1070 lines
42 KiB
Python
1070 lines
42 KiB
Python
""" Swin Transformer
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A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
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- https://arxiv.org/pdf/2103.14030
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Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
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S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weights from
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- https://github.com/microsoft/Cream/tree/main/AutoFormerV2
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Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
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"""
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# --------------------------------------------------------
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# Swin Transformer
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# --------------------------------------------------------
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import logging
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import math
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.layers import PatchEmbed, Mlp, DropPath, ClassifierHead, to_2tuple, to_ntuple, trunc_normal_, \
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_assert, use_fused_attn, resize_rel_pos_bias_table, resample_patch_embed, ndgrid
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from ._builder import build_model_with_cfg
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from ._features import feature_take_indices
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from ._features_fx import register_notrace_function
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from ._manipulate import checkpoint_seq, named_apply
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from ._registry import generate_default_cfgs, register_model, register_model_deprecations
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from .vision_transformer import get_init_weights_vit
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__all__ = ['SwinTransformer'] # model_registry will add each entrypoint fn to this
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_logger = logging.getLogger(__name__)
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_int_or_tuple_2_t = Union[int, Tuple[int, int]]
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def window_partition(
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x: torch.Tensor,
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window_size: Tuple[int, int],
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) -> torch.Tensor:
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"""
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Partition into non-overlapping windows with padding if needed.
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Args:
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x (tensor): input tokens with [B, H, W, C].
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window_size (int): window size.
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Returns:
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windows: windows after partition with [B * num_windows, window_size, window_size, C].
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(Hp, Wp): padded height and width before partition
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
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return windows
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@register_notrace_function # reason: int argument is a Proxy
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def window_reverse(windows, window_size: Tuple[int, int], H: int, W: int):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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C = windows.shape[-1]
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x = windows.view(-1, H // window_size[0], W // window_size[1], window_size[0], window_size[1], C)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
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return x
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def get_relative_position_index(win_h: int, win_w: int):
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# get pair-wise relative position index for each token inside the window
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coords = torch.stack(ndgrid(torch.arange(win_h), torch.arange(win_w))) # 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] += win_h - 1 # shift to start from 0
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relative_coords[:, :, 1] += win_w - 1
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relative_coords[:, :, 0] *= 2 * win_w - 1
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return relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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class WindowAttention(nn.Module):
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""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports shifted and non-shifted windows.
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"""
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fused_attn: torch.jit.Final[bool]
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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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head_dim: Optional[int] = None,
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window_size: _int_or_tuple_2_t = 7,
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qkv_bias: bool = True,
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attn_drop: float = 0.,
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proj_drop: float = 0.,
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):
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"""
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Args:
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dim: Number of input channels.
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num_heads: Number of attention heads.
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head_dim: Number of channels per head (dim // num_heads if not set)
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window_size: The height and width of the window.
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qkv_bias: If True, add a learnable bias to query, key, value.
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attn_drop: Dropout ratio of attention weight.
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proj_drop: Dropout ratio of output.
