Source code for torchvision.models.swin_transformer
import math
from functools import partial
from typing import Any, Callable, List, Optional
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from ..ops.misc import MLP, Permute
from ..ops.stochastic_depth import StochasticDepth
from ..transforms._presets import ImageClassification, InterpolationMode
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_named_param, handle_legacy_interface
__all__ = [
"SwinTransformer",
"Swin_T_Weights",
"Swin_S_Weights",
"Swin_B_Weights",
"Swin_V2_T_Weights",
"Swin_V2_S_Weights",
"Swin_V2_B_Weights",
"swin_t",
"swin_s",
"swin_b",
"swin_v2_t",
"swin_v2_s",
"swin_v2_b",
]
def _patch_merging_pad(x: torch.Tensor) -> torch.Tensor:
H, W, _ = x.shape[-3:]
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
x0 = x[..., 0::2, 0::2, :] # ... H/2 W/2 C
x1 = x[..., 1::2, 0::2, :] # ... H/2 W/2 C
x2 = x[..., 0::2, 1::2, :] # ... H/2 W/2 C
x3 = x[..., 1::2, 1::2, :] # ... H/2 W/2 C
x = torch.cat([x0, x1, x2, x3], -1) # ... H/2 W/2 4*C
return x
torch.fx.wrap("_patch_merging_pad")
def _get_relative_position_bias(
relative_position_bias_table: torch.Tensor, relative_position_index: torch.Tensor, window_size: List[int]
) -> torch.Tensor:
N = window_size[0] * window_size[1]
relative_position_bias = relative_position_bias_table[relative_position_index] # type: ignore[index]
relative_position_bias = relative_position_bias.view(N, N, -1)
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous().unsqueeze(0)
return relative_position_bias
torch.fx.wrap("_get_relative_position_bias")
class PatchMerging(nn.Module):
"""Patch Merging Layer.
Args:
dim (int): Number of input channels.
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
"""
def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm):
super().__init__()
_log_api_usage_once(self)
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(4 * dim)
def forward(self, x: Tensor):
"""
Args:
x (Tensor): input tensor with expected layout of [..., H, W, C]
Returns:
Tensor with layout of [..., H/2, W/2, 2*C]
"""
x = _patch_merging_pad(x)
x = self.norm(x)
x = self.reduction(x) # ... H/2 W/2 2*C
return x
class PatchMergingV2(nn.Module):
"""Patch Merging Layer for Swin Transformer V2.
Args:
dim (int): Number of input channels.
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
"""
def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm):
super().__init__()
_log_api_usage_once(self)
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(2 * dim) # difference
def forward(self, x: Tensor):
"""
Args:
x (Tensor): input tensor with expected layout of [..., H, W, C]
Returns:
Tensor with layout of [..., H/2, W/2, 2*C]
"""
x = _patch_merging_pad(x)
x = self.reduction(x) # ... H/2 W/2 2*C
x = self.norm(x)
return x
def shifted_window_attention(
input: Tensor,
qkv_weight: Tensor,
proj_weight: Tensor,
relative_position_bias: Tensor,
window_size: List[int],
num_heads: int,
shift_size: List[int],
attention_dropout: float = 0.0,
dropout: float = 0.0,
qkv_bias: Optional[Tensor] = None,
proj_bias: Optional[Tensor] = None,
logit_scale: Optional[torch.Tensor] = None,
training: bool = True,
) -> Tensor:
"""
Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
input (Tensor[N, H, W, C]): The input tensor or 4-dimensions.
qkv_weight (Tensor[in_dim, out_dim]): The weight tensor of query, key, value.
proj_weight (Tensor[out_dim, out_dim]): The weight tensor of projection.
relative_position_bias (Tensor): The learned relative position bias added to attention.
window_size (List[int]): Window size.
num_heads (int): Number of attention heads.
shift_size (List[int]): Shift size for shifted window attention.
attention_dropout (float): Dropout ratio of attention weight. Default: 0.0.
dropout (float): Dropout ratio of output. Default: 0.0.
qkv_bias (Tensor[out_dim], optional): The bias tensor of query, key, value. Default: None.
proj_bias (Tensor[out_dim], optional): The bias tensor of projection. Default: None.
logit_scale (Tensor[out_dim], optional): Logit scale of cosine attention for Swin Transformer V2. Default: None.
training (bool, optional): Training flag used by the dropout parameters. Default: True.
