Detectron2代码阅读

Git版本控制时的commit message

detectron2_0106

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the initial project version of detectron2_0106;
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待添加
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AdelaiDet_0107

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the initial project version of AdelaiDet_0107;
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待添加
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AdelaiDet中有关网络模型的默认参数配置位于以下脚本文件中:
adet/config/defaults.py

AdelaiDet_0420

# ---------------------------20210426分割线---------------------------
the initial project version of AdelaiDet_0420;
# ---------------------------20210505分割线---------------------------
add comments to the official code;
- adet/modeling/condinst/condinst.py
- adet/modeling/condinst/mask_branch.py
- adet/modeling/condinst/dynamic_mask_head.py
- adet/modeling/fcos/fcos.py
- adet/modeling/fcos/fcos_outputs.pywrite the code for the following schemes:
- CondInstv1v1_MS_R_50_1x
- DynamicMaskHeadV1v1,DynamicMaskHeadV1v2
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待添加
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暂时没看懂的代码

• metadata是什么意思

xxx\AdelaiDet_0107\tools\train_net_Detectron2.py
`metadata`是什么意思？

For attributes shared among the entire dataset, use Metadata (see below). To avoid extra memory, do not save such information inside each sample.

Each dataset is associated with some metadata, accessible through MetadataCatalog.get(dataset_name).some_metadata. Metadata is a key-value mapping that contains information that’s shared among the entire dataset, and usually is used to interpret what’s in the dataset, e.g., names of classes, colors of classes, root of files, etc. This information will be useful for augmentation, evaluation, visualization, logging, etc. The structure of metadata depends on what is needed from the corresponding downstream code.
摘自 ***Standard Dataset Dicts — detectron2 0.4 documentation

代码阅读记录

adet.utils.comm中的aligned_bilinear()函数

def aligned_bilinear(tensor, factor):assert tensor.dim() == 4assert factor >= 1assert int(factor) == factorif factor == 1:return tensorh, w = tensor.size()[2:]tensor = F.pad(tensor, pad=(0, 1, 0, 1), mode="replicate")oh = factor * h + 1ow = factor * w + 1tensor = F.interpolate(tensor, size=(oh, ow),mode='bilinear',align_corners=True)tensor = F.pad(tensor, pad=(factor // 2, 0, factor // 2, 0),mode="replicate")return tensor[:, :, :oh - 1, :ow - 1]

torch.Size([1, 1, 7, 5])
torch.Size([1, 1, 4, 3])torch.Size([1, 1, 8, 6])from adet.utils.comm import aligned_bilineartmp_in = torch.arange(12, dtype=torch.float).reshape(1, 1, 4, 3)
tmp_in
tensor([[[[ 0.,  1.,  2.],[ 3.,  4.,  5.],[ 6.,  7.,  8.],[ 9., 10., 11.]]]])
tmp_1 = F.pad(tmp_in, pad=(0, 1, 0, 1), mode="replicate")
tmp_1
tensor([[[[ 0.,  1.,  2.,  2.],[ 3.,  4.,  5.,  5.],[ 6.,  7.,  8.,  8.],[ 9., 10., 11., 11.],[ 9., 10., 11., 11.]]]])
tmp_1.shape
torch.Size([1, 1, 5, 4])tmp_2 = F.interpolate(tmp_1, size=(9,7), mode='bilinear', align_corners=True)
tmp_2
tensor([[[[ 0.0000,  0.5000,  1.0000,  1.5000,  2.0000,  2.0000,  2.0000],[ 1.5000,  2.0000,  2.5000,  3.0000,  3.5000,  3.5000,  3.5000],[ 3.0000,  3.5000,  4.0000,  4.5000,  5.0000,  5.0000,  5.0000],[ 4.5000,  5.0000,  5.5000,  6.0000,  6.5000,  6.5000,  6.5000],[ 6.0000,  6.5000,  7.0000,  7.5000,  8.0000,  8.0000,  8.0000],[ 7.5000,  8.0000,  8.5000,  9.0000,  9.5000,  9.5000,  9.5000],[ 9.0000,  9.5000, 10.0000, 10.5000, 11.0000, 11.0000, 11.0000],[ 9.0000,  9.5000, 10.0000, 10.5000, 11.0000, 11.0000, 11.0000],[ 9.0000,  9.5000, 10.0000, 10.5000, 11.0000, 11.0000, 11.0000]]]])
tmp_2.shape
torch.Size([1, 1, 9, 7])tmp_3 = F.pad(tmp_2, pad=(1, 0, 1, 0), mode="replicate")
tmp_3
tensor([[[[ 0.0000,  0.0000,  0.5000,  1.0000,  1.5000,  2.0000,  2.0000,  2.0000],[ 0.0000,  0.0000,  0.5000,  1.0000,  1.5000,  2.0000,  2.0000,  2.0000],[ 1.5000,  1.5000,  2.0000,  2.5000,  3.0000,  3.5000,  3.5000,  3.5000],[ 3.0000,  3.0000,  3.5000,  4.0000,  4.5000,  5.0000,  5.0000,  5.0000],[ 4.5000,  4.5000,  5.0000,  5.5000,  6.0000,  6.5000,  6.5000,  6.5000],[ 6.0000,  6.0000,  6.5000,  7.0000,  7.5000,  8.0000,  8.0000,  8.0000],[ 7.5000,  7.5000,  8.0000,  8.5000,  9.0000,  9.5000,  9.5000,  9.5000],[ 9.0000,  9.0000,  9.5000, 10.0000, 10.5000, 11.0000, 11.0000, 11.0000],[ 9.0000,  9.0000,  9.5000, 10.0000, 10.5000, 11.0000, 11.0000, 11.0000],[ 9.0000,  9.0000,  9.5000, 10.0000, 10.5000, 11.0000, 11.0000, 11.0000]]]])
tmp_3.shape
torch.Size([1, 1, 10, 8])tmp_out = tmp_3[:, :, :8, :6]
tmp_out
tensor([[[[ 0.0000,  0.0000,  0.5000,  1.0000,  1.5000,  2.0000],[ 0.0000,  0.0000,  0.5000,  1.0000,  1.5000,  2.0000],[ 1.5000,  1.5000,  2.0000,  2.5000,  3.0000,  3.5000],[ 3.0000,  3.0000,  3.5000,  4.0000,  4.5000,  5.0000],[ 4.5000,  4.5000,  5.0000,  5.5000,  6.0000,  6.5000],[ 6.0000,  6.0000,  6.5000,  7.0000,  7.5000,  8.0000],[ 7.5000,  7.5000,  8.0000,  8.5000,  9.0000,  9.5000],[ 9.0000,  9.0000,  9.5000, 10.0000, 10.5000, 11.0000]]]])
tmp_out.shape
torch.Size([1, 1, 8, 6])

