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737 lines
30 KiB
737 lines
30 KiB
3 years ago
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""" Swin Transformer V2
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A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
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- https://arxiv.org/abs/2111.09883
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Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
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Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
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"""
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# --------------------------------------------------------
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# Swin Transformer V2
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# Copyright (c) 2022 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 math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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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 .fx_features import register_notrace_function
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from .helpers import build_model_with_cfg, named_apply
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from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, to_ntuple, trunc_normal_, _assert
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from .registry import register_model
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from .vision_transformer import checkpoint_filter_fn, get_init_weights_vit
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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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'swinv2_tiny_window8_256.': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_tiny_window16_256': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_small_window8_256': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_small_window16_256': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_base_window8_256': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_base_window16_256': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_base_window12_192_22k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth',
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num_classes=21841, input_size=(3, 192, 192)
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),
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'swinv2_base_window12to16_192to256_22kft1k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_base_window12to24_192to384_22kft1k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth',
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input_size=(3, 384, 384)
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),
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'swinv2_large_window12_192_22k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth',
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num_classes=21841, input_size=(3, 192, 192)
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),
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'swinv2_large_window12to16_192to256_22kft1k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth',
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input_size=(3, 256, 256)
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),
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'swinv2_large_window12to24_192to384_22kft1k': _cfg(
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url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth',
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input_size=(3, 384, 384)
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),
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}
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def window_partition(x, window_size):
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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, H, W):
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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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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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pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
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"""
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def __init__(
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self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
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pretrained_window_size=[0, 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.pretrained_window_size = pretrained_window_size
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self.num_heads = num_heads
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self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
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# mlp to generate continuous relative position bias
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self.cpb_mlp = nn.Sequential(
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nn.Linear(2, 512, bias=True),
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nn.ReLU(inplace=True),
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nn.Linear(512, num_heads, bias=False)
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)
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# get relative_coords_table
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relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
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relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
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relative_coords_table = torch.stack(torch.meshgrid([
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relative_coords_h,
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relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
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if pretrained_window_size[0] > 0:
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relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
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relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
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else:
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relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
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relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
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relative_coords_table *= 8 # normalize to -8, 8
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relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
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torch.abs(relative_coords_table) + 1.0) / math.log2(8)
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self.register_buffer("relative_coords_table", relative_coords_table)
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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=False)
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if qkv_bias:
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self.q_bias = nn.Parameter(torch.zeros(dim))
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self.v_bias = nn.Parameter(torch.zeros(dim))
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else:
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self.q_bias = None
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self.v_bias = None
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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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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=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_bias = None
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if self.q_bias is not None:
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qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
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qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
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qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).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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# cosine attention
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attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
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logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp()
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attn = attn * logit_scale
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relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
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relative_position_bias = 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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relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
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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 resolution.
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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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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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pretrained_window_size (int): Window size in pretraining.
