]): New input resolution
        """
        # Update input resolution
        self.feat_size: Tuple[int, int] = new_feat_size
        self.window_size, self.shift_size = self._calc_window_shift(to_2tuple(new_window_size))
        self.window_area = self.window_size[0] * self.window_size[1]
        self.attn.update_input_size(new_window_size=self.window_size)
        self._make_attention_mask()

    def _shifted_window_attn(self, x):
        H, W = self.feat_size
        B, L, C = x.shape
        x = x.view(B, H, W, C)

        # cyclic shift
        sh, sw = self.shift_size
        do_shift: bool = any(self.shift_size)
        if do_shift:
            # FIXME PyTorch XLA needs cat impl, roll not lowered
            # x = torch.cat([x[:, sh:], x[:, :sh]], dim=1)
            # x = torch.cat([x[:, :, sw:], x[:, :, :sw]], dim=2)
            x = torch.roll(x, shifts=(-sh, -sw), dims=(1, 2))

        # partition windows
        x_windows = window_partition(x, self.window_size)  # num_windows * B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size[0] * self.window_size[1], C)

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # num_windows * B, window_size * window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
        x = window_reverse(attn_windows, self.window_size, self.feat_size)  # B H' W' C

        # reverse cyclic shift
        if do_shift:
            # FIXME PyTorch XLA needs cat impl, roll not lowered
            # x = torch.cat([x[:, -sh:], x[:, :-sh]], dim=1)
            # x = torch.cat([x[:, :, -sw:], x[:, :, :-sw]], dim=2)
            x = torch.roll(x, shifts=(sh, sw), dims=(1, 2))

        x = x.view(B, L, C)
        return x

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass.

        Args:
            x (torch.Tensor): Input tensor of the shape [B, C, H, W]

        Returns:
            output (torch.Tensor): Output tensor of the shape [B, C, H, W]
        """
        # NOTE post-norm branches (op -> norm -> drop)
        x = x + self.drop_path1(self.norm1(self._shifted_window_attn(x)))
        x = x + self.drop_path2(self.norm2(self.mlp(x)))
        x = self.norm3(x)  # main-branch norm enabled for some blocks / stages (every 6 for Huge/Giant)
        return x


class PatchMerging(nn.Module):
    """ This class implements the patch merging as a strided convolution with a normalization before.
    Args:
        dim (int): Number of input channels
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized.
    """

    def __init__(self, dim: int, norm_layer: Type[nn.Module] = nn.LayerNorm) -> None:
        super(PatchMerging, self).__init__()
        self.norm = norm_layer(4 * dim)
        self.reduction = nn.Linear(in_features=4 * dim, out_features=2 * dim, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """ Forward pass.
        Args:
            x (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]
        """
        B, C, H, W = x.shape
        # unfold + BCHW -> BHWC together
        # ordering, 5, 3, 1 instead of 3, 5, 1 maintains compat with original swin v1 merge
        x = x.reshape(B, C, H // 2, 2, W // 2, 2).permute(0, 2, 4, 5, 3, 1).flatten(3)
        x = self.norm(x)
        x = bhwc_to_bchw(self.reduction(x))
        return x


class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
        _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
        x = self.proj(x)
        x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        return x


class SwinTransformerStage(nn.Module):
    r"""This class implements a stage of the Swin transformer including multiple layers.

    Args:
        embed_dim (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)
        feat_size (Tuple[int, int]): input feature map size (H, W)
        num_heads (int): Number of attention heads to be utilized
        window_size (int): Window size to be utilized
        mlp_ratio (int): Ratio of the hidden dimension in the FFN to the input channels
        drop (float): Dropout in input mapping
        drop_attn (float): Dropout rate of attention map
        drop_path (float): Dropout in main path
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized. Default: nn.LayerNorm
        extra_norm_period (int): Insert extra norm layer on main branch every N (period) blocks
        extra_norm_stage (bool): End each stage with an extra norm layer in main branch
        sequential_attn (bool): If true sequential self-attention is performed
    """

    def __init__(
        self,
        embed_dim: int,
        depth: int,
        downscale: bool,
        num_heads: int,
        feat_size: Tuple[int, int],
        window_size: Tuple[int, int],
        mlp_ratio: float = 4.0,
        drop: float = 0.0,
        drop_attn: float = 0.0,
        drop_path: Union[List[float], float] = 0.0,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
        extra_norm_period: int = 0,
        extra_norm_stage: bool = False,
        sequential_attn: bool = False,
    ) -> None:
        super(SwinTransformerStage, self).__init__()
        self.downscale: bool = downscale
        self.grad_checkpointing: bool = False
        self.feat_size: Tuple[int, int] = (feat_size[0] // 2, feat_size[1] // 2) if downscale else feat_size

        self.downsample = PatchMerging(embed_dim, norm_layer=norm_layer) if downscale else nn.Identity()

        def _extra_norm(index):
            i = index + 1
            if extra_norm_period and i % extra_norm_period == 0:
                return True
            return i == depth if extra_norm_stage else False

        embed_dim = embed_dim * 2 if downscale else embed_dim
        self.blocks = nn.Sequential(*[
            SwinTransformerBlock(
                dim=embed_dim,
                num_heads=num_heads,
                feat_size=self.feat_size,
                window_size=window_size,
                shift_size=tuple([0 if ((index % 2) == 0) else w // 2 for w in window_size]),
                mlp_ratio=mlp_ratio,
                drop=drop,
                drop_attn=drop_attn,
                drop_path=drop_path[index] if isinstance(drop_path, list) else drop_path,
                extra_norm=_extra_norm(index),
                sequential_attn=sequential_attn,
                norm_layer=norm_layer,
            )
            for index in range(depth)]
        )

    def update_input_size(self, new_window_size: int, new_feat_size: 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_feat_size (Tuple[int, int]): New input resolution
        """
        self.feat_size: Tuple[int, int] = (
            (new_feat_size[0] // 2, new_feat_size[1] // 2) if self.downscale else new_feat_size
        )
        for block in self.blocks:
            block.update_input_size(new_window_size=new_window_size, new_feat_size=self.feat_size)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass.
        Args:
            x (torch.Tensor): Input tensor of the shape [B, C, H, W] or [B, L, C]
        Returns:
            output (torch.Tensor): Output tensor of the shape [B, 2 * C, H // 2, W // 2]
        """
        x = self.downsample(x)
        B, C, H, W = x.shape
        L = H * W

        x = bchw_to_bhwc(x).reshape(B, L, C)
        for block in self.blocks:
            # Perform checkpointing if utilized
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint.checkpoint(block, x)
            else:
                x = block(x)
        x = bhwc_to_bchw(x.reshape(B, H, W, -1))
        return x