Returns:
Tensor[N, H, W, C]: The output tensor after shifted window attention.
"""
B, H, W, C = input.shape
# pad feature maps to multiples of window size
pad_r = (window_size[1] - W % window_size[1]) % window_size[1]
pad_b = (window_size[0] - H % window_size[0]) % window_size[0]
x = F.pad(input, (0, 0, 0, pad_r, 0, pad_b))
_, pad_H, pad_W, _ = x.shape
shift_size = shift_size.copy()
# If window size is larger than feature size, there is no need to shift window
if window_size[0] >= pad_H:
shift_size[0] = 0
if window_size[1] >= pad_W:
shift_size[1] = 0
# cyclic shift
if sum(shift_size) > 0:
x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1]), dims=(1, 2))
# partition windows
num_windows = (pad_H // window_size[0]) * (pad_W // window_size[1])
x = x.view(B, pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).reshape(B * num_windows, window_size[0] * window_size[1], C) # B*nW, Ws*Ws, C
# multi-head attention
if logit_scale is not None and qkv_bias is not None:
qkv_bias = qkv_bias.clone()
length = qkv_bias.numel() // 3
qkv_bias[length : 2 * length].zero_()
qkv = F.linear(x, qkv_weight, qkv_bias)
qkv = qkv.reshape(x.size(0), x.size(1), 3, num_heads, C // num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
if logit_scale is not None:
# cosine attention
attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)
logit_scale = torch.clamp(logit_scale, max=math.log(100.0)).exp()
attn = attn * logit_scale
else:
q = q * (C // num_heads) ** -0.5
attn = q.matmul(k.transpose(-2, -1))
# add relative position bias
attn = attn + relative_position_bias
if sum(shift_size) > 0:
# generate attention mask
attn_mask = x.new_zeros((pad_H, pad_W))
h_slices = ((0, -window_size[0]), (-window_size[0], -shift_size[0]), (-shift_size[0], None))
w_slices = ((0, -window_size[1]), (-window_size[1], -shift_size[1]), (-shift_size[1], None))
count = 0
for h in h_slices:
for w in w_slices:
attn_mask[h[0] : h[1], w[0] : w[1]] = count
count += 1
attn_mask = attn_mask.view(pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1])
attn_mask = attn_mask.permute(0, 2, 1, 3).reshape(num_windows, window_size[0] * window_size[1])
attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
attn = attn.view(x.size(0) // num_windows, num_windows, num_heads, x.size(1), x.size(1))
attn = attn + attn_mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, num_heads, x.size(1), x.size(1))
attn = F.softmax(attn, dim=-1)
attn = F.dropout(attn, p=attention_dropout, training=training)
x = attn.matmul(v).transpose(1, 2).reshape(x.size(0), x.size(1), C)
x = F.linear(x, proj_weight, proj_bias)
x = F.dropout(x, p=dropout, training=training)
# reverse windows
x = x.view(B, pad_H // window_size[0], pad_W // window_size[1], window_size[0], window_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, pad_H, pad_W, C)
# reverse cyclic shift
if sum(shift_size) > 0:
x = torch.roll(x, shifts=(shift_size[0], shift_size[1]), dims=(1, 2))
# unpad features
x = x[:, :H, :W, :].contiguous()
return x
torch.fx.wrap("shifted_window_attention")
class ShiftedWindowAttention(nn.Module):
"""
See :func:`shifted_window_attention`.
