1、摘要

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3× or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pretraining and shows promising scaling behavior

本文证明了掩码自编码器(MAE)是一种可扩展的计算机视觉自监督学习算法。我们的MAE方法很简单:我们屏蔽输入图像的随机补丁并重建缺失的像素。它基于两个核心设计。首先，我们开发了一个非对称编码器-解码器架构，其中一个编码器仅对补丁的可见子集(没有掩码令牌)进行操作，以及一个轻量级解码器，该解码器从潜在表示和掩码令牌重建原始图像。其次，我们发现掩盖输入图像的高比例，例如75%，产生了一个重要的和有意义的自我监督任务。这两种设计的结合使我们能够高效地训练大型模型:我们加速了训练(3倍或更多)并提高了准确性。我们的可扩展方法允许学习泛化良好的大容量模型:例如，在仅使用ImageNet-1K数据的方法中，vanilla ViT-Huge模型达到了最好的准确率(87.8%)。下游任务的迁移性能优于监督预训练，并显示出有希望的缩放行为。

我们的MAE架构。在预训练过程中，图像补丁的一个大的随机子集(例如75%)被屏蔽掉。该编码器应用于可见补丁的小子集。在编码器之后引入掩码令牌，然后由一个小型解码器处理完整的编码补丁和掩码令牌，以像素为单位重建原始图像。预训练后，丢弃解码器，将编码器应用于未损坏的图像(完整的补丁集)进行识别任务。

2、 ImageNet Experiments

We do self-supervised pre-training on the ImageNet-1K (IN1K) [13] training set. Then we do supervised training to evaluate the representations with (i) end-to-end fine-tuning or (ii) linear probing. We report top-1 validation accuracy of a single 224×224 crop. Details are in Appendix

我们在ImageNet-1K (IN1K)训练集上进行自监督预训练。然后我们利用端到端微调或(i线性探测进行监督训练来评估表征。我们报告了单个224×224作物的前1验证精度。详情见附录。

(a)解码器深度。深度解码器可以提高线性探测精度。
(b)解码器宽度。解码器可以比编码器(1024-d)更窄。
©掩码令牌。没有掩码的编码器比kens更准确、更快(表2)。
(d)重建目标。像素作为重建目标是有效的。
(e)数据增强。我们的MAE工作在最小或没有增强。
(f)掩膜采样。随机抽样效果最好。

Table 1. MAE ablation experiments with ViT-L/16 on ImageNet-1K. We report fine-tuning (ft) and linear probing (lin) accuracy (%). If not specified, the default is: the decoder has depth 8 and width 512, the reconstruction target is unnormalized pixels, the data augmentation is random resized cropping, the masking ratio is 75%, and the pre-training length is 800 epochs. Default settings are marked in gray .

表1。viti - l /16在ImageNet-1K上的MAE消融实验。我们报告了微调(ft)和线性探测(lin)精度(%)。如果未指定，默认为:解码器深度8，宽度512，重建目标为非归一化像素，数据增强为随机调整大小裁剪，掩蔽比为75%，预训练长度为800 epoch。默认设置以灰色标记。

3、Transfer Learning Experiments

Table 3. Comparisons with previous results on ImageNet1K. The pre-training data is the ImageNet-1K training set (except the tokenizer in BEiT was pre-trained on 250M DALLE data [45]). All self-supervised methods are evaluated by end-to-end fine-tuning. The ViT models are B/16, L/16, H/14 [16]. The best for each column is underlined. All results are on an image size of 224, except for ViT-H with an extra result on 448. Here our MAE reconstructs normalized pixels and is pre-trained for 1600 epochs

表3。与先前ImageNet1K结果的比较。预训练数据为ImageNet-1K训练集(除了BEiT中的标记器是在250M DALLE数据上预训练的[45])。所有的自监督方法都是通过端到端微调来评估的。ViT模型为B/16、L/16、H/14[16]。每一栏最好的都用下划线标出。所有的结果都是224的图像大小，除了ViT-H的额外结果是448。在这里，我们的MAE重建归一化像素，并进行1600次预训练。

Object detection and segmentation.