class SwinTransformerV2Cr(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:
        img_size (Tuple[int, int]): Input resolution.
        window_size (Optional[int]): Window size. If None, img_size // window_div. Default: None
        img_window_ratio (int): Window size to image size ratio. Default: 32
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input channels.
        depths (int): Depth of the stage (number of layers).
        num_heads (int): Number of attention heads to be utilized.
        embed_dim (int): Patch embedding dimension. Default: 96
        num_classes (int): Number of output classes. Default: 1000
        mlp_ratio (int):  Ratio of the hidden dimension in the FFN to the input channels. Default: 4
        drop_rate (float): Dropout rate. Default: 0.0
        attn_drop_rate (float): Dropout rate of attention map. Default: 0.0
        drop_path_rate (float): Stochastic depth rate. Default: 0.0
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized. Default: nn.LayerNorm
        extra_norm_period (int): Insert extra norm layer on main branch every N (period) blocks in stage
        extra_norm_stage (bool): End each stage with an extra norm layer in main branch
        sequential_attn (bool): If true sequential self-attention is performed. Default: False
    """

    def __init__(
        self,
        img_size: Tuple[int, int] = (224, 224),
        patch_size: int = 4,
        window_size: Optional[int] = None,
        img_window_ratio: int = 32,
        in_chans: int = 3,
        num_classes: int = 1000,
        embed_dim: int = 96,
        depths: Tuple[int, ...] = (2, 2, 6, 2),
        num_heads: Tuple[int, ...] = (3, 6, 12, 24),
        mlp_ratio: float = 4.0,
        drop_rate: float = 0.0,
        attn_drop_rate: float = 0.0,
        drop_path_rate: float = 0.0,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
        extra_norm_period: int = 0,
        extra_norm_stage: bool = False,
        sequential_attn: bool = False,
        global_pool: str = 'avg',
        weight_init='skip',
        **kwargs: Any
    ) -> None:
        super(SwinTransformerV2Cr, self).__init__()
        img_size = to_2tuple(img_size)
        window_size = tuple([
            s // img_window_ratio for s in img_size]) if window_size is None else to_2tuple(window_size)

        self.num_classes: int = num_classes
        self.patch_size: int = patch_size
        self.img_size: Tuple[int, int] = img_size
        self.window_size: int = window_size
        self.num_features: int = int(embed_dim * 2 ** (len(depths) - 1))

        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans,
            embed_dim=embed_dim, norm_layer=norm_layer)
        patch_grid_size: Tuple[int, int] = self.patch_embed.grid_size

        drop_path_rate = torch.linspace(0.0, drop_path_rate, sum(depths)).tolist()
        stages = []
        for index, (depth, num_heads) in enumerate(zip(depths, num_heads)):
            stage_scale = 2 ** max(index - 1, 0)
            stages.append(
                SwinTransformerStage(
                    embed_dim=embed_dim * stage_scale,
                    depth=depth,
                    downscale=index != 0,
                    feat_size=(patch_grid_size[0] // stage_scale, patch_grid_size[1] // stage_scale),
                    num_heads=num_heads,
                    window_size=window_size,
                    mlp_ratio=mlp_ratio,
                    drop=drop_rate,
                    drop_attn=attn_drop_rate,
                    drop_path=drop_path_rate[sum(depths[:index]):sum(depths[:index + 1])],
                    extra_norm_period=extra_norm_period,
                    extra_norm_stage=extra_norm_stage or (index + 1) == len(depths),  # last stage ends w/ norm
                    sequential_attn=sequential_attn,
                    norm_layer=norm_layer,
                )
            )
        self.stages = nn.Sequential(*stages)

        self.global_pool: str = global_pool
        self.head = nn.Linear(self.num_features, num_classes) if num_classes else nn.Identity()

        # current weight init skips custom init and uses pytorch layer defaults, seems to work well
        # FIXME more experiments needed
        if weight_init != 'skip':
            named_apply(init_weights, self)

    def update_input_size(
            self,
            new_img_size: Optional[Tuple[int, int]] = None,
            new_window_size: Optional[int] = None,
            img_window_ratio: int = 32,
    ) -> 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 based on new_img_size // window_div
            new_img_size (Optional[Tuple[int, int]]): New input resolution, if None current resolution is used
            img_window_ratio (int): divisor for calculating window size from image size
        """
        # Check parameters
        if new_img_size is None:
            new_img_size = self.img_size
        else:
            new_img_size = to_2tuple(new_img_size)
        if new_window_size is None:
            new_window_size = tuple([s // img_window_ratio for s in new_img_size])
        # Compute new patch resolution & update resolution of each stage
        new_patch_grid_size = (new_img_size[0] // self.patch_size, new_img_size[1] // self.patch_size)
        for index, stage in enumerate(self.stages):
            stage_scale = 2 ** max(index - 1, 0)
            stage.update_input_size(
                new_window_size=new_window_size,
                new_img_size=(new_patch_grid_size[0] // stage_scale, new_patch_grid_size[1] // stage_scale),
            )

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        return dict(
            stem=r'^patch_embed',  # stem and embed
            blocks=r'^stages\.(\d+)' if coarse else [
                (r'^stages\.(\d+).downsample', (0,)),
                (r'^stages\.(\d+)\.\w+\.(\d+)', None),
            ]
        )

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        for s in self.stages:
            s.grad_checkpointing = enable

    @torch.jit.ignore()
    def get_classifier(self) -> nn.Module:
        """Method returns the classification head of the model.
        Returns:
            head (nn.Module): Current classification head
        """
        return self.head

    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None) -> None:
        """Method results the classification head

        Args:
            num_classes (int): Number of classes to be predicted
            global_pool (str): Unused
        """
        self.num_classes: int = num_classes
        if global_pool is not None:
            self.global_pool = global_pool
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
        x = self.patch_embed(x)
        x = self.stages(x)
        return x

    def forward_head(self, x, pre_logits: bool = False):
        if self.global_pool == 'avg':
            x = x.mean(dim=(2, 3))
        return x if pre_logits else self.head(x)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x


def init_weights(module: nn.Module, name: str = ''):
    # FIXME WIP determining if there's a better weight init
    if isinstance(module, nn.Linear):
        if 'qkv' in name:
            # treat the weights of Q, K, V separately
            val = math.sqrt(6. / float(module.weight.shape[0] // 3 + module.weight.shape[1]))
            nn.init.uniform_(module.weight, -val, val)
        elif 'head' in name:
            nn.init.zeros_(module.weight)
        else:
            nn.init.xavier_uniform_(module.weight)
        if module.bias is not None:
            nn.init.zeros_(module.bias)


def _create_swin_transformer_v2_cr(variant, pretrained=False, **kwargs):
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')
    model = build_model_with_cfg(
        SwinTransformerV2Cr, variant, pretrained,
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs
    )
    return model