"""
def __init__(
self,
dim: int,
window_size: List[int],
shift_size: List[int],
num_heads: int,
qkv_bias: bool = True,
proj_bias: bool = True,
attention_dropout: float = 0.0,
dropout: float = 0.0,
):
super().__init__()
if len(window_size) != 2 or len(shift_size) != 2:
raise ValueError("window_size and shift_size must be of length 2")
self.window_size = window_size
self.shift_size = shift_size
self.num_heads = num_heads
self.attention_dropout = attention_dropout
self.dropout = dropout
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.define_relative_position_bias_table()
self.define_relative_position_index()
def define_relative_position_bias_table(self):
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), self.num_heads)
) # 2*Wh-1 * 2*Ww-1, nH
nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02)
def define_relative_position_index(self):
# 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(torch.meshgrid(coords_h, coords_w, indexing="ij")) # 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).flatten() # Wh*Ww*Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
def get_relative_position_bias(self) -> torch.Tensor:
return _get_relative_position_bias(
self.relative_position_bias_table, self.relative_position_index, self.window_size # type: ignore[arg-type]
)
def forward(self, x: Tensor) -> Tensor:
"""
Args:
x (Tensor): Tensor with layout of [B, H, W, C]
Returns:
Tensor with same layout as input, i.e. [B, H, W, C]
"""
relative_position_bias = self.get_relative_position_bias()
return shifted_window_attention(
x,
self.qkv.weight,
self.proj.weight,
relative_position_bias,
self.window_size,
self.num_heads,
shift_size=self.shift_size,
attention_dropout=self.attention_dropout,
dropout=self.dropout,
qkv_bias=self.qkv.bias,
proj_bias=self.proj.bias,
training=self.training,
)
class ShiftedWindowAttentionV2(ShiftedWindowAttention):
"""
See :func:`shifted_window_attention_v2`.
"""
def __init__(
self,
dim: int,
window_size: List[int],
shift_size: List[int],
num_heads: int,
qkv_bias: bool = True,
proj_bias: bool = True,
attention_dropout: float = 0.0,
dropout: float = 0.0,
):
super().__init__(
dim,
window_size,
shift_size,
num_heads,
qkv_bias=qkv_bias,
proj_bias=proj_bias,
attention_dropout=attention_dropout,
dropout=dropout,
)
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)
)
if qkv_bias:
length = self.qkv.bias.numel() // 3
self.qkv.bias[length : 2 * length].data.zero_()
def define_relative_position_bias_table(self):
# get relative_coords_table
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w], indexing="ij"))
relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
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) / 3.0
)
self.register_buffer("relative_coords_table", relative_coords_table)
def get_relative_position_bias(self) -> torch.Tensor:
relative_position_bias = _get_relative_position_bias(
self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads),
self.relative_position_index, # type: ignore[arg-type]
self.window_size,
)
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
return relative_position_bias
def forward(self, x: Tensor):
"""
Args:
x (Tensor): Tensor with layout of [B, H, W, C]
Returns:
Tensor with same layout as input, i.e. [B, H, W, C]
"""
relative_position_bias = self.get_relative_position_bias()
return shifted_window_attention(
x,
self.qkv.weight,
self.proj.weight,
relative_position_bias,
self.window_size,
self.num_heads,
shift_size=self.shift_size,
attention_dropout=self.attention_dropout,
dropout=self.dropout,
qkv_bias=self.qkv.bias,
proj_bias=self.proj.bias,
logit_scale=self.logit_scale,
training=self.training,
)
class SwinTransformerBlock(nn.Module):
"""
Swin Transformer Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (List[int]): Window size.
shift_size (List[int]): Shift size for shifted window attention.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
dropout (float): Dropout rate. Default: 0.0.
attention_dropout (float): Attention dropout rate. Default: 0.0.
stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0.
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
attn_layer (nn.Module): Attention layer. Default: ShiftedWindowAttention
"""
def __init__(
self,
dim: int,
num_heads: int,
window_size: List[int],
shift_size: List[int],
mlp_ratio: float = 4.0,
dropout: float = 0.0,
attention_dropout: float = 0.0,
stochastic_depth_prob: float = 0.0,
norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
attn_layer: Callable[..., nn.Module] = ShiftedWindowAttention,
):
super().__init__()
_log_api_usage_once(self)
self.norm1 = norm_layer(dim)
self.attn = attn_layer(
dim,
window_size,
shift_size,
num_heads,
attention_dropout=attention_dropout,
dropout=dropout,
)
self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row")
self.norm2 = norm_layer(dim)
self.mlp = MLP(dim, [int(dim * mlp_ratio), dim], activation_layer=nn.GELU, inplace=None, dropout=dropout)
for m in self.mlp.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.normal_(m.bias, std=1e-6)
def forward(self, x: Tensor):
x = x + self.stochastic_depth(self.attn(self.norm1(x)))
x = x + self.stochastic_depth(self.mlp(self.norm2(x)))
return x
class SwinTransformerBlockV2(SwinTransformerBlock):
"""
Swin Transformer V2 Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (List[int]): Window size.
shift_size (List[int]): Shift size for shifted window attention.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
dropout (float): Dropout rate. Default: 0.0.
attention_dropout (float): Attention dropout rate. Default: 0.0.
stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0.
