fcjian / tood

TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

TOOD: Task-aligned One-stage Object Detection (ICCV 2021 Oral)

Paper     Website

News

• 【2023/01】 YOLOv8 adopts the Task Alignment Learning (TAL) of TOOD!
• 【2022/09】 YOLOv6 adopts the Task Alignment Learning (TAL) of TOOD!
• 【2022/03】 PP-YOLOE adopts the Task Alignment Learning (TAL) and an efficient version of the Task-aligned Head (T-Head)!

Introduction

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between learning task-interactive and task-specific features, as well as a greater flexibility to learn the alignment via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL) to explicitly pull closer (or even unify) the optimal anchors for the two tasks during training via a designed sample assignment scheme and a task-aligned loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a 51.1 AP at single-model single-scale testing. This surpasses the recent one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP), and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also demonstrate the effectiveness of TOOD for better aligning the tasks of object classification and localization.

Method overview

Parallel head vs. T-head

Prerequisites

• MMDetection version 2.14.0.

• Please see get_started.md for installation and the basic usage of MMDetection.

Train

# assume that you are under the root directory of this project,
# and you have activated your virtual environment if needed.
# and with COCO dataset in 'data/coco/'.

./tools/dist_train.sh configs/tood/tood_r50_fpn_1x_coco.py 4

Inference

./tools/dist_test.sh configs/tood/tood_r50_fpn_1x_coco.py work_dirs/tood_r50_fpn_1x_coco/epoch_12.pth 4 --eval bbox

Models

For your convenience, we provide the following trained models (TOOD). All models are trained with 16 images in a mini-batch.

Model	Anchor	MS train	DCN	Lr schd	AP (minival)	AP (test-dev)	Config	Download
TOOD_R_50_FPN_1x	Anchor-free	No	No	1x	42.5	42.7	config	google / baidu
TOOD_R_50_FPN_anchor_based_1x	Anchor-based	No	No	1x	42.4	42.8	config	google / baidu
TOOD_R_101_FPN_2x	Anchor-free	Yes	No	2x	46.2	46.7	config	google / baidu
TOOD_X_101_FPN_2x	Anchor-free	Yes	No	2x	47.6	48.5	config	google / baidu
TOOD_R_101_dcnv2_FPN_2x	Anchor-free	Yes	Yes	2x	49.2	49.6	config	google / baidu
TOOD_X_101_dcnv2_FPN_2x	Anchor-free	Yes	Yes	2x	50.5	51.1	config	google / baidu

[0] All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..
[1] dcnv2 denotes deformable convolutional networks v2.
[2] Refer to more details in config files in config/tood/.
[3] Extraction code of baidu netdisk: tood.

Acknowledgement

Thanks MMDetection team for the wonderful open source project!

Citation

If you find TOOD useful in your research, please consider citing:

@inproceedings{feng2021tood,
    title={TOOD: Task-aligned One-stage Object Detection},
    author={Feng, Chengjian and Zhong, Yujie and Gao, Yu and Scott, Matthew R and Huang, Weilin},
    booktitle={ICCV},
    year={2021}
}

tood's Issues

RuntimeError

RuntimeError: Given groups=1, weight of size [64, 512, 1, 1], expected input[2, 256, 128, 128] to have 512 channels, but got 256 channels instead

Coupled Head

Thank you for share this great project.
Have you consider Decoupled head issue ?

a little puzzled about the T-Head module

When reading your paper, I was a little puzzled about the T-Head module, and I hope to get your answer.
Why can "N consecutive conv layers" extract the task-interactive features?Compared with it, does the feature extracted by the previous backbone+FPN have no interactive information?

Can I use it in two-stage method?

Layer Attention instead of Channel Attention?

Why did you choose Layer Attention instead of normal Channel Attention?
Task-interactive features are concatenated after N consecutive Conv layers, then using Channel Attention could further separate each channels to specific task, instead of Layer Attention, which also conduct separation on channel dim, but can only separate in group of 6?

TAL样本分配策略与损失函数计算是否会造成网络输出的类别置信度偏低

你好，我尝试使用TAL样本分配策略和损失函数计算，网络输出的类别置信度最大接近0.95，整体偏低，不知道是否哪里出了问题？谢谢！

Plot result

Hi, thanks for your wonderful work, I have a question how do you plot the detection result in Figure1? especially the prediction score map and localization map.

