1033 lines
38 KiB
Python
1033 lines
38 KiB
Python
import math
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from functools import partial
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from typing import Any, Callable, List, Optional
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import torch
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import torch.nn.functional as F
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from torch import nn, Tensor
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from ..ops.misc import MLP, Permute
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from ..ops.stochastic_depth import StochasticDepth
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from ..transforms._presets import ImageClassification, InterpolationMode
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from ..utils import _log_api_usage_once
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from ._api import register_model, Weights, WeightsEnum
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from ._meta import _IMAGENET_CATEGORIES
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from ._utils import _ovewrite_named_param, handle_legacy_interface
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__all__ = [
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"SwinTransformer",
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"Swin_T_Weights",
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"Swin_S_Weights",
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"Swin_B_Weights",
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"Swin_V2_T_Weights",
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"Swin_V2_S_Weights",
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"Swin_V2_B_Weights",
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"swin_t",
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"swin_s",
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"swin_b",
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"swin_v2_t",
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"swin_v2_s",
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"swin_v2_b",
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]
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def _patch_merging_pad(x: torch.Tensor) -> torch.Tensor:
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H, W, _ = x.shape[-3:]
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x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
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x0 = x[..., 0::2, 0::2, :] # ... H/2 W/2 C
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x1 = x[..., 1::2, 0::2, :] # ... H/2 W/2 C
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x2 = x[..., 0::2, 1::2, :] # ... H/2 W/2 C
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x3 = x[..., 1::2, 1::2, :] # ... H/2 W/2 C
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x = torch.cat([x0, x1, x2, x3], -1) # ... H/2 W/2 4*C
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return x
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torch.fx.wrap("_patch_merging_pad")
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def _get_relative_position_bias(
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relative_position_bias_table: torch.Tensor, relative_position_index: torch.Tensor, window_size: List[int]
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) -> torch.Tensor:
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N = window_size[0] * window_size[1]
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relative_position_bias = relative_position_bias_table[relative_position_index] # type: ignore[index]
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relative_position_bias = relative_position_bias.view(N, N, -1)
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous().unsqueeze(0)
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return relative_position_bias
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torch.fx.wrap("_get_relative_position_bias")
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class PatchMerging(nn.Module):
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"""Patch Merging Layer.
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Args:
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dim (int): Number of input channels.
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norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
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"""
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def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm):
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super().__init__()
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_log_api_usage_once(self)
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self.dim = dim
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
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self.norm = norm_layer(4 * dim)
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def forward(self, x: Tensor):
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"""
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Args:
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x (Tensor): input tensor with expected layout of [..., H, W, C]
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Returns:
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Tensor with layout of [..., H/2, W/2, 2*C]
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"""
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x = _patch_merging_pad(x)
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x = self.norm(x)
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x = self.reduction(x) # ... H/2 W/2 2*C
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return x
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class PatchMergingV2(nn.Module):
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"""Patch Merging Layer for Swin Transformer V2.
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Args:
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dim (int): Number of input channels.
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norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
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"""
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def __init__(self, dim: int, norm_layer: Callable[..., nn.Module] = nn.LayerNorm):
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super().__init__()
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_log_api_usage_once(self)
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self.dim = dim
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
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self.norm = norm_layer(2 * dim) # difference
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def forward(self, x: Tensor):
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"""
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Args:
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x (Tensor): input tensor with expected layout of [..., H, W, C]
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Returns:
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Tensor with layout of [..., H/2, W/2, 2*C]
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"""
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x = _patch_merging_pad(x)
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x = self.reduction(x) # ... H/2 W/2 2*C
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x = self.norm(x)
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return x
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def shifted_window_attention(
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input: Tensor,
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qkv_weight: Tensor,
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proj_weight: Tensor,
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relative_position_bias: Tensor,
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window_size: List[int],
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num_heads: int,
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shift_size: List[int],
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attention_dropout: float = 0.0,
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dropout: float = 0.0,
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qkv_bias: Optional[Tensor] = None,
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proj_bias: Optional[Tensor] = None,
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logit_scale: Optional[torch.Tensor] = None,
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training: bool = True,
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) -> Tensor:
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"""
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Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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input (Tensor[N, H, W, C]): The input tensor or 4-dimensions.
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qkv_weight (Tensor[in_dim, out_dim]): The weight tensor of query, key, value.
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proj_weight (Tensor[out_dim, out_dim]): The weight tensor of projection.
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relative_position_bias (Tensor): The learned relative position bias added to attention.
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window_size (List[int]): Window size.
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num_heads (int): Number of attention heads.
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shift_size (List[int]): Shift size for shifted window attention.
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attention_dropout (float): Dropout ratio of attention weight. Default: 0.0.
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dropout (float): Dropout ratio of output. Default: 0.0.
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qkv_bias (Tensor[out_dim], optional): The bias tensor of query, key, value. Default: None.
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proj_bias (Tensor[out_dim], optional): The bias tensor of projection. Default: None.
