Source code for mmpretrain.models.selfsup.beit
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Dict, List, Optional, Tuple, Union
import torch
from einops import rearrange
from mmengine.model import BaseModule
from mmengine.model.weight_init import trunc_normal_
from torch import nn
from mmpretrain.models.backbones import BEiTViT
from mmpretrain.models.utils import NormEMAVectorQuantizer, resize_pos_embed
from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .base import BaseSelfSupervisor
[docs]@MODELS.register_module()
class VQKD(BaseModule):
"""Vector-Quantized Knowledge Distillation.
The module only contains encoder and VectorQuantizer part
Modified from https://github.com/microsoft/unilm/blob/master/beit2/modeling_vqkd.py
Args:
encoder_config (dict): The config of encoder.
decoder_config (dict, optional): The config of decoder. Currently,
VQKD only support to build encoder. Defaults to None.
num_embed (int): Number of embedding vectors in the codebook. Defaults
to 8192.
embed_dims (int) : The dimension of embedding vectors in the codebook.
Defaults to 32.
decay (float): The decay parameter of EMA. Defaults to 0.99.
beta (float): The mutiplier for VectorQuantizer loss. Defaults to 1.
quantize_kmeans_init (bool): Whether to use k-means to initialize the
VectorQuantizer. Defaults to True.
init_cfg (dict or List[dict], optional): Initialization config dict.
Defaults to None.
""" # noqa: E501
def __init__(self,
encoder_config: dict,
decoder_config: Optional[dict] = None,
num_embed: int = 8192,
embed_dims: int = 32,
decay: float = 0.99,
beta: float = 1.0,
quantize_kmeans_init: bool = True,
init_cfg: Optional[dict] = None) -> None:
super().__init__(init_cfg=init_cfg)
self.encoder = BEiTViT(**encoder_config)
if decoder_config is not None:
self.decoder = BEiTViT(**decoder_config)
self.quantize = NormEMAVectorQuantizer(
num_embed=num_embed,
embed_dims=embed_dims,
beta=beta,
decay=decay,
kmeans_init=quantize_kmeans_init,
)
# task layer
self.encode_task_layer = nn.Sequential(
nn.Linear(self.encoder.arch_settings['embed_dims'],
self.encoder.arch_settings['embed_dims']), nn.Tanh(),
nn.Linear(self.encoder.arch_settings['embed_dims'], embed_dims))
[docs] def get_tokens(self, x: torch.Tensor) -> dict:
"""Get tokens for beit pre-training."""
_, embed_ind, _ = self.encode(x)
output = {}
output['token'] = embed_ind.view(x.shape[0], -1)
output['input_img'] = x
return output
[docs] def encode(
self, x: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Encode the input images and get corresponding results."""
encoder_features = self.encoder(x)[0]
B, C, N1, N2 = encoder_features.shape
encoder_features = encoder_features.permute(0, 2, 3,
1).reshape(B, N1 * N2, C)
with torch.cuda.amp.autocast(enabled=False):
to_quantizer_features = self.encode_task_layer(
encoder_features.type_as(self.encode_task_layer[-1].weight))
N = to_quantizer_features.shape[1]
h, w = int(math.sqrt(N)), int(math.sqrt(N))
to_quantizer_features = rearrange(
to_quantizer_features, 'b (h w) c -> b c h w', h=h,
w=w) # reshape for quantizer
quantize, loss, embed_ind = self.quantize(to_quantizer_features)
return quantize, embed_ind, loss
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
"""The forward function.
Currently, only support to get tokens.
"""
return self.get_tokens(x)['token']
[docs]@MODELS.register_module()
class BEiTPretrainViT(BEiTViT):
"""Vision Transformer for BEiT pre-training.
Args:
arch (str | dict): Vision Transformer architecture. If use string,
choose from 'small', 'base' and '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.
- **feedforward_channels** (int): The hidden dimensions in
feedforward modules.
Defaults to 'base'.
img_size (int | tuple): The expected input image shape. Because we
support dynamic input shape, just set the argument to the most
common input image shape. Defaults to 224.
patch_size (int | tuple): The patch size in patch embedding.
Defaults to 16.
in_channels (int): The num of input channels. Defaults to 3.
out_indices (Sequence | int): Output from which stages.
Defaults to -1, means the last stage.
drop_rate (float): Probability of an element to be zeroed.
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')``.
final_norm (bool): Whether to add a additional layer to normalize
final feature map. Defaults to True.
out_type (str): The type of output features. Please choose from
- ``"cls_token"``: The class token tensor with shape (B, C).
- ``"featmap"``: The feature map tensor from the patch tokens
with shape (B, C, H, W).
- ``"avg_featmap"``: The global averaged feature map tensor
with shape (B, C).
- ``"raw"``: The raw feature tensor includes patch tokens and
class tokens with shape (B, L, C).
It only works without input mask. Defaults to ``"avg_featmap"``.
with_cls_token (bool): Whether concatenating class token into image
tokens as transformer input. Defaults to True.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Defaults to -1.
use_abs_pos_emb (bool): Whether or not use absolute position embedding.
Defaults to False.
use_rel_pos_bias (bool): Whether or not use relative position bias.
