You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
248 lines
9.8 KiB
248 lines
9.8 KiB
""" Transformer in Transformer (TNT) in PyTorch
|
|
|
|
A PyTorch implement of TNT as described in
|
|
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
|
|
|
|
The official mindspore code is released and available at
|
|
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
|
|
"""
|
|
import math
|
|
import torch
|
|
import torch.nn as nn
|
|
from functools import partial
|
|
|
|
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
|
from timm.models.helpers import load_pretrained
|
|
from timm.models.layers import DropPath, trunc_normal_
|
|
from timm.models.vision_transformer import Mlp
|
|
from timm.models.registry import register_model
|
|
|
|
|
|
def _cfg(url='', **kwargs):
|
|
return {
|
|
'url': url,
|
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
|
|
'crop_pct': .9, 'interpolation': 'bicubic',
|
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
|
'classifier': 'head',
|
|
**kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = {
|
|
'tnt_s_patch16_224': _cfg(
|
|
url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
),
|
|
'tnt_b_patch16_224': _cfg(
|
|
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
|
|
),
|
|
}
|
|
|
|
|
|
class Attention(nn.Module):
|
|
""" Multi-Head Attention
|
|
"""
|
|
def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
|
|
super().__init__()
|
|
self.hidden_dim = hidden_dim
|
|
self.num_heads = num_heads
|
|
head_dim = hidden_dim // num_heads
|
|
self.head_dim = head_dim
|
|
self.scale = head_dim ** -0.5
|
|
|
|
self.qk = nn.Linear(dim, hidden_dim * 2, bias=qkv_bias)
|
|
self.v = nn.Linear(dim, dim, bias=qkv_bias)
|
|
self.attn_drop = nn.Dropout(attn_drop, inplace=True)
|
|
self.proj = nn.Linear(dim, dim)
|
|
self.proj_drop = nn.Dropout(proj_drop, inplace=True)
|
|
|
|
def forward(self, x):
|
|
B, N, C = x.shape
|
|
qk = self.qk(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
|
|
q, k = qk[0], qk[1] # make torchscript happy (cannot use tensor as tuple)
|
|
v = self.v(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
|
|
|
|
attn = (q @ k.transpose(-2, -1)) * self.scale
|
|
attn = attn.softmax(dim=-1)
|
|
attn = self.attn_drop(attn)
|
|
|
|
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
return x
|
|
|
|
|
|
class Block(nn.Module):
|
|
""" TNT Block
|
|
"""
|
|
def __init__(self, dim, in_dim, num_pixel, num_heads=12, in_num_head=4, mlp_ratio=4.,
|
|
qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
|
super().__init__()
|
|
# Inner transformer
|
|
self.norm_in = norm_layer(in_dim)
|
|
self.attn_in = Attention(
|
|
in_dim, in_dim, num_heads=in_num_head, qkv_bias=qkv_bias,
|
|
attn_drop=attn_drop, proj_drop=drop)
|
|
|
|
self.norm_mlp_in = norm_layer(in_dim)
|
|
self.mlp_in = Mlp(in_features=in_dim, hidden_features=int(in_dim * 4),
|
|
out_features=in_dim, act_layer=act_layer, drop=drop)
|
|
|
|
self.norm1_proj = norm_layer(in_dim)
|
|
self.proj = nn.Linear(in_dim * num_pixel, dim, bias=True)
|
|
# Outer transformer
|
|
self.norm_out = norm_layer(dim)
|
|
self.attn_out = Attention(
|
|
dim, 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. else nn.Identity()
|
|
|
|
self.norm_mlp = norm_layer(dim)
|
|
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio),
|
|
out_features=dim, act_layer=act_layer, drop=drop)
|
|
|
|
def forward(self, pixel_embed, patch_embed):
|
|
# inner
|
|
pixel_embed = pixel_embed + self.drop_path(self.attn_in(self.norm_in(pixel_embed)))
|
|
pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
|
|
# outer
|
|
B, N, C = patch_embed.size()
|
|
patch_embed[:, 1:] = patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1))
|
|
patch_embed = patch_embed + self.drop_path(self.attn_out(self.norm_out(patch_embed)))
|
|
patch_embed = patch_embed + self.drop_path(self.mlp(self.norm_mlp(patch_embed)))
|
|
return pixel_embed, patch_embed
|
|
|
|
|
|
class PixelEmbed(nn.Module):
|
|
""" Image to Pixel Embedding
|
|
"""
|
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4):
|
|
super().__init__()
|
|
num_patches = (img_size // patch_size) ** 2
|
|
self.img_size = img_size
|
|
self.num_patches = num_patches
|
|
self.in_dim = in_dim
|
|
new_patch_size = math.ceil(patch_size / stride)
|
|
self.new_patch_size = new_patch_size
|
|
|
|
self.proj = nn.Conv2d(in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride)
|
|
self.unfold = nn.Unfold(kernel_size=new_patch_size, stride=new_patch_size)
|
|
|
|
def forward(self, x, pixel_pos):
|
|
B, C, H, W = x.shape
|
|
assert H == self.img_size and W == self.img_size, \
|
|
f"Input image size ({H}*{W}) doesn't match model ({self.img_size}*{self.img_size})."
