402 lines
14 KiB
Python
402 lines
14 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from copy import deepcopy
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from typing import Sequence
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import torch
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import torch.nn as nn
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from mmcv.cnn import build_norm_layer
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from mmcv.cnn.bricks.transformer import FFN
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from mmcv.cnn.utils.weight_init import trunc_normal_
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from mmcv.runner.base_module import BaseModule, ModuleList
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from ..builder import BACKBONES
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from ..utils import PatchEmbed, PatchMerging, ShiftWindowMSA
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from .base_backbone import BaseBackbone
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class SwinBlock(BaseModule):
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"""Swin Transformer block.
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Args:
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embed_dims (int): Number of input channels.
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input_resolution (Tuple[int, int]): The resolution of the input feature
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map.
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num_heads (int): Number of attention heads.
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window_size (int, optional): The height and width of the window.
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Defaults to 7.
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shift (bool, optional): Shift the attention window or not.
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Defaults to False.
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ffn_ratio (float, optional): The expansion ratio of feedforward network
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hidden layer channels. Defaults to 4.
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drop_path (float, optional): The drop path rate after attention and
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ffn. Defaults to 0.
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attn_cfgs (dict, optional): The extra config of Shift Window-MSA.
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Defaults to empty dict.
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ffn_cfgs (dict, optional): The extra config of FFN.
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Defaults to empty dict.
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norm_cfg (dict, optional): The config of norm layers.
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Defaults to dict(type='LN').
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auto_pad (bool, optional): Auto pad the feature map to be divisible by
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window_size, Defaults to False.
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init_cfg (dict, optional): The extra config for initialization.
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Default: None.
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"""
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def __init__(self,
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embed_dims,
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input_resolution,
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num_heads,
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window_size=7,
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shift=False,
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ffn_ratio=4.,
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drop_path=0.,
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attn_cfgs=dict(),
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ffn_cfgs=dict(),
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norm_cfg=dict(type='LN'),
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auto_pad=False,
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init_cfg=None):
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super(SwinBlock, self).__init__(init_cfg)
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_attn_cfgs = {
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'embed_dims': embed_dims,
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'input_resolution': input_resolution,
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'num_heads': num_heads,
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'shift_size': window_size // 2 if shift else 0,
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'window_size': window_size,
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'dropout_layer': dict(type='DropPath', drop_prob=drop_path),
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'auto_pad': auto_pad,
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**attn_cfgs
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}
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self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
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self.attn = ShiftWindowMSA(**_attn_cfgs)
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_ffn_cfgs = {
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'embed_dims': embed_dims,
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'feedforward_channels': int(embed_dims * ffn_ratio),
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'num_fcs': 2,
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'ffn_drop': 0,
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'dropout_layer': dict(type='DropPath', drop_prob=drop_path),
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'act_cfg': dict(type='GELU'),
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**ffn_cfgs
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}
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self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
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self.ffn = FFN(**_ffn_cfgs)
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def forward(self, x):
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identity = x
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x = self.norm1(x)
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x = self.attn(x)
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x = x + identity
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identity = x
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x = self.norm2(x)
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x = self.ffn(x, identity=identity)
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return x
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class SwinBlockSequence(BaseModule):
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"""Module with successive Swin Transformer blocks and downsample layer.
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Args:
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embed_dims (int): Number of input channels.
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input_resolution (Tuple[int, int]): The resolution of the input feature
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map.
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depth (int): Number of successive swin transformer blocks.
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num_heads (int): Number of attention heads.
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downsample (bool, optional): Downsample the output of blocks by patch
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merging. Defaults to False.
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downsample_cfg (dict, optional): The extra config of the patch merging
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layer. Defaults to empty dict.
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drop_paths (Sequence[float] | float, optional): The drop path rate in
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each block. Defaults to 0.
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block_cfgs (Sequence[dict] | dict, optional): The extra config of each
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block. Defaults to empty dicts.
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auto_pad (bool, optional): Auto pad the feature map to be divisible by
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window_size, Defaults to False.
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init_cfg (dict, optional): The extra config for initialization.
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Default: None.
