Add Swin Transformer models from https://github.com/microsoft/Swin-Transformer
Ross Wightman
4 years ago
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5a196dddf6
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""" 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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"""
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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 copy import deepcopy
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint as checkpoint
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg, overlay_external_default_cfg
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from .layers import DropPath, to_2tuple, trunc_normal_
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from .registry import register_model
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from .vision_transformer import checkpoint_filter_fn, Mlp, PatchEmbed, _init_vit_weights
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_logger = logging.getLogger(__name__)
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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# patch models (my experiments)
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'swin_base_patch4_window12_384': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth',
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input_size=(3, 384, 384), crop_pct=1.0),
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'swin_base_patch4_window7_224': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth',
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),
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'swin_large_patch4_window12_384': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth',
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input_size=(3, 384, 384), crop_pct=1.0),
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'swin_large_patch4_window7_224': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth',
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),
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'swin_small_patch4_window7_224': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',
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),
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'swin_tiny_patch4_window7_224': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',
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),
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'swin_base_patch4_window12_384_in22k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth',
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input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),
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'swin_base_patch4_window7_224_in22k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth',
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num_classes=21841),
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'swin_large_patch4_window12_384_in22k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
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input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),
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'swin_large_patch4_window7_224_in22k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
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num_classes=21841),
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}
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def window_partition(x, window_size: int):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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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, window_size, W // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows
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def window_reverse(windows, window_size: 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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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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class WindowAttention(nn.Module):
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r""" 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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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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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 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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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])) # 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) # Wh*Ww, Wh*Ww
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self.register_buffer("relative_position_index", relative_position_index)
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(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 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, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -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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attn = attn + relative_position_bias.unsqueeze(0)
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B_ // nW, nW, 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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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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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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r""" Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resulotion.
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num_heads (int): Number of attention heads.
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window_size (int): Window size.
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shift_size (int): Shift size for SW-MSA.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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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.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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if min(self.input_resolution) <= self.window_size:
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# if window size is larger than input resolution, we don't partition windows
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self.shift_size = 0
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self.window_size = min(self.input_resolution)
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assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(
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dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
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qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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if self.shift_size > 0:
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# calculate attention mask for SW-MSA
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H, W = self.input_resolution
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img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
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h_slices = (slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None))
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w_slices = (slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None))
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cnt = 0
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for h in h_slices:
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for w in w_slices:
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img_mask[:, h, w, :] = 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_size * self.window_size)
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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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self.register_buffer("attn_mask", attn_mask)
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def forward(self, x):
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H, W = self.input_resolution
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B, L, C = x.shape
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assert L == H * W, "input feature has wrong size"
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shortcut = x
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x = self.norm1(x)
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x = x.view(B, H, W, C)
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# cyclic shift
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if self.shift_size > 0:
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shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
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else:
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shifted_x = x
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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_size * self.window_size, C) # nW*B, window_size*window_size, C
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# W-MSA/SW-MSA
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attn_windows = self.attn(x_windows, mask=self.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, self.window_size, C)
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shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
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# reverse cyclic shift
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if self.shift_size > 0:
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x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
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else:
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x = shifted_x
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x = x.view(B, H * W, C)
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# FFN
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x = shortcut + self.drop_path(x)
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class PatchMerging(nn.Module):
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r""" Patch Merging Layer.
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Args:
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input_resolution (tuple[int]): Resolution of input feature.
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dim (int): Number of input channels.
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
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super().__init__()
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self.input_resolution = input_resolution
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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):
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"""
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x: B, H*W, C
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"""
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H, W = self.input_resolution
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B, L, C = x.shape
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assert L == H * W, "input feature has wrong size"
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assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
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x = x.view(B, H, W, C)
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x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
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x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
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x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
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x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
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x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
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x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
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x = self.norm(x)
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x = self.reduction(x)
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return x
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def extra_repr(self) -> str:
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return f"input_resolution={self.input_resolution}, dim={self.dim}"
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def flops(self):
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H, W = self.input_resolution
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flops = H * W * self.dim
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flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
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return flops
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class BasicLayer(nn.Module):
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""" A basic Swin Transformer layer for one stage.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resolution.
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depth (int): Number of blocks.
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num_heads (int): Number of attention heads.
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window_size (int): Local window size.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
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use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
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"""
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def __init__(self, dim, input_resolution, depth, num_heads, window_size,
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
|
||||
num_heads=num_heads, window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=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)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x):
|
||||
for blk in self.blocks:
|
||||
if not torch.jit.is_scripting() and self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x)
|
||||
else:
|
||||
x = blk(x)
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x)
|
||||
return x
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
r""" Swin Transformer
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
|
||||
Args:
|
||||
img_size (int | tuple(int)): Input image size. Default 224
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4
|
||||
in_chans (int): Number of input image channels. Default: 3
|
||||
num_classes (int): Number of classes for classification head. Default: 1000
|
||||
embed_dim (int): Patch embedding dimension. Default: 96
|
||||
depths (tuple(int)): Depth of each Swin Transformer layer.
|
||||
num_heads (tuple(int)): Number of attention heads in different layers.
