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tnt.py
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tnt.py
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""" 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
from typing import Optional
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import Mlp, DropPath, trunc_normal_, _assert, to_2tuple
from ._builder import build_model_with_cfg
from ._registry import register_model
from .vision_transformer import resize_pos_embed
__all__ = ['TNT'] # model_registry will add each entrypoint fn to this
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'pixel_embed.proj', '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.unbind(0) # 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,
dim_out,
num_pixel,
num_heads_in=4,
num_heads_out=12,
mlp_ratio=4.,
qkv_bias=False,
proj_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(dim)
self.attn_in = Attention(
dim,
dim,
num_heads=num_heads_in,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.norm_mlp_in = norm_layer(dim)
self.mlp_in = Mlp(
in_features=dim,
hidden_features=int(dim * 4),
out_features=dim,
act_layer=act_layer,
drop=proj_drop,
)
self.norm1_proj = norm_layer(dim)
self.proj = nn.Linear(dim * num_pixel, dim_out, bias=True)
# Outer transformer
self.norm_out = norm_layer(dim_out)
self.attn_out = Attention(
dim_out,
dim_out,
num_heads=num_heads_out,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm_mlp = norm_layer(dim_out)
self.mlp = Mlp(
in_features=dim_out,
hidden_features=int(dim_out * mlp_ratio),
out_features=dim_out,
act_layer=act_layer,
drop=proj_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 = torch.cat(
[patch_embed[:, 0:1], patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1))],
dim=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__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
# grid_size property necessary for resizing positional embedding
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
num_patches = (self.grid_size[0]) * (self.grid_size[1])
self.img_size = img_size
self.num_patches = num_patches
self.in_dim = in_dim
new_patch_size = [math.ceil(ps / stride) for ps in patch_size]
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[0],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
_assert(W == self.img_size[1],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
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[0], self.new_patch_size[1])
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,
global_pool='token',
embed_dim=768,
inner_dim=48,
depth=12,
num_heads_inner=4,
num_heads_outer=12,
mlp_ratio=4.,
qkv_bias=False,
drop_rate=0.,
pos_drop_rate=0.,
proj_drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=nn.LayerNorm,
first_stride=4,
):
super().__init__()
assert global_pool in ('', 'token', 'avg')
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.head_hidden_size = self.embed_dim = embed_dim # for consistency with other models
self.grad_checkpointing = False
self.pixel_embed = PixelEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
in_dim=inner_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[0] * new_patch_size[1]
self.norm1_proj = norm_layer(num_pixel * inner_dim)
self.proj = nn.Linear(num_pixel * inner_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, inner_dim, new_patch_size[0], new_patch_size[1]))
self.pos_drop = nn.Dropout(p=pos_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=inner_dim,
dim_out=embed_dim,
num_pixel=num_pixel,
num_heads_in=num_heads_inner,
num_heads_out=num_heads_outer,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_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_drop = nn.Dropout(drop_rate)
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'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^cls_token|patch_pos|pixel_pos|pixel_embed|norm[12]_proj|proj', # stem and embed / pos
blocks=[
(r'^blocks\.(\d+)', None),
(r'^norm', (99999,)),
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'token', 'avg')
self.global_pool = global_pool
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)
if self.grad_checkpointing and not torch.jit.is_scripting():
for blk in self.blocks:
pixel_embed, patch_embed = checkpoint(blk, pixel_embed, patch_embed)
else:
for blk in self.blocks:
pixel_embed, patch_embed = blk(pixel_embed, patch_embed)
patch_embed = self.norm(patch_embed)
return patch_embed
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, 1:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
if state_dict['patch_pos'].shape != model.patch_pos.shape:
state_dict['patch_pos'] = resize_pos_embed(state_dict['patch_pos'],
model.patch_pos, getattr(model, 'num_tokens', 1), model.pixel_embed.grid_size)
return state_dict
def _create_tnt(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(
TNT, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
return model
@register_model
def tnt_s_patch16_224(pretrained=False, **kwargs) -> TNT:
model_cfg = dict(
patch_size=16, embed_dim=384, inner_dim=24, depth=12, num_heads_outer=6,
qkv_bias=False)
model = _create_tnt('tnt_s_patch16_224', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model
@register_model
def tnt_b_patch16_224(pretrained=False, **kwargs) -> TNT:
model_cfg = dict(
patch_size=16, embed_dim=640, inner_dim=40, depth=12, num_heads_outer=10,
qkv_bias=False)
model = _create_tnt('tnt_b_patch16_224', pretrained=pretrained, **dict(model_cfg, **kwargs))
return model