# --------------------------------------------------------
# TinyViT Model Architecture
# Copyright (c) 2022 Microsoft
# Adapted from LeViT and Swin Transformer
# LeViT: (https://github.com/facebookresearch/levit)
# Swin: (https://github.com/microsoft/swin-transformer)
# Build the TinyViT Model
# --------------------------------------------------------
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath as TimmDropPath,\
to_2tuple, trunc_normal_
from timm.models.registry import register_model
from typing import Tuple
class Conv2d_BN(torch.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', torch.nn.Conv2d(
a, b, ks, stride, pad, dilation, groups, bias=False))
bn = torch.nn.BatchNorm2d(b)
torch.nn.init.constant_(bn.weight, bn_weight_init)
torch.nn.init.constant_(bn.bias, 0)
self.add_module('bn', bn)
@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 = torch.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)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class DropPath(TimmDropPath):
def __init__(self, drop_prob=None):
super().__init__(drop_prob=drop_prob)
self.drop_prob = drop_prob
def __repr__(self):
msg = super().__repr__()
msg += f'(drop_prob={self.drop_prob})'
return msg
class PatchEmbed(nn.Module):
def __init__(self, in_chans, embed_dim, resolution, activation):
super().__init__()
img_size: Tuple[int, int] = to_2tuple(resolution)
self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)
self.num_patches = self.patches_resolution[0] * \
self.patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
n = embed_dim
self.seq = nn.Sequential(
Conv2d_BN(in_chans, n // 2, 3, 2, 1),
activation(),
Conv2d_BN(n // 2, n, 3, 2, 1),
)
def forward(self, x):
return self.seq(x)
class MBConv(nn.Module):
def __init__(self, in_chans, out_chans, expand_ratio,
activation, drop_path):
super().__init__()
self.in_chans = in_chans
self.hidden_chans = int(in_chans * expand_ratio)
self.out_chans = out_chans
self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)
self.act1 = activation()
self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans,
ks=3, stride=1, pad=1, groups=self.hidden_chans)
self.act2 = activation()
self.conv3 = Conv2d_BN(
self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)
self.act3 = activation()
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
x = self.drop_path(x)
x += shortcut
x = self.act3(x)
return x
class PatchMerging(nn.Module):
def __init__(self, input_resolution, dim, out_dim, activation):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.out_dim = out_dim
self.act = activation()
self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)
stride_c=2
if(out_dim==320 or out_dim==448 or out_dim==576):
stride_c=1
self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)
self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)
def forward(self, x):
if x.ndim == 3:
H, W = self.input_resolution
B = len(x)
# (B, C, H, W)
x = x.view(B, H, W, -1).permute(0, 3, 1, 2)
x = self.conv1(x)
x = self.act(x)
x = self.conv2(x)
x = self.act(x)
x = self.conv3(x)
x = x.flatten(2).transpose(1, 2)
return x
class ConvLayer(nn.Module):
def __init__(self, dim, input_resolution, depth,
activation,
drop_path=0., downsample=None, use_checkpoint=False,
out_dim=None,
conv_expand_ratio=4.,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
MBConv(dim, dim, conv_expand_ratio, activation,
drop_path[i] if isinstance(drop_path, list) else drop_path,
)
for i in range(depth)])
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution, dim=dim, out_dim=out_dim, activation=activation)
else:
self.downsample = None
def forward(self, x):
for blk in self.blocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
if self.downsample is not None:
x = self.downsample(x)
return x
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None,
out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.norm = nn.LayerNorm(in_features)
self.fc1 = nn.Linear(in_features, hidden_features)
self.fc2 = nn.Linear(hidden_features, out_features)
self.act = act_layer()
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.norm(x)
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
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.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 TinyViTBlock(nn.Module):
r""" TinyViT Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int, int]): Input resolution.