@register_model
def swin_v2_cr_tiny_384(pretrained=False, **kwargs):
    """Swin-T V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 6, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_384', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_tiny_224(pretrained=False, **kwargs):
    """Swin-T V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 6, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_tiny_ns_224(pretrained=False, **kwargs):
    """Swin-T V2 CR @ 224x224, trained ImageNet-1k w/ extra stage norms.
    ** Experimental, may make default if results are improved. **
    """
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 6, 2),
        num_heads=(3, 6, 12, 24),
        extra_norm_stage=True,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_ns_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_small_384(pretrained=False, **kwargs):
    """Swin-S V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 18, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_small_384', pretrained=pretrained, **model_kwargs
    )


@register_model
def swin_v2_cr_small_224(pretrained=False, **kwargs):
    """Swin-S V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 18, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_small_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_base_384(pretrained=False, **kwargs):
    """Swin-B V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=128,
        depths=(2, 2, 18, 2),
        num_heads=(4, 8, 16, 32),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_base_384', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_base_224(pretrained=False, **kwargs):
    """Swin-B V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=128,
        depths=(2, 2, 18, 2),
        num_heads=(4, 8, 16, 32),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_base_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_large_384(pretrained=False, **kwargs):
    """Swin-L V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=192,
        depths=(2, 2, 18, 2),
        num_heads=(6, 12, 24, 48),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_large_384', pretrained=pretrained, **model_kwargs
    )


@register_model
def swin_v2_cr_large_224(pretrained=False, **kwargs):
    """Swin-L V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=192,
        depths=(2, 2, 18, 2),
        num_heads=(6, 12, 24, 48),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_large_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_huge_384(pretrained=False, **kwargs):
    """Swin-H V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=352,
        depths=(2, 2, 18, 2),
        num_heads=(11, 22, 44, 88),  # head count not certain for Huge, 384 & 224 trying diff values
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_huge_384', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_huge_224(pretrained=False, **kwargs):
    """Swin-H V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=352,
        depths=(2, 2, 18, 2),
        num_heads=(8, 16, 32, 64),  # head count not certain for Huge, 384 & 224 trying diff values
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_huge_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_giant_384(pretrained=False, **kwargs):
    """Swin-G V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=512,
        depths=(2, 2, 42, 2),
        num_heads=(16, 32, 64, 128),
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_giant_384', pretrained=pretrained, **model_kwargs
    )


@register_model
def swin_v2_cr_giant_224(pretrained=False, **kwargs):
    """Swin-G V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=512,
        depths=(2, 2, 42, 2),
        num_heads=(16, 32, 64, 128),
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_giant_224', pretrained=pretrained, **model_kwargs)

<filename>timm/models/swin_transformer_v2_cr.py
""" 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

This implementation is experimental and subject to change in manners that will break weight compat:
* Size of the pos embed MLP are not spelled out in paper in terms of dim, fixed for all models? vary with num_heads?
  * currently dim is fixed, I feel it may make sense to scale with num_heads (dim per head)
* The specifics of the memory saving 'sequential attention' are not detailed, Christoph Reich has an impl at
  GitHub link above. It needs further investigation as throughput vs mem tradeoff doesn't appear beneficial.
* num_heads per stage is not detailed for Huge and Giant model variants
* 'Giant' is 3B params in paper but ~2.6B here despite matching paper dim + block counts
* experiments are ongoing wrt to 'main branch' norm layer use and weight init scheme

Noteworthy additions over official Swin v1:
* MLP relative position embedding is looking promising and adapts to different image/window sizes
* This impl has been designed to allow easy change of image size with matching window size changes
* Non-square image size and window size are supported

Modifications and additions for timm hacked together by / Copyright 2022, <NAME>
"""
# --------------------------------------------------------
# Swin Transformer V2 reimplementation
# Copyright (c) 2021 <NAME>
# Licensed under The MIT License [see LICENSE for details]
# Written by <NAME>
# --------------------------------------------------------
import logging
import math
from copy import deepcopy
from typing import Tuple, Optional, List, Union, Any, Type

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .fx_features import register_notrace_function
from .helpers import build_model_with_cfg, named_apply
from .layers import DropPath, Mlp, to_2tuple, _assert
from .registry import register_model
from .vision_transformer import checkpoint_filter_fn

_logger = logging.getLogger(__name__)


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000,
        'input_size': (3, 224, 224),
        'pool_size': None,
        'crop_pct': 0.9,
        'interpolation': 'bicubic',
        'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN,
        'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.proj',
        'classifier': 'head',
        **kwargs,
    }


default_cfgs = {
    # patch models (my experiments)
    'swin_v2_cr_tiny_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_tiny_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_tiny_ns_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_small_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_small_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_base_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_base_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_large_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_large_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_huge_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_huge_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
    'swin_v2_cr_giant_384': _cfg(
        url="", input_size=(3, 384, 384), crop_pct=1.0),
    'swin_v2_cr_giant_224': _cfg(
        url="", input_size=(3, 224, 224), crop_pct=0.9),
}


def bchw_to_bhwc(x: torch.Tensor) -> torch.Tensor:
    """Permutes a tensor from the shape (B, C, H, W) to (B, H, W, C). """
    return x.permute(0, 2, 3, 1)


def bhwc_to_bchw(x: torch.Tensor) -> torch.Tensor:
    """Permutes a tensor from the shape (B, H, W, C) to (B, C, H, W). """
    return x.permute(0, 3, 1, 2)


def window_partition(x, window_size: Tuple[int, int]):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
    return windows


@register_notrace_function  # reason: int argument is a Proxy
def window_reverse(windows, window_size: Tuple[int, int], img_size: Tuple[int, int]):
    """
    Args:
        windows: (num_windows * B, window_size[0], window_size[1], C)
        window_size (Tuple[int, int]): Window size
        img_size (Tuple[int, int]): Image size

    Returns:
        x: (B, H, W, C)
    """
    H, W = img_size
    B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))
    x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowMultiHeadAttention(nn.Module):
    r"""This class implements window-based Multi-Head-Attention with log-spaced continuous position bias.