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"""
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super().__init__()
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self.dim = dim
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self.window_size = to_2tuple(window_size) # Wh, Ww
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win_h, win_w = self.window_size
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self.window_area = win_h * win_w
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self.num_heads = num_heads
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head_dim = head_dim or dim // num_heads
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attn_dim = head_dim * num_heads
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self.scale = head_dim ** -0.5
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self.fused_attn = use_fused_attn(experimental=True) # NOTE not tested for prime-time yet
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# define a parameter table of relative position bias, shape: 2*Wh-1 * 2*Ww-1, nH
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self.relative_position_bias_table = nn.Parameter(torch.zeros((2 * win_h - 1) * (2 * win_w - 1), num_heads))
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# get pair-wise relative position index for each token inside the window
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self.register_buffer("relative_position_index", get_relative_position_index(win_h, win_w), persistent=False)
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self.qkv = nn.Linear(dim, attn_dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(attn_dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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trunc_normal_(self.relative_position_bias_table, std=.02)
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self.softmax = nn.Softmax(dim=-1)
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def set_window_size(self, window_size: Tuple[int, int]) -> None:
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"""Update window size & interpolate position embeddings
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Args:
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window_size (int): New window size
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"""
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window_size = to_2tuple(window_size)
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if window_size == self.window_size:
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return
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self.window_size = window_size
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win_h, win_w = self.window_size
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self.window_area = win_h * win_w
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with torch.no_grad():
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new_bias_shape = (2 * win_h - 1) * (2 * win_w - 1), self.num_heads
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self.relative_position_bias_table = nn.Parameter(
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resize_rel_pos_bias_table(
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self.relative_position_bias_table,
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new_window_size=self.window_size,
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new_bias_shape=new_bias_shape,
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))
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self.register_buffer("relative_position_index", get_relative_position_index(win_h, win_w), persistent=False)
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def _get_rel_pos_bias(self) -> torch.Tensor:
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relative_position_bias = self.relative_position_bias_table[
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self.relative_position_index.view(-1)].view(self.window_area, self.window_area, -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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return relative_position_bias.unsqueeze(0)
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def forward(self, x, mask: Optional[torch.Tensor] = None):
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"""
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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"""
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0)
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if self.fused_attn:
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attn_mask = self._get_rel_pos_bias()
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if mask is not None:
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num_win = mask.shape[0]
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mask = mask.view(1, num_win, 1, N, N).expand(B_ // num_win, -1, self.num_heads, -1, -1)
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attn_mask = attn_mask + mask.reshape(-1, self.num_heads, N, N)
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x = torch.nn.functional.scaled_dot_product_attention(
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q, k, v,
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attn_mask=attn_mask,
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dropout_p=self.attn_drop.p if self.training else 0.,
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)
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else:
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q = q * self.scale
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attn = q @ k.transpose(-2, -1)
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attn = attn + self._get_rel_pos_bias()
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if mask is not None:
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num_win = mask.shape[0]
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attn = attn.view(-1, num_win, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = attn @ v
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x = x.transpose(1, 2).reshape(B_, N, -1)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class SwinTransformerBlock(nn.Module):
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""" Swin Transformer Block.
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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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input_resolution: _int_or_tuple_2_t,
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num_heads: int = 4,
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head_dim: Optional[int] = None,
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window_size: _int_or_tuple_2_t = 7,
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shift_size: int = 0,
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always_partition: bool = False,
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dynamic_mask: bool = False,
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mlp_ratio: float = 4.,
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qkv_bias: bool = True,
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proj_drop: float = 0.,
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attn_drop: float = 0.,
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drop_path: float = 0.,
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act_layer: Callable = nn.GELU,
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norm_layer: Callable = nn.LayerNorm,
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):
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"""
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Args:
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dim: Number of input channels.
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input_resolution: Input resolution.
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window_size: Window size.
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num_heads: Number of attention heads.
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head_dim: Enforce the number of channels per head
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shift_size: Shift size for SW-MSA.
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always_partition: Always partition into full windows and shift
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mlp_ratio: Ratio of mlp hidden dim to embedding dim.
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qkv_bias: If True, add a learnable bias to query, key, value.
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proj_drop: Dropout rate.
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attn_drop: Attention dropout rate.
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drop_path: Stochastic depth rate.
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act_layer: Activation layer.
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norm_layer: Normalization layer.