|
|
x = self.proj(x)
|
|
x = self.unfold(x)
|
|
x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size, self.new_patch_size)
|
|
x = x + pixel_pos
|
|
x = x.reshape(B * self.num_patches, self.in_dim, -1).transpose(1, 2)
|
|
return x
|
|
|
|
|
|
class TNT(nn.Module):
|
|
""" Transformer in Transformer - https://arxiv.org/abs/2103.00112
|
|
"""
|
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, in_dim=48, depth=12,
|
|
num_heads=12, in_num_head=4, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
|
|
drop_path_rate=0., norm_layer=nn.LayerNorm, first_stride=4):
|
|
super().__init__()
|
|
self.num_classes = num_classes
|
|
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
|
|
|
self.pixel_embed = PixelEmbed(
|
|
img_size=img_size, patch_size=patch_size, in_chans=in_chans, in_dim=in_dim, stride=first_stride)
|
|
num_patches = self.pixel_embed.num_patches
|
|
self.num_patches = num_patches
|
|
new_patch_size = self.pixel_embed.new_patch_size
|
|
num_pixel = new_patch_size ** 2
|
|
|
|
self.norm1_proj = norm_layer(num_pixel * in_dim)
|
|
self.proj = nn.Linear(num_pixel * in_dim, embed_dim)
|
|
self.norm2_proj = norm_layer(embed_dim)
|
|
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
|
self.patch_pos = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
|
|
self.pixel_pos = nn.Parameter(torch.zeros(1, in_dim, new_patch_size, new_patch_size))
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
|
blocks = []
|
|
for i in range(depth):
|
|
blocks.append(Block(
|
|
dim=embed_dim, in_dim=in_dim, num_pixel=num_pixel, num_heads=num_heads, in_num_head=in_num_head,
|
|
mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate,
|
|
drop_path=dpr[i], norm_layer=norm_layer))
|
|
self.blocks = nn.ModuleList(blocks)
|
|
self.norm = norm_layer(embed_dim)
|
|
|
|
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
trunc_normal_(self.cls_token, std=.02)
|
|
trunc_normal_(self.patch_pos, std=.02)
|
|
trunc_normal_(self.pixel_pos, std=.02)
|
|
self.apply(self._init_weights)
|
|
|
|
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(self):
|
|
return {'patch_pos', 'pixel_pos', 'cls_token'}
|
|
|
|
def get_classifier(self):
|
|
return self.head
|
|
|
|
def reset_classifier(self, num_classes, global_pool=''):
|
|
self.num_classes = num_classes
|
|
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
def forward_features(self, x):
|
|
B = x.shape[0]
|
|
pixel_embed = self.pixel_embed(x, self.pixel_pos)
|
|
|
|
patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, self.num_patches, -1))))
|
|
patch_embed = torch.cat((self.cls_token.expand(B, -1, -1), patch_embed), dim=1)
|
|
patch_embed = patch_embed + self.patch_pos
|
|
patch_embed = self.pos_drop(patch_embed)
|
|
|
|
for blk in self.blocks:
|
|
pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
|
|
|
|
patch_embed = self.norm(patch_embed)
|
|
return patch_embed[:, 0]
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.head(x)
|
|
return x
|
|
|
|
|
|
@register_model
|
|
def tnt_s_patch16_224(pretrained=False, **kwargs):
|
|
model = TNT(patch_size=16, embed_dim=384, in_dim=24, depth=12, num_heads=6, in_num_head=4,
|
|
qkv_bias=False, **kwargs)
|
|
model.default_cfg = default_cfgs['tnt_s_patch16_224']
|
|
if pretrained:
|
|
load_pretrained(
|
|
model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tnt_b_patch16_224(pretrained=False, **kwargs):
|
|
model = TNT(patch_size=16, embed_dim=640, in_dim=40, depth=12, num_heads=10, in_num_head=4,
|
|
qkv_bias=False, **kwargs)
|
|
model.default_cfg = default_cfgs['tnt_b_patch16_224']
|
|
if pretrained:
|
|
load_pretrained(
|
|
model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
|
|
return model
|