""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weights from
- https://github.com/microsoft/Cream/tree/main/AutoFormerV2
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import logging
import math
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, DropPath, ClassifierHead, to_2tuple, to_ntuple, trunc_normal_, \
_assert, use_fused_attn, resize_rel_pos_bias_table
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._manipulate import checkpoint_seq, named_apply
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
from .vision_transformer import get_init_weights_vit
__all__ = ['SwinTransformer'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
_int_or_tuple_2_t = Union[int, Tuple[int, int]]
def window_partition(
x: torch.Tensor,
window_size: Tuple[int, int],
) -> torch.Tensor:
"""
Partition into non-overlapping windows with padding if needed.
Args:
x (tensor): input tokens with [B, H, W, C].
window_size (int): window size.
Returns:
windows: windows after partition with [B * num_windows, window_size, window_size, C].
(Hp, Wp): padded height and width before partition
"""
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], H: int, W: int):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
C = windows.shape[-1]
x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
return x
def get_relative_position_index(win_h: int, win_w: int):
# get pair-wise relative position index for each token inside the window
coords = torch.stack(torch.meshgrid([torch.arange(win_h), torch.arange(win_w)])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += win_h - 1 # shift to start from 0
relative_coords[:, :, 1] += win_w - 1
relative_coords[:, :, 0] *= 2 * win_w - 1
return relative_coords.sum(-1) # Wh*Ww, Wh*Ww
class WindowAttention(nn.Module):
""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports shifted and non-shifted windows.
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim: int,
num_heads: int,
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
qkv_bias: bool = True,
attn_drop: float = 0.,
proj_drop: float = 0.,
):
"""
Args:
dim: Number of input channels.
num_heads: Number of attention heads.
head_dim: Number of channels per head (dim // num_heads if not set)
window_size: The height and width of the window.
qkv_bias: If True, add a learnable bias to query, key, value.
attn_drop: Dropout ratio of attention weight.
proj_drop: Dropout ratio of output.
"""
super().__init__()
self.dim = dim
self.window_size = to_2tuple(window_size) # Wh, Ww
win_h, win_w = self.window_size
self.window_area = win_h * win_w
self.num_heads = num_heads
head_dim = head_dim or dim // num_heads
attn_dim = head_dim * num_heads
self.scale = head_dim ** -0.5
self.fused_attn = use_fused_attn(experimental=True) # NOTE not tested for prime-time yet
# define a parameter table of relative position bias, shape: 2*Wh-1 * 2*Ww-1, nH
self.relative_position_bias_table = nn.Parameter(torch.zeros((2 * win_h - 1) * (2 * win_w - 1), num_heads))
# get pair-wise relative position index for each token inside the window
self.register_buffer("relative_position_index", get_relative_position_index(win_h, win_w), persistent=False)
self.qkv = nn.Linear(dim, attn_dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(attn_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1)
def _get_rel_pos_bias(self) -> torch.Tensor:
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)].view(self.window_area, self.window_area, -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
return relative_position_bias.unsqueeze(0)
def forward(self, x, mask: Optional[torch.Tensor] = None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
if self.fused_attn:
attn_mask = self._get_rel_pos_bias()
if mask is not None:
num_win = mask.shape[0]
mask = mask.view(1, num_win, 1, N, N).expand(B_ // num_win, -1, self.num_heads, -1, -1)
attn_mask = attn_mask + mask.reshape(-1, self.num_heads, N, N)
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_mask,
dropout_p=self.attn_drop.p,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn + self._get_rel_pos_bias()
if mask is not None:
num_win = mask.shape[0]
attn = attn.view(-1, num_win, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B_, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
class SwinTransformerBlock(nn.Module):
""" Swin Transformer Block.
"""
def __init__(
self,
dim: int,
input_resolution: _int_or_tuple_2_t,
num_heads: int = 4,
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
shift_size: int = 0,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
input_resolution: Input resolution.
window_size: Window size.
num_heads: Number of attention heads.
head_dim: Enforce the number of channels per head
shift_size: Shift size for SW-MSA.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
proj_drop: Dropout rate.
attn_drop: Attention dropout rate.
drop_path: Stochastic depth rate.
act_layer: Activation layer.