|
||||
window_size (int): Window size. Default: 7
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
|
||||
drop_rate (float): Dropout rate. Default: 0
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.1
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
|
||||
embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24),
|
||||
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
|
||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||||
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
||||
use_checkpoint=False, weight_init='', **kwargs):
|
||||
super().__init__()
|
||||
|
||||
self.num_classes = num_classes
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
|
||||
self.mlp_ratio = mlp_ratio
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
num_patches = self.patch_embed.num_patches
|
||||
self.patch_grid = self.patch_embed.patch_grid
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
else:
|
||||
self.absolute_pos_embed = None
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
layers = []
|
||||
for i_layer in range(self.num_layers):
|
||||
layers += [BasicLayer(
|
||||
dim=int(embed_dim * 2 ** i_layer),
|
||||
input_resolution=(self.patch_grid[0] // (2 ** i_layer), self.patch_grid[1] // (2 ** i_layer)),
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=self.mlp_ratio,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint)
|
||||
]
|
||||
self.layers = nn.Sequential(*layers)
|
||||
|
||||
self.norm = norm_layer(self.num_features)
|
||||
self.avgpool = nn.AdaptiveAvgPool1d(1)
|
||||
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
|
||||
head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
|
||||
if weight_init.startswith('jax'):
|
||||
for n, m in self.named_modules():
|
||||
_init_vit_weights(m, n, head_bias=head_bias, jax_impl=True)
|
||||
else:
|
||||
self.apply(_init_vit_weights)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'absolute_pos_embed'}
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay_keywords(self):
|
||||
return {'relative_position_bias_table'}
|
||||
|
||||
def forward_features(self, x):
|
||||
x = self.patch_embed(x)
|
||||
if self.absolute_pos_embed is not None:
|
||||
x = x + self.absolute_pos_embed
|
||||
x = self.pos_drop(x)
|
||||
x = self.layers(x)
|
||||
x = self.norm(x) # B L C
|
||||
x = self.avgpool(x.transpose(1, 2)) # B C 1
|
||||
x = torch.flatten(x, 1)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
|
||||
def _create_swin_transformer(variant, pretrained=False, default_cfg=None, **kwargs):
|
||||
if default_cfg is None:
|
||||
default_cfg = deepcopy(default_cfgs[variant])
|
||||
overlay_external_default_cfg(default_cfg, kwargs)
|
||||
default_num_classes = default_cfg['num_classes']
|
||||
default_img_size = default_cfg['input_size'][-2:]
|
||||
|
||||
num_classes = kwargs.pop('num_classes', default_num_classes)
|
||||
img_size = kwargs.pop('img_size', default_img_size)
|
||||
if kwargs.get('features_only', None):
|
||||
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
||||
|
||||
model = build_model_with_cfg(
|
||||
SwinTransformer, variant, pretrained,
|
||||
default_cfg=default_cfg,
|
||||
img_size=img_size,
|
||||
num_classes=num_classes,
|
||||
pretrained_filter_fn=checkpoint_filter_fn,
|
||||
**kwargs)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_base_patch4_window12_384(pretrained=False, **kwargs):
|
||||
""" Swin-B @ 384x384, pretrained ImageNet-22k, fine tune 1k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
|
||||
return _create_swin_transformer('swin_base_patch4_window12_384', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_base_patch4_window7_224(pretrained=False, **kwargs):
|
||||
""" Swin-B @ 224x224, pretrained ImageNet-22k, fine tune 1k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
|
||||
return _create_swin_transformer('swin_base_patch4_window7_224', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_large_patch4_window12_384(pretrained=False, **kwargs):
|
||||
""" Swin-L @ 384x384, pretrained ImageNet-22k, fine tune 1k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
|
||||
return _create_swin_transformer('swin_large_patch4_window12_384', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_large_patch4_window7_224(pretrained=False, **kwargs):
|
||||
""" Swin-L @ 224x224, pretrained ImageNet-22k, fine tune 1k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
|
||||
return _create_swin_transformer('swin_large_patch4_window7_224', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_small_patch4_window7_224(pretrained=False, **kwargs):
|
||||
""" Swin-S @ 224x224, trained ImageNet-1k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs)
|
||||
return _create_swin_transformer('swin_small_patch4_window7_224', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_tiny_patch4_window7_224(pretrained=False, **kwargs):
|
||||
""" Swin-T @ 224x224, trained ImageNet-1k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs)
|
||||
return _create_swin_transformer('swin_tiny_patch4_window7_224', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_base_patch4_window12_384_in22k(pretrained=False, **kwargs):
|
||||
""" Swin-B @ 384x384, trained ImageNet-22k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
|
||||
return _create_swin_transformer('swin_base_patch4_window12_384_in22k', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_base_patch4_window7_224_in22k(pretrained=False, **kwargs):
|
||||
""" Swin-B @ 224x224, trained ImageNet-22k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
|
||||
return _create_swin_transformer('swin_base_patch4_window7_224_in22k', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_large_patch4_window12_384_in22k(pretrained=False, **kwargs):
|
||||
""" Swin-L @ 384x384, trained ImageNet-22k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
|
||||
return _create_swin_transformer('swin_large_patch4_window12_384_in22k', pretrained=pretrained, **model_kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def swin_large_patch4_window7_224_in22k(pretrained=False, **kwargs):
|
||||
""" Swin-L @ 224x224, trained ImageNet-22k
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
|
||||
return _create_swin_transformer('swin_large_patch4_window7_224_in22k', pretrained=pretrained, **model_kwargs)
|
||||
Loading…
Reference in new issue