    Args:
        dim (int): Number of input features
        window_size (int): Window size
        num_heads (int): Number of attention heads
        drop_attn (float): Dropout rate of attention map
        drop_proj (float): Dropout rate after projection
        meta_hidden_dim (int): Number of hidden features in the two layer MLP meta network
        sequential_attn (bool): If true sequential self-attention is performed
    """

    def __init__(
        self,
        dim: int,
        num_heads: int,
        window_size: Tuple[int, int],
        drop_attn: float = 0.0,
        drop_proj: float = 0.0,
        meta_hidden_dim: int = 384,  # FIXME what's the optimal value?
        sequential_attn: bool = False,
    ) -> None:
        super(WindowMultiHeadAttention, self).__init__()
        assert dim % num_heads == 0, \
            "The number of input features (in_features) are not divisible by the number of heads (num_heads)."
        self.in_features: int = dim
        self.window_size: Tuple[int, int] = window_size
        self.num_heads: int = num_heads
        self.sequential_attn: bool = sequential_attn

        self.qkv = nn.Linear(in_features=dim, out_features=dim * 3, bias=True)
        self.attn_drop = nn.Dropout(drop_attn)
        self.proj = nn.Linear(in_features=dim, out_features=dim, bias=True)
        self.proj_drop = nn.Dropout(drop_proj)
        # meta network for positional encodings
        self.meta_mlp = Mlp(
            2,  # x, y
            hidden_features=meta_hidden_dim,
            out_features=num_heads,
            act_layer=nn.ReLU,
            drop=0.1  # FIXME should there be stochasticity, appears to 'overfit' without?
        )
        self.register_parameter("tau", torch.nn.Parameter(torch.ones(num_heads)))
        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."""
        device = self.tau.device
        coordinates = torch.stack(torch.meshgrid([
            torch.arange(self.window_size[0], device=device),
            torch.arange(self.window_size[1], device=device)]), dim=0).flatten(1)
        relative_coordinates = coordinates[:, :, None] - coordinates[:, None, :]
        relative_coordinates = relative_coordinates.permute(1, 2, 0).reshape(-1, 2).float()
        relative_coordinates_log = torch.sign(relative_coordinates) * torch.log(
            1.0 + relative_coordinates.abs())
        self.register_buffer("relative_coordinates_log", relative_coordinates_log, persistent=False)

    def update_input_size(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 and new pair-wise relative positions
        self.window_size: int = new_window_size
        self._make_pair_wise_relative_positions()

    def _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)
        """
        window_area = self.window_size[0] * self.window_size[1]
        relative_position_bias = self.meta_mlp(self.relative_coordinates_log)
        relative_position_bias = relative_position_bias.transpose(1, 0).reshape(
            self.num_heads, window_area, window_area
        )
        relative_position_bias = relative_position_bias.unsqueeze(0)
        return relative_position_bias

    def _forward_sequential(
        self,
        x: torch.Tensor,
        mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """
        """
        # FIXME TODO figure out 'sequential' attention mentioned in paper (should reduce GPU memory)
        assert False, "not implemented"

    def _forward_batch(
        self,
        x: torch.Tensor,
        mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """This function performs standard (non-sequential) scaled cosine self-attention.
        """
        Bw, L, C = x.shape

        qkv = self.qkv(x).view(Bw, L, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        query, key, value = qkv.unbind(0)

        # compute attention map with scaled cosine attention
        denom = torch.norm(query, dim=-1, keepdim=True) @ torch.norm(key, dim=-1, keepdim=True).transpose(-2, -1)
        attn = query @ key.transpose(-2, -1) / denom.clamp(min=1e-6)
        attn = attn / self.tau.clamp(min=0.01).reshape(1, self.num_heads, 1, 1)
        attn = attn + self._relative_positional_encodings()
        if mask is not None:
            # Apply mask if utilized
            num_win: int = mask.shape[0]
            attn = attn.view(Bw // num_win, num_win, self.num_heads, L, L)
            attn = attn + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, L, L)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ value).transpose(1, 2).reshape(Bw, L, -1)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        """ Forward pass.
        Args:
            x (torch.Tensor): Input tensor of the shape (B * windows, N, C)
            mask (Optional[torch.Tensor]): Attention mask for the shift case

        Returns:
            Output tensor of the shape [B * windows, N, C]
        """
        if self.sequential_attn:
            return self._forward_sequential(x, mask)
        else:
            return self._forward_batch(x, mask)


class SwinTransformerBlock(nn.Module):
    r"""This class implements the Swin transformer block.

    Args:
        dim (int): Number of input channels
        num_heads (int): Number of attention heads to be utilized
        feat_size (Tuple[int, int]): Input resolution
        window_size (Tuple[int, int]): Window size to be utilized
        shift_size (int): Shifting size to be used
        mlp_ratio (int): Ratio of the hidden dimension in the FFN to the input channels
        drop (float): Dropout in input mapping
        drop_attn (float): Dropout rate of attention map
        drop_path (float): Dropout in main path
        extra_norm (bool): Insert extra norm on 'main' branch if True
        sequential_attn (bool): If true sequential self-attention is performed
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized
    """

    def __init__(
        self,
        dim: int,
        num_heads: int,
        feat_size: Tuple[int, int],
        window_size: Tuple[int, int],
        shift_size: Tuple[int, int] = (0, 0),
        mlp_ratio: float = 4.0,
        drop: float = 0.0,
        drop_attn: float = 0.0,
        drop_path: float = 0.0,
        extra_norm: bool = False,
        sequential_attn: bool = False,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
    ) -> None:
        super(SwinTransformerBlock, self).__init__()
        self.dim: int = dim
        self.feat_size: Tuple[int, int] = feat_size
        self.target_shift_size: Tuple[int, int] = to_2tuple(shift_size)
        self.window_size, self.shift_size = self._calc_window_shift(to_2tuple(window_size))
        self.window_area = self.window_size[0] * self.window_size[1]

        # attn branch
        self.attn = WindowMultiHeadAttention(
            dim=dim,
            num_heads=num_heads,
            window_size=self.window_size,
            drop_attn=drop_attn,
            drop_proj=drop,
            sequential_attn=sequential_attn,
        )
        self.norm1 = norm_layer(dim)
        self.drop_path1 = DropPath(drop_prob=drop_path) if drop_path > 0.0 else nn.Identity()

        # mlp branch
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=int(dim * mlp_ratio),
            drop=drop,
            out_features=dim,
        )
        self.norm2 = norm_layer(dim)
        self.drop_path2 = DropPath(drop_prob=drop_path) if drop_path > 0.0 else nn.Identity()