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"""
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def __init__(self,
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embed_dims,
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input_resolution,
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depth,
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num_heads,
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downsample=False,
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downsample_cfg=dict(),
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drop_paths=0.,
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block_cfgs=dict(),
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auto_pad=False,
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init_cfg=None):
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super().__init__(init_cfg)
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if not isinstance(drop_paths, Sequence):
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drop_paths = [drop_paths] * depth
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if not isinstance(block_cfgs, Sequence):
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block_cfgs = [deepcopy(block_cfgs) for _ in range(depth)]
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self.embed_dims = embed_dims
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self.input_resolution = input_resolution
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self.blocks = ModuleList()
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for i in range(depth):
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_block_cfg = {
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'embed_dims': embed_dims,
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'input_resolution': input_resolution,
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'num_heads': num_heads,
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'shift': False if i % 2 == 0 else True,
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'drop_path': drop_paths[i],
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'auto_pad': auto_pad,
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**block_cfgs[i]
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}
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block = SwinBlock(**_block_cfg)
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self.blocks.append(block)
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if downsample:
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_downsample_cfg = {
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'input_resolution': input_resolution,
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'in_channels': embed_dims,
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'expansion_ratio': 2,
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'norm_cfg': dict(type='LN'),
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**downsample_cfg
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}
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self.downsample = PatchMerging(**_downsample_cfg)
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else:
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self.downsample = None
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def forward(self, x):
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for block in self.blocks:
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x = block(x)
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if self.downsample:
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x = self.downsample(x)
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return x
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@property
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def out_resolution(self):
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if self.downsample:
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return self.downsample.output_resolution
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else:
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return self.input_resolution
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@property
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def out_channels(self):
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if self.downsample:
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return self.downsample.out_channels
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else:
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return self.embed_dims
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@BACKBONES.register_module()
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class SwinTransformer(BaseBackbone):
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""" Swin Transformer
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A PyTorch implement of : `Swin Transformer:
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Hierarchical Vision Transformer using Shifted Windows
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<https://arxiv.org/abs/2103.14030>`_
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Inspiration from
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https://github.com/microsoft/Swin-Transformer
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Args:
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arch (str | dict): Swin Transformer architecture
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Defaults to 'T'.
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img_size (int | tuple): The size of input image.
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Defaults to 224.
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in_channels (int): The num of input channels.
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Defaults to 3.
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drop_rate (float): Dropout rate after embedding.
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Defaults to 0.
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drop_path_rate (float): Stochastic depth rate.
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Defaults to 0.1.
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use_abs_pos_embed (bool): If True, add absolute position embedding to
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the patch embedding. Defaults to False.
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auto_pad (bool): If True, auto pad feature map to fit window_size.
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Defaults to False.
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norm_cfg (dict, optional): Config dict for normalization layer at end
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of backone. Defaults to dict(type='LN')
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stage_cfgs (Sequence | dict, optional): Extra config dict for each
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stage. Defaults to empty dict.
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patch_cfg (dict, optional): Extra config dict for patch embedding.
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Defaults to empty dict.
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init_cfg (dict, optional): The Config for initialization.
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Defaults to None.
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Examples:
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>>> from mmcls.models import SwinTransformer
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>>> import torch
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>>> extra_config = dict(
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>>> arch='tiny',
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>>> stage_cfgs=dict(downsample_cfg={'kernel_size': 3,
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>>> 'expansion_ratio': 3}),
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>>> auto_pad=True)
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>>> self = SwinTransformer(**extra_config)
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>>> inputs = torch.rand(1, 3, 224, 224)
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>>> output = self.forward(inputs)
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>>> print(output.shape)
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(1, 2592, 4)
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"""
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arch_zoo = {
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**dict.fromkeys(['t', 'tiny'],
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{'embed_dims': 96,
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'depths': [2, 2, 6, 2],
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'num_heads': [3, 6, 12, 24]}),
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**dict.fromkeys(['s', 'small'],
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{'embed_dims': 96,
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'depths': [2, 2, 18, 2],
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'num_heads': [3, 6, 12, 24]}),
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**dict.fromkeys(['b', 'base'],
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{'embed_dims': 128,
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'depths': [2, 2, 18, 2],
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'num_heads': [4, 8, 16, 32]}),
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**dict.fromkeys(['l', 'large'],
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{'embed_dims': 192,
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'depths': [2, 2, 18, 2],
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'num_heads': [6, 12, 24, 48]}),
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} # yapf: disable
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_version = 2
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def __init__(self,
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arch='T',