Detectron2安装

20210106记：在GPU1080上
# create conda environment `usr_detectron2` with:
Add the conda-forge channel: conda config --add channels conda-forge
conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch -c conda-forgeconda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/win-64/# First install Detectron2 with:
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'# Then build AdelaiDet with:
git clone https://github.com/aim-uofa/AdelaiDet.git
cd AdelaiDet
python setup.py build develop20210507记：在GPU2080Ti上
conda info --env或conda env listconda config --add channels conda-forgeconda create -n usr_detectron2 python=3.9
conda activate usr_detectron2conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch -c conda-forge# to install Detectron2 from a local clone:
git clone -b v0.3 https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2# You often need to rebuild AdelaiDet after reinstalling Detectron2 with:
cd AdelaiDet
python setup.py build develop

入门Detectron2的参考博客

刚开始上手Detectron2时需要预先阅读的文档

***Detectron2_Tutorial.ipynb Colab Notebook
Getting Started with Detectron2 — detectron2 0.4 documentation
***Use Builtin Datasets — detectron2 0.4 documentation
***Standard Dataset Dicts — detectron2 0.4 documentation
Use Custom Datasets — detectron2 0.4 documentation
**Model Output Format - Use Models — detectron2 0.4 documentation
***detectron2.config — detectron2 0.4 documentation
***Detectron2训练自己的数据集（较详细）_丑小鸭-CSDN博客 20200615

Detectron2 源码分析_shang3988的专栏-CSDN博客 20200923
9.3.1 RetinaNet with Detectron2 · PyTorch深度学习 20200617
Detectron2 Beginner’s Tutorial（colabNotebook搬运，包括ipynb文件）_桜見的博客-CSDN博客 20200206
detectron2概述(各个脚本文件的功用)_艺的博客-CSDN博客_detectron2是什么 20200128

刚开始上手Detectron2时需要关注的类

cfg = get_cfg()  # detectron2/config/config.py
predictor = DefaultPredictor(cfg)  # detectron2/engine/defaults.py
trainer = DefaultTrainer(cfg)  # detectron2/engine/defaults.pyv = Visualizer(img, MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)  # detectron2/utils/visualizer.pyDatasetCatalog.register()  # detectron2/data/catalog.py
MetadataCatalog.get().set()  # detectron2/data/catalog.py
evaluator = COCOEvaluator("mydataset_val", ("bbox", "segm"), False, output_dir="./output/")  # detectron2/evaluation/coco_evaluation.py
val_loader = build_detection_test_loader(cfg, "mydataset_val")  # detectron2/data/build.py
print(inference_on_dataset(trainer.model, val_loader, evaluator))  # detectron2/evaluation/evaluator.py

detectron2.structures.instances — detectron2 0.4 documentation

Detectron2的使用记录

使用自己数据集时需要注意的地方

20210423记：
  自己数据集.json标注文件中的category_id需要是从0开始的;
  category_id (int, required): an integer in the range [0, num_categories-1] representing the category label. The value num_categories is reserved to represent the “background” category, if applicable.
Use Custom Datasets — detectron2 0.4 documentation

20210522记：
  COCO数据集的.json文件中the category ids是incontiguous；在Detectron2中，通过detectron2/data/datasets/coco.py def load_coco_json()加载Load a json file with COCO’s instances annotation format时会将其处理为contiguous ids in [0, 80)；

如何执行模型配置

detectron2.config — detectron2 0.4 documentation

Detectron2中的num_GPU,IMS_PER_BATCH,BASE_LR设置

Benchmarks — detectron2 0.4 documentation
Training & Evaluation in Command Line - Getting Started with Detectron2 — detectron2 0.4 documentation
staticauto_scale_workers() - detectron2.engine — detectron2 0.4 documentation

在mmdetection中, 原始为lr=0.01 with num_GPU=8, samples_per_gpu=2; 此时batch_size=16;
num_GPU=2, samples_per_gpu=6时, 则lr需乘以2∗6/(8∗2)2*6/(8*2)2∗6/(8∗2);

We evaluate CondInst on the large-scale benchmark MS-COCO [23]. Following the common practice [14, 22, 37], our models are trained with split train2017 (115K images) and all the ablation experiments are evaluated on split val2017 (5K images).
摘自 CondInst

在Detectron2中, 默认情况下, the configs are made for 8-GPU training, num_GPU=8, SOLVER.IMS_PER_BATCH=16, SOLVER.BASE_LR=0.02; 此时each GPU will see 2 images per batch;

• num_GPU=16, SOLVER.IMS_PER_BATCH=32, 则SOLVER.BASE_LR=0.02*32/16; 此时each GPU will see 2 images per batch;
• num_GPU=2, SOLVER.IMS_PER_BATCH=12, 则SOLVER.BASE_LR=0.02*12/16; 此时each GPU will see 6 images per batch;

关于iters_in_one_epoch, MAX_ITER的计算，

• d2.data.datasets.coco INFO: Loaded 118287 images in COCO format from coco/annotations/instances_train2017.json
  d2.data.build INFO: Removed 1021 images with no usable annotations. 117266 images left.