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"""
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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self.target_shift_size = to_2tuple(shift_size) # store for later resize
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self.always_partition = always_partition
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self.dynamic_mask = dynamic_mask
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self.window_size, self.shift_size = self._calc_window_shift(window_size, shift_size)
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self.window_area = self.window_size[0] * self.window_size[1]
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self.mlp_ratio = mlp_ratio
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(
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dim,
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num_heads=num_heads,
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head_dim=head_dim,
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window_size=self.window_size,
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qkv_bias=qkv_bias,
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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)
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.mlp = Mlp(
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in_features=dim,
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hidden_features=int(dim * mlp_ratio),
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act_layer=act_layer,
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drop=proj_drop,
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)
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.register_buffer(
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"attn_mask",
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None if self.dynamic_mask else self.get_attn_mask(),
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persistent=False,
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)
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def get_attn_mask(self, x: Optional[torch.Tensor] = None) -> Optional[torch.Tensor]:
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if any(self.shift_size):
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# calculate attention mask for SW-MSA
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if x is not None:
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H, W = x.shape[1], x.shape[2]
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device = x.device
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dtype = x.dtype
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else:
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H, W = self.input_resolution
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device = None
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dtype = None
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H = math.ceil(H / self.window_size[0]) * self.window_size[0]
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W = math.ceil(W / self.window_size[1]) * self.window_size[1]
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img_mask = torch.zeros((1, H, W, 1), dtype=dtype, device=device) # 1 H W 1
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cnt = 0
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for h in (
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(0, -self.window_size[0]),
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(-self.window_size[0], -self.shift_size[0]),
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(-self.shift_size[0], None),
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):
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for w in (
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(0, -self.window_size[1]),
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(-self.window_size[1], -self.shift_size[1]),
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(-self.shift_size[1], None),
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):
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img_mask[:, h[0]:h[1], w[0]:w[1], :] = cnt
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cnt += 1
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mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
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mask_windows = mask_windows.view(-1, self.window_area)
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.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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else:
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attn_mask = None
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return attn_mask
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def _calc_window_shift(
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self,
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target_window_size: Union[int, Tuple[int, int]],
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target_shift_size: Optional[Union[int, Tuple[int, int]]] = None,
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) -> Tuple[Tuple[int, int], Tuple[int, int]]:
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target_window_size = to_2tuple(target_window_size)
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if target_shift_size is None:
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# if passed value is None, recalculate from default window_size // 2 if it was previously non-zero
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target_shift_size = self.target_shift_size
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if any(target_shift_size):
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target_shift_size = (target_window_size[0] // 2, target_window_size[1] // 2)
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else:
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target_shift_size = to_2tuple(target_shift_size)
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if self.always_partition:
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return target_window_size, target_shift_size
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window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
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shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
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return tuple(window_size), tuple(shift_size)
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def set_input_size(
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self,
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feat_size: Tuple[int, int],
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window_size: Tuple[int, int],
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always_partition: Optional[bool] = None,
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):
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"""
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Args:
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feat_size: New input resolution
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window_size: New window size
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always_partition: Change always_partition attribute if not None
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"""
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self.input_resolution = feat_size
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if always_partition is not None:
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self.always_partition = always_partition
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self.window_size, self.shift_size = self._calc_window_shift(window_size)
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self.window_area = self.window_size[0] * self.window_size[1]
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self.attn.set_window_size(self.window_size)
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self.register_buffer(
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"attn_mask",
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None if self.dynamic_mask else self.get_attn_mask(),
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persistent=False,
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)
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def _attn(self, x):
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B, H, W, C = x.shape
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# cyclic shift
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has_shift = any(self.shift_size)
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if has_shift:
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shifted_x = torch.roll(x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))
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else:
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shifted_x = x
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# pad for resolution not divisible by window size
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pad_h = (self.window_size[0] - H % self.window_size[0]) % self.window_size[0]
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pad_w = (self.window_size[1] - W % self.window_size[1]) % self.window_size[1]
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shifted_x = torch.nn.functional.pad(shifted_x, (0, 0, 0, pad_w, 0, pad_h))
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_, Hp, Wp, _ = shifted_x.shape
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# partition windows
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x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
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x_windows = x_windows.view(-1, self.window_area, C) # nW*B, window_size*window_size, C
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# W-MSA/SW-MSA
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if getattr(self, 'dynamic_mask', False):
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attn_mask = self.get_attn_mask(shifted_x)
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else:
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attn_mask = self.attn_mask
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attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
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# merge windows
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attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
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shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
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shifted_x = shifted_x[:, :H, :W, :].contiguous()
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# reverse cyclic shift
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if has_shift:
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x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2))
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else:
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x = shifted_x
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return x
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def forward(self, x):
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B, H, W, C = x.shape
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x = x + self.drop_path1(self._attn(self.norm1(x)))
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x = x.reshape(B, -1, C)
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x = x + self.drop_path2(self.mlp(self.norm2(x)))
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x = x.reshape(B, H, W, C)
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return x
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|
|
|
|
class PatchMerging(nn.Module):
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|
""" Patch Merging Layer.
