Source code for mmpretrain.models.selfsup.mocov3
# Copyright (c) OpenMMLab. All rights reserved.
import math
from functools import reduce
from operator import mul
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from torch.nn.modules.batchnorm import _BatchNorm
from mmpretrain.models.backbones import VisionTransformer
from mmpretrain.models.utils import (build_2d_sincos_position_embedding,
to_2tuple)
from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from ..utils import CosineEMA
from .base import BaseSelfSupervisor
[docs]@MODELS.register_module()
class MoCoV3ViT(VisionTransformer):
"""Vision Transformer for MoCoV3 pre-training.
A pytorch implement of: `An Images is Worth 16x16 Words: Transformers for
Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.
Part of the code is modified from:
`<https://github.com/facebookresearch/moco-v3/blob/main/vits.py>`_.
Args:
stop_grad_conv1 (bool): whether to stop the gradient of
convolution layer in `PatchEmbed`. Defaults to False.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Defaults to -1.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. Defaults to False.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
stop_grad_conv1: bool = False,
frozen_stages: int = -1,
norm_eval: bool = False,
init_cfg: Optional[Union[dict, List[dict]]] = None,
**kwargs) -> None:
# add MoCoV3 ViT-small arch
self.arch_zoo.update(
dict.fromkeys(
['mocov3-s', 'mocov3-small'], {
'embed_dims': 384,
'num_layers': 12,
'num_heads': 12,
'feedforward_channels': 1536,
}))
super().__init__(init_cfg=init_cfg, **kwargs)
self.patch_size = kwargs['patch_size']
self.frozen_stages = frozen_stages
self.norm_eval = norm_eval
self.init_cfg = init_cfg
if stop_grad_conv1:
self.patch_embed.projection.weight.requires_grad = False
self.patch_embed.projection.bias.requires_grad = False
self._freeze_stages()
[docs] def init_weights(self) -> None:
"""Initialize position embedding, patch embedding, qkv layers and cls
token."""
super().init_weights()
if not (isinstance(self.init_cfg, dict)
and self.init_cfg['type'] == 'Pretrained'):
# Use fixed 2D sin-cos position embedding
pos_emb = build_2d_sincos_position_embedding(
patches_resolution=self.patch_resolution,
embed_dims=self.embed_dims,
cls_token=True)
self.pos_embed.data.copy_(pos_emb)
self.pos_embed.requires_grad = False
# xavier_uniform initialization for PatchEmbed
val = math.sqrt(
6. / float(3 * reduce(mul, to_2tuple(self.patch_size), 1) +
self.embed_dims))
nn.init.uniform_(self.patch_embed.projection.weight, -val, val)
nn.init.zeros_(self.patch_embed.projection.bias)
# initialization for linear layers
for name, m in self.named_modules():
if isinstance(m, nn.Linear):
if 'qkv' in name:
# treat the weights of Q, K, V separately
val = math.sqrt(
6. /
float(m.weight.shape[0] // 3 + m.weight.shape[1]))
nn.init.uniform_(m.weight, -val, val)
else:
nn.init.xavier_uniform_(m.weight)
nn.init.zeros_(m.bias)
nn.init.normal_(self.cls_token, std=1e-6)
def _freeze_stages(self) -> None:
"""Freeze patch_embed layer, some parameters and stages."""
if self.frozen_stages >= 0:
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
self.cls_token.requires_grad = False
self.pos_embed.requires_grad = False
for i in range(1, self.frozen_stages + 1):
m = self.layers[i - 1]
m.eval()
for param in m.parameters():
param.requires_grad = False
if i == (self.num_layers) and self.final_norm:
for param in getattr(self, 'norm1').parameters():
param.requires_grad = False
def train(self, mode: bool = True) -> None:
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only
if isinstance(m, _BatchNorm):
m.eval()
[docs]@MODELS.register_module()
class MoCoV3(BaseSelfSupervisor):
"""MoCo v3.
Implementation of `An Empirical Study of Training Self-Supervised Vision
Transformers <https://arxiv.org/abs/2104.02057>`_.
Args:
backbone (dict): Config dict for module of backbone
neck (dict): Config dict for module of deep features to compact feature
vectors.
head (dict): Config dict for module of head functions.
base_momentum (float): Momentum coefficient for the momentum-updated
encoder. Defaults to 0.01.
pretrained (str, optional): The pretrained checkpoint path, support
local path and remote path. Defaults to None.
data_preprocessor (dict, optional): The config for preprocessing
input data. If None or no specified type, it will use
"SelfSupDataPreprocessor" as type.
See :class:`SelfSupDataPreprocessor` for more details.
Defaults to None.
init_cfg (Union[List[dict], dict], optional): Config dict for weight
initialization. Defaults to None.
"""
def __init__(self,
backbone: dict,
neck: dict,
head: dict,
base_momentum: float = 0.01,
pretrained: Optional[str] = None,
data_preprocessor: Optional[dict] = None,
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(
backbone=backbone,
neck=neck,
head=head,
pretrained=pretrained,
data_preprocessor=data_preprocessor,
init_cfg=init_cfg)
# create momentum model
self.momentum_encoder = CosineEMA(
nn.Sequential(self.backbone, self.neck), momentum=base_momentum)
[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.
"""
assert isinstance(inputs, list)
view_1 = inputs[0]
view_2 = inputs[1]
# compute query features, [N, C] each
q1 = self.neck(self.backbone(view_1))[0]
q2 = self.neck(self.backbone(view_2))[0]
# compute key features, [N, C] each, no gradient
with torch.no_grad():
# update momentum encoder
self.momentum_encoder.update_parameters(
nn.Sequential(self.backbone, self.neck))
k1 = self.momentum_encoder(view_1)[0]
k2 = self.momentum_encoder(view_2)[0]
loss = self.head.loss(q1, k2) + self.head.loss(q2, k1)
losses = dict(loss=loss)
return losses