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"""
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def __init__(
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self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
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mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
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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.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, attn_drop=attn_drop, proj_drop=drop,
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pretrained_window_size=to_2tuple(pretrained_window_size))
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self.norm1 = norm_layer(dim)
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
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self.norm2 = norm_layer(dim)
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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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
|
||
|
|
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
|
||
|
|
cnt = 0
|
||
|
|
for h in (
|
||
|
|
slice(0, -self.window_size),
|
||
|
|
slice(-self.window_size, -self.shift_size),
|
||
|
|
slice(-self.shift_size, None)):
|
||
|
|
for w in (
|
||
|
|
slice(0, -self.window_size),
|
||
|
|
slice(-self.window_size, -self.shift_size),
|
||
|
|
slice(-self.shift_size, None)):
|
||
|
|
img_mask[:, h, w, :] = cnt
|
||
|
|
cnt += 1
|
||
|
|
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||
|
|
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||
|
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||
|
|
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||
|
|
else:
|
||
|
|
attn_mask = None
|
||
|
|
|
||
|
|
self.register_buffer("attn_mask", attn_mask)
|
||
|
|
|
||
|
|
def _attn(self, x):
|
||
|
|
H, W = self.input_resolution
|
||
|
|
B, L, C = x.shape
|
||
|
|
_assert(L == H * W, "input feature has wrong size")
|
||
|
|
x = x.view(B, H, W, C)
|
||
|
|
|
||
|
|
# cyclic shift
|
||
|
|
if self.shift_size > 0:
|
||
|
|
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||
|
|
else:
|
||
|
|
shifted_x = x
|
||
|
|
|
||
|
|
# partition windows
|
||
|
|
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||
|
|
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||
|
|
|
||
|
|
# W-MSA/SW-MSA
|
||
|
|
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
|
||
|
|
|
||
|
|
# merge windows
|
||
|
|
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||
|
|
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
|
||
|
|
|
||
|
|
# reverse cyclic shift
|
||
|
|
if self.shift_size > 0:
|
||
|
|
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||
|
|
else:
|
||
|
|
x = shifted_x
|
||
|
|
x = x.view(B, H * W, C)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = x + self.drop_path1(self.norm1(self._attn(x)))
|
||
|
|
x = x + self.drop_path2(self.norm2(self.mlp(x)))
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class PatchMerging(nn.Module):
|
||
|
|
r""" Patch Merging Layer.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input_resolution (tuple[int]): Resolution of input feature.
|
||
|
|
dim (int): Number of input channels.
|
||
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
|
||
|
|
super().__init__()
|
||
|
|
self.input_resolution = input_resolution
|
||
|
|
self.dim = dim
|
||
|
|
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||
|
|
self.norm = norm_layer(2 * dim)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
"""
|
||
|
|
x: B, H*W, C
|
||
|
|
"""
|
||
|
|
H, W = self.input_resolution
|
||
|
|
B, L, C = x.shape
|
||
|
|
_assert(L == H * W, "input feature has wrong size")
|
||
|
|
_assert(H % 2 == 0, f"x size ({H}*{W}) are not even.")
|
||
|
|
_assert(W % 2 == 0, f"x size ({H}*{W}) are not even.")
|
||
|
|
|
||
|
|
x = x.view(B, H, W, C)
|
||
|
|
|
||
|
|
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||
|
|
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||
|
|
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||
|
|
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||
|
|
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||
|
|
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||
|
|
|
||
|
|
x = self.reduction(x)
|
||
|
|
x = self.norm(x)
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class BasicLayer(nn.Module):
|
||
|
|
""" A basic Swin Transformer layer for one stage.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
dim (int): Number of input channels.
|
||
|
|
input_resolution (tuple[int]): Input resolution.
|
||
|
|
depth (int): Number of blocks.
|
||
|
|
num_heads (int): Number of attention heads.
|
||
|
|
window_size (int): Local window size.
