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pytorch-image-models/timm/models/beit.py

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""" BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
Model from official source: https://github.com/microsoft/unilm/tree/master/beit
At this point only the 1k fine-tuned classification weights and model configs have been added,
see original source above for pre-training models and procedure.
Modifications by / Copyright 2021 Ross Wightman, original copyrights below
"""
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# Based on timm and DeiT code bases
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit/
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
import math
from functools import partial
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .helpers import build_model_with_cfg
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
from .registry import register_model
from .vision_transformer import checkpoint_filter_fn
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': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = {
'beit_base_patch16_224': _cfg(
url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth'),
'beit_base_patch16_384': _cfg(
url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth',
input_size=(3, 384, 384), crop_pct=1.0,
),
'beit_base_patch16_224_in22k': _cfg(
url='https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k_ft22k.pth',
num_classes=21841,
),
'beit_large_patch16_224': _cfg(
url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth'),
'beit_large_patch16_384': _cfg(
url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth',
input_size=(3, 384, 384), crop_pct=1.0,
),
'beit_large_patch16_512': _cfg(
url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth',
input_size=(3, 512, 512), crop_pct=1.0,
),
'beit_large_patch16_224_in22k': _cfg(
url='https://unilm.blob.core.windows.net/beit/beit_large_patch16_224_pt22k_ft22k.pth',
num_classes=21841,
),
}
class Attention(nn.Module):
def __init__(
self, dim, num_heads=8, qkv_bias=False, attn_drop=0.,
proj_drop=0., window_size=None, attn_head_dim=None):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
self.register_buffer('k_bias', torch.zeros(all_head_dim), persistent=False)
self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
else:
self.q_bias = None
self.k_bias = None
self.v_bias = None
if window_size:
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(
torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(window_size[0])
coords_w = torch.arange(window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = \
torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index", relative_position_index)
else:
self.window_size = None
self.relative_position_bias_table = None
self.relative_position_index = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(all_head_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
B, N, C = x.shape
qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
if self.relative_position_bias_table is not None:
relative_position_bias = \
self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1] + 1,
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if rel_pos_bias is not None:
attn = attn + rel_pos_bias
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):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
window_size=None, attn_head_dim=None):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
window_size=window_size, attn_head_dim=attn_head_dim)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
if init_values:
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
else:
self.gamma_1, self.gamma_2 = None, None
def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
if self.gamma_1 is None:
x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
x = x + self.drop_path(self.mlp(self.norm2(x)))
else:
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class RelativePositionBias(nn.Module):
def __init__(self, window_size, num_heads):
super().__init__()
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(
torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(window_size[0])
coords_w = torch.arange(window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = \
torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = self.num_relative_distance - 3
relative_position_index[0:, 0] = self.num_relative_distance - 2
relative_position_index[0, 0] = self.num_relative_distance - 1
self.register_buffer("relative_position_index", relative_position_index)
# trunc_normal_(self.relative_position_bias_table, std=.02)
def forward(self):
relative_position_bias = \
self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1] + 1,
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
class Beit(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=None,
use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
use_mean_pooling=True, init_scale=0.001):
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
# self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
if use_abs_pos_emb:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
else:
self.pos_embed = None
self.pos_drop = nn.Dropout(p=drop_rate)
if use_shared_rel_pos_bias:
self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.grid_size, num_heads=num_heads)
else:
self.rel_pos_bias = None
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.use_rel_pos_bias = use_rel_pos_bias
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
init_values=init_values, window_size=self.patch_embed.grid_size if use_rel_pos_bias else None)
for i in range(depth)])
self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
# trunc_normal_(self.mask_token, std=.02)
self.fix_init_weight()
if isinstance(self.head, nn.Linear):
trunc_normal_(self.head.weight, std=.02)
self.head.weight.data.mul_(init_scale)
self.head.bias.data.mul_(init_scale)
def fix_init_weight(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
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)
def get_num_layers(self):
return len(self.blocks)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', '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):
x = self.patch_embed(x)
batch_size, seq_len, _ = x.size()
cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
if self.pos_embed is not None:
x = x + self.pos_embed
x = self.pos_drop(x)
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
for blk in self.blocks:
x = blk(x, rel_pos_bias=rel_pos_bias)
x = self.norm(x)
if self.fc_norm is not None:
t = x[:, 1:, :]
return self.fc_norm(t.mean(1))
else:
return x[:, 0]
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _create_beit(variant, pretrained=False, default_cfg=None, **kwargs):
default_cfg = default_cfg or default_cfgs[variant]
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Beit models.')
model = build_model_with_cfg(
Beit, variant, pretrained,
default_cfg=default_cfg,
# FIXME an updated filter fn needed to interpolate rel pos emb if fine tuning to diff model sizes
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
return model
@register_model
def beit_base_patch16_224(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
model = _create_beit('beit_base_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def beit_base_patch16_384(pretrained=False, **kwargs):
model_kwargs = dict(
img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
model = _create_beit('beit_base_patch16_384', pretrained=pretrained, **model_kwargs)
return model
@register_model
def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1, **kwargs)
model = _create_beit('beit_base_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
return model
@register_model
def beit_large_patch16_224(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def beit_large_patch16_384(pretrained=False, **kwargs):
model_kwargs = dict(
img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_384', pretrained=pretrained, **model_kwargs)
return model
@register_model
def beit_large_patch16_512(pretrained=False, **kwargs):
model_kwargs = dict(
img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_512', pretrained=pretrained, **model_kwargs)
return model
@register_model
def beit_large_patch16_224_in22k(pretrained=False, **kwargs):
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5, **kwargs)
model = _create_beit('beit_large_patch16_224_in22k', pretrained=pretrained, **model_kwargs)
return model
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