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|
"""
|
|
|
|
def __init__(
|
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self,
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dim: int,
|
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out_dim: Optional[int] = None,
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norm_layer: Callable = nn.LayerNorm,
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):
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"""
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|
Args:
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dim: Number of input channels.
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|
out_dim: Number of output channels (or 2 * dim if None)
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norm_layer: Normalization layer.
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"""
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super().__init__()
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self.dim = dim
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self.out_dim = out_dim or 2 * dim
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self.norm = norm_layer(4 * dim)
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self.reduction = nn.Linear(4 * dim, self.out_dim, bias=False)
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|
|
def forward(self, x):
|
|
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.norm(x)
|
|
x = self.reduction(x)
|
|
return x
|
|
|
|
|
|
class SwinTransformerStage(nn.Module):
|
|
""" A basic Swin Transformer layer for one stage.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
out_dim: int,
|
|
input_resolution: Tuple[int, int],
|
|
depth: int,
|
|
downsample: bool = True,
|
|
num_heads: int = 4,
|
|
head_dim: Optional[int] = None,
|
|
window_size: _int_or_tuple_2_t = 7,
|
|
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: Union[List[float], float] = 0.,
|
|
norm_layer: Callable = nn.LayerNorm,
|
|
):
|
|
"""
|
|
Args:
|
|
dim: Number of input channels.
|
|
out_dim: Number of output channels.
|
|
input_resolution: Input resolution.
|
|
depth: Number of blocks.
|
|
downsample: Downsample layer at the end of the layer.
|
|
num_heads: Number of attention heads.
|
|
head_dim: Channels per head (dim // num_heads if not set)
|
|
window_size: Local window size.
|
|
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
|
|
qkv_bias: If True, add a learnable bias to query, key, value.
|
|
proj_drop: Projection dropout rate.
|
|
attn_drop: Attention dropout rate.
|
|
drop_path: Stochastic depth rate.
|
|
norm_layer: Normalization layer.
|
|
"""
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.input_resolution = input_resolution
|
|
self.output_resolution = tuple(i // 2 for i in input_resolution) if downsample else input_resolution
|
|
self.depth = depth
|
|
self.grad_checkpointing = False
|
|
window_size = to_2tuple(window_size)
|
|
shift_size = tuple([w // 2 for w in window_size])
|
|
|
|
# patch merging layer
|
|
if downsample:
|
|
self.downsample = PatchMerging(
|
|
dim=dim,
|
|
out_dim=out_dim,
|
|
norm_layer=norm_layer,
|
|
)
|
|
else:
|
|
assert dim == out_dim
|
|
self.downsample = nn.Identity()
|
|
|
|
# build blocks
|
|
self.blocks = nn.Sequential(*[
|
|
SwinTransformerBlock(
|
|
dim=out_dim,
|
|
input_resolution=self.output_resolution,
|
|
num_heads=num_heads,
|
|
head_dim=head_dim,
|
|
window_size=window_size,
|
|
shift_size=0 if (i % 2 == 0) else shift_size,
|
|
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,
|
|
norm_layer=norm_layer,
|
|
)
|
|
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:
|
|
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):
|
|
x = self.downsample(x)
|
|
|
|
if self.grad_checkpointing and not torch.jit.is_scripting():
|
|
x = checkpoint_seq(self.blocks, x)
|
|
else:
|
|
x = self.blocks(x)
|
|
return x
|
|
|
|
|
|
class SwinTransformer(nn.Module):
|
|
""" Swin Transformer
|
|
|
|
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
|
https://arxiv.org/pdf/2103.14030
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
img_size: _int_or_tuple_2_t = 224,
|
|
patch_size: int = 4,
|
|
in_chans: int = 3,
|
|
num_classes: int = 1000,
|
|
global_pool: str = 'avg',
|
|
embed_dim: int = 96,
|
|
depths: Tuple[int, ...] = (2, 2, 6, 2),
|
|
num_heads: Tuple[int, ...] = (3, 6, 12, 24),
|
|
head_dim: Optional[int] = None,
|
|
window_size: _int_or_tuple_2_t = 7,
|
|
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,
|
|
embed_layer: Callable = PatchEmbed,
|
|
norm_layer: Union[str, Callable] = nn.LayerNorm,
|
|
weight_init: str = '',
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Args:
|
|
img_size: Input image size.
