1089 lines
43 KiB
Python
1089 lines
43 KiB
Python
""" Swin Transformer V2
|
|
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
|
|
- https://arxiv.org/abs/2111.09883
|
|
|
|
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
|
|
|
|
Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
|
|
"""
|
|
# --------------------------------------------------------
|
|
# Swin Transformer V2
|
|
# Copyright (c) 2022 Microsoft
|
|
# Licensed under The MIT License [see LICENSE for details]
|
|
# Written by Ze Liu
|
|
# --------------------------------------------------------
|
|
import math
|
|
from typing import Callable, List, Optional, Tuple, Type, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
|
from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, ClassifierHead,\
|
|
resample_patch_embed, ndgrid, get_act_layer, LayerType
|
|
from ._builder import build_model_with_cfg
|
|
from ._features import feature_take_indices
|
|
from ._features_fx import register_notrace_function
|
|
from ._manipulate import checkpoint
|
|
from ._registry import generate_default_cfgs, register_model, register_model_deprecations
|
|
|
|
__all__ = ['SwinTransformerV2'] # model_registry will add each entrypoint fn to this
|
|
|
|
_int_or_tuple_2_t = Union[int, Tuple[int, int]]
|
|
|
|
|
|
def window_partition(x: torch.Tensor, window_size: Tuple[int, int]) -> torch.Tensor:
|
|
"""
|
|
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: torch.Tensor, window_size: Tuple[int, int], img_size: Tuple[int, int]) -> torch.Tensor:
|
|
"""
|
|
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
|
|
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
|
|
|
|
|
|
class WindowAttention(nn.Module):
|
|
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
|
It supports both of shifted and non-shifted window.
|
|
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
window_size (tuple[int]): The height and width of the window.
|
|
num_heads (int): Number of attention heads.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
|
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
|
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
|
pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
window_size: Tuple[int, int],
|
|
num_heads: int,
|
|
qkv_bias: bool = True,
|
|
qkv_bias_separate: bool = False,
|
|
attn_drop: float = 0.,
|
|
proj_drop: float = 0.,
|
|
pretrained_window_size: Tuple[int, int] = (0, 0),
|
|
) -> None:
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.window_size = window_size # Wh, Ww
|
|
self.pretrained_window_size = to_2tuple(pretrained_window_size)
|
|
self.num_heads = num_heads
|
|
self.qkv_bias_separate = qkv_bias_separate
|
|
|
|
self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
|
|
|
|
# mlp to generate continuous relative position bias
|
|
self.cpb_mlp = nn.Sequential(
|
|
nn.Linear(2, 512, bias=True),
|
|
nn.ReLU(inplace=True),
|
|
nn.Linear(512, num_heads, bias=False)
|
|
)
|
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=False)
|
|
if qkv_bias:
|
|
self.q_bias = nn.Parameter(torch.zeros(dim))
|
|
self.register_buffer('k_bias', torch.zeros(dim), persistent=False)
|
|
self.v_bias = nn.Parameter(torch.zeros(dim))
|
|
else:
|
|
self.q_bias = None
|
|
self.k_bias = None
|
|
self.v_bias = None
|
|
self.attn_drop = nn.Dropout(attn_drop)
|
|
self.proj = nn.Linear(dim, dim)
|
|
self.proj_drop = nn.Dropout(proj_drop)
|
|
self.softmax = nn.Softmax(dim=-1)
|
|
|
|
self._make_pair_wise_relative_positions()
|
|
|
|
def _make_pair_wise_relative_positions(self):
|
|
# get relative_coords_table
|
|
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0]).to(torch.float32)
|
|
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1]).to(torch.float32)
|
|
relative_coords_table = torch.stack(ndgrid(relative_coords_h, relative_coords_w))
|
|
relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
|
|
if self.pretrained_window_size[0] > 0:
|
|
relative_coords_table[:, :, :, 0] /= (self.pretrained_window_size[0] - 1)
|
|
relative_coords_table[:, :, :, 1] /= (self.pretrained_window_size[1] - 1)
|
|
else:
|
|
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
|
|
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
|
|
relative_coords_table *= 8 # normalize to -8, 8
|
|
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
|
|
torch.abs(relative_coords_table) + 1.0) / math.log2(8)
|
|
self.register_buffer("relative_coords_table", relative_coords_table, persistent=False)
|
|
|
|
# get pair-wise relative position index for each token inside the window
|
|
coords_h = torch.arange(self.window_size[0])
|
|
coords_w = torch.arange(self.window_size[1])
|
|
coords = torch.stack(ndgrid(coords_h, coords_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] += self.window_size[0] - 1 # shift to start from 0
|
|
relative_coords[:, :, 1] += self.window_size[1] - 1
|
|
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
|
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
|
self.register_buffer("relative_position_index", relative_position_index, persistent=False)
|
|
|
|
def set_window_size(self, window_size: Tuple[int, int]) -> None:
|
|
"""Update window size & interpolate position embeddings
|
|
Args:
|
|
window_size (int): New window size
|
|
"""
|
|
window_size = to_2tuple(window_size)
|
|
if window_size != self.window_size:
|
|
self.window_size = window_size
|
|
self._make_pair_wise_relative_positions()
|
|
|
|
def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
"""
|
|
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
|
|
|
|
if self.q_bias is None:
|
|
qkv = self.qkv(x)
|
|
else:
|
|
qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias))
|
|
if self.qkv_bias_separate:
|
|
qkv = self.qkv(x)
|
|
qkv += qkv_bias
|
|
else:
|
|
qkv = F.linear(x, weight=self.qkv.weight, bias=qkv_bias)
|
|
qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
|
|
q, k, v = qkv.unbind(0)
|
|
|
|
# cosine attention
|
|
attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
|
|
logit_scale = torch.clamp(self.logit_scale, max=math.log(1. / 0.01)).exp()
|
|
attn = attn * logit_scale
|
|
|
|
relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
|
|
relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
|
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
|
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
|
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
|
|
attn = attn + relative_position_bias.unsqueeze(0)
|
|
|
|
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)
|
|
else:
|
|
attn = self.softmax(attn)
|
|
|
|
attn = self.attn_drop(attn)
|
|
|
|
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
return x
|
|
|
|
|
|
class SwinTransformerV2Block(nn.Module):
|
|
""" Swin Transformer Block.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
input_resolution: _int_or_tuple_2_t,
|
|
num_heads: int,
|
|
window_size: _int_or_tuple_2_t = 7,
|
|
shift_size: _int_or_tuple_2_t = 0,
|
|
always_partition: bool = False,
|
|
dynamic_mask: bool = False,
|
|
mlp_ratio: float = 4.,
|
|
qkv_bias: bool = True,
|
|
proj_drop: float = 0.,
|
|
attn_drop: float = 0.,
|
|
drop_path: float = 0.,
|
|
act_layer: LayerType = "gelu",
|
|
norm_layer: Type[nn.Module] = nn.LayerNorm,
|
|
pretrained_window_size: _int_or_tuple_2_t = 0,
|
|
):
|
|
"""
|
|
Args:
|
|
dim: Number of input channels.
|
|
input_resolution: Input resolution.
|
|
num_heads: Number of attention heads.
|
|
window_size: Window size.
|
|
shift_size: Shift size for SW-MSA.
|
|
always_partition: Always partition into full windows and shift
|
|
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.
|
|
pretrained_window_size: Window size in pretraining.
