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icd's Introduction

MegEngine

MegEngine is a fast, scalable, and user friendly deep learning framework with 3 key features.

  • Unified framework for both training and inference
    • Quantization, dynamic shape/image pre-processing, and even derivation with a single model.
    • After training, put everything into your model to inference on any platform with speed and precision. Check here for a quick guide.
  • The lowest hardware requirements
    • The memory usage of the GPU can be reduced to one-third of the original memory usage when DTR algorithm is enabled.
    • Inference models with the lowest memory usage by leveraging our Pushdown memory planner.
  • Inference efficiently on all platforms
    • Inference with speed and high-precision on x86, Arm, CUDA, and RoCM.
    • Supports Linux, Windows, iOS, Android, TEE, etc.
    • Optimize performance and memory usage by leveraging our advanced features.

Installation

NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.6 to 3.9. On Windows 10 you can either install the Linux distribution through Windows Subsystem for Linux (WSL) or install the Windows distribution directly. Many other platforms are supported for inference.

Binaries

To install the pre-built binaries via pip wheels:

python3 -m pip install --upgrade pip
python3 -m pip install megengine -f https://megengine.org.cn/whl/mge.html

Building from Source

How to Contribute

We strive to build an open and friendly community. We aim to power humanity with AI.

How to Contact Us

Resources

License

MegEngine is licensed under the Apache License, Version 2.0

Citation

If you use MegEngine in your publication,please cite it by using the following BibTeX entry.

@Misc{MegEngine,
  institution = {megvii},
  title =  {MegEngine:A fast, scalable and easy-to-use deep learning framework},
  howpublished = {\url{https://github.com/MegEngine/MegEngine}},
  year = {2020}
}

Copyright (c) 2014-2021 Megvii Inc. All rights reserved.

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icd's Issues

Can you provide me Pascal VOC configs?

Hello.
I'd like to try to replicate your great distortion methods at Pascal VOC.
However, this repository only provides MS-coco configurations.
you used extra 6k iterations for auxiliary task warm-up on the Pascal VOC dataset,
how can I reproduce this training details?

Is it alright to train teacher standalone / and distill students?

teacher standalone train
BASE: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
MASK_ON: False
RESNETS:
DEPTH: 101
ROI_HEADS:
NUM_CLASSES: 20
INPUT:
MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
MIN_SIZE_TEST: 800
DATASETS:
TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
TEST: ('voc_2007_test')
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000 # 17.4 epochs

student distillation
BASE: "../Base-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 20
DISTILLER:
MODEL_LOAD_OFFICIAL: False
MODEL_DISTILLER_CONFIG: 'PascalVOC-Detection/faster_rcnn_R_101_FPN.yaml'
INS_ATT_MIMIC:
WEIGHT_VALUE: 3.0
INS:
INPUT_FEATS: ['p2', 'p3', 'p4', 'p5', 'p6']
MAX_LABELS: 100
INPUT:
MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
MIN_SIZE_TEST: 800
DATASETS:
TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
TEST: ('voc_2007_test',)
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000 # 17.4 epochs
CLIP_GRADIENTS: {"ENABLED": True}

1GPU下效果不理想

我的的实验环境不具备8GPU条件,于是我在detectron2的官方说明指导下,将8GPU下运行的情况改为1GPU运行的情况,我使用如下命令
python train_baseline.py --num-gpus 1 --config-file configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
python3 train_distill.py --num-gpus 1 --resume --config-file configs/Distillation-ICD/retinanet_R_50_R101_icd_FPN_1x.yaml OUTPUT_DIR output/icd_retinanet SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
我得到的baseline效果为AP = 25.149,得到的蒸馏结果为AP = 35.860,baseline效果远低于论文给出的37.4,蒸馏结果也与39.9有一定差距。
请问这个结果正常吗?
我怎么样才能在1个GPU上达到与论文接近的水平?
十分感谢!

关于将Resnet50蒸馏成Resnet18的尝试

您好,最近我想将Resnet50蒸馏成Resnet18,但是在验证集上显示,模型并没有学到东西,AP值一直是零点几。
请问您在这个工程中做过相关类似的尝试吗?有什么经验分享吗?

AttributeError: BASE_LR_END

您好,我在命令python train_baseline.py --num-gpus 1 --config-file configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml 下可以正常运行并生成model_final.pth,但是在命令python3 train_distill.py --num-gpus 1 --config-file configs/Distillation-ICD/retinanet_R_50_R101_icd_FPN_1x.yaml OUTPUT_DIR output/icd_retinanet中,却报如下错误,可以请问您是什么原因吗?十分感谢!

Traceback (most recent call last):
File "train_distill.py", line 462, in
launch(
File "/home/sh/detectron2-main/detectron2/engine/launch.py", line 82, in launch
main_func(*args)
File "train_distill.py", line 447, in main
do_train(cfg, model, teacher, resume=args.resume)
File "train_distill.py", line 152, in do_train
teacher_sche = build_lr_scheduler(cfg.MODEL.DISTILLER, teacher_opt)
File "/home/sh/detectron2-main/detectron2/solver/build.py", line 275, in build_lr_scheduler
end_value = cfg.SOLVER.BASE_LR_END / cfg.SOLVER.BASE_LR
File "/home/sh/anaconda3/lib/python3.8/site-packages/yacs/config.py", line 141, in getattr
raise AttributeError(name)
AttributeError: BASE_LR_END
Segmentation fault (core dumped)

Some questions about Pascal Voc Evaluation index

Thank you for your work, I would like to know that in your work, Table 7 is about the performance on VOC. May I ask whether these evaluation indicators, such as AP and AP50, are calculated according to the COCO dataset?
When I independently trained RetinaNet-RestNet50 on VOC dataset without distillation, if according to the evaluation index of COCO, AP only reached 54.1, but AP50 could reach 82.4, I am quite confused about this. Could you please give me some suggestions.

问题1

您好,请问您在实验过程中尝试使用过yolo v3吗?

复现baseline有问题?

您好,我用如下命令,复现baseline
train_baseline.py --num-gpus 8 --config-file configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml
最后得到的结果是
image
即AP只有37.292,而该代码报的是37.4。是不是哪里超参改动了?

scale information是什么

image
1、如图,请问其中的scale information指代的是什么操作?
2、请问有没有只使用Identification和Localization,而没有scale information的实验数据?
3、Identification,Localization和scale information共同使用的数据仅比使用Localization和scale information增长了0.5个AP,这是否意味着中文所在设计的辅助优化任务仍然有待提升,Identification与Localization的配合并非最优!

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