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
attn_layer (nn.Module): Attention layer. Default: ShiftedWindowAttentionV2.
"""
def __init__(
self,
dim: int,
num_heads: int,
window_size: List[int],
shift_size: List[int],
mlp_ratio: float = 4.0,
dropout: float = 0.0,
attention_dropout: float = 0.0,
stochastic_depth_prob: float = 0.0,
norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
attn_layer: Callable[..., nn.Module] = ShiftedWindowAttentionV2,
):
super().__init__(
dim,
num_heads,
window_size,
shift_size,
mlp_ratio=mlp_ratio,
dropout=dropout,
attention_dropout=attention_dropout,
stochastic_depth_prob=stochastic_depth_prob,
norm_layer=norm_layer,
attn_layer=attn_layer,
)
def forward(self, x: Tensor):
# Here is the difference, we apply norm after the attention in V2.
# In V1 we applied norm before the attention.
x = x + self.stochastic_depth(self.norm1(self.attn(x)))
x = x + self.stochastic_depth(self.norm2(self.mlp(x)))
return x
class SwinTransformer(nn.Module):
"""
Implements Swin Transformer from the `"Swin Transformer: Hierarchical Vision Transformer using
Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_ paper.
Args:
patch_size (List[int]): Patch size.
embed_dim (int): Patch embedding dimension.
depths (List(int)): Depth of each Swin Transformer layer.
num_heads (List(int)): Number of attention heads in different layers.
window_size (List[int]): Window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
dropout (float): Dropout rate. Default: 0.0.
attention_dropout (float): Attention dropout rate. Default: 0.0.
stochastic_depth_prob (float): Stochastic depth rate. Default: 0.1.
num_classes (int): Number of classes for classification head. Default: 1000.
block (nn.Module, optional): SwinTransformer Block. Default: None.
norm_layer (nn.Module, optional): Normalization layer. Default: None.
downsample_layer (nn.Module): Downsample layer (patch merging). Default: PatchMerging.
"""
def __init__(
self,
patch_size: List[int],
embed_dim: int,
depths: List[int],
num_heads: List[int],
window_size: List[int],
mlp_ratio: float = 4.0,
dropout: float = 0.0,
attention_dropout: float = 0.0,
stochastic_depth_prob: float = 0.1,
num_classes: int = 1000,
norm_layer: Optional[Callable[..., nn.Module]] = None,
block: Optional[Callable[..., nn.Module]] = None,
downsample_layer: Callable[..., nn.Module] = PatchMerging,
):
super().__init__()
_log_api_usage_once(self)
self.num_classes = num_classes
if block is None:
block = SwinTransformerBlock
if norm_layer is None:
norm_layer = partial(nn.LayerNorm, eps=1e-5)
layers: List[nn.Module] = []
# split image into non-overlapping patches
layers.append(
nn.Sequential(
nn.Conv2d(
3, embed_dim, kernel_size=(patch_size[0], patch_size[1]), stride=(patch_size[0], patch_size[1])
),
Permute([0, 2, 3, 1]),
norm_layer(embed_dim),
)
)
total_stage_blocks = sum(depths)
stage_block_id = 0
# build SwinTransformer blocks
for i_stage in range(len(depths)):
stage: List[nn.Module] = []
dim = embed_dim * 2**i_stage
for i_layer in range(depths[i_stage]):
# adjust stochastic depth probability based on the depth of the stage block
sd_prob = stochastic_depth_prob * float(stage_block_id) / (total_stage_blocks - 1)
stage.append(
block(
dim,
num_heads[i_stage],
window_size=window_size,
shift_size=[0 if i_layer % 2 == 0 else w // 2 for w in window_size],
mlp_ratio=mlp_ratio,
dropout=dropout,
attention_dropout=attention_dropout,
stochastic_depth_prob=sd_prob,
norm_layer=norm_layer,
)
)
stage_block_id += 1
layers.append(nn.Sequential(*stage))
# add patch merging layer
if i_stage < (len(depths) - 1):
layers.append(downsample_layer(dim, norm_layer))
self.features = nn.Sequential(*layers)
num_features = embed_dim * 2 ** (len(depths) - 1)
self.norm = norm_layer(num_features)
self.permute = Permute([0, 3, 1, 2]) # B H W C -> B C H W
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.flatten = nn.Flatten(1)
self.head = nn.Linear(num_features, num_classes)
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.zeros_(m.bias)
def forward(self, x):
x = self.features(x)
