Problem in migrating InternImage into mmdetection 3

[VanLinLin]

Describe the issue

I'm migrating the InternImage backbone into mmdetection 3 from InternImage repo(it's built by mmdetection 2.28), it's almost done. However, when I try to train the model, it occurred some error and I can't figure out what happened.

Reproduction

1. What command or script did you run?

python mmdetection/tools/train.py

2. What config dir you run?

# --------------------------------------------------------
# InternImage
# Copyright (c) 2022 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
_base_ = [
    '../_base_/datasets/coco_detection.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_1x.py',
]
pretrained = 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth'
model = dict(
    type='DINO',
    num_queries=900,  # num_matching_queries
    with_box_refine=True,
    as_two_stage=True,
    backbone=dict(
        type='InternImage',
        core_op='DCNv3',
        channels=160,
        depths=[5, 5, 22, 5],
        groups=[10, 20, 40, 80],
        mlp_ratio=4.,
        drop_path_rate=0.4,
        norm_layer='LN',
        layer_scale=1.0,
        offset_scale=2.0,
        post_norm=True,
        with_cp=False,
        out_indices=(1, 2, 3),
        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
    neck=dict(
        type='ChannelMapper',
        in_channels=[320, 640, 1280],
        kernel_size=1,
        out_channels=256,
        act_cfg=None,
        norm_cfg=dict(type='GN', num_groups=32),
        num_outs=4),
    encoder=dict(
        num_layers=6,
        layer_cfg=dict(
            self_attn_cfg=dict(embed_dims=256, num_levels=4,
                               dropout=0.0),  # 0.1 for DeformDETR
            ffn_cfg=dict(
                embed_dims=256,
                feedforward_channels=2048,  # 1024 for DeformDETR
                ffn_drop=0.0))),  # 0.1 for DeformDETR
    decoder=dict(
        num_layers=6,
        return_intermediate=True,
        layer_cfg=dict(
            self_attn_cfg=dict(embed_dims=256, num_heads=8,
                               dropout=0.0),  # 0.1 for DeformDETR
            cross_attn_cfg=dict(embed_dims=256, num_levels=4,
                                dropout=0.0),  # 0.1 for DeformDETR
            ffn_cfg=dict(
                embed_dims=256,
                feedforward_channels=2048,  # 1024 for DeformDETR
                ffn_drop=0.0)),  # 0.1 for DeformDETR
        post_norm_cfg=None),
    positional_encoding=dict(
        num_feats=128,
        normalize=True,
        offset=0.0,  # -0.5 for DeformDETR
        temperature=20),  # 10000 for DeformDETR
    bbox_head=dict(
        type='DINOHead',
        # num_classes=80,
        num_classes=5,
        sync_cls_avg_factor=True,
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),  # 2.0 in DeformDETR
        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
    dn_cfg=dict(  # TODO: Move to model.train_cfg ?
        label_noise_scale=0.5,
        box_noise_scale=1.0,  # 0.4 for DN-DETR
        group_cfg=dict(dynamic=True, num_groups=None,
                       num_dn_queries=100)),  # TODO: half num_dn_queries
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='HungarianAssigner',
            match_costs=[
                dict(type='FocalLossCost', weight=2.0),
                dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
                dict(type='IoUCost', iou_mode='giou', weight=2.0)
            ])),
    test_cfg=dict(max_per_img=300))
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', prob=0.5),
    dict(
        type='AutoAugment',
        policies=[
            [
                dict(
                    type='RandomChoiceResize',
                    scales=[(480, 1333), (512, 1333), (544, 1333),
                            (576, 1333), (608, 1333), (640, 1333),
                            (672, 1333), (704, 1333), (736, 1333),
                            (768, 1333), (800, 1333)],
                    keep_ratio=True)
            ],
            [
                dict(
                    type='RandomChoiceResize',
                    scales=[(400, 4200), (500, 4200), (600, 4200)],
                    keep_ratio=True),
                dict(
                    type='RandomCrop',
                    crop_type='absolute_range',
                    crop_size=(384, 600),
                    allow_negative_crop=False),
                dict(
                    type='RandomChoiceResize',
                    scales=[(480, 1333), (512, 1333), (544, 1333),
                            (576, 1333), (608, 1333), (640, 1333),
                            (672, 1333), (704, 1333), (736, 1333),
                            (768, 1333), (800, 1333)],
                    # override=True,
                    keep_ratio=True)
            ]
        ]),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='PackDetInputs')
]
# By default, models are trained on 8 GPUs with 2 images per GPU
dataset_type = 'CocoDataset'  # 数据集类型，这将被用来定义数据集。
data_root = 'data/packages_coco/'
metainfo = {
    'classes': ("Folding_Knife", "Straight_Knife", "Scissor",
                "Utility_Knife", "Multi-tool_Knife")
}
train_dataloader = dict(
    batch_size=6,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    # 批数据采样器，用于确保每一批次内的数据拥有相似的长宽比，可用于节省显存
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type=dataset_type,
        metainfo=metainfo,
        data_root=data_root,
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),  # 图片路径前缀
        pipeline=train_pipeline),
)