|
|
"""
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.input_resolution = to_2tuple(input_resolution)
|
|
self.num_heads = num_heads
|
|
self.target_shift_size = to_2tuple(shift_size) # store for later resize
|
|
self.always_partition = always_partition
|
|
self.dynamic_mask = dynamic_mask
|
|
self.window_size, self.shift_size = self._calc_window_shift(window_size, shift_size)
|
|
self.window_area = self.window_size[0] * self.window_size[1]
|
|
self.mlp_ratio = mlp_ratio
|
|
act_layer = get_act_layer(act_layer)
|
|
|
|
self.attn = WindowAttention(
|
|
dim,
|
|
window_size=to_2tuple(self.window_size),
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
attn_drop=attn_drop,
|
|
proj_drop=proj_drop,
|
|
pretrained_window_size=to_2tuple(pretrained_window_size),
|
|
)
|
|
self.norm1 = norm_layer(dim)
|
|
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
|
|
self.mlp = Mlp(
|
|
in_features=dim,
|
|
hidden_features=int(dim * mlp_ratio),
|
|
act_layer=act_layer,
|
|
drop=proj_drop,
|
|
)
|
|
self.norm2 = norm_layer(dim)
|
|
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
|
|
self.register_buffer(
|
|
"attn_mask",
|
|
None if self.dynamic_mask else self.get_attn_mask(),
|
|
persistent=False,
|
|
)
|
|
|
|
def get_attn_mask(self, x: Optional[torch.Tensor] = None) -> Optional[torch.Tensor]:
|
|
if any(self.shift_size):
|
|
# calculate attention mask for SW-MSA
|
|
if x is None:
|
|
img_mask = torch.zeros((1, *self.input_resolution, 1)) # 1 H W 1
|
|
else:
|
|
img_mask = torch.zeros((1, x.shape[1], x.shape[2], 1), dtype=x.dtype, device=x.device) # 1 H W 1
|
|
cnt = 0
|
|
for h in (
|
|
(0, -self.window_size[0]),
|
|
(-self.window_size[0], -self.shift_size[0]),
|
|
(-self.shift_size[0], None),
|
|
):
|
|
for w in (
|
|
(0, -self.window_size[1]),
|
|
(-self.window_size[1], -self.shift_size[1]),
|
|
(-self.shift_size[1], None),
|
|
):
|
|
img_mask[:, h[0]:h[1], w[0]:w[1], :] = 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
|
|
return attn_mask
|
|
|
|
def _calc_window_shift(
|
|
self,
|
|
target_window_size: _int_or_tuple_2_t,
|
|
target_shift_size: Optional[_int_or_tuple_2_t] = None,
|
|
) -> Tuple[Tuple[int, int], Tuple[int, int]]:
|
|
target_window_size = to_2tuple(target_window_size)
|
|
if target_shift_size is None:
|
|
# if passed value is None, recalculate from default window_size // 2 if it was active
|
|
target_shift_size = self.target_shift_size
|
|
if any(target_shift_size):
|
|
# if there was previously a non-zero shift, recalculate based on current window_size
|
|
target_shift_size = (target_window_size[0] // 2, target_window_size[1] // 2)
|
|
else:
|
|
target_shift_size = to_2tuple(target_shift_size)
|
|
|
|
if self.always_partition:
|
|
return target_window_size, target_shift_size
|
|
|
|
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 set_input_size(
|
|
self,
|
|
feat_size: Tuple[int, int],
|
|
window_size: Tuple[int, int],
|
|
always_partition: Optional[bool] = None,
|
|
):
|
|
""" Updates the input resolution, window size.
