• mmdetection用mmclassification的backbone

    接上篇[1]，现需要将 backbone 换成 DeiT-tiny[2,3]。

    MMDetection[4] 不直接支持 DeiT（backbones/ 下没有），但 MMClassification 有实现。参考 [6,7]，可以直接在 MMDetection 中调用 MMClassification 的模型。

    由于 DeiT 是 transformer 结构，而 MMDetection 直接支持的 DETR[8,9]也是，考虑基于它的配置文件[10]来改。

    Configuration

    配置文件的介绍见 [11-16]。对照已有的配置文件和 mmdetection/mmdet/models/ 的类定义来看，配置文件中 model 里配置的域，对应相应模型类的构造函数参数，所以替换 DeiT-tiny 做 backbone 时 model/backbone 要写哪些项，是看 deit.py 及其父类的构造函数有什么参数。

    由 [10]，它引用的数据集相关的配置文件是 coco_detection.py，类似 [1] 中，两个配置文件分别修改：

    scannet_detection.py

    • 修数据集类集的方法见 [17,18]，要改 / 加 classes、data/train/dataset/classes、data/val/classes、data/test/classes。类集见 [19]。
    # Inherited from: mmdetection/configs/_base_/datasets/coco_detection.py
# fit to ScanNet-frames-25k

dataset_type = 'CocoDataset'
classes = (
    "wall", "floor", "cabinet", "bed", "chair",
    "sofa", "table", "door", "window", "bookshelf",
    "picture", "counter", "desk", "curtain", "refrigerator",
    "shower curtain", "toilet", "sink", "bathtub", "otherfurniture"
)
data_root = 'data/scannet-frames/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'scannet_objdet_train.json',
        img_prefix=data_root + 'train/',
        pipeline=train_pipeline,
        classes=classes),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'scannet_objdet_val.json',
        img_prefix=data_root + 'val/',
        pipeline=test_pipeline,
        classes=classes),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'scannet_objdet_val.json',
        img_prefix=data_root + 'val/',
        pipeline=test_pipeline,
        classes=classes))
evaluation = dict(interval=1, metric='bbox')

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    detr_deit_tiny_8x1_150e_scannet.py

    • model/backbone
    • model/bbox_head
      • num_classes 参考 [17,18]，类数也是数自 [19]；
      • in_channels 即 backbone 最后一层输出的特征维度，见 arch_zoo 中的 deit-tiny/embed_dims；
      • transformer/encoder 和 decoder/transformerlayers/attn_cfgs/embed_dims 和 ffn_cfgs/embed_dims（见 BaseTransformerLayer 中的 __init__/ffn_cfgs/embed_dims）也要相应改成跟 in_channels 同维；
      • positional_encoding/num_feats 要恰好是 in_channels 的一半。
    • data/samples_per_gpu 改成 1，即每块卡 batch_size = 1，否则我这会爆显存。按 [11] 的命名规则，配置文件名中改成 8x1。
    # Inherited from mmdetection/configs/detr/detr_r50_8x2_150e_coco.py
## Modifications
# 1. use DeiT-tiny as backbone
# 2. use ScanNet-frames-25k