N个连续的卷积层

请问作者，他这个N个连续的卷积层是如何去提取一个多尺度信息的呢

T-Head

作者你好，对于这个T-head+TAL模块，可以在孪生网络目标跟踪中使用吗？在目标跟踪中也存在分类回归不对齐问题，和检测有什么区别吗？

Tood's onnx file request

Hello, I was not able to export the onnx file successfully using mmdet.Is there a Tood onnx file available? I would like to further visualize the network structure for learning, thank you!

TOOD

作者你好我想在其他模型上使用TOOD head，但是我有点读不懂TOOD代码，不知道TOOD核心具体是在哪几个类

Error during training (Assertion input_val >= zero && input_val <= one failed.)

Problem

thank you for contribution, I encountered gradient exploding during training the model tood_r50_fpn_1x_coco.

• I tried to train this model in Mix-Precision Training strategy, and the loss scale was set 'dynamic'. The training soon stopped, and raise RuntimeError: CUDA error: device-side assert triggered.

• I also retrained the model with FP32 precision, but it did not work.

• A lower lr did not address gradient exploding.

• Gradient cutting helps avoid training failure (Mix-Precision Training, loss scale=512.) , but the model can not converge.

  I try to google this issue. I think it is not OOM. It seems to relate with the NaN value in prediction head and further cause the error at calculating loss. I do not know if the environment(mmdet-1.15.0) affects with training.

My modification

• I port the TOOD code to my working environment (MMDet-1.15.0), without edit.
• I edit the training config to train my own dataset.

Environment

2021-12-09 16:50:01,643 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 2070
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.4.r11.4/compiler.30033411_0
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.9.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210617 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - 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 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.10.0
OpenCV: 4.5.3
MMCV: 1.3.10
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.1
MMDetection: 2.15.0+87eda06
------------------------------------------------------------

Error Report

/opt/conda/conda-bld/pytorch_1623448265233/work/aten/src/ATen/native/cuda/Loss.cu:111: operator(): block: [19,0,0], thread: [32,0,0] Assertion `input_val >= zero && input_val <= one` failed.
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/opt/conda/conda-bld/pytorch_1623448265233/work/aten/src/ATen/native/cuda/Loss.cu:111: operator(): block: [31,0,0], thread: [27,0,0] Assertion `input_val >= zero && input_val <= one` failed.
/opt/conda/conda-bld/pytorch_1623448265233/work/aten/src/ATen/native/cuda/Loss.cu:111: operator(): block: [31,0,0], thread: [28,0,0] Assertion `input_val >= zero && input_val <= one` failed.
/opt/conda/conda-bld/pytorch_1623448265233/work/aten/src/ATen/native/cuda/Loss.cu:111: operator(): block: [31,0,0], thread: [29,0,0] Assertion `input_val >= zero && input_val <= one` failed.
/opt/conda/conda-bld/pytorch_1623448265233/work/aten/src/ATen/native/cuda/Loss.cu:111: operator(): block: [31,0,0], thread: [30,0,0] Assertion `input_val >= zero && input_val <= one` failed.
/opt/conda/conda-bld/pytorch_1623448265233/work/aten/src/ATen/native/cuda/Loss.cu:111: operator(): block: [31,0,0], thread: [31,0,0] Assertion `input_val >= zero && input_val <= one` failed.
Traceback (most recent call last):
  File "tools/train.py", line 188, in <module>
    main()
  File "tools/train.py", line 184, in main
    meta=meta)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/apis/train.py", line 170, in train_detector
    runner.run(data_loaders, cfg.workflow)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
    self.run_iter(data_batch, train_mode=True, **kwargs)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter
    **kwargs)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 67, in train_step
    return self.module.train_step(*inputs[0], **kwargs[0])
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/models/detectors/base.py", line 237, in train_step
    losses = self(**data)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 97, in new_func
    return old_func(*args, **kwargs)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/models/detectors/base.py", line 171, in forward
    return self.forward_train(img, img_metas, **kwargs)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/models/detectors/single_stage.py", line 83, in forward_train
    gt_labels, gt_bboxes_ignore)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/models/dense_heads/base_dense_head.py", line 54, in forward_train
    losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 185, in new_func
    return old_func(*args, **kwargs)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/models/dense_heads/tood_head.py", line 426, in loss
    num_total_samples=num_total_samples)
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/core/utils/misc.py", line 29, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/mmdet-2.15.0-py3.7.egg/mmdet/models/dense_heads/tood_head.py", line 333, in loss_single
    & (labels < bg_class_ind)).nonzero().squeeze(1)
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
terminate called after throwing an instance of 'c10::CUDAError'
  what():  CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Exception raised from create_event_internal at /opt/conda/conda-bld/pytorch_1623448265233/work/c10/cuda/CUDACachingAllocator.cpp:1055 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7f12c21efa22 in /root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x10ac3 (0x7f12c2451ac3 in /root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #2: c10::cuda::CUDACachingAllocator::raw_delete(void*) + 0x1a7 (0x7f12c2453167 in /root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10::TensorImpl::release_resources() + 0x54 (0x7f12c21d95a4 in /root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #4: <unknown function> + 0xa2bb12 (0x7f133bad0b12 in /root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: <unknown function> + 0xa2bbb1 (0x7f133bad0bb1 in /root/anaconda3/envs/openmmlab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #24: __libc_start_main + 0xe7 (0x7f1376d75bf7 in /lib/x86_64-linux-gnu/libc.so.6)