We fine-tune Mask R-CNN [24] end-to-end on COCO [33]. The ViT backbone is adapted for use with FPN [32] (see A.3). We apply this approach for all entries in Table 4. We report box AP for object detection and mask AP for instance segmentation

目标检测和分割。我们在COCO[33]上端到端微调Mask R-CNN。ViT骨干网适用于FPN。我们将此方法应用于表4中的所有条目。我们报告了用于对象检测的框AP和用于实例分割的掩码AP。

COCO object detection and segmentation using a ViT Mask R-CNN baseline. All entries are based on our implementation. Self-supervised entries use IN1K data without labels. Mask AP follows a similar trend as box AP

使用ViT Mask R-CNN基线的COCO对象检测和分割。所有条目都基于我们的实现。自监督条目使用不带标签的IN1K数据。掩码AP的趋势与方框AP相似

Compared to supervised pre-training, our MAE performs better under all configurations (Table 4). With the smaller ViT-B, our MAE is 2.4 points higher than supervised pretraining (50.3 vs. 47.9, APbox). More significantly, with the larger ViT-L, our MAE pre-training outperforms supervised pre-training by 4.0 points (53.3 vs. 49.3)

与监督预训练相比，我们的MAE在所有配置下都表现更好(表4)。在ViT-B较小的情况下，我们的MAE比监督预训练高2.4分(50.3 vs. 47.9, APbox)。更重要的是，随着ViT-L的增大，我们的MAE预训练比监督预训练高出4.0分(53.3比49.3)。

Semantic segmentation.

We experiment on ADE20K [66] using UperNet [57] (see A.4). Table 5 shows that our pretraining significantly improves results over supervised pretraining, e.g., by 3.7 points for ViT-L. Our pixel-based MAE also outperforms the token-based BEiT. These observations are consistent with those in COCO

语义分割。我们使用UperNet[57]对ADE20K[66]进行了实验(见A.4)。表5显示，我们的预训练显著提高了监督预训练的结果，例如viti - l提高了3.7分。我们基于像素的MAE也优于基于令牌的MAE。这些观测结果与COCO的观测结果一致。

MMSegmention用法

要使用其他存储库的预训练模型，需要转换关键字。
我们在tools目录中提供了一个beit2mmseg.py脚本，用于将MAE模型的关键字从官方存储库转换为MMSegmentation样式。

python tools/model_converters/beit2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}

python tools/model_converters/beit2mmseg.py https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth pretrain/mae_pretrain_vit_base_mmcls.pth

python tools/model_converters/beit2mmseg.py https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large.pth pretrain/mae_pretrain_vit_large_mmcls.pth

此脚本转换模型并将PRETRAIN_PATH转换后的模型存储在STORE_PATH.
在我们的默认设置中，预训练模型可以定义如下：

验证模型的单尺度结果：

sh tools/dist_test.sh \
work_dirs/runs/train/selfup/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py checkpoints/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth  --eval mIoU

python  tools/test.py --config work_dirs/runs/train/selfup/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py  --checkpoint checkpoints/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth  --eval  mIoU

由于相对位置嵌入要求输入长宽相等，因此采用滑动窗口进行多尺度推理。所以我们设置min_size=512，即最短边为512。所以config的多尺度推理是单独进行的，而不是’–aug-test’。对于多尺度推理：

sh tools/dist_test.sh \
configs/mae/upernet_mae-base_fp16_512x512_160k_ade20k_ms.py \
upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth $GPUS --eval mIoU

mmsegmentation/mmseg/models/backbones/mae.py

# Copyright (c) OpenMMLab. All rights reserved.import math
import math

import torch
import torch.nn as nn
from mmcv.cnn.utils.weight_init import (constant_init, kaiming_init,
                                        trunc_normal_)
from mmcv.runner import ModuleList, _load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm

from mmseg.utils import get_root_logger
from ..builder import BACKBONES
from .beit import BEiT, BEiTAttention, BEiTTransformerEncoderLayer


class MAEAttention(BEiTAttention):
    """Multi-head self-attention with relative position bias used in MAE.
    具有相对位置偏差的多头自注意在MAE中的应用

    This module is different from ``BEiTAttention`` by initializing the
    relative bias table with zeros.
    """

    def init_weights(self):
        """Initialize relative position bias with zeros."""

        # As MAE initializes relative position bias as zeros and this class
        # inherited from BEiT which initializes relative position bias
        # with `trunc_normal`, `init_weights` here does
        # nothing and just passes directly

        pass


class MAETransformerEncoderLayer(BEiTTransformerEncoderLayer):
    """Implements one encoder layer in Vision Transformer.