val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,  # 是否丢弃最后未能组成一个批次的数据
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        metainfo=metainfo,
        data_root=data_root,
        ann_file='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
    ),
)
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader

val_evaluator = dict(  # 验证过程使用的评测器
    type='CocoMetric',  # 用于评估检测和实例分割的 AR、AP 和 mAP 的 coco 评价指标
    ann_file=data_root + 'annotations/instances_val2017.json',  # 标注文件路径
    metric=['bbox'],  # 需要计算的评价指标，`bbox` 用于检测，`segm` 用于实例分割
    format_only=False)
test_evaluator = val_evaluator  # 测试过程使用的评测器

train_cfg = dict(
    # 训练循环的类型，请参考 https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py
    type='EpochBasedTrainLoop',
    max_epochs=30,  # 最大训练轮次
    val_interval=1)  # 验证间隔。每个 epoch 验证一次
val_cfg = dict(type='ValLoop')  # 验证循环的类型
test_cfg = dict(type='TestLoop')  # 测试循环的类型

# optimizer
optim_wrapper_cfg = dict(  # 优化器封装的配置
    type='OptimWrapper',  # 优化器封装的类型。可以切换至 AmpOptimWrapper 来启用混合精度训练
    optimizer=dict(
        _delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001,
        constructor='CustomLayerDecayOptimizerConstructor',
        paramwise_cfg=dict(num_layers=37, layer_decay_rate=0.90,
                           depths=[5, 5, 22, 5])),
    optimizer_config=dict(
        _delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2))
)

# learning policy
param_scheduler = [
    dict(
        type='LinearLR',  # 使用线性学习率预热
        start_factor=0.001,  # 学习率预热的系数
        by_epoch=False,  # 按 iteration 更新预热学习率
        begin=0,  # 从第一个 iteration 开始
        end=500),  # 到第 500 个 iteration 结束
    dict(
        type='MultiStepLR',  # 在训练过程中使用 multi step 学习率策略
        by_epoch=True,  # 按 epoch 更新学习率
        begin=0,   # 从第一个 epoch 开始
        end=12,  # 到第 12 个 epoch 结束
        milestones=[8, 11],  # 在哪几个 epoch 进行学习率衰减
        gamma=0.1)  # 学习率衰减系数
]

default_hooks = dict(
    checkpoint=dict(
        type='CheckpointHook',
        save_best='auto',
        max_keep_ckpts=3,
        interval=1))

vis_backends = [
    dict(type='LocalVisBackend'),
    dict(type='TensorboardVisBackend')
]
visualizer = dict(
    type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')

3. Did you make any modifications on the code or config? Did you understand what you have modified? Yes
4. What dataset did you use? SIXray

