385 lines
15 KiB
Python
385 lines
15 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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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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from mmcv.cnn.bricks.drop import build_dropout
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from mmengine.model import BaseModule
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from mmengine.model.utils import trunc_normal_
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from mmcls.registry import MODELS
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from .helpers import to_2tuple
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class WindowMSA(BaseModule):
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"""Window based multi-head self-attention (W-MSA) module with relative
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position bias.
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Args:
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embed_dims (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 q, k, v.
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Defaults to True.
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qk_scale (float, optional): Override default qk scale of
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``head_dim ** -0.5`` if set. Defaults to None.
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attn_drop (float, optional): Dropout ratio of attention weight.
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Defaults to 0.
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proj_drop (float, optional): Dropout ratio of output. Defaults to 0.
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init_cfg (dict, optional): The extra config for initialization.
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Defaults to None.
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"""
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def __init__(self,
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embed_dims,
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window_size,
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num_heads,
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qkv_bias=True,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.,
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init_cfg=None):
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super().__init__(init_cfg)
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self.embed_dims = embed_dims
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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_embed_dims = embed_dims // num_heads
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self.scale = qk_scale or head_embed_dims**-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),
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num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# About 2x faster than original impl
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Wh, Ww = self.window_size
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rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
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rel_position_index = rel_index_coords + rel_index_coords.T
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rel_position_index = rel_position_index.flip(1).contiguous()
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self.register_buffer('relative_position_index', rel_position_index)
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self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(embed_dims, embed_dims)
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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 init_weights(self):
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super(WindowMSA, self).init_weights()
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trunc_normal_(self.relative_position_bias_table, std=0.02)
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def forward(self, x, mask=None):
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"""
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Args:
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x (tensor): input features with shape of (num_windows*B, N, C)
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mask (tensor, Optional): mask with shape of (num_windows, Wh*Ww,
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Wh*Ww), value should be between (-inf, 0].
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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,
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C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[
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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[
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self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1],
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self.window_size[0] * self.window_size[1],
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-1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(
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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,
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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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@staticmethod
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def double_step_seq(step1, len1, step2, len2):
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seq1 = torch.arange(0, step1 * len1, step1)
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seq2 = torch.arange(0, step2 * len2, step2)
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return (seq1[:, None] + seq2[None, :]).reshape(1, -1)
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@MODELS.register_module()
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class ShiftWindowMSA(BaseModule):
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"""Shift Window Multihead Self-Attention Module.
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Args:
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embed_dims (int): Number of input channels.
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num_heads (int): Number of attention heads.
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window_size (int): The height and width of the window.
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shift_size (int, optional): The shift step of each window towards
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right-bottom. If zero, act as regular window-msa. Defaults to 0.
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qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
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Defaults to True
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qk_scale (float | None, optional): Override default qk scale of
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head_dim ** -0.5 if set. Defaults to None.
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attn_drop (float, optional): Dropout ratio of attention weight.
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Defaults to 0.0.
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proj_drop (float, optional): Dropout ratio of output. Defaults to 0.
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dropout_layer (dict, optional): The dropout_layer used before output.
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Defaults to dict(type='DropPath', drop_prob=0.).
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pad_small_map (bool): If True, pad the small feature map to the window
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size, which is common used in detection and segmentation. If False,
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avoid shifting window and shrink the window size to the size of
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feature map, which is common used in classification.
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Defaults to False.
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init_cfg (dict, optional): The extra config for initialization.
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Defaults to None.
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"""
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def __init__(self,
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embed_dims,
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num_heads,
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window_size,
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shift_size=0,
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qkv_bias=True,
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qk_scale=None,
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attn_drop=0,
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proj_drop=0,
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dropout_layer=dict(type='DropPath', drop_prob=0.),
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pad_small_map=False,
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input_resolution=None,
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auto_pad=None,
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init_cfg=None):
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super().__init__(init_cfg)
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if input_resolution is not None or auto_pad is not None:
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warnings.warn(
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'The ShiftWindowMSA in new version has supported auto padding '
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'and dynamic input shape in all condition. And the argument '
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'`auto_pad` and `input_resolution` have been deprecated.',
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DeprecationWarning)
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self.shift_size = shift_size
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self.window_size = window_size
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assert 0 <= self.shift_size < self.window_size
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self.w_msa = WindowMSA(
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embed_dims=embed_dims,
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window_size=to_2tuple(self.window_size),
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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)
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self.drop = build_dropout(dropout_layer)
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self.pad_small_map = pad_small_map
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def forward(self, query, hw_shape):
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B, L, C = query.shape
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H, W = hw_shape
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assert L == H * W, f"The query length {L} doesn't match the input "\
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f'shape ({H}, {W}).'
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query = query.view(B, H, W, C)
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window_size = self.window_size
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shift_size = self.shift_size
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if min(H, W) == window_size:
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# If not pad small feature map, avoid shifting when the window size
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# is equal to the size of feature map. It's to align with the
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# behavior of the original implementation.
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shift_size = shift_size if self.pad_small_map else 0
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elif min(H, W) < window_size:
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# In the original implementation, the window size will be shrunk
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# to the size of feature map. The behavior is different with
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# swin-transformer for downstream tasks. To support dynamic input
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# shape, we don't allow this feature.
