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mmpretrain.models.selfsup.mocov3 源代码

# 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


[文档]@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()
[文档] 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()
[文档]@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)
[文档] 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
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