    This module is different from ``BEiTTransformerEncoderLayer`` by replacing
    ``BEiTAttention`` with ``MAEAttention``.
    """

    def build_attn(self, attn_cfg):
        self.attn = MAEAttention(**attn_cfg)


@BACKBONES.register_module()
class MAE(BEiT):
    """VisionTransformer with support for patch.

    Args:
        img_size (int | tuple): Input image size. Default: 224.
        patch_size (int): The patch size. Default: 16.
        in_channels (int): Number of input channels. Default: 3.
        embed_dims (int): embedding dimension. Default: 768.
        num_layers (int): depth of transformer. Default: 12.
        num_heads (int): number of attention heads. Default: 12.
        mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
            Default: 4.MLP隐藏维度与编码维度之比
        out_indices (list | tuple | int): Output from which stages.
            Default: -1.
        attn_drop_rate (float): The drop out rate for attention layer.
            Default 0.0
        drop_path_rate (float): stochastic depth rate. Default 0.0.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN')
        act_cfg (dict): The activation config for FFNs.
            Default: dict(type='GELU').
        patch_norm (bool): Whether to add a norm in PatchEmbed Block.
            Default: False.
        final_norm (bool): Whether to add a additional layer to normalize
            final feature map. Default: False.
        num_fcs (int): The number of fully-connected layers for FFNs.
            Default: 2.
        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. Default: False.
        pretrained (str, optional): model pretrained path. Default: None.
        init_values (float): Initialize the values of Attention and FFN
            with learnable scaling. Defaults to 0.1.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None.
    """

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_channels=3,
                 embed_dims=768,
                 num_layers=12,
                 num_heads=12,
                 mlp_ratio=4,
                 out_indices=-1,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 norm_cfg=dict(type='LN'),
                 act_cfg=dict(type='GELU'),
                 patch_norm=False,
                 final_norm=False,
                 num_fcs=2,
                 norm_eval=False,
                 pretrained=None,
                 init_values=0.1,
                 init_cfg=None):
        super(MAE, self).__init__(
            img_size=img_size,
            patch_size=patch_size,
            in_channels=in_channels,
            embed_dims=embed_dims,
            num_layers=num_layers,
            num_heads=num_heads,
            mlp_ratio=mlp_ratio,
            out_indices=out_indices,
            qv_bias=False,
            attn_drop_rate=attn_drop_rate,
            drop_path_rate=drop_path_rate,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg,
            patch_norm=patch_norm,
            final_norm=final_norm,
            num_fcs=num_fcs,
            norm_eval=norm_eval,
            pretrained=pretrained,
            init_values=init_values,
            init_cfg=init_cfg)

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))

        self.num_patches = self.patch_shape[0] * self.patch_shape[1]
        self.pos_embed = nn.Parameter(
            torch.zeros(1, self.num_patches + 1, embed_dims))

    def _build_layers(self):
        dpr = [
            x.item()
            for x in torch.linspace(0, self.drop_path_rate, self.num_layers)
        ]
        self.layers = ModuleList()
        for i in range(self.num_layers):
            self.layers.append(
                MAETransformerEncoderLayer(
                    embed_dims=self.embed_dims,
                    num_heads=self.num_heads,
                    feedforward_channels=self.mlp_ratio * self.embed_dims,
                    attn_drop_rate=self.attn_drop_rate,
                    drop_path_rate=dpr[i],
                    num_fcs=self.num_fcs,
                    bias=True,
                    act_cfg=self.act_cfg,
                    norm_cfg=self.norm_cfg,
                    window_size=self.patch_shape,
                    init_values=self.init_values))

    def fix_init_weight(self):
        """Rescale the initialization according to layer id.

        This function is copied from  https://github.com/microsoft/unilm/blob/master/beit/modeling_pretrain.py. # noqa: E501
        Copyright (c) Microsoft Corporation
        Licensed under the MIT License
        """

        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)

    def init_weights(self):