        # Extra main branch norm layer mentioned for Huge/Giant models in V2 paper.
        # Also being used as final network norm and optional stage ending norm while still in a C-last format.
        self.norm3 = norm_layer(dim) if extra_norm else nn.Identity()

        self._make_attention_mask()

    def _calc_window_shift(self, target_window_size):
        window_size = [f if f <= w else w for f, w in zip(self.feat_size, target_window_size)]
        shift_size = [0 if f <= w else s for f, w, s in zip(self.feat_size, window_size, self.target_shift_size)]
        return tuple(window_size), tuple(shift_size)

    def _make_attention_mask(self) -> None:
        """Method generates the attention mask used in shift case."""
        # Make masks for shift case
        if any(self.shift_size):
            # calculate attention mask for SW-MSA
            H, W = self.feat_size
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            cnt = 0
            for h in (
                    slice(0, -self.window_size[0]),
                    slice(-self.window_size[0], -self.shift_size[0]),
                    slice(-self.shift_size[0], None)):
                for w in (
                        slice(0, -self.window_size[1]),
                        slice(-self.window_size[1], -self.shift_size[1]),
                        slice(-self.shift_size[1], None)):
                    img_mask[:, h, w, :] = cnt
                    cnt += 1
            mask_windows = window_partition(img_mask, self.window_size)  # num_windows, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_area)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None
        self.register_buffer("attn_mask", attn_mask, persistent=False)

    def update_input_size(self, new_window_size: Tuple[int, int], new_feat_size: 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_feat_size (Tuple[int, int]): New input resolution
        """
        # Update input resolution
        self.feat_size: Tuple[int, int] = new_feat_size
        self.window_size, self.shift_size = self._calc_window_shift(to_2tuple(new_window_size))
        self.window_area = self.window_size[0] * self.window_size[1]
        self.attn.update_input_size(new_window_size=self.window_size)
        self._make_attention_mask()

    def _shifted_window_attn(self, x):
        H, W = self.feat_size
        B, L, C = x.shape
        x = x.view(B, H, W, C)

        # cyclic shift
        sh, sw = self.shift_size
        do_shift: bool = any(self.shift_size)
        if do_shift:
            # FIXME PyTorch XLA needs cat impl, roll not lowered
            # x = torch.cat([x[:, sh:], x[:, :sh]], dim=1)
            # x = torch.cat([x[:, :, sw:], x[:, :, :sw]], dim=2)
            x = torch.roll(x, shifts=(-sh, -sw), dims=(1, 2))

        # partition windows
        x_windows = window_partition(x, self.window_size)  # num_windows * B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size[0] * self.window_size[1], C)

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # num_windows * B, window_size * window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
        x = window_reverse(attn_windows, self.window_size, self.feat_size)  # B H' W' C

        # reverse cyclic shift
        if do_shift:
            # FIXME PyTorch XLA needs cat impl, roll not lowered
            # x = torch.cat([x[:, -sh:], x[:, :-sh]], dim=1)
            # x = torch.cat([x[:, :, -sw:], x[:, :, :-sw]], dim=2)
            x = torch.roll(x, shifts=(sh, sw), dims=(1, 2))

        x = x.view(B, L, C)
        return x

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass.

        Args:
            x (torch.Tensor): Input tensor of the shape [B, C, H, W]

        Returns:
            output (torch.Tensor): Output tensor of the shape [B, C, H, W]
        """
        # NOTE post-norm branches (op -> norm -> drop)
        x = x + self.drop_path1(self.norm1(self._shifted_window_attn(x)))
        x = x + self.drop_path2(self.norm2(self.mlp(x)))
        x = self.norm3(x)  # main-branch norm enabled for some blocks / stages (every 6 for Huge/Giant)
        return x


class PatchMerging(nn.Module):
    """ This class implements the patch merging as a strided convolution with a normalization before.
    Args:
        dim (int): Number of input channels
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized.
    """

    def __init__(self, dim: int, norm_layer: Type[nn.Module] = nn.LayerNorm) -> None:
        super(PatchMerging, self).__init__()
        self.norm = norm_layer(4 * dim)
        self.reduction = nn.Linear(in_features=4 * dim, out_features=2 * dim, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """ Forward pass.
        Args:
            x (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]
        """
        B, C, H, W = x.shape
        # unfold + BCHW -> BHWC together
        # ordering, 5, 3, 1 instead of 3, 5, 1 maintains compat with original swin v1 merge
        x = x.reshape(B, C, H // 2, 2, W // 2, 2).permute(0, 2, 4, 5, 3, 1).flatten(3)
        x = self.norm(x)
        x = bhwc_to_bchw(self.reduction(x))
        return x


class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
        _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
        x = self.proj(x)
        x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        return x


class SwinTransformerStage(nn.Module):
    r"""This class implements a stage of the Swin transformer including multiple layers.

    Args:
        embed_dim (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)
        feat_size (Tuple[int, int]): input feature map size (H, W)
        num_heads (int): Number of attention heads to be utilized
        window_size (int): Window size to be utilized
        mlp_ratio (int): Ratio of the hidden dimension in the FFN to the input channels
        drop (float): Dropout in input mapping
        drop_attn (float): Dropout rate of attention map
        drop_path (float): Dropout in main path
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized. Default: nn.LayerNorm
        extra_norm_period (int): Insert extra norm layer on main branch every N (period) blocks
        extra_norm_stage (bool): End each stage with an extra norm layer in main branch
        sequential_attn (bool): If true sequential self-attention is performed
    """

    def __init__(
        self,
        embed_dim: int,
        depth: int,
        downscale: bool,
        num_heads: int,
        feat_size: Tuple[int, int],
        window_size: Tuple[int, int],
        mlp_ratio: float = 4.0,
        drop: float = 0.0,
        drop_attn: float = 0.0,
        drop_path: Union[List[float], float] = 0.0,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
        extra_norm_period: int = 0,
        extra_norm_stage: bool = False,
        sequential_attn: bool = False,
    ) -> None:
        super(SwinTransformerStage, self).__init__()
        self.downscale: bool = downscale
        self.grad_checkpointing: bool = False
        self.feat_size: Tuple[int, int] = (feat_size[0] // 2, feat_size[1] // 2) if downscale else feat_size

        self.downsample = PatchMerging(embed_dim, norm_layer=norm_layer) if downscale else nn.Identity()

        def _extra_norm(index):
            i = index + 1
            if extra_norm_period and i % extra_norm_period == 0:
                return True
            return i == depth if extra_norm_stage else False

        embed_dim = embed_dim * 2 if downscale else embed_dim
        self.blocks = nn.Sequential(*[
            SwinTransformerBlock(
                dim=embed_dim,
                num_heads=num_heads,
                feat_size=self.feat_size,
                window_size=window_size,
                shift_size=tuple([0 if ((index % 2) == 0) else w // 2 for w in window_size]),
                mlp_ratio=mlp_ratio,
                drop=drop,
                drop_attn=drop_attn,
                drop_path=drop_path[index] if isinstance(drop_path, list) else drop_path,
                extra_norm=_extra_norm(index),
                sequential_attn=sequential_attn,
                norm_layer=norm_layer,
            )
            for index in range(depth)]
        )