• COCO train17中总共有115k张图片, 若设置batch_size=16, 则iters_in_one_epoch = int(dataset_imgs/batch_size) + 1 = int(115k/16) + 1 = int(7.1875k) + 1 = 7188; 若训练12个epoch,则可指定最大迭代次数cfg.SOLVER.MAX_ITER = (iters_in_one_epoch * 12) = 86256;

• LSCD train总共有6735张图片, LSCD val总共有1000张图片; 若设置batch_size=8, 则iters_in_one_epoch = int(6735/8) + 1 = int(841.875) + 1 = 842; 若训练12个epoch,则可指定最大迭代次数cfg.SOLVER.MAX_ITER = (iters_in_one_epoch * 12) = 10104;

Detectron2中的其它设置

  设置/AdelaiDet_0420/detectron2/detectron2/engine/defaults.py中的period=10参数，可以改变log.txt中的iter: 19, metrics.json中的"iteration": 19间隔周期；

/AdelaiDet_0420/detectron2/detectron2/engine/defaults.py
def build_hooks(self): 略ret.append(hooks.PeriodicWriter(self.build_writers(), period=10))  # line375略/AdelaiDet_0420/detectron2/detectron2/engine/hooks.py
class PeriodicWriter(HookBase):def __init__(self, writers, period=20):  # line155略/AdelaiDet_0420/detectron2/tools/plain_train_net.py
def do_train(cfg, model, resume=False):略if iteration - start_iter > 5 and ((iteration + 1) % 20 == 0 or iteration == max_iter - 1):for writer in writers:writer.write()略此外，右键"Project | Find in Path.."搜索"Starting training from iteration，可以看到相关的脚本文件；

warmup 策略

detectron2.solver.lr_scheduler — detectron2 0.4 documentation
深度学习训练策略-学习率预热Warmup_豆芽菜-CSDN博客 20200405
深度学习 warmup 策略_comway_Li的博客-CSDN博客 20200321
深度学习之“训练热身”（warm up）–学习率的设置_dreamandgo的博客-CSDN博客 20200720

Mask R-CNN的Detectron2, MMDetection性能对比

detectron2/MODEL_ZOO.md at master · facebookresearch/detectron2 · GitHub

• COCO Instance Segmentation Baselines with Mask R-CNN in Detectron2

Name	lr sched	train time (s/iter)	inference time (s/iter)	train mem (GB)	box AP	mask AP	model id	备注
R50-FPN	1x	0.261	0.043	3.4	38.6	35.2	137260431	比较
R101-FPN	3x	0.340	0.056	4.6	42.9	38.6	138205316	-

mmdetection/configs/mask_rcnn at master · open-mmlab/mmdetection · GitHub

• Results and models of Mask R-CNN in MMDetection

Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	box AP	mask AP	备注
R-50-FPN	caffe	1x	4.3	-	38.0	34.4	-
R-50-FPN	pytorch	1x	4.4	16.1	38.2	34.7	比较
R-50-FPN	pytorch	2x	-	-	39.2	35.4	-
R-101-FPN	caffe	1x	-	-	40.4	36.4	-
R-101-FPN	pytorch	1x	6.4	13.5	40.0	36.1	-
R-101-FPN	pytorch	2x	-	-	40.8	36.6	-