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logit_scale (Tensor[out_dim], optional): Logit scale of cosine attention for Swin Transformer V2. Default: None.
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training (bool, optional): Training flag used by the dropout parameters. Default: True.
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Returns:
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Tensor[N, H, W, C]: The output tensor after shifted window attention.
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"""
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B, H, W, C = input.shape
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# pad feature maps to multiples of window size
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pad_r = (window_size[1] - W % window_size[1]) % window_size[1]
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pad_b = (window_size[0] - H % window_size[0]) % window_size[0]
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x = F.pad(input, (0, 0, 0, pad_r, 0, pad_b))
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_, pad_H, pad_W, _ = x.shape
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shift_size = shift_size.copy()
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# If window size is larger than feature size, there is no need to shift window
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if window_size[0] >= pad_H:
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shift_size[0] = 0
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if window_size[1] >= pad_W:
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shift_size[1] = 0
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# cyclic shift
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if sum(shift_size) > 0:
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x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1]), dims=(1, 2))
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# partition windows
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num_windows = (pad_H // window_size[0]) * (pad_W // window_size[1])
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x = x.view(B, pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1], C)
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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
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# multi-head attention
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if logit_scale is not None and qkv_bias is not None:
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qkv_bias = qkv_bias.clone()
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length = qkv_bias.numel() // 3
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qkv_bias[length : 2 * length].zero_()
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qkv = F.linear(x, qkv_weight, qkv_bias)
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qkv = qkv.reshape(x.size(0), x.size(1), 3, num_heads, C // num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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if logit_scale is not None:
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# cosine attention
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attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)
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logit_scale = torch.clamp(logit_scale, max=math.log(100.0)).exp()
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attn = attn * logit_scale
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else:
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q = q * (C // num_heads) ** -0.5
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attn = q.matmul(k.transpose(-2, -1))
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# add relative position bias
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attn = attn + relative_position_bias
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if sum(shift_size) > 0:
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# generate attention mask
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attn_mask = x.new_zeros((pad_H, pad_W))
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h_slices = ((0, -window_size[0]), (-window_size[0], -shift_size[0]), (-shift_size[0], None))
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w_slices = ((0, -window_size[1]), (-window_size[1], -shift_size[1]), (-shift_size[1], None))
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count = 0
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for h in h_slices:
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for w in w_slices:
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attn_mask[h[0] : h[1], w[0] : w[1]] = count
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count += 1
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attn_mask = attn_mask.view(pad_H // window_size[0], window_size[0], pad_W // window_size[1], window_size[1])
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attn_mask = attn_mask.permute(0, 2, 1, 3).reshape(num_windows, window_size[0] * window_size[1])
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attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2)
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attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
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attn = attn.view(x.size(0) // num_windows, num_windows, num_heads, x.size(1), x.size(1))
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attn = attn + attn_mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, num_heads, x.size(1), x.size(1))
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attn = F.softmax(attn, dim=-1)
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attn = F.dropout(attn, p=attention_dropout, training=training)
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x = attn.matmul(v).transpose(1, 2).reshape(x.size(0), x.size(1), C)
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x = F.linear(x, proj_weight, proj_bias)
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x = F.dropout(x, p=dropout, training=training)
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# reverse windows
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x = x.view(B, pad_H // window_size[0], pad_W // window_size[1], window_size[0], window_size[1], C)
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x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, pad_H, pad_W, C)
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# reverse cyclic shift
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if sum(shift_size) > 0:
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x = torch.roll(x, shifts=(shift_size[0], shift_size[1]), dims=(1, 2))
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# unpad features
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x = x[:, :H, :W, :].contiguous()
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return x
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torch.fx.wrap("shifted_window_attention")
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class ShiftedWindowAttention(nn.Module):
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"""
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See :func:`shifted_window_attention`.
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"""
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def __init__(
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self,
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dim: int,
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window_size: List[int],
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shift_size: List[int],
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num_heads: int,
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qkv_bias: bool = True,
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proj_bias: bool = True,
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attention_dropout: float = 0.0,
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dropout: float = 0.0,
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):
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super().__init__()
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if len(window_size) != 2 or len(shift_size) != 2:
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raise ValueError("window_size and shift_size must be of length 2")
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self.window_size = window_size
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self.shift_size = shift_size
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self.num_heads = num_heads
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self.attention_dropout = attention_dropout
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self.dropout = dropout
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.proj = nn.Linear(dim, dim, bias=proj_bias)
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self.define_relative_position_bias_table()
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self.define_relative_position_index()
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def define_relative_position_bias_table(self):
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# define a parameter table of relative position bias
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), self.num_heads)
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) # 2*Wh-1 * 2*Ww-1, nH
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nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02)
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def define_relative_position_index(self):
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(self.window_size[0])
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coords_w = torch.arange(self.window_size[1])
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coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij")) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
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relative_position_index = relative_coords.sum(-1).flatten() # Wh*Ww*Wh*Ww
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self.register_buffer("relative_position_index", relative_position_index)
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def get_relative_position_bias(self) -> torch.Tensor:
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return _get_relative_position_bias(
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self.relative_position_bias_table, self.relative_position_index, self.window_size # type: ignore[arg-type]
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)
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def forward(self, x: Tensor) -> Tensor:
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"""
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Args:
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x (Tensor): Tensor with layout of [B, H, W, C]
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Returns:
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Tensor with same layout as input, i.e. [B, H, W, C]
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"""
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relative_position_bias = self.get_relative_position_bias()
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return shifted_window_attention(
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x,
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self.qkv.weight,
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self.proj.weight,
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relative_position_bias,
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self.window_size,
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self.num_heads,
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shift_size=self.shift_size,
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attention_dropout=self.attention_dropout,
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dropout=self.dropout,
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qkv_bias=self.qkv.bias,
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proj_bias=self.proj.bias,
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training=self.training,
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)
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class ShiftedWindowAttentionV2(ShiftedWindowAttention):
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"""
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See :func:`shifted_window_attention_v2`.