    def update_input_size(self, new_window_size: int, new_feat_size: 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_feat_size (Tuple[int, int]): New input resolution
        """
        self.feat_size: Tuple[int, int] = (
            (new_feat_size[0] // 2, new_feat_size[1] // 2) if self.downscale else new_feat_size
        )
        for block in self.blocks:
            block.update_input_size(new_window_size=new_window_size, new_feat_size=self.feat_size)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass.
        Args:
            x (torch.Tensor): Input tensor of the shape [B, C, H, W] or [B, L, C]
        Returns:
            output (torch.Tensor): Output tensor of the shape [B, 2 * C, H // 2, W // 2]
        """
        x = self.downsample(x)
        B, C, H, W = x.shape
        L = H * W

        x = bchw_to_bhwc(x).reshape(B, L, C)
        for block in self.blocks:
            # Perform checkpointing if utilized
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint.checkpoint(block, x)
            else:
                x = block(x)
        x = bhwc_to_bchw(x.reshape(B, H, W, -1))
        return x


class SwinTransformerV2Cr(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:
        img_size (Tuple[int, int]): Input resolution.
        window_size (Optional[int]): Window size. If None, img_size // window_div. Default: None
        img_window_ratio (int): Window size to image size ratio. Default: 32
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input channels.
        depths (int): Depth of the stage (number of layers).
        num_heads (int): Number of attention heads to be utilized.
        embed_dim (int): Patch embedding dimension. Default: 96
        num_classes (int): Number of output classes. Default: 1000
        mlp_ratio (int):  Ratio of the hidden dimension in the FFN to the input channels. Default: 4
        drop_rate (float): Dropout rate. Default: 0.0
        attn_drop_rate (float): Dropout rate of attention map. Default: 0.0
        drop_path_rate (float): Stochastic depth rate. Default: 0.0
        norm_layer (Type[nn.Module]): Type of normalization layer to be utilized. Default: nn.LayerNorm
        extra_norm_period (int): Insert extra norm layer on main branch every N (period) blocks in stage
        extra_norm_stage (bool): End each stage with an extra norm layer in main branch
        sequential_attn (bool): If true sequential self-attention is performed. Default: False
    """

    def __init__(
        self,
        img_size: Tuple[int, int] = (224, 224),
        patch_size: int = 4,
        window_size: Optional[int] = None,
        img_window_ratio: int = 32,
        in_chans: int = 3,
        num_classes: int = 1000,
        embed_dim: int = 96,
        depths: Tuple[int, ...] = (2, 2, 6, 2),
        num_heads: Tuple[int, ...] = (3, 6, 12, 24),
        mlp_ratio: float = 4.0,
        drop_rate: float = 0.0,
        attn_drop_rate: float = 0.0,
        drop_path_rate: float = 0.0,
        norm_layer: Type[nn.Module] = nn.LayerNorm,
        extra_norm_period: int = 0,
        extra_norm_stage: bool = False,
        sequential_attn: bool = False,
        global_pool: str = 'avg',
        weight_init='skip',
        **kwargs: Any
    ) -> None:
        super(SwinTransformerV2Cr, self).__init__()
        img_size = to_2tuple(img_size)
        window_size = tuple([
            s // img_window_ratio for s in img_size]) if window_size is None else to_2tuple(window_size)

        self.num_classes: int = num_classes
        self.patch_size: int = patch_size
        self.img_size: Tuple[int, int] = img_size
        self.window_size: int = window_size
        self.num_features: int = int(embed_dim * 2 ** (len(depths) - 1))

        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans,
            embed_dim=embed_dim, norm_layer=norm_layer)
        patch_grid_size: Tuple[int, int] = self.patch_embed.grid_size

        drop_path_rate = torch.linspace(0.0, drop_path_rate, sum(depths)).tolist()
        stages = []
        for index, (depth, num_heads) in enumerate(zip(depths, num_heads)):
            stage_scale = 2 ** max(index - 1, 0)
            stages.append(
                SwinTransformerStage(
                    embed_dim=embed_dim * stage_scale,
                    depth=depth,
                    downscale=index != 0,
                    feat_size=(patch_grid_size[0] // stage_scale, patch_grid_size[1] // stage_scale),
                    num_heads=num_heads,
                    window_size=window_size,
                    mlp_ratio=mlp_ratio,
                    drop=drop_rate,
                    drop_attn=attn_drop_rate,
                    drop_path=drop_path_rate[sum(depths[:index]):sum(depths[:index + 1])],
                    extra_norm_period=extra_norm_period,
                    extra_norm_stage=extra_norm_stage or (index + 1) == len(depths),  # last stage ends w/ norm
                    sequential_attn=sequential_attn,
                    norm_layer=norm_layer,
                )
            )
        self.stages = nn.Sequential(*stages)

        self.global_pool: str = global_pool
        self.head = nn.Linear(self.num_features, num_classes) if num_classes else nn.Identity()

        # current weight init skips custom init and uses pytorch layer defaults, seems to work well
        # FIXME more experiments needed
        if weight_init != 'skip':
            named_apply(init_weights, self)

    def update_input_size(
            self,
            new_img_size: Optional[Tuple[int, int]] = None,
            new_window_size: Optional[int] = None,
            img_window_ratio: int = 32,
    ) -> 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 based on new_img_size // window_div
            new_img_size (Optional[Tuple[int, int]]): New input resolution, if None current resolution is used
            img_window_ratio (int): divisor for calculating window size from image size
        """
        # Check parameters
        if new_img_size is None:
            new_img_size = self.img_size
        else:
            new_img_size = to_2tuple(new_img_size)
        if new_window_size is None:
            new_window_size = tuple([s // img_window_ratio for s in new_img_size])
        # Compute new patch resolution & update resolution of each stage
        new_patch_grid_size = (new_img_size[0] // self.patch_size, new_img_size[1] // self.patch_size)
        for index, stage in enumerate(self.stages):
            stage_scale = 2 ** max(index - 1, 0)
            stage.update_input_size(
                new_window_size=new_window_size,
                new_img_size=(new_patch_grid_size[0] // stage_scale, new_patch_grid_size[1] // stage_scale),
            )

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        return dict(
            stem=r'^patch_embed',  # stem and embed
            blocks=r'^stages\.(\d+)' if coarse else [
                (r'^stages\.(\d+).downsample', (0,)),
                (r'^stages\.(\d+)\.\w+\.(\d+)', None),
            ]
        )

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        for s in self.stages:
            s.grad_checkpointing = enable

    @torch.jit.ignore()
    def get_classifier(self) -> nn.Module:
        """Method returns the classification head of the model.
        Returns:
            head (nn.Module): Current classification head
        """
        return self.head

    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None) -> None:
        """Method results the classification head