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img_size=224,
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in_channels=3,
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drop_rate=0.,
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drop_path_rate=0.1,
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out_indices=(3, ),
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use_abs_pos_embed=False,
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auto_pad=False,
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norm_cfg=dict(type='LN'),
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stage_cfgs=dict(),
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patch_cfg=dict(),
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init_cfg=None):
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super(SwinTransformer, self).__init__(init_cfg)
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if isinstance(arch, str):
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arch = arch.lower()
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assert arch in set(self.arch_zoo), \
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f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
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self.arch_settings = self.arch_zoo[arch]
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else:
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essential_keys = {'embed_dims', 'depths', 'num_head'}
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assert isinstance(arch, dict) and set(arch) == essential_keys, \
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f'Custom arch needs a dict with keys {essential_keys}'
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self.arch_settings = arch
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self.embed_dims = self.arch_settings['embed_dims']
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self.depths = self.arch_settings['depths']
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self.num_heads = self.arch_settings['num_heads']
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self.num_layers = len(self.depths)
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self.out_indices = out_indices
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self.use_abs_pos_embed = use_abs_pos_embed
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self.auto_pad = auto_pad
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_patch_cfg = {
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'img_size': img_size,
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'in_channels': in_channels,
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'embed_dims': self.embed_dims,
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'conv_cfg': dict(type='Conv2d', kernel_size=4, stride=4),
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'norm_cfg': dict(type='LN'),
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**patch_cfg
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}
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self.patch_embed = PatchEmbed(**_patch_cfg)
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num_patches = self.patch_embed.num_patches
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patches_resolution = self.patch_embed.patches_resolution
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self.patches_resolution = patches_resolution
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if self.use_abs_pos_embed:
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self.absolute_pos_embed = nn.Parameter(
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torch.zeros(1, num_patches, self.embed_dims))
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self.drop_after_pos = nn.Dropout(p=drop_rate)
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# stochastic depth
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total_depth = sum(self.depths)
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dpr = [
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x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
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] # stochastic depth decay rule
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self.stages = ModuleList()
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embed_dims = self.embed_dims
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input_resolution = patches_resolution
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for i, (depth,
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num_heads) in enumerate(zip(self.depths, self.num_heads)):
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if isinstance(stage_cfgs, Sequence):
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stage_cfg = stage_cfgs[i]
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else:
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stage_cfg = deepcopy(stage_cfgs)
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downsample = True if i < self.num_layers - 1 else False
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_stage_cfg = {
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'embed_dims': embed_dims,
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'depth': depth,
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'num_heads': num_heads,
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'downsample': downsample,
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'input_resolution': input_resolution,
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'drop_paths': dpr[:depth],
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'auto_pad': auto_pad,
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**stage_cfg
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}
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stage = SwinBlockSequence(**_stage_cfg)
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self.stages.append(stage)
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dpr = dpr[depth:]
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embed_dims = stage.out_channels
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input_resolution = stage.out_resolution
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for i in out_indices:
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if norm_cfg is not None:
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norm_layer = build_norm_layer(norm_cfg, embed_dims)[1]
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else:
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norm_layer = nn.Identity()
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self.add_module(f'norm{i}', norm_layer)
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def init_weights(self):
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super(SwinTransformer, self).init_weights()
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if (isinstance(self.init_cfg, dict)
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and self.init_cfg['type'] == 'Pretrained'):
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# Suppress default init if use pretrained model.
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return
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if self.use_abs_pos_embed:
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trunc_normal_(self.absolute_pos_embed, std=0.02)
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def forward(self, x):
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x = self.patch_embed(x)
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if self.use_abs_pos_embed:
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x = x + self.absolute_pos_embed
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x = self.drop_after_pos(x)
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outs = []
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for i, stage in enumerate(self.stages):
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x = stage(x)
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if i in self.out_indices:
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norm_layer = getattr(self, f'norm{i}')
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out = norm_layer(x)
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out = out.view(-1, *stage.out_resolution,
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stage.out_channels).permute(0, 3, 1,
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2).contiguous()
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outs.append(out)
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return tuple(outs)
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, *args,
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**kwargs):
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"""load checkpoints."""
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# Names of some parameters in has been changed.
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version = local_metadata.get('version', None)
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if (version is None
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or version < 2) and self.__class__ is SwinTransformer:
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final_stage_num = len(self.stages) - 1
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state_dict_keys = list(state_dict.keys())
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for k in state_dict_keys:
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if k.startswith('norm.') or k.startswith('backbone.norm.'):
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convert_key = k.replace('norm.', f'norm{final_stage_num}.')
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state_dict[convert_key] = state_dict[k]
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del state_dict[k]
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super()._load_from_state_dict(state_dict, prefix, local_metadata,
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*args, **kwargs)
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