Source code for mmpretrain.models.backbones.vit_eva02
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn.bricks.drop import build_dropout
from mmengine.model import BaseModule, ModuleList
from mmpretrain.registry import MODELS
from ..utils import (RotaryEmbeddingFast, SwiGLUFFN, build_norm_layer,
resize_pos_embed)
from .vision_transformer import VisionTransformer
class AttentionWithRoPE(BaseModule):
"""Multi-head Attention Module with 2D sincos position embedding (RoPE).
Args:
embed_dims (int): The embedding dimension.
num_heads (int): Parallel attention heads.
attn_drop (float): Dropout rate of the dropout layer after the
attention calculation of query and key. Defaults to 0.
proj_drop (float): Dropout rate of the dropout layer after the
output projection. Defaults to 0.
qkv_bias (bool): If True, add a learnable bias to q and v. Note
that we follows the official implementation where ``k_bias``
is 0. Defaults to True.
qk_scale (float, optional): Override default qk scale of
``head_dim ** -0.5`` if set. Defaults to None.
proj_bias (bool) If True, add a learnable bias to output projection.
Defaults to True.
rope (:obj:`torch.nn.Module`, optional): If it is an object of the
``RotaryEmbedding``, the rotation of the token position will be
performed before the softmax. Defaults to None.
with_cls_token (bool): Whether concatenating class token into image
tokens as transformer input. Defaults to True.
init_cfg (dict, optional): The Config for initialization.
Defaults to None.
"""
def __init__(self,
embed_dims,
num_heads,
attn_drop=0.,
proj_drop=0.,
qkv_bias=True,
qk_scale=None,
proj_bias=True,
rope=None,
with_cls_token=True,
init_cfg=None):
super(AttentionWithRoPE, self).__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
self.num_heads = num_heads
self.head_dims = embed_dims // num_heads
self.scale = qk_scale or self.head_dims**-0.5
self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(embed_dims, embed_dims, bias=proj_bias)
self.proj_drop = nn.Dropout(proj_drop)
self.with_cls_token = with_cls_token
self.rope = rope
def forward(self, x, patch_resolution):
B, N, _ = x.shape
qkv = self.qkv(x)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(dim=0)
if self.rope:
if self.with_cls_token:
q_t = q[:, :, 1:, :]
ro_q_t = self.rope(q_t, patch_resolution)
q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)
k_t = k[:, :, 1:, :] if self.with_cls_token else k
ro_k_t = self.rope(k_t, patch_resolution)
k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)
else:
q = self.rope(q, patch_resolution)
k = self.rope(k, patch_resolution)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1).type_as(x)
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 EVA02EndcoderLayer(BaseModule):
"""Implements one encoder EVA02EndcoderLayer in EVA02.
Args:
embed_dims (int): The feature dimension
num_heads (int): Parallel attention heads
feedforward_channels (int): The hidden dimension of FFNs.
sub_ln (bool): Whether to add the sub layer normalization
in the attention module. Defaults to False.
attn_drop (float): Dropout rate of the dropout layer after the
attention calculation of query and key. Defaults to 0.
proj_drop (float): Dropout rate of the dropout layer after the
output projection. Defaults to 0.
qkv_bias (bool): enable bias for qkv if True. Defaults to True.
qk_scale (float, optional): Override default qk scale of
``head_dim ** -0.5`` if set. Defaults to None.
proj_bias (bool): enable bias for projection in the attention module
if True. Defaults to True.
rope (:obj:`torch.nn.Module`, optional): RotaryEmbedding object
in the attention module. Defaults to None.
drop_rate (float): Dropout rate in the mlp module. Defaults to 0.
drop_path_rate (float): Stochastic depth rate. Defaults to 0.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
embed_dims,
num_heads,
feedforward_channels,
sub_ln=False,
attn_drop=0.,
proj_drop=0.,
qkv_bias=False,
qk_scale=None,
proj_bias=True,
rope=None,
with_cls_token=True,
drop_rate=0.,
drop_path_rate=0.,
norm_cfg=dict(type='LN'),
init_cfg=None):
super(EVA02EndcoderLayer, self).__init__(init_cfg=init_cfg)
self.norm1 = build_norm_layer(norm_cfg, embed_dims)
self.attn = AttentionWithRoPE(
embed_dims=embed_dims,
num_heads=num_heads,
attn_drop=attn_drop,
proj_drop=proj_drop,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
proj_bias=proj_bias,
rope=rope,
with_cls_token=with_cls_token)
self.drop_path = build_dropout(
dict(type='DropPath', drop_prob=drop_path_rate))
self.norm2 = build_norm_layer(norm_cfg, embed_dims)
if drop_rate > 0:
dropout_layer = dict(type='Dropout', drop_prob=drop_rate)
else:
dropout_layer = None
if sub_ln:
ffn_norm = norm_cfg
else:
ffn_norm = None
self.mlp = SwiGLUFFN(
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
dropout_layer=dropout_layer,
norm_cfg=ffn_norm,
add_identity=False,
)
def forward(self, x, patch_resolution):
inputs = x
x = self.norm1(x)
x = self.attn(x, patch_resolution)
x = self.drop_path(x)
x = inputs + x
inputs = x
x = self.norm2(x)
x = self.mlp(x)
x = self.drop_path(x)
x = inputs + x
return x
[docs]@MODELS.register_module()
class ViTEVA02(VisionTransformer):
"""EVA02 Vision Transformer.