Defaults to False.
use_shared_rel_pos_bias (bool): Whether or not use shared relative
position bias. Defaults to True.
layer_scale_init_value (float): The initialization value for
the learnable scaling of attention and FFN. Defaults to 0.1.
interpolate_mode (str): Select the interpolate mode for position
embeding vector resize. Defaults to "bicubic".
patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict.
layer_cfgs (Sequence | dict): Configs of each transformer layer in
encoder. Defaults to an empty dict.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
arch: str = 'base',
img_size: int = 224,
patch_size: int = 16,
in_channels: int = 3,
out_indices: int = -1,
drop_rate: float = 0,
drop_path_rate: float = 0,
norm_cfg: dict = dict(type='LN', eps=1e-6),
final_norm: bool = True,
out_type: str = 'raw',
frozen_stages: int = -1,
use_abs_pos_emb: bool = False,
use_rel_pos_bias: bool = False,
use_shared_rel_pos_bias: bool = True,
layer_scale_init_value: int = 0.1,
interpolate_mode: str = 'bicubic',
patch_cfg: dict = dict(padding=0),
layer_cfgs: dict = dict(),
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(
arch=arch,
img_size=img_size,
patch_size=patch_size,
in_channels=in_channels,
out_indices=out_indices,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
norm_cfg=norm_cfg,
final_norm=final_norm,
out_type=out_type,
with_cls_token=True,
frozen_stages=frozen_stages,
use_abs_pos_emb=use_abs_pos_emb,
use_shared_rel_pos_bias=use_shared_rel_pos_bias,
use_rel_pos_bias=use_rel_pos_bias,
layer_scale_init_value=layer_scale_init_value,
interpolate_mode=interpolate_mode,
patch_cfg=patch_cfg,
layer_cfgs=layer_cfgs,
init_cfg=init_cfg)
self.mask_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims))
[docs] def init_weights(self) -> None:
"""Initialize position embedding, patch embedding and cls token."""
super().init_weights()
if (isinstance(self.init_cfg, dict)
and self.init_cfg['type'] == 'Pretrained'):
# Suppress default init if use pretrained model.
return
trunc_normal_(self.cls_token, std=0.02)
trunc_normal_(self.mask_token, std=0.02)
self.rescale_init_weight()
[docs] def rescale_init_weight(self) -> None:
"""Rescale the initialized weights."""
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.layers):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.ffn.layers[1].weight.data, layer_id + 1)
[docs] def forward(self, x: torch.Tensor,
mask: Optional[torch.Tensor]) -> Tuple[torch.Tensor]:
"""The BEiT style forward function.
The function supports two kind of forward behaviors. If the ``mask`` is
not ``None``, the forward function will be executed as masked image
modeling pre-training; if the ``mask`` is ``None``, the forward
function will call ``super().forward()``, which extract features from
images without mask.
Args:
x (torch.Tensor): Input images, which is of shape (B x C x H x W).
mask (torch.Tensor, optional): Mask for input, which is of shape
(B x patch_resolution[0] x patch_resolution[1]).
Returns:
Tuple[torch.Tensor]: Hidden features.
"""
if mask is None:
return super().forward(x)
else:
x, patch_resolution = self.patch_embed(x)
# replace the masked visual tokens by mask_token
B, L, _ = x.shape
mask_token = self.mask_token.expand(B, L, -1)
w = mask.flatten(1).unsqueeze(-1).type_as(mask_token)
x = x * (1. - w) + mask_token * w
# 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)
if self.pos_embed is not None:
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)
self.shared_rel_pos_bias = self.rel_pos_bias().to(
mask.device) if self.rel_pos_bias is not None else None
outs = []
for i, layer in enumerate(self.layers):
x = layer(x, rel_pos_bias=self.shared_rel_pos_bias)
if i == len(self.layers) - 1 and self.final_norm:
x = self.norm1(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)
[docs]@MODELS.register_module()
class BEiT(BaseSelfSupervisor):
"""BEiT v1/v2.
Implementation of `BEiT: BERT Pre-Training of Image Transformers
<https://arxiv.org/abs/2106.08254>`_ and `BEiT v2: Masked Image Modeling
with Vector-Quantized Visual Tokenizers
<https://arxiv.org/abs/2208.06366>`_.
"""
def extract_feat(self, inputs: torch.Tensor):
return self.backbone(inputs, mask=None)
[docs] def loss(self, inputs: List[torch.Tensor], data_samples: List[DataSample],
**kwargs) -> Dict[str, torch.Tensor]:
"""The forward function in training.
Args:
inputs (List[torch.Tensor]): The input images.
data_samples (List[DataSample]): All elements required
during the forward function.
Returns:
Dict[str, torch.Tensor]: A dictionary of loss components.
"""
mask = torch.stack([data_sample.mask for data_sample in data_samples])
img_latent = self.backbone(inputs[0], mask)
# inputs[1] is the target image
with torch.no_grad():
target = self.target_generator(inputs[1])
target = target.detach()
if self.with_neck:
# BEiT v2
feats, feats_cls_pt = self.neck(
img_latent, rel_pos_bias=self.backbone.shared_rel_pos_bias)
loss = self.head.loss(feats, feats_cls_pt, target, mask)
else:
# BEiT v1
loss = self.head.loss(img_latent[0], target, mask)
if isinstance(loss, torch.Tensor):
losses = dict(loss=loss)
return losses
elif isinstance(loss, Tuple):
# the loss_1 and loss_2 are general reconstruction loss (patch
# feature vectors from last layer of backbone) and early state
# reconstruction loss (patch feature vectors from intermediate
# layer of backbone)
loss_1, loss_2 = loss[0], loss[1]
losses = dict()
# the key with prefix 'loss', like loss_1 and loss_2, will be used
# as the final criterion
losses['loss_1'] = loss_1
losses['loss_2'] = loss_2
return losses