x = self.norm(x)
x = self.permute(x)
x = self.avgpool(x)
x = self.flatten(x)
x = self.head(x)
return x
def _swin_transformer(
patch_size: List[int],
embed_dim: int,
depths: List[int],
num_heads: List[int],
window_size: List[int],
stochastic_depth_prob: float,
weights: Optional[WeightsEnum],
progress: bool,
**kwargs: Any,
) -> SwinTransformer:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = SwinTransformer(
patch_size=patch_size,
embed_dim=embed_dim,
depths=depths,
num_heads=num_heads,
window_size=window_size,
stochastic_depth_prob=stochastic_depth_prob,
**kwargs,
)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress))
return model
_COMMON_META = {
"categories": _IMAGENET_CATEGORIES,
}
[docs]class Swin_T_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/swin_t-704ceda3.pth",
transforms=partial(
ImageClassification, crop_size=224, resize_size=232, interpolation=InterpolationMode.BICUBIC
),
meta={
**_COMMON_META,
"num_params": 28288354,
"min_size": (224, 224),
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer",
"_metrics": {
"ImageNet-1K": {
"acc@1": 81.474,
"acc@5": 95.776,
}
},
"_ops": 4.491,
"_file_size": 108.19,
"_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
[docs]class Swin_S_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/swin_s-5e29d889.pth",
transforms=partial(
ImageClassification, crop_size=224, resize_size=246, interpolation=InterpolationMode.BICUBIC
),
meta={
**_COMMON_META,
"num_params": 49606258,
"min_size": (224, 224),
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer",
"_metrics": {
"ImageNet-1K": {
"acc@1": 83.196,
"acc@5": 96.360,
}
},
"_ops": 8.741,
"_file_size": 189.786,
"_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
[docs]class Swin_B_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/swin_b-68c6b09e.pth",
transforms=partial(
ImageClassification, crop_size=224, resize_size=238, interpolation=InterpolationMode.BICUBIC
),
meta={
**_COMMON_META,
"num_params": 87768224,
"min_size": (224, 224),
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer",
"_metrics": {
"ImageNet-1K": {
"acc@1": 83.582,
"acc@5": 96.640,
}
},
"_ops": 15.431,
"_file_size": 335.364,
"_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
[docs]class Swin_V2_T_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/swin_v2_t-b137f0e2.pth",
transforms=partial(
ImageClassification, crop_size=256, resize_size=260, interpolation=InterpolationMode.BICUBIC
),
meta={
**_COMMON_META,
"num_params": 28351570,
"min_size": (256, 256),
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2",
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.072,
"acc@5": 96.132,
}
},
"_ops": 5.94,
"_file_size": 108.626,
"_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
[docs]class Swin_V2_S_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/swin_v2_s-637d8ceb.pth",
transforms=partial(
ImageClassification, crop_size=256, resize_size=260, interpolation=InterpolationMode.BICUBIC
),
meta={
**_COMMON_META,
"num_params": 49737442,
"min_size": (256, 256),
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2",
"_metrics": {
"ImageNet-1K": {
"acc@1": 83.712,
"acc@5": 96.816,
}
},
"_ops": 11.546,
"_file_size": 190.675,
"_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
[docs]class Swin_V2_B_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/swin_v2_b-781e5279.pth",
transforms=partial(
ImageClassification, crop_size=256, resize_size=272, interpolation=InterpolationMode.BICUBIC
),
meta={
**_COMMON_META,
"num_params": 87930848,
"min_size": (256, 256),
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#swintransformer-v2",
"_metrics": {
"ImageNet-1K": {
"acc@1": 84.112,
"acc@5": 96.864,
}
},
"_ops": 20.325,
"_file_size": 336.372,
"_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin_T_Weights.IMAGENET1K_V1))
def swin_t(*, weights: Optional[Swin_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer:
"""
Constructs a swin_tiny architecture from
`Swin Transformer: Hierarchical Vision Transformer using Shifted Windows <https://arxiv.org/pdf/2103.14030>`_.