A PyTorch implement of : `EVA-02: A Visual Representation for Neon Genesis
<https://arxiv.org/abs/2303.11331>`_
Args:
arch (str | dict): Vision Transformer architecture. If use string,
choose from 'tiny', 'small', 'base', 'large'. If use dict,
it should have below keys:
- **embed_dims** (int): The dimensions of embedding.
- **num_layers** (int): The number of transformer encoder layers.
- **num_heads** (int): The number of heads in attention modules.
- **mlp_ratio** (float): The ratio of the mlp module.
Defaults to 'tiny'.
sub_ln (bool): Whether to add the sub layer normalization in swiglu.
Defaults to False.
drop_rate (float): Probability of an element to be zeroed in the
mlp module. Defaults to 0.
attn_drop_rate (float): Probability of an element to be zeroed after
the softmax in the attention. Defaults to 0.
proj_drop_rate (float): Probability of an element to be zeroed after
projection in the attention. Defaults to 0.
drop_path_rate (float): stochastic depth rate. Defaults to 0.
qkv_bias (bool): Whether to add bias for qkv in attention modules.
Defaults to True.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
with_cls_token (bool): Whether concatenating class token into image
tokens as transformer input. Defaults to True.
layer_cfgs (Sequence | dict): Configs of each transformer layer in
encoder. Defaults to an empty dict.
**kwargs(dict, optional): Other args for Vision Transformer.
"""
arch_zoo = {
**dict.fromkeys(
['t', 'ti', 'tiny'], {
'embed_dims': 192,
'num_layers': 12,
'num_heads': 3,
'feedforward_channels': int(192 * 4 * 2 / 3)
}),
**dict.fromkeys(
['s', 'small'], {
'embed_dims': 384,
'num_layers': 12,
'num_heads': 6,
'feedforward_channels': int(384 * 4 * 2 / 3)
}),
**dict.fromkeys(
['b', 'base'], {
'embed_dims': 768,
'num_layers': 12,
'num_heads': 12,
'feedforward_channels': int(768 * 4 * 2 / 3)
}),
**dict.fromkeys(
['l', 'large'], {
'embed_dims': 1024,
'num_layers': 24,
'num_heads': 16,
'feedforward_channels': int(1024 * 4 * 2 / 3)
})
}
num_extra_tokens = 1 # class token
OUT_TYPES = {'raw', 'cls_token', 'featmap', 'avg_featmap'}
def __init__(self,
arch='tiny',
sub_ln=False,
drop_rate=0.,
attn_drop_rate=0.,
proj_drop_rate=0.,
drop_path_rate=0.,
qkv_bias=True,
norm_cfg=dict(type='LN'),
with_cls_token=True,
layer_cfgs=dict(),
**kwargs):
# set essential args for Vision Transformer
kwargs.update(
arch=arch,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
norm_cfg=norm_cfg,
with_cls_token=with_cls_token)
super(ViTEVA02, self).__init__(**kwargs)
self.num_heads = self.arch_settings['num_heads']
# Set RoPE
head_dim = self.embed_dims // self.num_heads
self.rope = RotaryEmbeddingFast(
embed_dims=head_dim, patch_resolution=self.patch_resolution)
# stochastic depth decay rule
dpr = np.linspace(0, drop_path_rate, self.num_layers)
self.layers = ModuleList()
if isinstance(layer_cfgs, dict):
layer_cfgs = [layer_cfgs] * self.num_layers
for i in range(self.num_layers):
_layer_cfg = dict(
embed_dims=self.embed_dims,
num_heads=self.num_heads,
feedforward_channels=self.
arch_settings['feedforward_channels'],
sub_ln=sub_ln,
norm_cfg=norm_cfg,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_rate=drop_rate,
qkv_bias=qkv_bias,
rope=self.rope,
with_cls_token=with_cls_token,
drop_path_rate=dpr[i])
_layer_cfg.update(layer_cfgs[i])
self.layers.append(EVA02EndcoderLayer(**_layer_cfg))
def forward(self, x):
B = x.shape[0]
x, patch_resolution = self.patch_embed(x)
if self.cls_token is not None:
# stole cls_tokens impl from Phil Wang, thanks
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x = x + resize_pos_embed(
self.pos_embed,
self.patch_resolution,
patch_resolution,
mode=self.interpolate_mode,
num_extra_tokens=self.num_extra_tokens)
x = self.drop_after_pos(x)
x = self.pre_norm(x)
outs = []
for i, layer in enumerate(self.layers):
x = layer(x, patch_resolution)
if i == len(self.layers) - 1 and self.final_norm:
x = self.ln1(x)
if i in self.out_indices:
outs.append(self._format_output(x, patch_resolution))
return tuple(outs)