        Args:
            num_classes (int): Number of classes to be predicted
            global_pool (str): Unused
        """
        self.num_classes: int = num_classes
        if global_pool is not None:
            self.global_pool = global_pool
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
        x = self.patch_embed(x)
        x = self.stages(x)
        return x

    def forward_head(self, x, pre_logits: bool = False):
        if self.global_pool == 'avg':
            x = x.mean(dim=(2, 3))
        return x if pre_logits else self.head(x)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x


def init_weights(module: nn.Module, name: str = ''):
    # FIXME WIP determining if there's a better weight init
    if isinstance(module, nn.Linear):
        if 'qkv' in name:
            # treat the weights of Q, K, V separately
            val = math.sqrt(6. / float(module.weight.shape[0] // 3 + module.weight.shape[1]))
            nn.init.uniform_(module.weight, -val, val)
        elif 'head' in name:
            nn.init.zeros_(module.weight)
        else:
            nn.init.xavier_uniform_(module.weight)
        if module.bias is not None:
            nn.init.zeros_(module.bias)


def _create_swin_transformer_v2_cr(variant, pretrained=False, **kwargs):
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')
    model = build_model_with_cfg(
        SwinTransformerV2Cr, variant, pretrained,
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs
    )
    return model


@register_model
def swin_v2_cr_tiny_384(pretrained=False, **kwargs):
    """Swin-T V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 6, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_384', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_tiny_224(pretrained=False, **kwargs):
    """Swin-T V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 6, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_tiny_ns_224(pretrained=False, **kwargs):
    """Swin-T V2 CR @ 224x224, trained ImageNet-1k w/ extra stage norms.
    ** Experimental, may make default if results are improved. **
    """
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 6, 2),
        num_heads=(3, 6, 12, 24),
        extra_norm_stage=True,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_tiny_ns_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_small_384(pretrained=False, **kwargs):
    """Swin-S V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 18, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_small_384', pretrained=pretrained, **model_kwargs
    )


@register_model
def swin_v2_cr_small_224(pretrained=False, **kwargs):
    """Swin-S V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=96,
        depths=(2, 2, 18, 2),
        num_heads=(3, 6, 12, 24),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_small_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_base_384(pretrained=False, **kwargs):
    """Swin-B V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=128,
        depths=(2, 2, 18, 2),
        num_heads=(4, 8, 16, 32),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_base_384', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_base_224(pretrained=False, **kwargs):
    """Swin-B V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=128,
        depths=(2, 2, 18, 2),
        num_heads=(4, 8, 16, 32),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_base_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_large_384(pretrained=False, **kwargs):
    """Swin-L V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=192,
        depths=(2, 2, 18, 2),
        num_heads=(6, 12, 24, 48),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_large_384', pretrained=pretrained, **model_kwargs
    )


@register_model
def swin_v2_cr_large_224(pretrained=False, **kwargs):
    """Swin-L V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=192,
        depths=(2, 2, 18, 2),
        num_heads=(6, 12, 24, 48),
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_large_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_huge_384(pretrained=False, **kwargs):
    """Swin-H V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=352,
        depths=(2, 2, 18, 2),
        num_heads=(11, 22, 44, 88),  # head count not certain for Huge, 384 & 224 trying diff values
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_huge_384', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_huge_224(pretrained=False, **kwargs):
    """Swin-H V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=352,
        depths=(2, 2, 18, 2),
        num_heads=(8, 16, 32, 64),  # head count not certain for Huge, 384 & 224 trying diff values
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_huge_224', pretrained=pretrained, **model_kwargs)


@register_model
def swin_v2_cr_giant_384(pretrained=False, **kwargs):
    """Swin-G V2 CR @ 384x384, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=512,
        depths=(2, 2, 42, 2),
        num_heads=(16, 32, 64, 128),
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_giant_384', pretrained=pretrained, **model_kwargs
    )


@register_model
def swin_v2_cr_giant_224(pretrained=False, **kwargs):
    """Swin-G V2 CR @ 224x224, trained ImageNet-1k"""
    model_kwargs = dict(
        embed_dim=512,
        depths=(2, 2, 42, 2),
        num_heads=(16, 32, 64, 128),
        extra_norm_period=6,
        **kwargs
    )
    return _create_swin_transformer_v2_cr('swin_v2_cr_giant_224', pretrained=pretrained, **model_kwargs)

