foreword

running PaddleDetectionversion ofsolov2

1. solov2

The deep learning example segmentation model is said to be very powerful:
insert image description here

2. Implementation steps

1. Build the dataset

I don’t pay too much attention to labeling tools. I’m currently using SenseTime’s labelBee, which feels very light in use, so I’ve been using it all the time. As for the format of the output labeling file, it is acceptable. The big deal is pywriting A series of scripts are enough;
the newly created project is as shown in the figure below:
insert image description here
After marking, the marking information needs to be output, and exportthis window will pop up after selecting , and you can choose a more general one coco; the content of the

saved jsonfile is as follows:

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			[665.7601771115822, 269.53170795345187, 680.5967809202416, 264.6328293373851, 691.2343459151294, 263.5130856537127, 713.5330235768897, 264.2182323939071, 717.2308951964027, 265.67911994729496, 721.2026832321759, 265.4052035310347, 720.1983230392218, 263.8073577695168, 710.2460265817671, 260.7029717185676, 703.945949007782, 257.6442384036618, 697.5171594824766, 254.26763108506208, 693.2967028060517, 255.10024269403067, 689.4749864563443, 255.8770143098249, 686.7407503687488, 256.529502467092, 684.690073303052, 252.4592192003304, 682.6692159737936, 250.65167351749346, 664.9189145810122, 247.67420360644627, 662.9721073314814, 249.44923374572443, 662.8003302212287, 259.52682421388414, 663.5446976989905, 266.97049899150215]
		],
		"area": 593.736997365937,
		"bbox": [662.8003302212287, 247.67420360644627, 58.402353010947195, 21.8575043470056],
		"category_id": 0,
		"order": 3
	}, {
    
    
		"image_id": 8,
		"id": 39,
		"iscrowd": 0,
		"segmentation": [
			[639.6359848037292, 774.5020180770084, 635.1590007180087, 771.4770288299, 628.9880226539073, 768.5730391526757, 625.6000346971456, 765.7900490453359, 623.6640415789961, 749.9391053904872, 616.4040673859357, 743.0421299070797, 607.3250150933964, 737.9142049701919, 600.1496603547893, 737.273548297102, 592.9743056161822, 738.6829929778999, 584.1332435275414, 739.8361749894617, 580.4174348236198, 743.6801150280012, 580.1611721543838, 748.8053684127206, 576.3172321158444, 752.3930457820242, 574.5233934311926, 759.1840065167772, 575.0359187696645, 764.8217852399686, 573.6264740888666, 768.793856613126, 580.6736974928558, 771.4846146401037, 592.2055176084743, 773.0221906555195, 607.7094090972503, 772.3815339824296, 622.0601185744645, 774.0472413324633, 632.3106253439032, 777.250524697913]
		],
		"area": 1619.7162347433623,
		"bbox": [573.6264740888666, 737.273548297102, 66.00951071486259, 39.97697640081094],
		"category_id": 0,
		"order": 4
	}, {
    
    
		"image_id": 8,
		"id": 40,
		"iscrowd": 0,
		"segmentation": [
			[630.0817640932652, 781.5082893196262, 629.8793962845028, 794.1225493991504, 645.7315413042257, 793.7852697178797, 647.4179397105793, 788.1864270087862, 639.3543323321237, 781.6107714105622]
		],
		"area": 184.18309333355865,
		"bbox": [629.8793962845028, 781.5082893196262, 17.538543426076444, 12.614260079524229],
		"category_id": 0,
		"order": 5
	}],
	"categories": [{
    
    
		"id": 0,
		"name": "",
		"supercategory": ""
	}]
}

2. Write configuration files

There are many configuration files, but fortunately, the logic is clear enough. Since I am modifying it myself, I first make copya copy of the source file. The following is a brief analysis of several files that will be used, as an example solov2_r50_fpn_3x_coco:
Main file: defines the learning rate and trains the model Save path, epoch, bsetc.
Among them, trainthe datasetrelated settings are all in test_coco_instance.ymlthe file