• Mask R-CNN的训练log.json

Detectron2 R50-FPN | 1x
https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json{"data_time": 0.002598743070848286, "fast_rcnn/cls_accuracy": 0.97265625, "fast_rcnn/false_negative": 1.0, "fast_rcnn/fg_cls_accuracy": 0.0, "iteration": 19,
"loss_box_reg": 0.032130290812347084, "loss_cls": 0.639292468316853, "loss_mask": 0.6927643306553364,
"loss_rpn_cls": 0.6572066731750965, "loss_rpn_loc": 0.16647980932611972,
"lr": 0.00039962, "mask_rcnn/accuracy": 0.49910617656153367, "mask_rcnn/false_negative": 0.3886190705390907, "mask_rcnn/false_positive": 0.6187156134052185, "roi_head/num_bg_samples": 502.5, "roi_head/num_fg_samples": 9.5, "rpn/num_neg_anchors": 225.25, "rpn/num_pos_anchors": 30.75, "time": 0.25318309606518596,
"total_loss": 2.24487855611369}{"data_time": 0.0025207019643858075, "fast_rcnn/cls_accuracy": 0.9716796875, "fast_rcnn/false_negative": 1.0, "fast_rcnn/fg_cls_accuracy": 0.0, "iteration": 39,
"loss_box_reg": 0.09106488188263029, "loss_cls": 0.5796218859031796, "loss_mask": 0.6907654628157616,
"loss_rpn_cls": 0.45275296457111835, "loss_rpn_loc": 0.1657287187408656,
"lr": 0.0007992199999999999, "mask_rcnn/accuracy": 0.531749595389413, "mask_rcnn/false_negative": 0.11595830385098957, "mask_rcnn/false_positive": 0.8639941425849713, "roi_head/num_bg_samples": 497.5, "roi_head/num_fg_samples": 14.5, "rpn/num_neg_anchors": 230.25, "rpn/num_pos_anchors": 25.75, "time": 0.2263991323998198,
"total_loss": 2.01636832227814}{"data_time": 0.0023499008966609836, "fast_rcnn/cls_accuracy": 0.96435546875, "fast_rcnn/false_negative": 1.0, "fast_rcnn/fg_cls_accuracy": 0.0, "iteration": 59,
"loss_box_reg": 0.12914634053595364, "loss_cls": 0.4017837508581579, "loss_mask": 0.690228708088398,
"loss_rpn_cls": 0.31959519628435373, "loss_rpn_loc": 0.13147040538024157,
"lr": 0.00119882, "mask_rcnn/accuracy": 0.5578946873589731, "mask_rcnn/false_negative": 0.08273137213201628, "mask_rcnn/false_positive": 0.9230921910050085, "roi_head/num_bg_samples": 493.75, "roi_head/num_fg_samples": 18.25, "rpn/num_neg_anchors": 235.75, "rpn/num_pos_anchors": 20.25, "time": 0.23010813840664923,
"total_loss": 1.6990175214014016}{"data_time": 0.0023776369635015726, "fast_rcnn/cls_accuracy": 0.955078125, "fast_rcnn/false_negative": 1.0, "fast_rcnn/fg_cls_accuracy": 0.0, "iteration": 79, "loss_box_reg": 0.1658823168836534, "loss_cls": 0.37299142871052027, "loss_mask": 0.6893040910363197,
"loss_rpn_cls": 0.2666563978418708, "loss_rpn_loc": 0.1486980152549222,
"lr": 0.0015984200000000001, "mask_rcnn/accuracy": 0.5633592547171357, "mask_rcnn/false_negative": 0.04547704788242386, "mask_rcnn/false_positive": 0.9446711285129321, "roi_head/num_bg_samples": 489.0, "roi_head/num_fg_samples": 23.0, "rpn/num_neg_anchors": 226.5, "rpn/num_pos_anchors": 29.5, "time": 0.23420310846995562,
"total_loss": 1.6437977316963952}--------------------{"data_time": 0.002329822047613561, "fast_rcnn/cls_accuracy": 0.94970703125, "fast_rcnn/false_negative": 0.3859087630724847, "fast_rcnn/fg_cls_accuracy": 0.591547539893617, "iteration": 44979,
"loss_box_reg": 0.2508180043660104, "loss_cls": 0.227490158053115, "loss_mask": 0.27231015264987946,
"loss_rpn_cls": 0.04181082101422362, "loss_rpn_loc": 0.07604182060458697,
"lr": 0.02, "mask_rcnn/accuracy": 0.8922324188088697, "mask_rcnn/false_negative": 0.10118279120451368, "mask_rcnn/false_positive": 0.12274398554618456, "roi_head/num_bg_samples": 449.75, "roi_head/num_fg_samples": 62.25, "rpn/num_neg_anchors": 230.75, "rpn/num_pos_anchors": 25.25, "time": 0.25466170033905655,
"total_loss": 0.8704715783533175}{"data_time": 0.002378121018409729, "fast_rcnn/cls_accuracy": 0.9287109375, "fast_rcnn/false_negative": 0.388353581901969, "fast_rcnn/fg_cls_accuracy": 0.5810359231411864, "iteration": 44999,
"loss_box_reg": 0.2347505206707865, "loss_cls": 0.2301798826083541, "loss_mask": 0.27007893938571215,
"loss_rpn_cls": 0.04352796872262843, "loss_rpn_loc": 0.08090459188679233,
"lr": 0.02, "mask_rcnn/accuracy": 0.8877158907400843, "mask_rcnn/false_negative": 0.089878663914568, "mask_rcnn/false_positive": 0.1376528427255747, "roi_head/num_bg_samples": 443.25, "roi_head/num_fg_samples": 68.75, "rpn/num_neg_anchors": 229.75, "rpn/num_pos_anchors": 26.25, "time": 0.2536838488886133,
"total_loss": 0.8512819005991332,
"bbox/AP": 29.6848, "bbox/AP50": 49.736, "bbox/AP75": 31.6153, "bbox/APs": 17.0325, "bbox/APm": 32.3724, "bbox/APl": 38.9994,
"segm/AP": 27.9303, "segm/AP50": 46.4976, "segm/AP75": 29.4796, "segm/APs": 12.7666, "segm/APm": 30.2016, "segm/APl": 41.198}{"data_time": 0.002324605593457818, "fast_rcnn/cls_accuracy": 0.9228515625, "fast_rcnn/false_negative": 0.4123746826728626, "fast_rcnn/fg_cls_accuracy": 0.5709211553473849, "iteration": 45019,
"loss_box_reg": 0.23696620645932853, "loss_cls": 0.24506191909313202, "loss_mask": 0.2732751062139869,
"loss_rpn_cls": 0.046370368072530255, "loss_rpn_loc": 0.07181740016676486,
"lr": 0.02, "mask_rcnn/accuracy": 0.897779099108315, "mask_rcnn/false_negative": 0.09148855694390579, "mask_rcnn/false_positive": 0.10694905510167176, "roi_head/num_bg_samples": 440.0, "roi_head/num_fg_samples": 72.0, "rpn/num_neg_anchors": 220.0, "rpn/num_pos_anchors": 36.0, "time": 0.2497749866452068,
"total_loss": 0.8699611588672269}--------------------{"data_time": 0.0026094965869560838, "fast_rcnn/cls_accuracy": 0.93701171875, "fast_rcnn/false_negative": 0.3187358810240964, "fast_rcnn/fg_cls_accuracy": 0.6734516189759037, "iteration": 89959,
"loss_box_reg": 0.2107297400943935, "loss_cls": 0.19628392602317035, "loss_mask": 0.23165522469207644,
"loss_rpn_cls": 0.035993807558043045, "loss_rpn_loc": 0.0715790189569816,
"lr": 0.00020000000000000004, "mask_rcnn/accuracy": 0.8943493506033365, "mask_rcnn/false_negative": 0.09956806044260469, "mask_rcnn/false_positive": 0.1258371461357612, "roi_head/num_bg_samples": 443.75, "roi_head/num_fg_samples": 68.25, "rpn/num_neg_anchors": 218.75, "rpn/num_pos_anchors": 37.25, "time": 0.2603230450768024,
"total_loss": 0.7695412554403447}{"data_time": 0.0024753035977482796, "fast_rcnn/cls_accuracy": 0.94482421875, "fast_rcnn/false_negative": 0.2629855958096901, "fast_rcnn/fg_cls_accuracy": 0.6700900961473812, "iteration": 89979,
"loss_box_reg": 0.2162564373575151, "loss_cls": 0.18739765952341259, "loss_mask": 0.24809803813695908,
"loss_rpn_cls": 0.031174708376056515, "loss_rpn_loc": 0.07452140923123807,
"lr": 0.00020000000000000004, "mask_rcnn/accuracy": 0.8987971277948823, "mask_rcnn/false_negative": 0.08529945120560417, "mask_rcnn/false_positive": 0.11964548731891606, "roi_head/num_bg_samples": 443.75, "roi_head/num_fg_samples": 68.25, "rpn/num_neg_anchors": 227.25, "rpn/num_pos_anchors": 28.75, "time": 0.26268363802228123,
"total_loss": 0.739351074967999}{"bbox/AP": 38.64992550664329, "bbox/AP50": 59.47717812766804, "bbox/AP75": 42.12190719744732, "bbox/APl": 49.888680953062895, "bbox/APm": 42.02547896334861, "bbox/APs": 22.480254061778187, "data_time": 0.0024516225093975663, "fast_rcnn/cls_accuracy": 0.9326171875, "fast_rcnn/false_negative": 0.3482697426796806, "fast_rcnn/fg_cls_accuracy": 0.6329931972789116, "iteration": 89999,
"loss_box_reg": 0.21414381475187838, "loss_cls": 0.1887940641026944, "loss_mask": 0.23086919263005257,
"loss_rpn_cls": 0.027912706733332016, "loss_rpn_loc": 0.06928918394260108,
"lr": 0.00020000000000000004, "mask_rcnn/accuracy": 0.8918846949181082, "mask_rcnn/false_negative": 0.08113309024818016, "mask_rcnn/false_positive": 0.135757795864421, "roi_head/num_bg_samples": 440.5, "roi_head/num_fg_samples": 71.5, "rpn/num_neg_anchors": 228.75, "rpn/num_pos_anchors": 27.25,
"segm/AP": 35.24377920043061, "segm/AP50": 56.31648013808561, "segm/AP75": 37.49476279170336, "segm/APl": 50.34300779993869, "segm/APm": 37.70836473109089, "segm/APs": 17.1625607932606, "time": 0.26572991348803043, "total_loss": 0.7517079937970266}