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assert self.pad_small_map, \
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f'The input shape ({H}, {W}) is smaller than the window ' \
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f'size ({window_size}). Please set `pad_small_map=True`, or ' \
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'decrease the `window_size`.'
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pad_r = (window_size - W % window_size) % window_size
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pad_b = (window_size - H % window_size) % window_size
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query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b))
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H_pad, W_pad = query.shape[1], query.shape[2]
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# cyclic shift
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if shift_size > 0:
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query = torch.roll(
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query, shifts=(-shift_size, -shift_size), dims=(1, 2))
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attn_mask = self.get_attn_mask((H_pad, W_pad),
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window_size=window_size,
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shift_size=shift_size,
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device=query.device)
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# nW*B, window_size, window_size, C
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query_windows = self.window_partition(query, window_size)
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# nW*B, window_size*window_size, C
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query_windows = query_windows.view(-1, window_size**2, C)
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# W-MSA/SW-MSA (nW*B, window_size*window_size, C)
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attn_windows = self.w_msa(query_windows, mask=attn_mask)
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# merge windows
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attn_windows = attn_windows.view(-1, window_size, window_size, C)
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# B H' W' C
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shifted_x = self.window_reverse(attn_windows, H_pad, W_pad,
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window_size)
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# reverse cyclic shift
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if self.shift_size > 0:
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x = torch.roll(
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shifted_x, shifts=(shift_size, shift_size), dims=(1, 2))
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else:
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x = shifted_x
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if H != H_pad or W != W_pad:
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x = x[:, :H, :W, :].contiguous()
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x = x.view(B, H * W, C)
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x = self.drop(x)
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return x
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@staticmethod
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def window_reverse(windows, H, W, window_size):
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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,
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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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@staticmethod
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def window_partition(x, window_size):
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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,
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window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
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windows = windows.view(-1, window_size, window_size, C)
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return windows
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@staticmethod
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def get_attn_mask(hw_shape, window_size, shift_size, device=None):
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if shift_size > 0:
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img_mask = torch.zeros(1, *hw_shape, 1, device=device)
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h_slices = (slice(0, -window_size), slice(-window_size,
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-shift_size),
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slice(-shift_size, None))
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w_slices = (slice(0, -window_size), slice(-window_size,
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-shift_size),
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slice(-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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# nW, window_size, window_size, 1
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mask_windows = ShiftWindowMSA.window_partition(
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img_mask, window_size)
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mask_windows = mask_windows.view(-1, window_size * 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, -100.0)
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attn_mask = attn_mask.masked_fill(attn_mask == 0, 0.0)
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else:
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attn_mask = None
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return attn_mask
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class MultiheadAttention(BaseModule):
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"""Multi-head Attention Module.
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This module implements multi-head attention that supports different input
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dims and embed dims. And it also supports a shortcut from ``value``, which
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is useful if input dims is not the same with embed dims.
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Args:
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embed_dims (int): The embedding dimension.
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num_heads (int): Parallel attention heads.
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input_dims (int, optional): The input dimension, and if None,
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use ``embed_dims``. Defaults to None.
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attn_drop (float): Dropout rate of the dropout layer after the
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attention calculation of query and key. Defaults to 0.
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proj_drop (float): Dropout rate of the dropout layer after the
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output projection. Defaults to 0.
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dropout_layer (dict): The dropout config before adding the shortcut.
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Defaults to ``dict(type='Dropout', drop_prob=0.)``.
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qkv_bias (bool): If True, add a learnable bias to q, k, v.
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Defaults to True.
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qk_scale (float, optional): Override default qk scale of
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``head_dim ** -0.5`` if set. Defaults to None.
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proj_bias (bool) If True, add a learnable bias to output projection.
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Defaults to True.
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v_shortcut (bool): Add a shortcut from value to output. It's usually
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used if ``input_dims`` is different from ``embed_dims``.
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Defaults to False.
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init_cfg (dict, optional): The Config for initialization.
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Defaults to None.
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"""
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def __init__(self,
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embed_dims,
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num_heads,
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input_dims=None,
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attn_drop=0.,
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proj_drop=0.,
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dropout_layer=dict(type='Dropout', drop_prob=0.),
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qkv_bias=True,
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qk_scale=None,
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proj_bias=True,
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v_shortcut=False,
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init_cfg=None):
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super(MultiheadAttention, self).__init__(init_cfg=init_cfg)
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self.input_dims = input_dims or embed_dims
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self.embed_dims = embed_dims
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self.num_heads = num_heads
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self.v_shortcut = v_shortcut
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self.head_dims = embed_dims // num_heads
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self.scale = qk_scale or self.head_dims**-0.5
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self.qkv = nn.Linear(self.input_dims, embed_dims * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(embed_dims, embed_dims, bias=proj_bias)
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self.proj_drop = nn.Dropout(proj_drop)
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self.out_drop = build_dropout(dropout_layer)
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def forward(self, x):
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B, N, _ = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
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self.head_dims).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, self.embed_dims)
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x = self.proj(x)
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x = self.out_drop(self.proj_drop(x))
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if self.v_shortcut:
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x = v.squeeze(1) + x
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return x
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