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  This implementation is experimental and subject to change in manners that will break weight compat: * Size of the pos embed MLP are not spelled out in paper in terms of dim, fixed for all models? vary with num_heads?   * currently dim is fixed, I feel it may make sense to scale with num_heads (dim per head) * The specifics of the memory saving 'sequential attention' are not detailed, Christoph Reich has an impl at   GitHub link above. It needs further investigation as throughput vs mem tradeoff doesn't appear beneficial. * num_heads per stage is not detailed for Huge and Giant model variants * 'Giant' is 3B params in paper but ~2.6B here despite matching paper dim + block counts * experiments are ongoing wrt to 'main branch' norm layer use and weight init scheme  Noteworthy additions over official Swin v1: * MLP relative position embedding is looking promising and adapts to different image/window sizes * This impl has been designed to allow easy change of image size with matching window size changes * Non-square image size and window size are supported  Modifications and additions for timm hacked together by / Copyright 2022, <NAME> # -------------------------------------------------------- # Swin Transformer V2 reimplementation # Copyright (c) 2021 <NAME> # Licensed under The MIT License [see LICENSE for details] # Written by <NAME> # -------------------------------------------------------- # patch models (my experiments) Permutes a tensor from the shape (B, C, H, W) to (B, H, W, C). Permutes a tensor from the shape (B, H, W, C) to (B, C, H, W). Args:         x: (B, H, W, C)         window_size (int): window size      Returns:         windows: (num_windows*B, window_size, window_size, C) # reason: int argument is a Proxy Args:         windows: (num_windows * B, window_size[0], window_size[1], C)         window_size (Tuple[int, int]): Window size         img_size (Tuple[int, int]): Image size      Returns:         x: (B, H, W, C) This class implements window-based Multi-Head-Attention with log-spaced continuous position bias.      Args:         dim (int): Number of input features         window_size (int): Window size         num_heads (int): Number of attention heads         drop_attn (float): Dropout rate of attention map         drop_proj (float): Dropout rate after projection         meta_hidden_dim (int): Number of hidden features in the two layer MLP meta network         sequential_attn (bool): If true sequential self-attention is performed # FIXME what's the optimal value? # meta network for positional encodings # x, y # FIXME should there be stochasticity, appears to 'overfit' without? Method initializes the pair-wise relative positions to compute the positional biases. 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 and new pair-wise relative positions 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) # FIXME TODO figure out 'sequential' attention mentioned in paper (should reduce GPU memory) This function performs standard (non-sequential) scaled cosine self-attention. # compute attention map with scaled cosine attention # Apply mask if utilized Forward pass.         Args:             x (torch.Tensor): Input tensor of the shape (B * windows, N, C)             mask (Optional[torch.Tensor]): Attention mask for the shift case          Returns:             Output tensor of the shape [B * windows, N, C] This class implements the Swin transformer block.      Args:         dim (int): Number of input channels         num_heads (int): Number of attention heads to be utilized         feat_size (Tuple[int, int]): Input resolution         window_size (Tuple[int, int]): Window size to be utilized         shift_size (int): Shifting size to be used         mlp_ratio (int): Ratio of the hidden dimension in the FFN to the input channels         drop (float): Dropout in input mapping         drop_attn (float): Dropout rate of attention map         drop_path (float): Dropout in main path         extra_norm (bool): Insert extra norm on 'main' branch if True         sequential_attn (bool): If true sequential self-attention is performed         norm_layer (Type[nn.Module]): Type of normalization layer to be utilized # attn branch # mlp branch # Extra main branch norm layer mentioned for Huge/Giant models in V2 paper. # Also being used as final network norm and optional stage ending norm while still in a C-last format. Method generates the attention mask used in shift case. # Make masks for shift case # calculate attention mask for SW-MSA # 1 H W 1 # num_windows, window_size, window_size, 1 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_feat_size (Tuple[int, int]): New input resolution # Update input resolution # cyclic shift # FIXME PyTorch XLA needs cat impl, roll not lowered # x = torch.cat([x[:, sh:], x[:, :sh]], dim=1) # x = torch.cat([x[:, :, sw:], x[:, :, :sw]], dim=2) # partition windows # num_windows * B, window_size, window_size, C # W-MSA/SW-MSA # num_windows * B, window_size * window_size, C # merge windows # B H' W' C # reverse cyclic shift # FIXME PyTorch XLA needs cat impl, roll not lowered # x = torch.cat([x[:, -sh:], x[:, :-sh]], dim=1) # x = torch.cat([x[:, :, -sw:], x[:, :, :-sw]], dim=2) Forward pass.          Args:             x (torch.Tensor): Input tensor of the shape [B, C, H, W]          Returns:             output (torch.Tensor): Output tensor of the shape [B, C, H, W] # NOTE post-norm branches (op -> norm -> drop) # main-branch norm enabled for some blocks / stages (every 6 for Huge/Giant) This class implements the patch merging as a strided convolution with a normalization before.     Args:         dim (int): Number of input channels         norm_layer (Type[nn.Module]): Type of normalization layer to be utilized. Forward pass.         Args:             x (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] # unfold + BCHW -> BHWC together # ordering, 5, 3, 1 instead of 3, 5, 1 maintains compat with original swin v1 merge 2D Image to Patch Embedding This class implements a stage of the Swin transformer including multiple layers.      Args:         embed_dim (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)         feat_size (Tuple[int, int]): input feature map size (H, W)         num_heads (int): Number of attention heads to be utilized         window_size (int): Window size to be utilized         mlp_ratio (int): Ratio of the hidden dimension in the FFN to the input channels         drop (float): Dropout in input mapping         drop_attn (float): Dropout rate of attention map         drop_path (float): Dropout in main path         norm_layer (Type[nn.Module]): Type of normalization layer to be utilized. Default: nn.LayerNorm         extra_norm_period (int): Insert extra norm layer on main branch every N (period) blocks         extra_norm_stage (bool): End each stage with an extra norm layer in main branch         sequential_attn (bool): If true sequential self-attention is performed 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_feat_size (Tuple[int, int]): New input resolution Forward pass.         Args:             x (torch.Tensor): Input tensor of the shape [B, C, H, W] or [B, L, C]         Returns:             output (torch.Tensor): Output tensor of the shape [B, 2 * C, H // 2, W // 2] # Perform checkpointing if utilized Swin Transformer V2         A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`  -           https://arxiv.org/pdf/2111.09883      Args:         img_size (Tuple[int, int]): Input resolution.         window_size (Optional[int]): Window size. If None, img_size // window_div. Default: None         img_window_ratio (int): Window size to image size ratio. Default: 32         patch_size (int | tuple(int)): Patch size. Default: 4         in_chans (int): Number of input channels.         depths (int): Depth of the stage (number of layers).         num_heads (int): Number of attention heads to be utilized.         embed_dim (int): Patch embedding dimension. Default: 96         num_classes (int): Number of output classes. Default: 1000         mlp_ratio (int):  Ratio of the hidden dimension in the FFN to the input channels. Default: 4         drop_rate (float): Dropout rate. Default: 0.0         attn_drop_rate (float): Dropout rate of attention map. Default: 0.0         drop_path_rate (float): Stochastic depth rate. Default: 0.0         norm_layer (Type[nn.Module]): Type of normalization layer to be utilized. Default: nn.LayerNorm         extra_norm_period (int): Insert extra norm layer on main branch every N (period) blocks in stage         extra_norm_stage (bool): End each stage with an extra norm layer in main branch         sequential_attn (bool): If true sequential self-attention is performed. Default: False # last stage ends w/ norm # current weight init skips custom init and uses pytorch layer defaults, seems to work well # FIXME more experiments needed 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 based on new_img_size // window_div             new_img_size (Optional[Tuple[int, int]]): New input resolution, if None current resolution is used             img_window_ratio (int): divisor for calculating window size from image size # Check parameters # Compute new patch resolution & update resolution of each stage # stem and embed Method returns the classification head of the model.         Returns:             head (nn.Module): Current classification head Method results the classification head          Args:             num_classes (int): Number of classes to be predicted             global_pool (str): Unused # FIXME WIP determining if there's a better weight init # treat the weights of Q, K, V separately Swin-T V2 CR @ 384x384, trained ImageNet-1k Swin-T V2 CR @ 224x224, trained ImageNet-1k Swin-T V2 CR @ 224x224, trained ImageNet-1k w/ extra stage norms.     ** Experimental, may make default if results are improved. ** Swin-S V2 CR @ 384x384, trained ImageNet-1k Swin-S V2 CR @ 224x224, trained ImageNet-1k Swin-B V2 CR @ 384x384, trained ImageNet-1k Swin-B V2 CR @ 224x224, trained ImageNet-1k Swin-L V2 CR @ 384x384, trained ImageNet-1k Swin-L V2 CR @ 224x224, trained ImageNet-1k Swin-H V2 CR @ 384x384, trained ImageNet-1k # head count not certain for Huge, 384 & 224 trying diff values Swin-H V2 CR @ 224x224, trained ImageNet-1k # head count not certain for Huge, 384 & 224 trying diff values Swin-G V2 CR @ 384x384, trained ImageNet-1k Swin-G V2 CR @ 224x224, trained ImageNet-1k