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
ws, ss = self._calc_window_shift(window_size, shift_size)
self.window_size: Tuple[int, int] = ws
self.shift_size: Tuple[int, int] = ss
self.window_area = self.window_size[0] * self.window_size[1]
self.mlp_ratio = mlp_ratio
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
num_heads=num_heads,
head_dim=head_dim,
window_size=to_2tuple(self.window_size),
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
if any(self.shift_size):
# calculate attention mask for SW-MSA
H, W = self.input_resolution
H = math.ceil(H / self.window_size[0]) * self.window_size[0]
W = math.ceil(W / self.window_size[1]) * self.window_size[1]
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) # nW, 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 _calc_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]:
target_window_size = to_2tuple(target_window_size)
target_shift_size = to_2tuple(target_shift_size)
window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
return tuple(window_size), tuple(shift_size)
def _attn(self, x):
B, H, W, C = x.shape
# cyclic shift
has_shift = any(self.shift_size)
if has_shift:
shifted_x = torch.roll(x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))
else:
shifted_x = x
# pad for resolution not divisible by window size
pad_h = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0]
pad_w = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1]
shifted_x = torch.nn.functional.pad(shifted_x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = H + pad_h, W + pad_w
# partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_area, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
shifted_x = shifted_x[:, :H, :W, :].contiguous()
# reverse cyclic shift
if has_shift:
x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2))
else:
x = shifted_x
return x
def forward(self, x):
B, H, W, C = x.shape
x = x + self.drop_path1(self._attn(self.norm1(x)))
x = x.reshape(B, -1, C)
x = x + self.drop_path2(self.mlp(self.norm2(x)))
x = x.reshape(B, H, W, C)
return x
class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(
self,
dim: int,
out_dim: Optional[int] = None,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
out_dim: Number of output channels (or 2 * dim if None)
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.out_dim = out_dim or 2 * dim
self.norm = norm_layer(4 * dim)
self.reduction = nn.Linear(4 * dim, self.out_dim, bias=False)
def forward(self, x):
B, H, W, C = x.shape
_assert(H % 2 == 0, f"x height ({H}) is not even.")
_assert(W % 2 == 0, f"x width ({W}) is not even.")
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.norm(x)
x = self.reduction(x)
return x
class SwinTransformerStage(nn.Module):
""" A basic Swin Transformer layer for one stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
input_resolution: Tuple[int, int],
depth: int,
downsample: bool = True,
num_heads: int = 4,
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: Union[List[float], float] = 0.,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
input_resolution: Input resolution.
depth: Number of blocks.
downsample: Downsample layer at the end of the layer.
num_heads: Number of attention heads.
head_dim: Channels per head (dim // num_heads if not set)
window_size: Local window size.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
proj_drop: Projection dropout rate.
attn_drop: Attention dropout rate.
drop_path: Stochastic depth rate.
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.output_resolution = tuple(i // 2 for i in input_resolution) if downsample else input_resolution
self.depth = depth
self.grad_checkpointing = False
window_size = to_2tuple(window_size)
shift_size = tuple([w // 2 for w in window_size])
# patch merging layer
if downsample:
self.downsample = PatchMerging(
dim=dim,
out_dim=out_dim,
norm_layer=norm_layer,
)
else:
assert dim == out_dim
self.downsample = nn.Identity()
# build blocks
self.blocks = nn.Sequential(*[
SwinTransformerBlock(
dim=out_dim,
input_resolution=self.output_resolution,
num_heads=num_heads,
head_dim=head_dim,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else shift_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
)
for i in range(depth)])
def forward(self, x):
x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
class SwinTransformer(nn.Module):
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
https://arxiv.org/pdf/2103.14030
"""
def __init__(
self,
img_size: _int_or_tuple_2_t = 224,
patch_size: int = 4,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
embed_dim: int = 96,
depths: Tuple[int, ...] = (2, 2, 6, 2),
num_heads: Tuple[int, ...] = (3, 6, 12, 24),
head_dim: Optional[int] = None,
window_size: _int_or_tuple_2_t = 7,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.1,
norm_layer: Union[str, Callable] = nn.LayerNorm,
weight_init: str = '',
**kwargs,
):
"""
Args:
img_size: Input image size.
patch_size: Patch size.
in_chans: Number of input image channels.
num_classes: Number of classes for classification head.
embed_dim: Patch embedding dimension.
depths: Depth of each Swin Transformer layer.
num_heads: Number of attention heads in different layers.
head_dim: Dimension of self-attention heads.
window_size: Window size.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: If True, add a learnable bias to query, key, value.
drop_rate: Dropout rate.
attn_drop_rate (float): Attention dropout rate.
drop_path_rate (float): Stochastic depth rate.
norm_layer (nn.Module): Normalization layer.