|
|
patch_size: Patch size.
|
|
in_chans: Number of input image channels.
|
|
num_classes: Number of classes for classification head.
|
|
embed_dim: Patch embedding dimension.
|
|
depths: Depth of each Swin Transformer layer.
|
|
num_heads: Number of attention heads in different layers.
|
|
head_dim: Dimension of self-attention heads.
|
|
window_size: Window size.
|
|
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
|
|
qkv_bias: If True, add a learnable bias to query, key, value.
|
|
drop_rate: Dropout rate.
|
|
attn_drop_rate (float): Attention dropout rate.
|
|
drop_path_rate (float): Stochastic depth rate.
|
|
embed_layer: Patch embedding layer.
|
|
norm_layer (nn.Module): Normalization layer.
|
|
"""
|
|
super().__init__()
|
|
assert global_pool in ('', 'avg')
|
|
self.num_classes = num_classes
|
|
self.global_pool = global_pool
|
|
self.output_fmt = 'NHWC'
|
|
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.num_features = 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 = embed_layer(
|
|
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',
|
|
)
|
|
patch_grid = self.patch_embed.grid_size
|
|
|
|
# build layers
|
|
head_dim = to_ntuple(self.num_layers)(head_dim)
|
|
if not isinstance(window_size, (list, tuple)):
|
|
window_size = to_ntuple(self.num_layers)(window_size)
|
|
elif len(window_size) == 2:
|
|
window_size = (window_size,) * self.num_layers
|
|
assert len(window_size) == self.num_layers
|
|
mlp_ratio = to_ntuple(self.num_layers)(mlp_ratio)
|
|
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
|
|
layers = []
|
|
in_dim = embed_dim[0]
|
|
scale = 1
|
|
for i in range(self.num_layers):
|
|
out_dim = embed_dim[i]
|
|
layers += [SwinTransformerStage(
|
|
dim=in_dim,
|
|
out_dim=out_dim,
|
|
input_resolution=(
|
|
patch_grid[0] // scale,
|
|
patch_grid[1] // scale
|
|
),
|
|
depth=depths[i],
|
|
downsample=i > 0,
|
|
num_heads=num_heads[i],
|
|
head_dim=head_dim[i],
|
|
window_size=window_size[i],
|
|
always_partition=always_partition,
|
|
dynamic_mask=not strict_img_size,
|
|
mlp_ratio=mlp_ratio[i],
|
|
qkv_bias=qkv_bias,
|
|
proj_drop=proj_drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i],
|
|
norm_layer=norm_layer,
|
|
)]
|
|
in_dim = out_dim
|
|
if i > 0:
|
|
scale *= 2
|
|
self.feature_info += [dict(num_chs=out_dim, reduction=patch_size * scale, module=f'layers.{i}')]
|
|
self.layers = nn.Sequential(*layers)
|
|
|
|
self.norm = norm_layer(self.num_features)
|
|
self.head = ClassifierHead(
|
|
self.num_features,
|
|
num_classes,
|
|
pool_type=global_pool,
|
|
drop_rate=drop_rate,
|
|
input_fmt=self.output_fmt,
|
|
)
|
|
if weight_init != 'skip':
|
|
self.init_weights(weight_init)
|
|
|
|
@torch.jit.ignore
|
|
def init_weights(self, mode=''):
|
|
assert mode in ('jax', 'jax_nlhb', 'moco', '')
|
|
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
|
|
named_apply(get_init_weights_vit(mode, head_bias=head_bias), self)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
nwd = set()
|
|
for n, _ in self.named_parameters():
|
|
if 'relative_position_bias_table' in n:
|
|
nwd.add(n)
|
|
return nwd
|
|
|
|
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: int = 8,
|
|
always_partition: Optional[bool] = None,
|
|
) -> None:
|
|
""" Updates the image resolution and window size.
