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Source: https://github.com/novendrastywn/MicroViT (model/shvit.py)
Included verbatim; do not edit — keep in sync with upstream if needed.
"""
from __future__ import annotations
import itertools
import torch
import torch.nn as nn
from timm.models.vision_transformer import trunc_normal_
from timm.models.layers import SqueezeExcite
class GroupNorm(nn.GroupNorm):
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs)
class Conv2d_BN(nn.Sequential):
def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
groups=1, bn_weight_init=1):
super().__init__()
self.add_module('c', nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False))
self.add_module('bn', nn.BatchNorm2d(b))
nn.init.constant_(self.bn.weight, bn_weight_init)
nn.init.constant_(self.bn.bias, 0)
@torch.no_grad()
def fuse(self):
c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
m = nn.Conv2d(
w.size(1) * self.c.groups, w.size(0), w.shape[2:],
stride=self.c.stride, padding=self.c.padding,
dilation=self.c.dilation, groups=self.c.groups,
device=c.weight.device,
)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class BN_Linear(nn.Sequential):
def __init__(self, a, b, bias=True, std=0.02):
super().__init__()
self.add_module('bn', nn.BatchNorm1d(a))
self.add_module('l', nn.Linear(a, b, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
nn.init.constant_(self.l.bias, 0)
@torch.no_grad()
def fuse(self):
bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
b = (l.weight @ b[:, None]).view(-1) + self.l.bias
m = nn.Linear(w.size(1), w.size(0))
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class PatchMerging(nn.Module):
def __init__(self, dim, out_dim):
super().__init__()
hid_dim = int(dim * 4)
self.conv1 = Conv2d_BN(dim, hid_dim, 1, 1, 0)
self.act = nn.ReLU()
self.conv2 = Conv2d_BN(hid_dim, hid_dim, 3, 2, 1, groups=hid_dim)
self.se = SqueezeExcite(hid_dim, 0.25)
self.conv3 = Conv2d_BN(hid_dim, out_dim, 1, 1, 0)
def forward(self, x):
return self.conv3(self.se(self.act(self.conv2(self.act(self.conv1(x))))))
class Residual(nn.Module):
def __init__(self, m, drop=0.0):
super().__init__()
self.m = m
self.drop = drop
def forward(self, x):
if self.training and self.drop > 0:
return x + self.m(x) * torch.rand(
x.size(0), 1, 1, 1, device=x.device
).ge_(self.drop).div(1 - self.drop).detach()
return x + self.m(x)
@torch.no_grad()
def fuse(self):
if isinstance(self.m, Conv2d_BN):
m = self.m.fuse()
assert m.groups == m.in_channels
identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)
identity = nn.functional.pad(identity, [1, 1, 1, 1])
m.weight += identity.to(m.weight.device)
return m
return self
class FFN(nn.Module):
def __init__(self, ed, h):
super().__init__()
self.pw1 = Conv2d_BN(ed, h)
self.act = nn.ReLU()
self.pw2 = Conv2d_BN(h, ed, bn_weight_init=0)
def forward(self, x):
return self.pw2(self.act(self.pw1(x)))
class SHSA(nn.Module):
"""Single-Head Self-Attention."""
def __init__(self, dim, qk_dim, pdim):
super().__init__()
self.scale = qk_dim ** -0.5
self.qk_dim = qk_dim
self.dim = dim
self.pdim = pdim
self.pre_norm = GroupNorm(pdim)
self.qkv = Conv2d_BN(pdim, qk_dim * 2 + pdim)
self.proj = nn.Sequential(nn.ReLU(), Conv2d_BN(dim, dim, bn_weight_init=0))
def forward(self, x):
B, C, H, W = x.shape
x1, x2 = torch.split(x, [self.pdim, self.dim - self.pdim], dim=1)
x1 = self.pre_norm(x1)
qkv = self.qkv(x1)
q, k, v = qkv.split([self.qk_dim, self.qk_dim, self.pdim], dim=1)
q, k, v = q.flatten(2), k.flatten(2), v.flatten(2)
attn = (q.transpose(-2, -1) @ k) * self.scale
attn = attn.softmax(dim=-1)
x1 = (v @ attn.transpose(-2, -1)).reshape(B, self.pdim, H, W)
return self.proj(torch.cat([x1, x2], dim=1))
class BasicBlock(nn.Module):
def __init__(self, dim, qk_dim, pdim, type):
super().__init__()
if type == "s":
self.conv = Residual(Conv2d_BN(dim, dim, 3, 1, 1, groups=dim, bn_weight_init=0))
self.mixer = Residual(SHSA(dim, qk_dim, pdim))
self.ffn = Residual(FFN(dim, int(dim * 2)))
elif type == "i":
self.conv = Residual(Conv2d_BN(dim, dim, 3, 1, 1, groups=dim, bn_weight_init=0))
self.mixer = nn.Identity()
self.ffn = Residual(FFN(dim, int(dim * 2)))
def forward(self, x):
return self.ffn(self.mixer(self.conv(x)))
class SHViT(nn.Module):
def __init__(
self,
in_chans: int = 3,
num_classes: int = 1000,
embed_dim: list[int] = [128, 256, 384],
partial_dim: list[int] = [32, 64, 96],
qk_dim: list[int] = [16, 16, 16],
depth: list[int] = [1, 2, 3],
types: list[str] = ["s", "s", "s"],
down_ops: list = [["subsample", 2], ["subsample", 2], [""]],
distillation: bool = False,
):
super().__init__()
self.patch_embed = nn.Sequential(
Conv2d_BN(in_chans, embed_dim[0] // 8, 3, 2, 1), nn.ReLU(),
Conv2d_BN(embed_dim[0] // 8, embed_dim[0] // 4, 3, 2, 1), nn.ReLU(),
Conv2d_BN(embed_dim[0] // 4, embed_dim[0] // 2, 3, 2, 1), nn.ReLU(),
Conv2d_BN(embed_dim[0] // 2, embed_dim[0], 3, 2, 1),
)
self.blocks1: list = []
self.blocks2: list = []
self.blocks3: list = []
for i, (ed, kd, pd, dpth, do, t) in enumerate(
zip(embed_dim, qk_dim, partial_dim, depth, down_ops, types)
):
for _ in range(dpth):
getattr(self, f"blocks{i + 1}").append(BasicBlock(ed, kd, pd, t))
if do[0] == "subsample":
blk = getattr(self, f"blocks{i + 2}")
blk.append(nn.Sequential(
Residual(Conv2d_BN(embed_dim[i], embed_dim[i], 3, 1, 1, groups=embed_dim[i])),
Residual(FFN(embed_dim[i], int(embed_dim[i] * 2))),
))
blk.append(PatchMerging(*embed_dim[i: i + 2]))
blk.append(nn.Sequential(
Residual(Conv2d_BN(embed_dim[i + 1], embed_dim[i + 1], 3, 1, 1, groups=embed_dim[i + 1])),
Residual(FFN(embed_dim[i + 1], int(embed_dim[i + 1] * 2))),
))
self.blocks1 = nn.Sequential(*self.blocks1)
self.blocks2 = nn.Sequential(*self.blocks2)
self.blocks3 = nn.Sequential(*self.blocks3)
self.head = BN_Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
self.distillation = distillation
if distillation:
self.head_dist = BN_Linear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x):
x = self.patch_embed(x)
x = self.blocks1(x)
x = self.blocks2(x)
x = self.blocks3(x)
x = nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
if self.distillation:
x = self.head(x), self.head_dist(x)
if not self.training:
x = (x[0] + x[1]) / 2
else:
x = self.head(x)
return x
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