|
||
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
||
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||
|
|
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||
|
|
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||
|
|
pretrained_window_size (int): Local window size in pre-training.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self, dim, input_resolution, depth, num_heads, window_size,
|
||
|
|
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
|
||
|
|
norm_layer=nn.LayerNorm, downsample=None, pretrained_window_size=0):
|
||
|
|
|
||
|
|
super().__init__()
|
||
|
|
self.dim = dim
|
||
|
|
self.input_resolution = input_resolution
|
||
|
|
self.depth = depth
|
||
|
|
self.grad_checkpointing = False
|
||
|
|
|
||
|
|
# 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,
|
||
|
|
drop=drop, attn_drop=attn_drop,
|
||
|
|
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||
|
|
norm_layer=norm_layer,
|
||
|
|
pretrained_window_size=pretrained_window_size)
|
||
|
|
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 = nn.Identity()
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
for blk in self.blocks:
|
||
|
|
if self.grad_checkpointing:
|
||
|
|
x = checkpoint.checkpoint(blk, x)
|
||
|
|
else:
|
||
|
|
x = blk(x)
|
||
|
|
x = self.downsample(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def _init_respostnorm(self):
|
||
|
|
for blk in self.blocks:
|
||
|
|
nn.init.constant_(blk.norm1.bias, 0)
|
||
|
|
nn.init.constant_(blk.norm1.weight, 0)
|
||
|
|
nn.init.constant_(blk.norm2.bias, 0)
|
||
|
|
nn.init.constant_(blk.norm2.weight, 0)
|
||
|
|
|
||
|
|
|
||
|
|
class SwinTransformerV2(nn.Module):
|
||
|
|
r""" Swin Transformer V2
|
||
|
|
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
|
||
|
|
- https://arxiv.org/abs/2111.09883
|
||
|
|
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
|
||
|
|
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
|
||
|
|
pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, global_pool='avg',
|
||
|
|
embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24),
|
||
|
|
window_size=7, mlp_ratio=4., qkv_bias=True,
|
||
|
|
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||
|
|
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
|
||
|
|
pretrained_window_sizes=(0, 0, 0, 0), **kwargs):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.num_classes = num_classes
|
||
|
|
assert global_pool in ('', 'avg')
|
||
|
|
self.global_pool = global_pool
|
||
|
|
self.num_layers = len(depths)
|
||
|
|
self.embed_dim = embed_dim
|
||
|
|
self.patch_norm = patch_norm
|
||
|
|
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
|
||
|
|
|
||
|
|
# 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
|
||
|
|
|
||
|
|
# absolute position embedding
|
||
|
|
if 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
|
||
|
|
self.layers = nn.ModuleList()
|
||
|
|
for i_layer in range(self.num_layers):
|
||
|
|
layer = BasicLayer(
|
||
|
|
dim=int(embed_dim * 2 ** i_layer),
|
||
|
|
input_resolution=(
|
||
|
|
self.patch_embed.grid_size[0] // (2 ** i_layer),
|
||
|
|
self.patch_embed.grid_size[1] // (2 ** i_layer)),
|
||
|
|
depth=depths[i_layer],
|
||
|
|
num_heads=num_heads[i_layer],
|
||
|
|
window_size=window_size,
|
||
|
|
mlp_ratio=mlp_ratio,
|
||
|
|
qkv_bias=qkv_bias,
|
||
|
|
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,
|
||
|
|
pretrained_window_size=pretrained_window_sizes[i_layer]
|
||
|
|
)
|
||
|
|
self.layers.append(layer)
|
||
|
|
|
||
|
|
self.norm = norm_layer(self.num_features)
|
||
|
|
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||
|
|
|
||
|
|
self.apply(self._init_weights)
|
||
|
|
for bly in self.layers:
|
||
|
|
bly._init_respostnorm()
|
||
|
|
|
||
|
|
def _init_weights(self, m):
|
||
|
|
if isinstance(m, nn.Linear):
|
||
|
|
trunc_normal_(m.weight, std=.02)
|
||
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
||
|
|
nn.init.constant_(m.bias, 0)
|
||
|
|
elif isinstance(m, nn.LayerNorm):
|
||
|
|
nn.init.constant_(m.bias, 0)
|
||
|
|
nn.init.constant_(m.weight, 1.0)
|
||
|
|
|
||
|
|
@torch.jit.ignore
|
||
|
|
def no_weight_decay(self):
|
||
|
|
nod = {'absolute_pos_embed'}
|
||
|
|
for n, m in self.named_modules():
|
||
|
|
if any([kw in n for kw in ("cpb_mlp", "logit_scale", 'relative_position_bias_table')]):
|
||
|
|
nod.add(n)
|
||
|
|
return nod
|
||
|
|
|
||
|
|
@torch.jit.ignore
|
||
|
|
def group_matcher(self, coarse=False):
|
||
|
|
return dict(
|
||
|
|
stem=r'^absolute_pos_embed|patch_embed', # stem and embed
|
||
|
|
blocks=r'^layers\.(\d+)' if coarse else [
|
||
|
|
(r'^layers\.(\d+).downsample', (0,)),
|
||
|
|
(r'^layers\.(\d+)\.\w+\.(\d+)', None),
|
||
|
|
(r'^norm', (99999,)),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
@torch.jit.ignore
|
||
|
|
def set_grad_checkpointing(self, enable=True):
|
||
|
|
for l in self.layers:
|
||
|
|
l.grad_checkpointing = enable
|
||
|
|
|
||
|
|
@torch.jit.ignore
|
||
|
|
def get_classifier(self):
|
||
|
|
return self.head
|
||
|
|
|
||
|
|
def reset_classifier(self, num_classes, global_pool=None):