|
|
|
|
Args:
|
|
feat_size (Tuple[int, int]): New input resolution
|
|
window_size (int): New window size
|
|
always_partition: Change always_partition attribute if not None
|
|
"""
|
|
# Update input resolution
|
|
self.input_resolution = feat_size
|
|
if always_partition is not None:
|
|
self.always_partition = always_partition
|
|
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]
|
|
self.attn.set_window_size(self.window_size)
|
|
self.register_buffer(
|
|
"attn_mask",
|
|
None if self.dynamic_mask else self.get_attn_mask(),
|
|
persistent=False,
|
|
)
|
|
|
|
def _attn(self, x: torch.Tensor) -> torch.Tensor:
|
|
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_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, _ = shifted_x.shape
|
|
|
|
# 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
|
|
if getattr(self, 'dynamic_mask', False):
|
|
attn_mask = self.get_attn_mask(shifted_x)
|
|
else:
|
|
attn_mask = self.attn_mask
|
|
attn_windows = self.attn(x_windows, mask=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: torch.Tensor) -> torch.Tensor:
|
|
B, H, W, C = x.shape
|
|
x = x + self.drop_path1(self.norm1(self._attn(x)))
|
|
x = x.reshape(B, -1, C)
|
|
x = x + self.drop_path2(self.norm2(self.mlp(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: Type[nn.Module] = nn.LayerNorm
|
|
):
|
|
"""
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
out_dim (int): Number of output channels (or 2 * dim if None)
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
|
"""
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.out_dim = out_dim or 2 * dim
|
|
self.reduction = nn.Linear(4 * dim, self.out_dim, bias=False)
|
|
self.norm = norm_layer(self.out_dim)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
B, H, W, C = x.shape
|
|
|
|
pad_values = (0, 0, 0, W % 2, 0, H % 2)
|
|
x = nn.functional.pad(x, pad_values)
|
|
_, H, W, _ = x.shape
|
|
|
|
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
|
|
x = self.reduction(x)
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
|
|
class SwinTransformerV2Stage(nn.Module):
|
|
""" A Swin Transformer V2 Stage.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
out_dim: int,
|
|
input_resolution: _int_or_tuple_2_t,
|
|
depth: int,
|
|
num_heads: int,
|
|
window_size: _int_or_tuple_2_t,
|
|
always_partition: bool = False,
|
|
dynamic_mask: bool = False,
|
|
downsample: bool = False,
|
|
mlp_ratio: float = 4.,
|
|
qkv_bias: bool = True,
|
|
proj_drop: float = 0.,
|
|
attn_drop: float = 0.,
|
|
drop_path: float = 0.,
|
|
act_layer: Union[str, Callable] = 'gelu',
|
|
norm_layer: Type[nn.Module] = nn.LayerNorm,
|
|
pretrained_window_size: _int_or_tuple_2_t = 0,
|
|
output_nchw: bool = False,
|
|
) -> None:
|
|
"""
|
|
Args:
|
|
dim: Number of input channels.
|
|
out_dim: Number of output channels.
|
|
input_resolution: Input resolution.
|
|
depth: Number of blocks.
|
|
num_heads: Number of attention heads.
|
|
window_size: Local window size.
|
|
always_partition: Always partition into full windows and shift
|
|
dynamic_mask: Create attention mask in forward based on current input size
|
|
downsample: Use downsample layer at start of the block.
|
|
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.
|
|
act_layer: Activation layer type.
|
|
norm_layer: Normalization layer.
|
|
pretrained_window_size: Local window size in pretraining.
|
|
output_nchw: Output tensors on NCHW format instead of NHWC.
|
|
"""
|
|
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.output_nchw = output_nchw
|
|
self.grad_checkpointing = False
|
|
window_size = to_2tuple(window_size)
|
|
shift_size = tuple([w // 2 for w in window_size])
|
|
|
|
# patch merging / downsample 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.ModuleList([
|
|
SwinTransformerV2Block(
|
|
dim=out_dim,
|
|
input_resolution=self.output_resolution,
|
|
num_heads=num_heads,
|
|
window_size=window_size,
|
|
shift_size=0 if (i % 2 == 0) else shift_size,
|
|
always_partition=always_partition,
|
|
dynamic_mask=dynamic_mask,
|
|
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,
|
|
act_layer=act_layer,
|
|
norm_layer=norm_layer,
|
|
pretrained_window_size=pretrained_window_size,
|
|
)
|
|
for i in range(depth)])
|
|
|
|
def set_input_size(
|
|
self,
|
|
feat_size: Tuple[int, int],
|
|
window_size: int,
|
|
always_partition: Optional[bool] = None,
|
|
):
|
|
""" Updates the resolution, window size and so the pair-wise relative positions.
