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from itertools import repeat
import torch
import torch.nn as nn
import torch.nn.functional as F
from .droppath import DropPath
from .swin import Mlp
def constant_init(tensor, constant=0.0):
nn.init.constant_(tensor, constant)
return tensor
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return x
return tuple(repeat(x, n))
return parse
class Mlp(nn.Module):
def __init__(
self,
in_features=None,
hidden_features=None,
out_features=None,
activation=F.gelu,
drop=0.0,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = activation
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x, train: bool = True):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x) if train else x
x = self.fc2(x)
x = self.drop(x) if train else x
return x
class Attention(nn.Module):
"""
Default multihead attention
"""
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
nn.init.xavier_uniform_(self.qkv.weight)
nn.init.xavier_uniform_(self.proj.weight)
def forward(self, x, train: bool = True):
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn) if train else attn
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x) if train else x
return x
def window_partition1d(x, window_size):
B, W, C = x.shape
x = x.view(B, W // window_size, window_size, C)
windows = x.view(-1, window_size, C)
return windows
def window_reverse1d(windows, window_size, W: int):
B = int(windows.shape[0] / (W / window_size))
x = windows.view(B, W // window_size, window_size, -1)
x = x.view(B, W, -1)
return x
def get_relative_position_index1d(win_w):
# get pair-wise relative position index for each token inside the window
coords = torch.stack(torch.meshgrid(torch.arange(win_w)))
relative_coords = coords[:, :, None] - coords[:, None, :] # 1, Ww, Ww
relative_coords = relative_coords.permute(1, 2, 0) # Ww, Ww, 1
relative_coords[:, :, 0] += win_w - 1 # shift to start from 0
return relative_coords.sum(-1) # Ww*Ww
class WindowedAttentionHead(nn.Module):
def __init__(self, head_dim, window_size, shift_windows=False, attn_drop=0.0):
super().__init__()
self.head_dim = head_dim
self.window_size = window_size
self.shift_windows = shift_windows
self.attn_drop = attn_drop
self.scale = self.head_dim**-0.5
self.window_area = self.window_size * 1
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * window_size - 1, 1))
)
nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02)
# Get relative position index
self.register_buffer(
"relative_position_index", get_relative_position_index1d(window_size)
)
self.drop_layer = nn.Dropout(attn_drop) if attn_drop > 0 else None
if shift_windows:
self.shift_size = window_size // 2
else:
self.shift_size = 0
assert 0 <= self.shift_size < self.window_size, (
"shift_size must in 0-window_size"
)
def forward(self, q, k, v, train: bool = True):
B, W, C = q.shape
mask = None
if self.shift_size > 0:
img_mask = torch.zeros((1, W, 1), device=q.device)
cnt = 0
for w in (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
):
img_mask[:, w, :] = cnt
cnt += 1
mask_windows = window_partition1d(img_mask, self.window_size)
mask_windows = mask_windows.view(-1, self.window_size)
mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
mask = mask.masked_fill(mask != 0, -100.0).masked_fill(mask == 0, 0.0)
q = torch.roll(q, shifts=-self.shift_size, dims=1)
k = torch.roll(k, shifts=-self.shift_size, dims=1)
v = torch.roll(v, shifts=-self.shift_size, dims=1)
q = window_partition1d(q, self.window_size)
k = window_partition1d(k, self.window_size)
v = window_partition1d(v, self.window_size)
attn = (q @ k.transpose(-2, -1)) * self.scale
if train:
attn = attn + self._get_rel_pos_bias()
else:
attn = attn + self._get_rel_pos_bias()
if mask is not None:
B_, N, _ = attn.shape
num_win = mask.shape[0]
attn = attn.view(B_ // num_win, num_win, N, N) + mask.unsqueeze(0)
attn = attn.view(-1, N, N)
attn = attn.softmax(dim=-1)
else:
attn = attn.softmax(dim=-1)
if self.drop_layer is not None and train:
attn = self.drop_layer(attn)
x = attn @ v
# merge windows
shifted_x = window_reverse1d(x, self.window_size, W=W)
if self.shift_size > 0:
x = torch.roll(shifted_x, shifts=self.shift_size, dims=1)
else:
x = shifted_x
return x, attn
def _get_rel_pos_bias(self):