Aborted

是否可以提供没有使用tap的tal训练config？

hi，有看到论文中有使用非tap头，并同时使用tal作为la的实验结果。请问是否可以提供这部分实验的训练config？

RuntimeError

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:2 and cuda:0!

how to stead of deform_sampling?

Hi Author:
def deform_sampling(self, feat, offset): """ Sampling the feature x according to offset. Args: feat (Tensor): Feature offset (Tensor): Spatial offset for for feature sampliing """ # it is an equivalent implementation of bilinear interpolation b, c, h, w = feat.shape weight = feat.new_ones(c, 1, 1, 1) y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c) return y
https://github.com/fcjian/TOOD/blob/master/mmdet/models/dense_heads/tood_head.py
how to use bilinear interpolation stead of deform_conv2d?

请问TAL中，t的计算公式是否为：t = (s**alpha) * (u**beta)，t = t/t.max * u.max？

model weight

Can you offer the weight in Baidu Netdisk?

Support for Swin backbone

Hello
How are you?
Thanks for contributing to this project.
Could u support the SwinTransformer backbone?

If I want to load the resnet50-19c8e357.pth from my local path instead of downloading from the terminal,how can I change the code?

I failed to download resnet50-19c8e357.pth in terminal,so I downloaded it and saved it to the local path.But I don't know what to do next.

How to apply TOOD in YOLOX

It seems that T-head and TAL have better performance than Decoupled head(like YOLOX), how can we apply this in YOLOX?

It might have conflict with simOTA?

the image size

what's the input image size? [3,640,640]????

i changed the number of ratios, then model can not train ,where should i have to modify futher?

i modify the ratios=[1] to ratios=[2.444, 3.182, 1.574, 1.721, 0.994, 1.163, 0.751, 0.534] then have a error like this：

2022-03-29 15:12:23,844 - mmdet - INFO - workflow: [('train', 1)], max: 100 epochs
2022-03-29 15:12:23,844 - mmdet - INFO - Checkpoints will be saved to E:\Object-Detection\Github\radar-detection\work_dirs\radar_tood by HardDiskBackend.
D:\App\anaconda\envs\swin-t\lib\site-packages\torch\nn\functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  ..\c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Traceback (most recent call last):
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\runner\epoch_based_runner.py", line 50, in train
    self.run_iter(data_batch, train_mode=True, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\runner\epoch_based_runner.py", line 30, in run_iter
    **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\parallel\data_parallel.py", line 75, in train_step
    return self.module.train_step(*inputs[0], **kwargs[0])
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\detectors\base.py", line 248, in train_step
    losses = self(**data)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmcv\runner\fp16_utils.py", line 98, in new_func
    return old_func(*args, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\detectors\base.py", line 172, in forward
    return self.forward_train(img, img_metas, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\detectors\single_stage.py", line 84, in forward_train
    gt_labels, gt_bboxes_ignore)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\dense_heads\base_dense_head.py", line 330, in forward_train
    outs = self(x)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\App\anaconda\envs\swin-t\lib\site-packages\mmdet\models\dense_heads\tood_head.py", line 263, in forward
    b, h, w, 4).permute(0, 3, 1, 2) / stride[0]
RuntimeError: shape '[8, 32, 168, 4]' is invalid for input of size 1376256