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, 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.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
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)
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)
x = self.attn(x)
# 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))
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}"
class BasicLayer(nn.Module):
""" A basic TinyViT layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): Dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
local_conv_size: the kernel size of the depthwise convolution between attention and MLP. Default: 3
activation: the activation function. Default: nn.GELU
out_dim: the output dimension of the layer. Default: dim
"""
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
mlp_ratio=4., drop=0.,
drop_path=0., downsample=None, use_checkpoint=False,
local_conv_size=3,
activation=nn.GELU,
out_dim=None,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
TinyViTBlock(dim=dim, input_resolution=input_resolution,
num_heads=num_heads, window_size=window_size,
mlp_ratio=mlp_ratio,
drop=drop,
drop_path=drop_path[i] if isinstance(
drop_path, list) else drop_path,
local_conv_size=local_conv_size,
activation=activation,
)
for i in range(depth)])
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution, dim=dim, out_dim=out_dim, activation=activation)
else:
self.downsample = None
def forward(self, x):
for blk in self.blocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
if self.downsample is not None:
x = self.downsample(x)
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class TinyViT(nn.Module):
def __init__(self, img_size=224, in_chans=3, num_classes=1000,
embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_sizes=[7, 7, 14, 7],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.1,
use_checkpoint=False,
mbconv_expand_ratio=4.0,
local_conv_size=3,
layer_lr_decay=1.0,
):
super().__init__()
self.img_size=img_size
self.num_classes = num_classes
self.depths = depths
self.num_layers = len(depths)
self.mlp_ratio = mlp_ratio
activation = nn.GELU
self.patch_embed = PatchEmbed(in_chans=in_chans,
embed_dim=embed_dims[0],
resolution=img_size,
activation=activation)
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# stochastic depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate,
sum(depths))] # stochastic depth decay rule
# build layers
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
kwargs = dict(dim=embed_dims[i_layer],
input_resolution=(patches_resolution[0] // (2 ** (i_layer-1 if i_layer == 3 else i_layer)),
patches_resolution[1] // (2 ** (i_layer-1 if i_layer == 3 else i_layer))),
# input_resolution=(patches_resolution[0] // (2 ** i_layer),
# patches_resolution[1] // (2 ** i_layer)),
depth=depths[i_layer],
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
downsample=PatchMerging if (
i_layer < self.num_layers - 1) else None,
use_checkpoint=use_checkpoint,
out_dim=embed_dims[min(
i_layer + 1, len(embed_dims) - 1)],
activation=activation,
)
if i_layer == 0:
layer = ConvLayer(
conv_expand_ratio=mbconv_expand_ratio,
**kwargs,
)
else:
layer = BasicLayer(
num_heads=num_heads[i_layer],
window_size=window_sizes[i_layer],
mlp_ratio=self.mlp_ratio,
drop=drop_rate,
local_conv_size=local_conv_size,
**kwargs)
self.layers.append(layer)
# Classifier head
self.norm_head = nn.LayerNorm(embed_dims[-1])
self.head = nn.Linear(
embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
# init weights
self.apply(self._init_weights)
self.set_layer_lr_decay(layer_lr_decay)
self.neck = nn.Sequential(
nn.Conv2d(
embed_dims[-1],
256,
kernel_size=1,
bias=False,
),
LayerNorm2d(256),
nn.Conv2d(
256,
256,
kernel_size=3,
padding=1,
bias=False,
),
LayerNorm2d(256),
)
def set_layer_lr_decay(self, layer_lr_decay):
decay_rate = layer_lr_decay
# layers -> blocks (depth)
depth = sum(self.depths)
lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]