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"""
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def __init__(
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self,
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dim: int,
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window_size: List[int],
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shift_size: List[int],
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num_heads: int,
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qkv_bias: bool = True,
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proj_bias: bool = True,
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attention_dropout: float = 0.0,
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dropout: float = 0.0,
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):
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super().__init__(
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dim,
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window_size,
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shift_size,
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num_heads,
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qkv_bias=qkv_bias,
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proj_bias=proj_bias,
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attention_dropout=attention_dropout,
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dropout=dropout,
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)
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self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
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# mlp to generate continuous relative position bias
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self.cpb_mlp = nn.Sequential(
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nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True), nn.Linear(512, num_heads, bias=False)
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)
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if qkv_bias:
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length = self.qkv.bias.numel() // 3
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self.qkv.bias[length : 2 * length].data.zero_()
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def define_relative_position_bias_table(self):
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# get relative_coords_table
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relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
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relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
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relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w], indexing="ij"))
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relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
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relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1
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relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1
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relative_coords_table *= 8 # normalize to -8, 8
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relative_coords_table = (
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torch.sign(relative_coords_table) * torch.log2(torch.abs(relative_coords_table) + 1.0) / 3.0
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)
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self.register_buffer("relative_coords_table", relative_coords_table)
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def get_relative_position_bias(self) -> torch.Tensor:
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relative_position_bias = _get_relative_position_bias(
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self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads),
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self.relative_position_index, # type: ignore[arg-type]
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self.window_size,
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)
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relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
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return relative_position_bias
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|
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def forward(self, x: Tensor):
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"""
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Args:
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x (Tensor): Tensor with layout of [B, H, W, C]
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Returns:
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Tensor with same layout as input, i.e. [B, H, W, C]
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"""
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relative_position_bias = self.get_relative_position_bias()
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return shifted_window_attention(
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x,
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self.qkv.weight,
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self.proj.weight,
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relative_position_bias,
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self.window_size,
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self.num_heads,
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shift_size=self.shift_size,
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attention_dropout=self.attention_dropout,
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dropout=self.dropout,
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qkv_bias=self.qkv.bias,
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proj_bias=self.proj.bias,
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logit_scale=self.logit_scale,
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training=self.training,
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)
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|
|
|
|
class SwinTransformerBlock(nn.Module):
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"""
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Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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window_size (List[int]): Window size.
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shift_size (List[int]): Shift size for shifted window attention.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
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dropout (float): Dropout rate. Default: 0.0.
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attention_dropout (float): Attention dropout rate. Default: 0.0.
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stochastic_depth_prob: (float): Stochastic depth rate. Default: 0.0.
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norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
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attn_layer (nn.Module): Attention layer. Default: ShiftedWindowAttention
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"""
|
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|
|
def __init__(
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self,
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dim: int,
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num_heads: int,
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window_size: List[int],
|
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shift_size: List[int],
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mlp_ratio: float = 4.0,
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dropout: float = 0.0,
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attention_dropout: float = 0.0,
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|
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/abs/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, check_hash=True))
|
|
|
|
return model
|
|
|
|
|
|
_COMMON_META = {
|
|
"categories": _IMAGENET_CATEGORIES,
|
|
}
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
@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/abs/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,
|
|
)
|
|
|
|
|
|
@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/abs/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,
|
|
)
|
|
|
|
|
|
@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/abs/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
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|
:members:
|
|
"""
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|
weights = Swin_B_Weights.verify(weights)
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|
|
|
return _swin_transformer(
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|
patch_size=[4, 4],
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|
embed_dim=128,
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|
depths=[2, 2, 18, 2],
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|
num_heads=[4, 8, 16, 32],
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|
window_size=[7, 7],
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|
stochastic_depth_prob=0.5,
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|
weights=weights,
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|
progress=progress,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
@register_model()
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|
@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/abs/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,
|
|
)
|
|
|
|
|
|
@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/abs/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,
|
|
)
|
|
|
|
|
|
@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/abs/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,
|
|
)
|