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)
].view(self.window_area, self.window_area, -1) # Ww,Ww,1
relative_position_bias = relative_position_bias.permute(2, 0, 1) # 1, Ww, Ww
return relative_position_bias
class AttentionHead(nn.Module):
def __init__(self, head_dim, attn_drop=0.0):
super().__init__()
self.head_dim = head_dim
self.scale = head_dim**-0.5
self.drop_layer = nn.Dropout(attn_drop) if attn_drop > 0 else None
def forward(self, q, k, v, train: bool = True):
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
if self.drop_layer is not None and train:
attn = self.drop_layer(attn)
x = attn @ v
return x, attn
class WindowedMultiHeadAttention(nn.Module):
def __init__(
self,
dim,
window_sizes,
shift_windows=False,
num_heads=8,
qkv_bias=False,
attn_drop=0.0,
proj_drop=0.0,
):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
nn.init.xavier_uniform_(self.qkv.weight)
if isinstance(window_sizes, int):
window_sizes = _ntuple(num_heads)(window_sizes)
else:
assert len(window_sizes) == num_heads
self.attn_heads = nn.ModuleList()
for i in range(num_heads):
ws_i = window_sizes[i]
if ws_i == 0:
self.attn_heads.append(AttentionHead(self.head_dim, attn_drop))
else:
self.attn_heads.append(
WindowedAttentionHead(
self.head_dim,
window_size=ws_i,
shift_windows=shift_windows,
attn_drop=attn_drop,
)
)
self.proj = nn.Linear(dim, dim)
nn.init.xavier_uniform_(self.proj.weight)
self.drop_layer = nn.Dropout(proj_drop) if proj_drop > 0 else None
def forward(self, x, train: bool = True):
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 3, 0, 1, 4)
)
q, k, v = qkv[0], qkv[1], qkv[2]
o = []
for i in range(self.num_heads):
head_i, attn_i = self.attn_heads[i](q[i], k[i], v[i], train=train)
o.append(head_i.unsqueeze(0))
o = torch.cat(o, dim=0)
o = o.permute(1, 2, 0, 3).reshape(B, N, -1)
o = self.proj(o)
if self.drop_layer is not None and train:
o = self.drop_layer(o)
return o
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5):
super().__init__()
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x * self.gamma
class BNWrapper(nn.Module):
def __init__(
self, num_features, use_running_average=True, use_bias=True, use_scale=True
):
super().__init__()
self.bn = nn.BatchNorm1d(num_features, affine=use_scale or use_bias)
def forward(self, x, train=True):
return self.bn(x, train)
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
drop=0.0,
attn_drop=0.0,
init_values=None,
drop_path=0.0,
act_layer=F.gelu,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
out_features=dim,
activation=act_layer,
drop=drop,
)
self.init_values = init_values
if init_values is not None:
self.layer_scale1 = LayerScale(dim, init_values)
self.layer_scale2 = LayerScale(dim, init_values)
def forward(self, x, train: bool = True):
outputs1 = self.attn(self.norm1(x), train=train)
if self.init_values is not None:
outputs1 = self.layer_scale1(outputs1)
x = x + self.drop_path(outputs1) if train else x + outputs1
outputs2 = self.mlp(self.norm2(x), train=train)
if self.init_values is not None:
outputs2 = self.layer_scale2(outputs2)
x = x + self.drop_path(outputs2) if train else x + outputs2
return x
class MWMHABlock(nn.Module):
def __init__(
self,
dim,
num_heads,
window_sizes,
shift_windows=False,
mlp_ratio=4.0,
qkv_bias=False,
drop=0.0,
attn_drop=0.0,
init_values=None,
drop_path=0.0,
act_layer=F.gelu,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.wmha = WindowedMultiHeadAttention(
dim,
window_sizes=window_sizes,
shift_windows=shift_windows,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
out_features=dim,
activation=act_layer,
drop=drop,
)
self.init_values = init_values
if init_values is not None:
self.layer_scale1 = LayerScale(dim, init_values)
self.layer_scale2 = LayerScale(dim, init_values)
def forward(self, x, train: bool = True):
outputs1 = self.wmha(self.norm1(x), train=train)
if self.init_values is not None:
outputs1 = self.layer_scale1(outputs1)
x = x + self.drop_path(outputs1) if train else x + outputs1
outputs2 = self.mlp(self.norm2(x), train=train)
if self.init_values is not None:
outputs2 = self.layer_scale2(outputs2)
x = x + self.drop_path(outputs2) if train else x + outputs2
return x
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