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import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...common import Adapter
from .utils import Conv2d_BN, DropPath, Mlp
class Attention(torch.nn.Module):
def __init__(self, dim, key_dim, num_heads=8,
attn_ratio=4,
resolution=(14, 14),
):
super().__init__()
# (h, w)
assert isinstance(resolution, tuple) and len(resolution) == 2
self.num_heads = num_heads
self.scale = key_dim ** -0.5
self.key_dim = key_dim
self.nh_kd = nh_kd = key_dim * num_heads
self.d = int(attn_ratio * key_dim)
self.dh = int(attn_ratio * key_dim) * num_heads
self.attn_ratio = attn_ratio
h = self.dh + nh_kd * 2
self.norm = nn.LayerNorm(dim)
self.qkv = nn.Linear(dim, h)
self.proj = nn.Linear(self.dh, dim)
points = list(itertools.product(
range(resolution[0]), range(resolution[1])))
N = len(points)
attention_offsets = {}
idxs = []
for p1 in points:
for p2 in points:
offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
if offset not in attention_offsets:
attention_offsets[offset] = len(attention_offsets)
idxs.append(attention_offsets[offset])
self.attention_biases = torch.nn.Parameter(
torch.zeros(num_heads, len(attention_offsets)))
self.register_buffer('attention_bias_idxs',
torch.LongTensor(idxs).view(N, N),
persistent=False)
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and hasattr(self, 'ab'):
del self.ab
else:
self.ab = self.attention_biases[:, self.attention_bias_idxs]
# self.register_buffer('ab',
# self.attention_biases[:, self.attention_bias_idxs],
# persistent=False)
def forward(self, x): # x (B,N,C)
B, N, _ = x.shape
# Normalization
x = self.norm(x)
qkv = self.qkv(x)
# (B, N, num_heads, d)
q, k, v = qkv.view(B, N, self.num_heads, -
1).split([self.key_dim, self.key_dim, self.d], dim=3)
# (B, num_heads, N, d)
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
attn = (
(q @ k.transpose(-2, -1)) * self.scale
+
(self.attention_biases[:, self.attention_bias_idxs] if self.training else self.ab)
)
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
x = self.proj(x)
return x
class TinyViTAdapterBlock(nn.Module):
r""" TinyViT Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int, int]): Input resulotion.
num_heads (int): Number of attention heads.
window_size (int): Window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): Dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
local_conv_size (int): the kernel size of the convolution between
Attention and MLP. Default: 3
activation: the activation function. Default: nn.GELU
"""
def __init__(self, args, dim, input_resolution, num_heads, window_size=7,
mlp_ratio=4., drop=0., drop_path=0.,
local_conv_size=3,
activation=nn.GELU,
):
super().__init__()
self.args = args,
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
assert window_size > 0, 'window_size must be greater than 0'
self.window_size = window_size
self.mlp_ratio = mlp_ratio
if(args.mid_dim != None):
adapter_dim = args.mid_dim
else:
adapter_dim = dim
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
assert dim % num_heads == 0, 'dim must be divisible by num_heads'
head_dim = dim // num_heads
window_resolution = (window_size, window_size)
self.attn = Attention(dim, head_dim, num_heads,
attn_ratio=1, resolution=window_resolution)
mlp_hidden_dim = int(dim * mlp_ratio)
mlp_activation = activation
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
act_layer=mlp_activation, drop=drop)
self.MLP_Adapter = Adapter(adapter_dim, skip_connect=False) # MLP-adapter, no skip connection
self.Space_Adapter = Adapter(adapter_dim) # with skip connection
self.Depth_Adapter = Adapter(adapter_dim, skip_connect=False) # no skip connection
pad = local_conv_size // 2
self.local_conv = Conv2d_BN(
dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)
def forward(self, x):
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
res_x = x
if H == self.window_size and W == self.window_size:
x = self.attn(x)
else:
x = x.view(B, H, W, C)
pad_b = (self.window_size - H %
self.window_size) % self.window_size
pad_r = (self.window_size - W %
self.window_size) % self.window_size
padding = pad_b > 0 or pad_r > 0
if padding:
x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))
pH, pW = H + pad_b, W + pad_r
nH = pH // self.window_size
nW = pW // self.window_size
# window partition
x = x.view(B, nH, self.window_size, nW, self.window_size, C).transpose(2, 3).reshape(
B * nH * nW, self.window_size * self.window_size, C)
## 3d branch
if self.args[0].thd:
from einops import rearrange
hh, ww = x.shape[1], x.shape[2]
depth = self.args.chunk
xd = rearrange(x, '(b d) h w c -> (b h w) d c ', d=depth)
# xd = rearrange(xd, '(b d) n c -> (b n) d c', d=self.in_chans)
xd = self.norm1(xd)
dh, _ = closest_numbers(depth)
xd = rearrange(xd, 'bhw (dh dw) c -> bhw dh dw c', dh= dh)
xd = self.Depth_Adapter(self.attn(xd))
xd = rearrange(xd, '(b n) dh dw c ->(b dh dw) n c', n= hh * ww )
x = self.attn(x)
x = self.Space_Adapter(x)
if self.args[0].thd:
xd = rearrange(xd, 'b (hh ww) c -> b hh ww c', hh= hh )
x = x + xd
# window reverse
x = x.view(B, nH, nW, self.window_size, self.window_size,
C).transpose(2, 3).reshape(B, pH, pW, C)
if padding:
x = x[:, :H, :W].contiguous()
x = x.view(B, L, C)
x = res_x + self.drop_path(x)
x = x.transpose(1, 2).reshape(B, C, H, W)
x = self.local_conv(x)
x = x.view(B, C, L).transpose(1, 2)
x = x + self.drop_path(self.mlp(x)) + 0.5 * self.MLP_Adapter(x)
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}"
def closest_numbers(target):
a = int(target ** 0.5)
b = a + 1
while True:
if a * b == target:
return (a, b)
elif a * b < target:
b += 1
else:
a -= 1 |