_base_ = [
    '../_base_/datasets/scannet_detection.py',
    '../../mmdetection/configs/_base_/default_runtime.py'
]
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
model = dict(
    type='DETR',
    backbone=dict(
        # _delete_=True, # Delete the backbone field in _base_
        # from: mmclassification/configs/deit/deit-tiny_pt-4xb256_in1k.py
        type='mmcls.VisionTransformer',
        arch='deit-tiny',
        img_size=224,
        patch_size=16,
        with_cls_token=False,
        output_cls_token=False,
        out_indices=-1,
        # norm_cfg=dict(type='BN', requires_grad=False),
        # norm_eval=True,
        # style='pytorch',
        init_cfg=dict(
            type='Pretrained',
            # from: mmclassification/configs/deit/README.md -> DeiT-tiny
            checkpoint='https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth',
            prefix='backbone.',
        ),
    ),
    bbox_head=dict(
        type='DETRHead',
        num_classes=20,  # from: convert-scannet-coco-objdet.py
        # from: mmclassification/mmcls/models/backbones/vision_transformer.py
        #   -> arch_zoo["deit-tiny"]["embed_dims"]
        in_channels=192,
        transformer=dict(
            type='Transformer',
            encoder=dict(
                type='DetrTransformerEncoder',
                num_layers=6,
                transformerlayers=dict(
                    type='BaseTransformerLayer',
                    attn_cfgs=[
                        dict(
                            type='MultiheadAttention',
                            embed_dims=192,#256,
                            num_heads=8,
                            dropout=0.1)
                    ],
                    feedforward_channels=2048,
                    ffn_dropout=0.1,
                    # from: mmcv/mmcv/cnn/bricks/transformer.py
                    #    -> BaseTransformerLayer/__init__/ffn_cfgs
                    ffn_cfgs=dict(
                        embed_dims=192,
                        # feedforward_channels=2048,
                        # ffn_drop=0.1,
                    ),
                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
            decoder=dict(
                type='DetrTransformerDecoder',
                return_intermediate=True,
                num_layers=6,
                transformerlayers=dict(
                    type='DetrTransformerDecoderLayer',
                    attn_cfgs=dict(
                        type='MultiheadAttention',
                        embed_dims=192,#256,
                        num_heads=8,
                        dropout=0.1),
                    feedforward_channels=2048,
                    ffn_dropout=0.1,
                    # from: mmcv/mmcv/cnn/bricks/transformer.py
                    #    -> BaseTransformerLayer/__init__/ffn_cfgs
                    ffn_cfgs=dict(
                        embed_dims=192,
                        # feedforward_channels=2048,
                        # ffn_drop=0.1,
                    ),
                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
                                     'ffn', 'norm')),
            )),
        positional_encoding=dict(
            # type='SinePositionalEncoding', num_feats=128, normalize=True),
            type='SinePositionalEncoding', num_feats=int(192 // 2), normalize=True),
        loss_cls=dict(
            type='CrossEntropyLoss',
            bg_cls_weight=0.1,
            use_sigmoid=False,
            loss_weight=1.0,
            class_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='HungarianAssigner',
            cls_cost=dict(type='ClassificationCost', weight=1.),
            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
    test_cfg=dict(max_per_img=100))
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='AutoAugment',
        policies=[[
            dict(
                type='Resize',
                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                           (736, 1333), (768, 1333), (800, 1333)],
                multiscale_mode='value',
                keep_ratio=True)
        ], [
            dict(
                type='Resize',
                img_scale=[(400, 1333), (500, 1333), (600, 1333)],
                multiscale_mode='value',
                keep_ratio=True),
            dict(
                type='RandomCrop',
                crop_type='absolute_range',
                crop_size=(384, 600),
                allow_negative_crop=True),
            dict(
                type='Resize',
                img_scale=[(480, 1333), (512, 1333), (544, 1333),
                            (576, 1333), (608, 1333), (640, 1333),
                            (672, 1333), (704, 1333), (736, 1333),
                            (768, 1333), (800, 1333)],
                multiscale_mode='value',
                override=True,
                keep_ratio=True)
        ]]),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=1),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=1),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=1,#2,
    workers_per_gpu=2,
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
    type='AdamW',
    lr=0.0001,
    weight_decay=0.0001,
    paramwise_cfg=dict(
        custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
# learning policy
lr_config = dict(policy='step', step=[100])
runner = dict(type='EpochBasedRunner', max_epochs=150)

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    Training

    代码结构类似 [1]，这里只展示必要的部分：

    my-project/
|- mmdetection/
|- configs/
|  |- _base_/
|  |  `- datasets/
|  |     `- mstrain_3x_scannet.py
|  `- detr/
|     `- detr_deit_tiny_8x1_150e_scannet.py
`- scripts/
   |- find_gpu.sh
   `- train-scannet-frames.sh

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    其中，训练脚本：

    #!/bin/bash
# train-scannet-frames.sh
clear

# run `conda activate openmmlab` first

config=configs/detr/detr_deit_tiny_8x2_150e_scannet.py

. scripts/find_gpu.sh -1 14787

echo begin: $(date) > scripts/RUN-`basename $0`.txt

PATH=/usr/local/cuda/bin:$PATH \
PYTHONPATH=mmdetection/mmdet:$PYTHONPATH \
CUDA_VISIBLE_DEVICES=${gpu_id} \
MMDET_DATASETS=`pwd`/data/scannet-frames/ \
bash mmdetection/tools/dist_train.sh \
    $config ${n_gpu_found}
# python mmdetection/tools/train.py \
#     $config

echo end: $(date) >> scripts/RUN-`basename $0`.txt

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    References

    1. MMDetection在ScanNet上训练
    2. (ICLR 2021) Training data-efficient image transformers & distillation through attention
    3. facebookresearch/deit
    4. open-mmlab/mmdetection
    5. open-mmlab/mmclassification
    6. How to change the model of mmclassification to mmdetection？ #7761
    7. Use backbone network implemented in MMClassification / Use backbone network implemented in MMClassification
    8. (ECCV 2020) End-to-End Object Detection with Transformers - paper, supplementary
    9. facebookresearch/detr
    10. open-mmlab/mmdetection/configs/detr/detr_r50_8x2_150e_coco.py
    11. Tutorial 1: Learn about Configs
    12. Config
    13. MMDetection框架入门教程（二）：快速上手教程
    14. MMDetection框架入门教程（三）：配置文件详细解析
    15. 【MMDetection-学习记录】config配置文件说明
    16. mmdetection的config配置文件参数介绍
    17. AssertionError: The num_classes (3) in Shared2FCBBoxHead of MMDataParallel does not matches the length of CLASSES 80) in CocoDataset #4828
    18. Prepare a config / Prepare a config
    19. ScanNet/BenchmarkScripts/convert2panoptic.py
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  • 原文地址：https://blog.csdn.net/HackerTom/article/details/126906561