Source code for mmselfsup.models.backbones.mocov3_vit
# Copyright (c) OpenMMLab. All rights reserved.
import math
from functools import reduce
from operator import mul
from typing import List, Optional, Union
import torch.nn as nn
from mmcls.models.backbones import VisionTransformer
from mmcls.models.utils import to_2tuple
from mmcv.cnn.bricks.transformer import PatchEmbed
from torch.nn.modules.batchnorm import _BatchNorm
from mmselfsup.models.utils import build_2d_sincos_position_embedding
from mmselfsup.registry import MODELS
[docs]@MODELS.register_module()
class MoCoV3ViT(VisionTransformer):
"""Vision Transformer.
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 isinstance(self.patch_embed, PatchEmbed):
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
if isinstance(self.patch_embed, 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
[docs] 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()