MMDetection R-50-FPN | pytorch | 1x
http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json{"CUDA_HOME": "/mnt/lustre/share/cuda-10.0", "GPU 0,1,2,3,4,5,6,7": "Tesla V100-SXM2-32GB", "PyTorch": "1.3.1", "TorchVision": "0.4.2", "MMCV": "0.2.16", "MMDetection": "1.0rc1+d1c761c", "seed": 0}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00797, "time": 3.87105, "data_time": 3.52078, "memory": 4207,
"loss_rpn_cls": 0.31397, "loss_rpn_bbox": 0.09809, "loss_cls": 0.68102, "acc": 93.2146, "loss_bbox": 0.15389, "loss_mask": 0.71273, "loss": 1.95971}{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00931, "time": 0.33465, "data_time": 0.00731, "memory": 4207,
"loss_rpn_cls": 0.17393, "loss_rpn_bbox": 0.09358, "loss_cls": 0.48114, "acc": 93.85815, "loss_bbox": 0.2136, "loss_mask": 0.6744, "loss": 1.63665}{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.01064, "time": 0.30104, "data_time": 0.00618, "memory": 4207,
"loss_rpn_cls": 0.14034, "loss_rpn_bbox": 0.0951, "loss_cls": 0.4919, "acc": 92.52588, "loss_bbox": 0.26593, "loss_mask": 0.64107, "loss": 1.63434}{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01197, "time": 0.30138, "data_time": 0.00583, "memory": 4207,
"loss_rpn_cls": 0.13552, "loss_rpn_bbox": 0.09778, "loss_cls": 0.52423, "acc": 91.6001, "loss_bbox": 0.30218, "loss_mask": 0.62185, "loss": 1.68157}--------------------{"mode": "train", "epoch": 6, "iter": 7300, "lr": 0.02, "time": 0.31512, "data_time": 0.00871, "memory": 4441,
"loss_rpn_cls": 0.04038, "loss_rpn_bbox": 0.05534, "loss_cls": 0.25806, "acc": 91.79053, "loss_bbox": 0.25501, "loss_mask": 0.27165, "loss": 0.88044}{"mode": "val", "epoch": 6, "iter": 7330, "lr": 0.02,
"bbox_mAP": 0.298, "bbox_mAP_50": 0.494, "bbox_mAP_75": 0.319, "bbox_mAP_s": 0.167, "bbox_mAP_m": 0.332, "bbox_mAP_l": 0.376,
"bbox_mAP_copypaste": "0.298 0.494 0.319 0.167 0.332 0.376", "segm_mAP": 0.283, "segm_mAP_50": 0.469, "segm_mAP_75": 0.301, "segm_mAP_s": 0.145, "segm_mAP_m": 0.313, "segm_mAP_l": 0.374,
"segm_mAP_copypaste": "0.283 0.469 0.301 0.145 0.313 0.374"}{"mode": "train", "epoch": 7, "iter": 50, "lr": 0.02, "time": 3.53933, "data_time": 3.1868, "memory": 4441,
"loss_rpn_cls": 0.0374, "loss_rpn_bbox": 0.05666, "loss_cls": 0.24946, "acc": 91.99194, "loss_bbox": 0.25296, "loss_mask": 0.26755, "loss": 0.86403}--------------------{"mode": "train", "epoch": 12, "iter": 7200, "lr": 0.0002, "time": 0.30634, "data_time": 0.00588, "memory": 4441,
"loss_rpn_cls": 0.02146, "loss_rpn_bbox": 0.04223, "loss_cls": 0.17699, "acc": 93.70728, "loss_bbox": 0.20831, "loss_mask": 0.229, "loss": 0.678}{"mode": "train", "epoch": 12, "iter": 7250, "lr": 0.0002, "time": 0.33768, "data_time": 0.00644, "memory": 4441,
"loss_rpn_cls": 0.0231, "loss_rpn_bbox": 0.04785, "loss_cls": 0.19285, "acc": 93.18335, "loss_bbox": 0.22734, "loss_mask": 0.24249, "loss": 0.73362}{"mode": "train", "epoch": 12, "iter": 7300, "lr": 0.0002, "time": 0.30858, "data_time": 0.00727, "memory": 4441,
"loss_rpn_cls": 0.02357, "loss_rpn_bbox": 0.04924, "loss_cls": 0.19678, "acc": 93.0603, "loss_bbox": 0.22557, "loss_mask": 0.24389, "loss": 0.73906}{"mode": "val", "epoch": 12, "iter": 7330, "lr": 0.0002,
"bbox_mAP": 0.382, "bbox_mAP_50": 0.588, "bbox_mAP_75": 0.414, "bbox_mAP_s": 0.219, "bbox_mAP_m": 0.409, "bbox_mAP_l": 0.495,
"bbox_mAP_copypaste": "0.382 0.588 0.414 0.219 0.409 0.495", "segm_mAP": 0.347, "segm_mAP_50": 0.557, "segm_mAP_75": 0.372, "segm_mAP_s": 0.183, "segm_mAP_m": 0.374, "segm_mAP_l": 0.472,
"segm_mAP_copypaste": "0.347 0.557 0.372 0.183 0.374 0.472"}