Args:
weights (:class:`~torchvision.models.Swin_T_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.Swin_T_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.Swin_T_Weights
:members:
"""
weights = Swin_T_Weights.verify(weights)
return _swin_transformer(
patch_size=[4, 4],
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=[7, 7],
stochastic_depth_prob=0.2,
weights=weights,
progress=progress,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin_S_Weights.IMAGENET1K_V1))
def swin_s(*, weights: Optional[Swin_S_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer:
"""
Constructs a swin_small architecture from
`Swin Transformer: Hierarchical Vision Transformer using Shifted Windows <https://arxiv.org/pdf/2103.14030>`_.
Args:
weights (:class:`~torchvision.models.Swin_S_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.Swin_S_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.Swin_S_Weights
:members:
"""
weights = Swin_S_Weights.verify(weights)
return _swin_transformer(
patch_size=[4, 4],
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=[7, 7],
stochastic_depth_prob=0.3,
weights=weights,
progress=progress,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin_B_Weights.IMAGENET1K_V1))
def swin_b(*, weights: Optional[Swin_B_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer:
"""
Constructs a swin_base architecture from
`Swin Transformer: Hierarchical Vision Transformer using Shifted Windows <https://arxiv.org/pdf/2103.14030>`_.
Args:
weights (:class:`~torchvision.models.Swin_B_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.Swin_B_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.Swin_B_Weights
:members:
"""
weights = Swin_B_Weights.verify(weights)
return _swin_transformer(
patch_size=[4, 4],
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=[7, 7],
stochastic_depth_prob=0.5,
weights=weights,
progress=progress,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin_V2_T_Weights.IMAGENET1K_V1))
def swin_v2_t(*, weights: Optional[Swin_V2_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer:
"""
Constructs a swin_v2_tiny architecture from
`Swin Transformer V2: Scaling Up Capacity and Resolution <https://arxiv.org/pdf/2111.09883>`_.
Args:
weights (:class:`~torchvision.models.Swin_V2_T_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.Swin_V2_T_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.Swin_V2_T_Weights
:members:
"""
weights = Swin_V2_T_Weights.verify(weights)
return _swin_transformer(
patch_size=[4, 4],
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=[8, 8],
stochastic_depth_prob=0.2,
weights=weights,
progress=progress,
block=SwinTransformerBlockV2,
downsample_layer=PatchMergingV2,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin_V2_S_Weights.IMAGENET1K_V1))
def swin_v2_s(*, weights: Optional[Swin_V2_S_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer:
"""
Constructs a swin_v2_small architecture from
`Swin Transformer V2: Scaling Up Capacity and Resolution <https://arxiv.org/pdf/2111.09883>`_.
Args:
weights (:class:`~torchvision.models.Swin_V2_S_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.Swin_V2_S_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.Swin_V2_S_Weights
:members:
"""
weights = Swin_V2_S_Weights.verify(weights)
return _swin_transformer(
patch_size=[4, 4],
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=[8, 8],
stochastic_depth_prob=0.3,
weights=weights,
progress=progress,
block=SwinTransformerBlockV2,
downsample_layer=PatchMergingV2,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin_V2_B_Weights.IMAGENET1K_V1))
def swin_v2_b(*, weights: Optional[Swin_V2_B_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer:
"""
Constructs a swin_v2_base architecture from
`Swin Transformer V2: Scaling Up Capacity and Resolution <https://arxiv.org/pdf/2111.09883>`_.
Args:
weights (:class:`~torchvision.models.Swin_V2_B_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.Swin_V2_B_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.Swin_V2_B_Weights
:members:
"""
weights = Swin_V2_B_Weights.verify(weights)
return _swin_transformer(
patch_size=[4, 4],
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=[8, 8],
stochastic_depth_prob=0.5,
weights=weights,
progress=progress,
block=SwinTransformerBlockV2,
downsample_layer=PatchMergingV2,
**kwargs,
)