|
|
|
|
Args:
|
|
feat_size: New input (feature) resolution
|
|
window_size: New window size
|
|
always_partition: Always partition / shift the window
|
|
"""
|
|
self.input_resolution = feat_size
|
|
if isinstance(self.downsample, nn.Identity):
|
|
self.output_resolution = feat_size
|
|
else:
|
|
assert isinstance(self.downsample, PatchMerging)
|
|
self.output_resolution = tuple(i // 2 for i in feat_size)
|
|
for block in self.blocks:
|
|
block.set_input_size(
|
|
feat_size=self.output_resolution,
|
|
window_size=window_size,
|
|
always_partition=always_partition,
|
|
)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.downsample(x)
|
|
|
|
for blk in self.blocks:
|
|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
|
x = checkpoint.checkpoint(blk, x)
|
|
else:
|
|
x = blk(x)
|
|
return x
|
|
|
|
def _init_respostnorm(self) -> None:
|
|
for blk in self.blocks:
|
|
nn.init.constant_(blk.norm1.bias, 0)
|
|
nn.init.constant_(blk.norm1.weight, 0)
|
|
nn.init.constant_(blk.norm2.bias, 0)
|
|
nn.init.constant_(blk.norm2.weight, 0)
|
|
|
|
|
|
class SwinTransformerV2(nn.Module):
|
|
""" Swin Transformer V2
|
|
|
|
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
|
|
- https://arxiv.org/abs/2111.09883
|
|
"""
|
|
|
|
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),
|
|
window_size: _int_or_tuple_2_t = 7,
|
|
always_partition: bool = False,
|
|
strict_img_size: bool = True,
|
|
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,
|
|
act_layer: Union[str, Callable] = 'gelu',
|
|
norm_layer: Callable = nn.LayerNorm,
|
|
pretrained_window_sizes: Tuple[int, ...] = (0, 0, 0, 0),
|
|
**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 stage (layer).
|
|
num_heads: Number of attention heads in different layers.
|
|
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: Head dropout rate.
|
|
proj_drop_rate: Projection dropout rate.
|
|
attn_drop_rate: Attention dropout rate.
|
|
drop_path_rate: Stochastic depth rate.
|
|
norm_layer: Normalization layer.
|
|
act_layer: Activation layer type.
|
|
patch_norm: If True, add normalization after patch embedding.
|
|
pretrained_window_sizes: Pretrained window sizes of each layer.
|
|
output_fmt: Output tensor format if not None, otherwise output 'NHWC' by default.
|
|
"""
|
|
super().__init__()
|
|
|
|
self.num_classes = num_classes
|
|
assert global_pool in ('', 'avg')
|
|
self.global_pool = global_pool
|
|
self.output_fmt = 'NHWC'
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.num_features = self.head_hidden_size = 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,
|
|
strict_img_size=strict_img_size,
|
|
output_fmt='NHWC',
|
|
)
|
|
grid_size = self.patch_embed.grid_size
|
|
|
|
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 += [SwinTransformerV2Stage(
|
|
dim=in_dim,
|
|
out_dim=out_dim,
|
|
input_resolution=(grid_size[0] // scale, grid_size[1] // scale),
|
|
depth=depths[i],
|
|
downsample=i > 0,
|
|
num_heads=num_heads[i],
|
|
window_size=window_size,
|
|
always_partition=always_partition,
|
|
dynamic_mask=not strict_img_size,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
proj_drop=proj_drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
act_layer=act_layer,
|
|
norm_layer=norm_layer,
|
|
pretrained_window_size=pretrained_window_sizes[i],
|
|
)]
|
|
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,
|
|
)
|
|
|
|
self.apply(self._init_weights)
|
|
for bly in self.layers:
|
|
bly._init_respostnorm()
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
def set_input_size(
|
|
self,
|
|
img_size: Optional[Tuple[int, int]] = None,
|
|
patch_size: Optional[Tuple[int, int]] = None,
|
|
window_size: Optional[Tuple[int, int]] = None,
|
|
window_ratio: Optional[int] = 8,
|
|
always_partition: Optional[bool] = None,
|
|
):
|
|
"""Updates the image resolution, window size, and so the pair-wise relative positions.