问题记录

问题描述：
  开始
原因分析：
  开始
解决方案：
  开始

ImportError: cannot import name ‘_C’ from ‘detectron2’

问题描述：

cd xxx/Pytorch_WorkSpace/OpenSourcePlatform/detectron2python demo/demo.py \--config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \--input my_workspace/test_demo_model/000019.jpg \--opts MODEL.WEIGHTS checkpoints/mask_rcnn_r50_fpn_1x_coco_137260431final_a54504.pkl \MODEL.DEVICE cpuTraceback (most recent call last):File "xxx/OpenSourcePlatform/detectron2/detectron2/layers/__init__.py", line 3, in <module>from .deform_conv import DeformConv, ModulatedDeformConvFile "xxx/OpenSourcePlatform/detectron2/detectron2/layers/deform_conv.py", line 11, in <module>from detectron2 import _C
ImportError: cannot import name '_C' from 'detectron2' (xxx/OpenSourcePlatform/detectron2/detectron2/__init__.py)

原因分析：

ppwwyyxx commented on 10 Mar 2020
If you install detectron2 following INSTALL.md, you cannot import detectron2 at the root of the github repo because it will not find the one you installed, but will find the one in the repo (which has not been compiled).
Try a different directory. You should execute the code in any different directory.
ImportError: cannot import name ‘_C’ from ‘detectron2’ · Issue #1018 · facebookresearch/detectron2

  创建from_detectron2_import_C.py，文件内容如下：

import os
print('current working directory of this process:\n', os.getcwd())
from detectron2 import _C

  在不同路径下，执行该脚本文件，会发现在/AdelaiDet/路径下执行会报错，而在除了该路径之外的其它路径下执行就不会报错。原因在于在/AdelaiDet/路径下存在detectron2文件夹，与环境中的detectron2包重名。