Environment

sys.platform: win32
Python: 3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)]
CUDA available: True
numpy_random_seed: 2147483648
GPU 0: NVIDIA GeForce RTX 3060
CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7
NVCC: Cuda compilation tools, release 11.7, V11.7.99
MSVC: Microsoft (R) C/C++ Optimizing Compiler Version 19.32.31332 for x64
GCC: n/a
PyTorch: 1.13.1+cu117
PyTorch compiling details: PyTorch built with:
  - C++ Version: 199711
  - MSVC 192829337
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 2019
  - LAPACK is enabled (usually provided by MKL)
  - CPU capability usage: AVX2
  - CUDA Runtime 11.7
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.5
  - Magma 2.5.4
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/actions-runner/_work/pytorch/pytorch/builder/windows/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.14.1+cu117
OpenCV: 4.8.1
MMEngine: 0.9.0
MMDetection: 3.1.0+f78af77

Results

Traceback (most recent call last):
  File "F:\Lab\mmdetection3\tools\train.py", line 137, in <module>
    main()
  File "F:\Lab\mmdetection3\tools\train.py", line 133, in main
    runner.train()
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmengine\runner\runner.py", line 1777, in train
    model = self.train_loop.run()  # type: ignore
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmengine\runner\loops.py", line 96, in run
    self.run_epoch()
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmengine\runner\loops.py", line 112, in run_epoch
    self.run_iter(idx, data_batch)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmengine\runner\loops.py", line 128, in run_iter
    outputs = self.runner.model.train_step(
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmengine\model\base_model\base_model.py", line 114, in train_step
    losses = self._run_forward(data, mode='loss')  # type: ignore
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmengine\model\base_model\base_model.py", line 346, in _run_forward
    results = self(**data, mode=mode)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmdet-3.1.0-py3.9.egg\mmdet\models\detectors\base.py", line 92, in forward
    return self.loss(inputs, data_samples)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmdet-3.1.0-py3.9.egg\mmdet\models\detectors\base_detr.py", line 98, in loss
    img_feats = self.extract_feat(batch_inputs)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmdet-3.1.0-py3.9.egg\mmdet\models\detectors\base_detr.py", line 237, in extract_feat
    x = self.backbone(batch_inputs)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmdet-3.1.0-py3.9.egg\mmdet\models\backbones\intern_image.py", line 706, in forward
    x = self.patch_embed(x)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\mmdet-3.1.0-py3.9.egg\mmdet\models\backbones\intern_image.py", line 275, in forward
    x = self.conv1(x)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "F:\Lab\mmdetection3\.venv\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
TypeError: conv2d() received an invalid combination of arguments - got (list, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (list, Parameter, Parameter, tuple, tuple, tuple, int)
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (list, Parameter, Parameter, tuple, tuple, tuple, int)

Issue fix

Not yet

[hhaAndroid]

@VanLinLin The configuration seems fine. I suspect there might be an error in your "InternImage" code.

[VanLinLin]

@hhaAndroid Hi, thanks for your reply. I'm finished migrating InternImage into mmdetection3, that's pretty cool and thank you again!

[shreejalt]

@VanLinLin What changes did you make to make it run in v3 of mmdetection? I only have one mmdetection repo with version 3.X, so i want to know that how hard is it to change the source code

[AlanBlanchet]

I'm having the same problem. The batch_size information is done list-wise while conv2d expects the item to be a tensor of shape (B, C, H, W). Instead we have a list of tensors of shape (C, H, W) :

TypeError: conv2d() received an invalid combination of arguments - got (list, Parameter, NoneType, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (list of [Tensor, Tensor], Parameter, NoneType, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (list of [Tensor, Tensor], Parameter, NoneType, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)

Here I have a batch size of 2 ("list of [Tensor, Tensor]")

[AlanBlanchet]

Solution found in this issue It seams that you have to specify the collate_fn in your dataloader config :

train_dataloader = dict(
    batch_size=2,  # Batch size of a single GPU
    num_workers=2,  # Worker to pre-fetch data for each single GPU
    persistent_workers=True,
    collate_fn=dict(type="default_collate"),
    ....
)