and this is my config file

dataset_type = 'CocoDataset'
data_root = 'data/coco/'
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',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    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',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=1,
    train=dict(
        type='CocoDataset',
        ann_file='E:/Object-Detection/data_radar/devkit/voc07_train.json',
        img_prefix='E:/Object-Detection/data_radar/devkit/',
        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',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        classes=('loose_l', 'loose_s', 'poor_l', 'porous')),
    val=dict(
        type='CocoDataset',
        ann_file='E:/Object-Detection/data_radar/devkit/voc07_val.json',
        img_prefix='E:/Object-Detection/data_radar/devkit/',
        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',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('loose_l', 'loose_s', 'poor_l', 'porous')),
    test=dict(
        type='CocoDataset',
        ann_file='E:/Object-Detection/data_radar/devkit/voc07_test.json',
        img_prefix='E:/Object-Detection/data_radar/devkit/',
        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',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        classes=('loose_l', 'loose_s', 'poor_l', 'porous')))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=100)
checkpoint_config = dict(interval=10)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='SetEpochInfoHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
model = dict(
    type='TOOD',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='TOODHead',
        num_classes=4,
        in_channels=256,
        stacked_convs=6,
        feat_channels=256,
        anchor_type='anchor_based',
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[2.444, 3.182, 1.574, 1.721, 0.994, 1.163, 0.751, 0.534],
            octave_base_scale=1,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        initial_loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            activated=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            activated=True,
            beta=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
    train_cfg=dict(
        initial_epoch=4,
        initial_assigner=dict(type='ATSSAssigner', topk=9),
        assigner=dict(type='TaskAlignedAssigner', topk=13),
        alpha=1,
        beta=6,
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=100))
classes = ('loose_l', 'loose_s', 'poor_l', 'porous')
work_dir = './work_dirs\radar_tood'
auto_resume = False
gpu_ids = [0]

How about the ATSS assigner as initial static assignment method?

In the initial stage of training, the scores of task alignment learning metric are so small, so that theirs value are almost zero, because of low ious and classification scores. As my point of view, using the ATSS is aim to select positive samples closing to gt center points in order to accelerate the model convergence in the early training?

Can I ask some questions about TOOD/mmdet/core/bbox/assigners/task_aligned_assigner.py ?

line 65: decode_bboxes (Tensor): predicted bounding boxes, shape(n, 80)
Why the shape of bbox here is (n,80)?
I started reading from tood_head.py and I wonder why the shape here is not the (n,4)?

Welcome update to OpenMMLab 2.0

Welcome update to OpenMMLab 2.0

I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/amFNsyUBvm or add me on WeChat (van-sin) and I will invite you to the OpenMMLab WeChat group.

Here are the OpenMMLab 2.0 repos branches:

	OpenMMLab 1.0 branch	OpenMMLab 2.0 branch
MMEngine		0.x
MMCV	1.x	2.x
MMDetection	0.x 、1.x、2.x	3.x
MMAction2	0.x	1.x
MMClassification	0.x	1.x
MMSegmentation	0.x	1.x
MMDetection3D	0.x	1.x
MMEditing	0.x	1.x
MMPose	0.x	1.x
MMDeploy	0.x	1.x
MMTracking	0.x	1.x
MMOCR	0.x	1.x
MMRazor	0.x	1.x
MMSelfSup	0.x	1.x
MMRotate	1.x	1.x
MMYOLO		0.x

Attention: please create a new virtual environment for OpenMMLab 2.0.

Benchmark / FPS

Hi,

Is there any benchmark including the fps or comparing with the yolov7 vs.?