(detectron2) xxx@xxx-System-Product-Name:xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet$
python xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/my_workspace/usr_from_detectron2_import_C.py
current working directory of this process:xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet
Traceback (most recent call last):File "xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/my_workspace/usr_from_detectron2_import_C.py", line 23, in <module>from detectron2 import _C
ImportError: cannot import name '_C' from 'detectron2' (xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/detectron2/__init__.py)(detectron2) xxx@xxx-System-Product-Name:xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet$
cd my_workspace/
(detectron2) xxx@xxx-System-Product-Name:xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/my_workspace$
python xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/my_workspace/usr_from_detectron2_import_C.py
current working directory of this process:xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/my_workspace(detectron2) xxx@xxx-System-Product-Name:xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/my_workspace$
cd ../..
(detectron2) xxx@xxx-System-Product-Name:xxx/Pytorch_WorkSpace/OpenSourcePlatform$
python xxx/Pytorch_WorkSpace/OpenSourcePlatform/AdelaiDet/my_workspace/usr_from_detectron2_import_C.py
current working directory of this process:xxx/Pytorch_WorkSpace/OpenSourcePlatform(detectron2) xxx@xxx-System-Product-Name:xxx/Pytorch_WorkSpace/OpenSourcePlatform$ 

解决方案：
  to “execute” the code in a different directory.

cd ./my_workspacepython ../demo/demo.py \--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \--input test_demo_model/000019.jpg \--output ./ \--opts MODEL.WEIGHTS ../checkpoints/mask_rcnn_r50_fpn_1x_coco_137260431final_a54504.pkl \MODEL.DEVICE cpu

  当使用Run/Debug Configuration: Python—PyCharm进行Debug/Run时，在"Run…/Debug… | Edit Configurations… | Configuration tab"下，注意要取消勾选"Add content roots to PYTHONPATH", 因为Content root—PyCharm中包含了/AdelaiDet/路径。

raise SizeMismatchError(detectron2.data.detection_utils.SizeMismatchError: Mismatched image shape for image

问题描述：

  File "/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/detectron2/data/dataset_mapper.py", line 126, in __call__utils.check_image_size(dataset_dict, image)File "/home/usrname/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/detectron2/data/detection_utils.py", line 194, in check_image_sizeraise SizeMismatchError(
detectron2.data.detection_utils.SizeMismatchError: Mismatched image shape for image /data/carton_net_/images/train2017/net (4114).jpg, got (500, 375), expect (500, 500). Please check the width/height in your annotation.

原因分析and解决方案：
  20210423经核查,carton_net_数据集json标注文件中的width/height有误,与图片的实际宽高不符;

将instances_train2017.json中的数据
{"height": 500,"width": 500,"id": 5782,"file_name": "net (4114).jpg"
},
改为
{"height": 375,"width": 500,"id": 5782,"file_name": "net (4114).jpg"
},
结果仍然还有其他图片的数据有问题。于是决定暂时注释掉File "/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/detectron2/data/dataset_mapper.py",
line 126, in __call__ utils.check_image_size(dataset_dict, image)以跳过此问题；
后续有待重新生成数据正确的instances_train2017.json；

  以下的解决方案暂时未尝试，
where I need to change the code · Issue #2463 · facebookresearch/detectron2 · GitHub 20210109
SizeMismatchError When trying to train a model using a custom dataset · Issue #194 · facebookresearch/detectron2 · GitHub 20191029

由于Tensor.shape尺寸过大导致执行F.interpolate(), .float()操作时报错RuntimeError: CUDA out of memory.

20210424记：
RuntimeError: CUDA out of memory. Tried to allocate 4.47 GiB; UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown;
问题描述：

[05/07 12:21:05 d2.evaluation.evaluator]: Inference done 276/500. 0.5077 s / img. ETA=0:04:39
val2017/818.jpg h:3087 w:2561
val2017/2394 (2).jpg h:3024 w:2956
val2017/2 (76).jpg h:4000 w:3000
val2017/4 (213).jpg h:3264 w:2448
val2017/4 (315).jpg h:3264 w:2243
val2017/5 (8).jpg h:4000 w:3000
Traceback (most recent call last):File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/tools/train_net_usr.py", line 367, in <module>launch(File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/detectron2/detectron2/engine/launch.py", line 55, in launchmp.spawn(File "/home/amax/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 230, in spawnreturn start_processes(fn, args, nprocs, join, daemon, start_method='spawn')File "/home/amax/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processeswhile not context.join():File "/home/amax/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 150, in joinraise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error:
Traceback (most recent call last):File "/home/amax/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 59, in _wrapfn(i, *args)File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/detectron2/detectron2/engine/launch.py", line 94, in _distributed_workermain_func(*args)File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/tools/train_net_usr.py", line 344, in mainres = Trainer.test(cfg, model)File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/detectron2/detectron2/engine/defaults.py", line 534, in testresults_i = inference_on_dataset(model, data_loader, evaluator)File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/detectron2/detectron2/evaluation/evaluator.py", line 141, in inference_on_datasetoutputs = model(inputs)File "/home/amax/miniconda2/envs/usr_detectron2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_implresult = self.forward(*input, **kwargs)File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/adet/modeling/condinst/condinst.py", line 181, in forwardinstances_per_im = self.postprocess(  File "/home/amax/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/adet/modeling/condinst/condinst.py", line 396, in postprocessresults.pred_masks = (pred_global_masks > mask_threshold).float()
RuntimeError: CUDA out of memory. Tried to allocate 4.47 GiB (GPU 0; 10.73 GiB total capacity; 6.10 GiB already allocated; 1.05 GiB free; 8.69 GiB reserved in total by PyTorch)(usr_detectron2) amax@amax:~/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/tools$ /home/amax/miniconda2/envs/usr_detectron2/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdownwarnings.warn('resource_tracker: There appear to be %d '
(usr_detectron2) amax@amax:~/usr_users/usrname/OpenSourcePlatform/AdelaiDet_0420/tools$ 

原因分析：

/adet/modeling/condinst/condinst.py line363def postprocess(self, results, output_height, output_width, padded_im_h, padded_im_w, mask_threshold=0.5):略pred_global_masks = F.interpolate(pred_global_masks,size=(output_height, output_width),mode="bilinear", align_corners=False)pred_global_masks = pred_global_masks[:, 0, :, :]results.pred_masks = (pred_global_masks > mask_threshold).float()略pred_masks_tmp = (pred_global_masks > mask_threshold)
pred_masks_tmp2 = pred_masks_tmp.clone().detach()
pred_masks_tmp2[0].float()
运行上面三个命令时均不会报错，但运行pred_masks_tmp2.float()时就会报错"RuntimeError: CUDA out of memory."