|
|
|
|
Args:
|
|
img_size: 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: New window size, if None based on new_img_size // window_div
|
|
window_ratio: divisor for calculating window size from grid size
|
|
always_partition: always partition into windows and shift (even if window size < feat size)
|
|
"""
|
|
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)
|
|
patch_grid = self.patch_embed.grid_size
|
|
|
|
if window_size is None:
|
|
window_size = tuple([pg // window_ratio for pg in patch_grid])
|
|
|
|
for index, stage in enumerate(self.layers):
|
|
stage_scale = 2 ** max(index - 1, 0)
|
|
stage.set_input_size(
|
|
feat_size=(patch_grid[0] // stage_scale, patch_grid[1] // stage_scale),
|
|
window_size=window_size,
|
|
always_partition=always_partition,
|
|
)
|
|
|
|
@torch.jit.ignore
|
|
def group_matcher(self, coarse=False):
|
|
return dict(
|
|
stem=r'^patch_embed', # stem and embed
|
|
blocks=r'^layers\.(\d+)' if coarse else [
|
|
(r'^layers\.(\d+).downsample', (0,)),
|
|
(r'^layers\.(\d+)\.\w+\.(\d+)', None),
|
|
(r'^norm', (99999,)),
|
|
]
|
|
)
|
|
|
|
@torch.jit.ignore
|
|
def set_grad_checkpointing(self, enable=True):
|
|
for l in self.layers:
|
|
l.grad_checkpointing = enable
|
|
|
|
@torch.jit.ignore
|
|
def get_classifier(self) -> 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, pool_type=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):
|
|
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
|
old_weights = True
|
|
if 'head.fc.weight' in state_dict:
|
|
old_weights = False
|
|
import re
|
|
out_dict = {}
|
|
state_dict = state_dict.get('model', state_dict)
|
|
state_dict = state_dict.get('state_dict', state_dict)
|
|
for k, v in state_dict.items():
|
|
if any([n in k for n in ('relative_position_index', 'attn_mask')]):
|
|
continue # skip buffers that should not be persistent
|
|
|
|
if '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 k.endswith('relative_position_bias_table'):
|
|
m = model.get_submodule(k[:-29])
|
|
if v.shape != m.relative_position_bias_table.shape or m.window_size[0] != m.window_size[1]:
|
|
v = resize_rel_pos_bias_table(
|
|
v,
|
|
new_window_size=m.window_size,
|
|
new_bias_shape=m.relative_position_bias_table.shape,
|
|
)
|
|
|
|
if old_weights:
|
|
k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k)
|
|
k = k.replace('head.', 'head.fc.')