"""
super().__init__()
assert global_pool in ('', 'avg')
self.num_classes = num_classes
self.global_pool = global_pool
self.output_fmt = 'NHWC'
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.feature_info = []
if not isinstance(embed_dim, (tuple, list)):
embed_dim = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim[0],
norm_layer=norm_layer,
output_fmt='NHWC',
)
self.patch_grid = self.patch_embed.grid_size
# build layers
head_dim = to_ntuple(self.num_layers)(head_dim)
if not isinstance(window_size, (list, tuple)):
window_size = to_ntuple(self.num_layers)(window_size)
elif len(window_size) == 2:
window_size = (window_size,) * self.num_layers
assert len(window_size) == self.num_layers
mlp_ratio = to_ntuple(self.num_layers)(mlp_ratio)
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
layers = []
in_dim = embed_dim[0]
scale = 1
for i in range(self.num_layers):
out_dim = embed_dim[i]
layers += [SwinTransformerStage(
dim=in_dim,
out_dim=out_dim,
input_resolution=(
self.patch_grid[0] // scale,
self.patch_grid[1] // scale
),
depth=depths[i],
downsample=i > 0,
num_heads=num_heads[i],
head_dim=head_dim[i],
window_size=window_size[i],
mlp_ratio=mlp_ratio[i],
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
)]
in_dim = out_dim
if i > 0:
scale *= 2
self.feature_info += [dict(num_chs=out_dim, reduction=4 * scale, module=f'layers.{i}')]
self.layers = nn.Sequential(*layers)
self.norm = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
input_fmt=self.output_fmt,
)
if weight_init != 'skip':
self.init_weights(weight_init)
@torch.jit.ignore
def init_weights(self, mode=''):
assert mode in ('jax', 'jax_nlhb', 'moco', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
named_apply(get_init_weights_vit(mode, head_bias=head_bias), self)
@torch.jit.ignore
def no_weight_decay(self):
nwd = set()
for n, _ in self.named_parameters():
if 'relative_position_bias_table' in n:
nwd.add(n)
return nwd
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^patch_embed', # stem and embed
blocks=r'^layers\.(\d+)' if coarse else [
(r'^layers\.(\d+).downsample', (0,)),
(r'^layers\.(\d+)\.\w+\.(\d+)', None),
(r'^norm', (99999,)),
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for l in self.layers:
l.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.patch_embed(x)
x = self.layers(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=True) if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
old_weights = True
if 'head.fc.weight' in state_dict:
old_weights = False
import re
out_dict = {}
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
for k, v in state_dict.items():
if any([n in k for n in ('relative_position_index', 'attn_mask')]):
continue # skip buffers that should not be persistent
if k.endswith('relative_position_bias_table'):
m = model.get_submodule(k[:-29])
if v.shape != m.relative_position_bias_table.shape or m.window_size[0] != m.window_size[1]:
v = resize_rel_pos_bias_table(
v,
new_window_size=m.window_size,
new_bias_shape=m.relative_position_bias_table.shape,
)
if old_weights:
k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k)
k = k.replace('head.', 'head.fc.')
out_dict[k] = v
return out_dict
def _create_swin_transformer(variant, pretrained=False, **kwargs):
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
SwinTransformer, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head.fc',
'license': 'mit', **kwargs
}
default_cfgs = generate_default_cfgs({
'swin_small_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22kto1k_finetune.pth', ),
'swin_base_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth',),
'swin_base_patch4_window12_384.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'swin_large_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth',),
'swin_large_patch4_window12_384.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
'swin_tiny_patch4_window7_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',),
'swin_small_patch4_window7_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',),
'swin_base_patch4_window7_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth',),
'swin_base_patch4_window12_384.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
# tiny 22k pretrain is worse than 1k, so moved after (untagged priority is based on order)
'swin_tiny_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22kto1k_finetune.pth',),
'swin_tiny_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_small_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_base_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_base_patch4_window12_384.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21841),
'swin_large_patch4_window7_224.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_large_patch4_window12_384.ms_in22k': _cfg(
hf_hub_id='timm/',
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21841),
'swin_s3_tiny_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_t-1d53f6a8.pth'),
'swin_s3_small_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_s-3bb4c69d.pth'),
'swin_s3_base_224.ms_in1k': _cfg(
hf_hub_id='timm/',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_b-a1e95db4.pth'),
})
@register_model
def swin_tiny_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-T @ 224x224, trained ImageNet-1k
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer(
'swin_tiny_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_small_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S @ 224x224
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer(
'swin_small_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_base_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-B @ 224x224
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
return _create_swin_transformer(
'swin_base_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_base_patch4_window12_384(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-B @ 384x384
"""
model_args = dict(patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
return _create_swin_transformer(
'swin_base_patch4_window12_384', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_large_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-L @ 224x224
"""
model_args = dict(patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
return _create_swin_transformer(
'swin_large_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_large_patch4_window12_384(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-L @ 384x384
"""
model_args = dict(patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
return _create_swin_transformer(
'swin_large_patch4_window12_384', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_s3_tiny_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S3-T @ 224x224, https://arxiv.org/abs/2111.14725
"""
model_args = dict(
patch_size=4, window_size=(7, 7, 14, 7), embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer('swin_s3_tiny_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_s3_small_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S3-S @ 224x224, https://arxiv.org/abs/2111.14725
"""
model_args = dict(
patch_size=4, window_size=(14, 14, 14, 7), embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer('swin_s3_small_224', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def swin_s3_base_224(pretrained=False, **kwargs) -> SwinTransformer:
""" Swin-S3-B @ 224x224, https://arxiv.org/abs/2111.14725
"""
model_args = dict(
patch_size=4, window_size=(7, 7, 14, 7), embed_dim=96, depths=(2, 2, 30, 2), num_heads=(3, 6, 12, 24))
return _create_swin_transformer('swin_s3_base_224', pretrained=pretrained, **dict(model_args, **kwargs))
register_model_deprecations(__name__, {
'swin_base_patch4_window7_224_in22k': 'swin_base_patch4_window7_224.ms_in22k',
'swin_base_patch4_window12_384_in22k': 'swin_base_patch4_window12_384.ms_in22k',
'swin_large_patch4_window7_224_in22k': 'swin_large_patch4_window7_224.ms_in22k',
'swin_large_patch4_window12_384_in22k': 'swin_large_patch4_window12_384.ms_in22k',
})