|
|
|
|
Args:
|
|
img_size (Optional[Tuple[int, int]]): New input resolution, if None current resolution is used
|
|
patch_size (Optional[Tuple[int, int]): New patch size, if None use current patch size
|
|
window_size (Optional[int]): New window size, if None based on new_img_size // window_div
|
|
window_ratio (int): divisor for calculating window size from patch grid size
|
|
always_partition: always partition / shift windows even if feat size is < window
|
|
"""
|
|
if img_size is not None or patch_size is not None:
|
|
self.patch_embed.set_input_size(img_size=img_size, patch_size=patch_size)
|
|
grid_size = self.patch_embed.grid_size
|
|
|
|
if window_size is None and window_ratio is not None:
|
|
window_size = tuple([s // window_ratio for s in grid_size])
|
|
|
|
for index, stage in enumerate(self.layers):
|
|
stage_scale = 2 ** max(index - 1, 0)
|
|
stage.set_input_size(
|
|
feat_size=(grid_size[0] // stage_scale, grid_size[1] // stage_scale),
|
|
window_size=window_size,
|
|
always_partition=always_partition,
|
|
)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
nod = set()
|
|
for n, m in self.named_modules():
|
|
if any([kw in n for kw in ("cpb_mlp", "logit_scale")]):
|
|
nod.add(n)
|
|
return nod
|
|
|
|
@torch.jit.ignore
|
|
def group_matcher(self, coarse=False):
|
|
return dict(
|
|
stem=r'^absolute_pos_embed|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) -> nn.Module:
|
|
return self.head.fc
|
|
|
|
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
|
|
self.num_classes = num_classes
|
|
self.head.reset(num_classes, global_pool)
|
|
|
|
def forward_intermediates(
|
|
self,
|
|
x: torch.Tensor,
|
|
indices: Optional[Union[int, List[int]]] = None,
|
|
norm: bool = False,
|
|
stop_early: bool = False,
|
|
output_fmt: str = 'NCHW',
|
|
intermediates_only: bool = False,
|
|
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
|
|
""" Forward features that returns intermediates.
|
|
|
|
Args:
|
|
x: Input image tensor
|
|
indices: Take last n blocks if int, all if None, select matching indices if sequence
|
|
norm: Apply norm layer to compatible intermediates
|
|
stop_early: Stop iterating over blocks when last desired intermediate hit
|
|
output_fmt: Shape of intermediate feature outputs
|
|
intermediates_only: Only return intermediate features
|
|
Returns:
|
|
|
|
"""
|
|
assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
|
|
intermediates = []
|
|
take_indices, max_index = feature_take_indices(len(self.layers), indices)
|
|
|
|
# forward pass
|
|
x = self.patch_embed(x)
|
|
|
|
num_stages = len(self.layers)
|
|
if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
|
|
stages = self.layers
|
|
else:
|
|
stages = self.layers[:max_index + 1]
|
|
for i, stage in enumerate(stages):
|
|
x = stage(x)
|
|
if i in take_indices:
|
|
if norm and i == num_stages - 1:
|
|
x_inter = self.norm(x) # applying final norm last intermediate
|
|
else:
|
|
x_inter = x
|
|
x_inter = x_inter.permute(0, 3, 1, 2).contiguous()
|
|
intermediates.append(x_inter)
|
|
|
|
if intermediates_only:
|
|
return intermediates
|
|
|
|
x = self.norm(x)
|
|
|
|
return x, intermediates
|
|
|
|
def prune_intermediate_layers(
|
|
self,
|
|
indices: Union[int, List[int]] = 1,
|
|
prune_norm: bool = False,
|
|
prune_head: bool = True,
|
|
):
|
|
""" Prune layers not required for specified intermediates.