|
|
|
|
out_dict[k] = v
|
|
return out_dict
|
|
|
|
|
|
def _create_swin_transformer(variant, pretrained=False, **kwargs):
|
|
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
|
|
out_indices = kwargs.pop('out_indices', default_out_indices)
|
|
|
|
model = build_model_with_cfg(
|
|
SwinTransformer, variant, pretrained,
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
|
|
**kwargs)
|
|
|
|
return model
|
|
|
|
|
|
def _cfg(url='', **kwargs):
|
|
return {
|
|
'url': url,
|
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
|
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
|
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
|
'first_conv': 'patch_embed.proj', 'classifier': 'head.fc',
|
|
'license': 'mit', **kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = generate_default_cfgs({
|
|
'swin_small_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22kto1k_finetune.pth', ),
|
|
'swin_base_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth',),
|
|
'swin_base_patch4_window12_384.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
|
'swin_large_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth',),
|
|
'swin_large_patch4_window12_384.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
'swin_tiny_patch4_window7_224.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',),
|
|
'swin_small_patch4_window7_224.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',),
|
|
'swin_base_patch4_window7_224.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth',),
|
|
'swin_base_patch4_window12_384.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0),
|
|
|
|
# tiny 22k pretrain is worse than 1k, so moved after (untagged priority is based on order)
|
|
'swin_tiny_patch4_window7_224.ms_in22k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22kto1k_finetune.pth',),
|
|
|
|
'swin_tiny_patch4_window7_224.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth',
|
|
num_classes=21841),
|
|
'swin_small_patch4_window7_224.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_small_patch4_window7_224_22k.pth',
|
|
num_classes=21841),
|
|
'swin_base_patch4_window7_224.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth',
|
|
num_classes=21841),
|
|
'swin_base_patch4_window12_384.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21841),
|
|
'swin_large_patch4_window7_224.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
|
|
num_classes=21841),
|
|
'swin_large_patch4_window12_384.ms_in22k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
|
|
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21841),
|
|
|
|
'swin_s3_tiny_224.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_t-1d53f6a8.pth'),
|
|
'swin_s3_small_224.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_s-3bb4c69d.pth'),
|
|
'swin_s3_base_224.ms_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_b-a1e95db4.pth'),
|
|
})
|
|
|
|
|
|
@register_model
|
|
def swin_tiny_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-T @ 224x224, trained ImageNet-1k
|
|
"""
|
|
model_args = dict(patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer(
|
|
'swin_tiny_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_small_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-S @ 224x224
|
|
"""
|
|
model_args = dict(patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer(
|
|
'swin_small_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_base_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-B @ 224x224
|
|
"""
|
|
model_args = dict(patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
|
|
return _create_swin_transformer(
|
|
'swin_base_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_base_patch4_window12_384(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-B @ 384x384
|
|
"""
|
|
model_args = dict(patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32))
|
|
return _create_swin_transformer(
|
|
'swin_base_patch4_window12_384', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_large_patch4_window7_224(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-L @ 224x224
|
|
"""
|
|
model_args = dict(patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
|
|
return _create_swin_transformer(
|
|
'swin_large_patch4_window7_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_large_patch4_window12_384(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-L @ 384x384
|
|
"""
|
|
model_args = dict(patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48))
|
|
return _create_swin_transformer(
|
|
'swin_large_patch4_window12_384', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_s3_tiny_224(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-S3-T @ 224x224, https://arxiv.org/abs/2111.14725
|
|
"""
|
|
model_args = dict(
|
|
patch_size=4, window_size=(7, 7, 14, 7), embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer('swin_s3_tiny_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_s3_small_224(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-S3-S @ 224x224, https://arxiv.org/abs/2111.14725
|
|
"""
|
|
model_args = dict(
|
|
patch_size=4, window_size=(14, 14, 14, 7), embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer('swin_s3_small_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
@register_model
|
|
def swin_s3_base_224(pretrained=False, **kwargs) -> SwinTransformer:
|
|
""" Swin-S3-B @ 224x224, https://arxiv.org/abs/2111.14725
|
|
"""
|
|
model_args = dict(
|
|
patch_size=4, window_size=(7, 7, 14, 7), embed_dim=96, depths=(2, 2, 30, 2), num_heads=(3, 6, 12, 24))
|
|
return _create_swin_transformer('swin_s3_base_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
|
|
|
|
register_model_deprecations(__name__, {
|
|
'swin_base_patch4_window7_224_in22k': 'swin_base_patch4_window7_224.ms_in22k',
|
|
'swin_base_patch4_window12_384_in22k': 'swin_base_patch4_window12_384.ms_in22k',
|
|
'swin_large_patch4_window7_224_in22k': 'swin_large_patch4_window7_224.ms_in22k',
|
|
'swin_large_patch4_window12_384_in22k': 'swin_large_patch4_window12_384.ms_in22k',
|
|
})
|