        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        self.apply(_init_weights)
        self.fix_init_weight()

        if (isinstance(self.init_cfg, dict)
                and self.init_cfg.get('type') == 'Pretrained'):
            logger = get_root_logger()
            checkpoint = _load_checkpoint(
                self.init_cfg['checkpoint'], logger=logger, map_location='cpu')
            state_dict = self.resize_rel_pos_embed(checkpoint)
            state_dict = self.resize_abs_pos_embed(state_dict)
            self.load_state_dict(state_dict, False)
        elif self.init_cfg is not None:
            super(MAE, self).init_weights()
        else:
            # We only implement the 'jax_impl' initialization implemented at
            # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353  # noqa: E501
            # Copyright 2019 Ross Wightman
            # Licensed under the Apache License, Version 2.0 (the "License")
            trunc_normal_(self.cls_token, std=.02)
            for n, m in self.named_modules():
                if isinstance(m, nn.Linear):
                    trunc_normal_(m.weight, std=.02)
                    if m.bias is not None:
                        if 'ffn' in n:
                            nn.init.normal_(m.bias, mean=0., std=1e-6)
                        else:
                            nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Conv2d):
                    kaiming_init(m, mode='fan_in', bias=0.)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
                    constant_init(m, val=1.0, bias=0.)

    def resize_abs_pos_embed(self, state_dict):
        if 'pos_embed' in state_dict:
            pos_embed_checkpoint = state_dict['pos_embed']
            embedding_size = pos_embed_checkpoint.shape[-1]
            num_extra_tokens = self.pos_embed.shape[-2] - self.num_patches
            # height (== width) for the checkpoint position embedding
            orig_size = int(
                (pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
            # height (== width) for the new position embedding
            new_size = int(self.num_patches**0.5)
            # class_token and dist_token are kept unchanged
            if orig_size != new_size:
                extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
                # only the position tokens are interpolated
                pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
                pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,
                                                embedding_size).permute(
                                                    0, 3, 1, 2)
                pos_tokens = torch.nn.functional.interpolate(
                    pos_tokens,
                    size=(new_size, new_size),
                    mode='bicubic',
                    align_corners=False)
                pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
                new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
                state_dict['pos_embed'] = new_pos_embed
        return state_dict

    def forward(self, inputs):
        B = inputs.shape[0]

        x, hw_shape = self.patch_embed(inputs)

        # 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 + self.pos_embed

        outs = []
        for i, layer in enumerate(self.layers):
            x = layer(x)
            if i == len(self.layers) - 1:
                if self.final_norm:
                    x = self.norm1(x)
            if i in self.out_indices:
                out = x[:, 1:]
                B, _, C = out.shape
                out = out.reshape(B, hw_shape[0], hw_shape[1],
                                  C).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        return tuple(outs)

配置文件解析

norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder', #type:要构建的模型的类型
    pretrained='work_dirs/pretrain/mae/mae_pretrain_vit_base_mmcls.pth',#预训练模型的位置
    backbone=dict(
        type='MAE',#骨干模块的类型
        img_size=(512, 512),
        patch_size=16,
        in_channels=3,
        embed_dims=768,
        num_layers=12,
        num_heads=12,
        mlp_ratio=4,
        out_indices=[3, 5, 7, 11],
        attn_drop_rate=0.0,
        drop_path_rate=0.1,
        norm_cfg=dict(type='LN', eps=1e-06),
        act_cfg=dict(type='GELU'),
        norm_eval=False,
        init_values=1.0),           
    neck=dict(type='Feature2Pyramid', embed_dim=768, rescales=[4, 2, 1, 0.5]),#颈部结构连接ViT主干和解码器头。
    decode_head=dict(
        type='UPerHead',
        in_channels=[768, 768, 768, 768],
        in_index=[0, 1, 2, 3],
        pool_scales=(1, 2, 3, 6),
        channels=768,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=768,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)))
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2048, 512),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=4,
    train=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/training',
        ann_dir='annotations/training',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', reduce_zero_label=True),
            dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PhotoMetricDistortion'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
        ]),
    val=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(2048, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(2048, 512),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook', by_epoch=False),
        dict(type='TensorboardLoggerHook')
    ])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(
    type='AdamW',
    lr=0.0001,
    betas=(0.9, 0.999),
    weight_decay=0.05,
    constructor='LayerDecayOptimizerConstructor',
    paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.65))
optimizer_config = dict()
lr_config = dict(
    policy='poly',
    warmup='linear',
    warmup_iters=1500,
    warmup_ratio=1e-06,
    power=1.0,
    min_lr=0.0,
    by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, interval=16000)
evaluation = dict(
    interval=16000,
    metric=['mIoU', 'mFscore'],
    pre_eval=True,
    save_best='auto')
fp16 = dict(loss_scale='dynamic')
work_dir = 'work_dirs/runs/train/selfup/mae'
gpu_ids = [0]
auto_resume = False