|
|
"""
|
|
take_indices, max_index = feature_take_indices(len(self.layers), indices)
|
|
self.layers = self.layers[:max_index + 1] # truncate blocks
|
|
if prune_norm:
|
|
self.norm = nn.Identity()
|
|
if prune_head:
|
|
self.reset_classifier(0, '')
|
|
return take_indices
|
|
|
|
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):
|
|
state_dict = state_dict.get('model', state_dict)
|
|
state_dict = state_dict.get('state_dict', state_dict)
|
|
native_checkpoint = 'head.fc.weight' in state_dict
|
|
out_dict = {}
|
|
import re
|
|
for k, v in state_dict.items():
|
|
if any([n in k for n in ('relative_position_index', 'relative_coords_table', 'attn_mask')]):
|
|
continue # skip buffers that should not be persistent
|
|
|
|
if 'patch_embed.proj.weight' in k:
|
|
_, _, H, W = model.patch_embed.proj.weight.shape
|
|
if v.shape[-2] != H or v.shape[-1] != W:
|
|
v = resample_patch_embed(
|
|
v,
|
|
(H, W),
|
|
interpolation='bicubic',
|
|
antialias=True,
|
|
verbose=True,
|
|
)
|
|
|
|
if not native_checkpoint:
|
|
# skip layer remapping for updated checkpoints
|
|
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_v2(variant, pretrained=False, **kwargs):
|
|
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 1, 1))))
|
|
out_indices = kwargs.pop('out_indices', default_out_indices)
|
|
|
|
model = build_model_with_cfg(
|
|
SwinTransformerV2, 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, 256, 256), 'pool_size': (8, 8),
|
|
'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({
|
|
'swinv2_base_window12to16_192to256.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth',
|
|
),
|
|
'swinv2_base_window12to24_192to384.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
|
|
),
|
|
'swinv2_large_window12to16_192to256.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth',
|
|
),
|
|
'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0,
|
|
),
|
|
|
|
'swinv2_tiny_window8_256.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth',
|
|
),
|
|
'swinv2_tiny_window16_256.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth',
|
|
),
|
|
'swinv2_small_window8_256.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth',
|
|
),
|
|
'swinv2_small_window16_256.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth',
|
|
),
|
|
'swinv2_base_window8_256.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth',
|
|
),
|
|
'swinv2_base_window16_256.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth',
|
|
),
|
|
|
|
'swinv2_base_window12_192.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth',
|
|
num_classes=21841, input_size=(3, 192, 192), pool_size=(6, 6)
|
|
),
|
|
'swinv2_large_window12_192.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth',
|
|
num_classes=21841, input_size=(3, 192, 192), pool_size=(6, 6)
|
|
),
|
|
})
|
|
|
|
|
|
@register_model
|
|
def swinv2_tiny_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=16, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_tiny_window16_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_tiny_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=8, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_tiny_window8_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_small_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=16, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_small_window16_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_small_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=8, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_small_window8_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_base_window16_256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_base_window16_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_base_window8_256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=8, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_base_window8_256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_base_window12_192(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_base_window12_192', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_base_window12to16_192to256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(
|
|
window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
|
|
pretrained_window_sizes=(12, 12, 12, 6))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_base_window12to16_192to256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_base_window12to24_192to384(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(
|
|
window_size=24, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
|
|
pretrained_window_sizes=(12, 12, 12, 6))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_base_window12to24_192to384', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_large_window12_192(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_large_window12_192', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_large_window12to16_192to256(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(
|
|
window_size=16, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
|
|
pretrained_window_sizes=(12, 12, 12, 6))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_large_window12to16_192to256', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swinv2_large_window12to24_192to384(pretrained=False, **kwargs) -> SwinTransformerV2:
|
|
"""
|
|
"""
|
|
model_args = dict(
|
|
window_size=24, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
|
|
pretrained_window_sizes=(12, 12, 12, 6))
|
|
return _create_swin_transformer_v2(
|
|
'swinv2_large_window12to24_192to384', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
register_model_deprecations(__name__, {
|
|
'swinv2_base_window12_192_22k': 'swinv2_base_window12_192.ms_in22k',
|
|
'swinv2_base_window12to16_192to256_22kft1k': 'swinv2_base_window12to16_192to256.ms_in22k_ft_in1k',
|
|
'swinv2_base_window12to24_192to384_22kft1k': 'swinv2_base_window12to24_192to384.ms_in22k_ft_in1k',
|
|
'swinv2_large_window12_192_22k': 'swinv2_large_window12_192.ms_in22k',
|
|
'swinv2_large_window12to16_192to256_22kft1k': 'swinv2_large_window12to16_192to256.ms_in22k_ft_in1k',
|
|
'swinv2_large_window12to24_192to384_22kft1k': 'swinv2_large_window12to24_192to384.ms_in22k_ft_in1k',
|
|
})
|