/detectron2/evaluation/coco_evaluation.py line384def instances_to_coco_json(instances, img_id):略has_mask = instances.has("pred_masks")if has_mask:# use RLE to encode the masks, because they are too large and takes memory# since this evaluator stores outputs of the entire datasetrles = [mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]for mask in instances.pred_masks]略pred_masks_adarray1 = np.array(instances.pred_masks[0][:, :, None], order="F", dtype="uint8")
pred_masks_adarray2 = np.array(instances.pred_masks[0].float()[:, :, None], order="F", dtype="uint8")

解决方案：
  pred_global_masks的shape是torch.Size([100, 800, 608]), 将其上采样至原始图片大小torch.Size([100, 3648, 2736])时, 计算量过大, 从而导致报错"RuntimeError: CUDA out of memory."。解决方案之一是将F.interpolate(), .float()操作放到CPU上运行。

RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1556653114079/work/torch/lib/c10d/ProcessGroupNCCL.cpp

(未阅读未尝试)重新配置语义分割实验环境遇到的坑 - Oliver-cs - 博客园

The environment is inconsistent, please check the package plan carefully. The following packages are causing the inconsistency

20210505记：
问题描述：

(base) amax@amax:~$ conda update conda
Collecting package metadata (current_repodata.json): done
Solving environment: /
The environment is inconsistent, please check the package plan carefully
The following packages are causing the inconsistency:- conda-forge/linux-64::libgfortran4==7.5.0=h14aa051_19- defaults/linux-64::numpy-base==1.15.4=py27hde5b4d6_0- defaults/linux-64::mkl_fft==1.0.6=py27hd81dba3_0- defaults/linux-64::mkl_random==1.0.2=py27hd81dba3_0- defaults/linux-64::blas==1.0=mkl
failed with repodata from current_repodata.json, will retry with next repodata source.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
略详情见：GPU2080Ti操作日志_20200505 environment is inconsistent.log

原因分析与解决方案:
  尝试了以下博客中的方法，均未解决；只好冷静下来，逐行观察上述报错提示，觉得要不试试将上述inconsistent的包逐个卸载掉；使用conda list命令查看当前环境中已安装的包，发现不同包的Channel不同，于是决定先将Channel不是defaults的包逐个卸载掉；在卸载其中的某些包时，仍然会报类似于上述的错误；后来，通过轮换键入conda uninstall package_name, pip uninstall package_name, conda clean -y --all命令，终于卸载掉了其中的一些包；接着，通过conda config --show命令查看conda的channels，通过conda config --remove channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/命令移除除defaults之外的其它channels；最后，通过conda clean -y --all,conda update -n base conda命令，此问题似乎得到了解决；

*** anaconda - The environment is inconsistent, please check the package plan carefully - Stack Overflow
解决conda update -n base -c defaults conda报错问题_ljx0951的博客-CSDN博客 20200428

Solving environment: failed with initial frozen solve. Retrying with flexible solve; Found conflicts! Looking for incompatible packages. UnsatisfiableError; Package xxx conflicts for;

20210505记：
问题描述：

(base) amax@amax:~$ conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch -c conda-forge
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: /
Found conflicts! Looking for incompatible packages.
Examining torchaudio:   4%|鈻堚枅鈻堚枅鈻�  详情见：GPU2080Ti操作日志_20200505 Package xxx conflicts for.log

原因分析与解决方案:
  尝试了以下博客中的方法，均未解决；只好冷静下来，逐行观察上述报错提示，觉得要不试试将上述inconsistent的包逐个卸载掉；使用conda list命令查看当前环境中已安装的包，发现不同包的Channel不同，于是决定先将Channel不是defaults的包逐个卸载掉；在卸载其中的某些包时，仍然会报类似于上述的错误；后来，通过轮换键入conda uninstall package_name, pip uninstall package_name, conda clean -y --all命令，终于卸载掉了其中的一些包；接着，通过conda config --show命令查看conda的channels，通过conda config --remove channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/命令移除除defaults之外的其它channels；最后，通过conda clean -y --all,conda update -n base conda命令，此问题似乎得到了解决；
conda安装环境报错 - Tools&Platform Guide

二级标题

  

待补充

  

待补充

  

文字居中

数学公式粗体\textbf{}或者memory{\bf memory}memory
数学公式粗斜体\bm{}

摘录自“bookname_author”
此文系转载，原文链接：名称 20200505

高亮颜色说明：突出重点
个人觉得，：待核准个人观点是否有误

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我是字号为2的字体
我是颜色为00ffff, 字号为2的字体
我是字体类型为微软雅黑, 颜色为00ffff, 字号为2的字体

分割线

分割线
问题描述：
原因分析：
解决方案：

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内容	内容	内容
内容	内容	内容

为长文本指定列宽实现换行

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| xxx

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