#print("LR SCALES:", lr_scales)
def _set_lr_scale(m, scale):
for p in m.parameters():
p.lr_scale = scale
self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))
i = 0
for layer in self.layers:
for block in layer.blocks:
block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))
i += 1
if layer.downsample is not None:
layer.downsample.apply(
lambda x: _set_lr_scale(x, lr_scales[i - 1]))
assert i == depth
for m in [self.norm_head, self.head]:
m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))
for k, p in self.named_parameters():
p.param_name = k
def _check_lr_scale(m):
for p in m.parameters():
assert hasattr(p, 'lr_scale'), p.param_name
self.apply(_check_lr_scale)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {'attention_biases'}
def forward_features(self, x):
# x: (N, C, H, W)
x = self.patch_embed(x)
x = self.layers[0](x)
start_i = 1
interm_embeddings=[]
for i in range(start_i, len(self.layers)):
layer = self.layers[i]
x = layer(x)
# print('x shape:', x.shape, '---i:', i)
if i == 1:
interm_embeddings.append(x.view(x.shape[0], 64, 64, -1))
B,_,C=x.size()
x = x.view(B, 64, 64, C)
x=x.permute(0, 3, 1, 2)
x=self.neck(x)
return x, interm_embeddings
def forward(self, x):
x, interm_embeddings = self.forward_features(x)
#x = self.norm_head(x)
#x = self.head(x)
# print('come to here is correct'* 3)
return x, interm_embeddings
_checkpoint_url_format = \
'https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/{}.pth'
_provided_checkpoints = {
'tiny_vit_5m_224': 'tiny_vit_5m_22kto1k_distill',
'tiny_vit_11m_224': 'tiny_vit_11m_22kto1k_distill',
'tiny_vit_21m_224': 'tiny_vit_21m_22kto1k_distill',
'tiny_vit_21m_384': 'tiny_vit_21m_22kto1k_384_distill',
'tiny_vit_21m_512': 'tiny_vit_21m_22kto1k_512_distill',
}
def register_tiny_vit_model(fn):
'''Register a TinyViT model
It is a wrapper of `register_model` with loading the pretrained checkpoint.
'''
def fn_wrapper(pretrained=False, **kwargs):
model = fn()
if pretrained:
model_name = fn.__name__
assert model_name in _provided_checkpoints, \
f'Sorry that the checkpoint `{model_name}` is not provided yet.'
url = _checkpoint_url_format.format(
_provided_checkpoints[model_name])
checkpoint = torch.hub.load_state_dict_from_url(
url=url,
map_location='cpu', check_hash=False,
)
model.load_state_dict(checkpoint['model'])
return model
# rename the name of fn_wrapper
fn_wrapper.__name__ = fn.__name__
return register_model(fn_wrapper)
@register_tiny_vit_model
def tiny_vit_5m_224(pretrained=False, num_classes=1000, drop_path_rate=0.0):
return TinyViT(
num_classes=num_classes,
embed_dims=[64, 128, 160, 320],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 5, 10],
window_sizes=[7, 7, 14, 7],
drop_path_rate=drop_path_rate,
)
@register_tiny_vit_model
def tiny_vit_11m_224(pretrained=False, num_classes=1000, drop_path_rate=0.1):
return TinyViT(
num_classes=num_classes,
embed_dims=[64, 128, 256, 448],
depths=[2, 2, 6, 2],
num_heads=[2, 4, 8, 14],
window_sizes=[7, 7, 14, 7],
drop_path_rate=drop_path_rate,
)
@register_tiny_vit_model
def tiny_vit_21m_224(pretrained=False, num_classes=1000, drop_path_rate=0.2):
return TinyViT(
num_classes=num_classes,
embed_dims=[96, 192, 384, 576],
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 18],
window_sizes=[7, 7, 14, 7],
drop_path_rate=drop_path_rate,
)
@register_tiny_vit_model
def tiny_vit_21m_384(pretrained=False, num_classes=1000, drop_path_rate=0.1):
return TinyViT(
img_size=384,
num_classes=num_classes,
embed_dims=[96, 192, 384, 576],
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 18],
window_sizes=[12, 12, 24, 12],
drop_path_rate=drop_path_rate,
)
@register_tiny_vit_model
def tiny_vit_21m_512(pretrained=False, num_classes=1000, drop_path_rate=0.1):
return TinyViT(
img_size=512,
num_classes=num_classes,
embed_dims=[96, 192, 384, 576],
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 18],
window_sizes=[16, 16, 32, 16],
drop_path_rate=drop_path_rate,
)