|
||
|
|
self.num_classes = num_classes
|
||
|
|
if global_pool is not None:
|
||
|
|
assert global_pool in ('', 'avg')
|
||
|
|
self.global_pool = global_pool
|
||
|
|
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
for layer in self.layers:
|
||
|
|
x = layer(x)
|
||
|
|
|
||
|
|
x = self.norm(x) # B L C
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward_head(self, x, pre_logits: bool = False):
|
||
|
|
if self.global_pool == 'avg':
|
||
|
|
x = x.mean(dim=1)
|
||
|
|
return x if pre_logits else self.head(x)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.forward_features(x)
|
||
|
|
x = self.forward_head(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def _create_swin_transformer_v2(variant, pretrained=False, **kwargs):
|
||
|
|
model = build_model_with_cfg(
|
||
|
|
SwinTransformerV2, variant, pretrained,
|
||
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
||
|
|
**kwargs)
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_tiny_window16_256(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
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window_size=16, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs)
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||
|
|
return _create_swin_transformer_v2('swinv2_tiny_window16_256', pretrained=pretrained, **model_kwargs)
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||
|
|
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||
|
|
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|
@register_model
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||
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|
def swinv2_tiny_window8_256(pretrained=False, **kwargs):
|
||
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|
"""
|
||
|
|
"""
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|
model_kwargs = dict(
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||
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|
window_size=8, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs)
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||
|
|
return _create_swin_transformer_v2('swinv2_tiny_window8_256', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_small_window16_256(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=16, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs)
|
||
|
|
return _create_swin_transformer_v2('swinv2_small_window16_256', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_small_window8_256(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=8, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs)
|
||
|
|
return _create_swin_transformer_v2('swinv2_small_window8_256', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_base_window16_256(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
|
||
|
|
return _create_swin_transformer_v2('swinv2_base_window16_256', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_base_window8_256(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=8, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
|
||
|
|
return _create_swin_transformer_v2('swinv2_base_window8_256', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_base_window12_192_22k(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
|
||
|
|
return _create_swin_transformer_v2('swinv2_base_window12_192_22k', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_base_window12to16_192to256_22kft1k(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
|
||
|
|
pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
|
||
|
|
return _create_swin_transformer_v2(
|
||
|
|
'swinv2_base_window12to16_192to256_22kft1k', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_base_window12to24_192to384_22kft1k(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=24, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
|
||
|
|
pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
|
||
|
|
return _create_swin_transformer_v2(
|
||
|
|
'swinv2_base_window12to24_192to384_22kft1k', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_large_window12_192_22k(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
|
||
|
|
return _create_swin_transformer_v2('swinv2_large_window12_192_22k', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_large_window12to16_192to256_22kft1k(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=16, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
|
||
|
|
pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
|
||
|
|
return _create_swin_transformer_v2(
|
||
|
|
'swinv2_large_window12to16_192to256_22kft1k', pretrained=pretrained, **model_kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def swinv2_large_window12to24_192to384_22kft1k(pretrained=False, **kwargs):
|
||
|
|
"""
|
||
|
|
"""
|
||
|
|
model_kwargs = dict(
|
||
|
|
window_size=24, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
|
||
|
|
pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
|
||
|
|
return _create_swin_transformer_v2(
|
||
|
|
'swinv2_large_window12to24_192to384_22kft1k', pretrained=pretrained, **model_kwargs)
|