【test_solov2_r50_fpn_3x_coco.yml】
_BASE_: [
  '../datasets/test_coco_instance.yml',
  '../runtime.yml',
  '_base_/solov2_r50_fpn.yml',
  '_base_/optimizer_1x.yml',
  '_base_/solov2_reader.yml',
]
weights: output/test_solov2_r50_fpn_3x_coco/model_final
epoch: 36

LearningRate:
  base_lr: 0.01
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [24, 33]
  - !LinearWarmup
    start_factor: 0.
    steps: 1000

TrainReader:
  sample_transforms:
  - Decode: {
    
    }
  - Poly2Mask: {
    
    }
  - RandomResize: {
    
    interp: 1,
                     target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]],
                     keep_ratio: True}
  - RandomFlip: {
    
    }
  - NormalizeImage: {
    
    is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {
    
    }
  batch_transforms:
  - PadBatch: {
    
    pad_to_stride: 32}
  - Gt2Solov2Target: {
    
    num_grids: [40, 36, 24, 16, 12],
                        scale_ranges: [[1, 96], [48, 192], [96, 384], [192, 768], [384, 2048]],
                        coord_sigma: 0.2}
  batch_size: 1
  shuffle: true
  drop_last: true

The data configuration file mainly includes train/testrelated data path issues. If only the data source is changed, this file should be fine.

[test_coco_instance.yml]
metric: COCO
num_classes: 80

TrainDataset:
  !COCODataSet
    image_dir: images
    anno_path: instances_train_battery.json # annotations/instances_train2017.json
    dataset_dir: dataset/coco/battery/train
    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_poly', 'is_crowd']

EvalDataset:
  !COCODataSet
    image_dir: images
    anno_path: instances_test_battery.json
    dataset_dir: dataset/coco/battery/test

TestDataset:
  !ImageFolder
    anno_path: instances_test_battery.json # also support txt (like VOC's label_list.txt)
    dataset_dir: dataset/coco/battery/test # if set, anno_path will be 'dataset_dir/anno_path'

# copy from coco_instance.yml

There is also a tasteless file: definition is_use_gpuand other information

【runtime.yml】
use_gpu: True
use_xpu: false
log_iter: 20
save_dir: output
snapshot_epoch: 1
print_flops: false

# Exporting the model
export:
  post_process: True  # Whether post-processing is included in the network when export model.
  nms: True           # Whether NMS is included in the network when export model.
  benchmark: False    # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
  fuse_conv_bn: False

3.train

Setting the parameters is the most time-consuming and the least brain-intensive train, just one sentence:

python tools/train.py -c ./configs/solov2/test_solov2_r50_fpn_3x_coco.yml

It’s a long process. At trainthe beginning, you may encounter GPUproblems such as video memory. It’s good to see the tricks.
For example, my bug:

Out of memory error on GPU 0. Cannot allocate 264.375000MB memory on GPU 0, 3.999695GB memory has been allocated and available memory is only 0.000000B.

Please check whether there is any other process using GPU 0.
1. If yes, please stop them, or start PaddlePaddle on another GPU.
2. If no, please decrease the batch size of your model.
If the above ways do not solve the out of memory problem, you can try to use CUDA managed memory. The command is `export FLAGS_use_cuda_managed_memory=false`.
 (at /paddle/paddle/fluid/memory/allocation/cuda_allocator.cc:87)
 (at /paddle/paddle/fluid/imperative/basic_engine.cc:586)

The solution is to find it batch_size, just lower this value, but mine is 1650a lowbit cruel, so I can only batch_sizeset it 1to ;
during normal operation:

[09/07 06:23:19] ppdet.engine INFO: Epoch: [0] [ 0/60] learning_rate: 0.000000 loss_ins: 2.997996 loss_cate: 1.103366 loss: 4.101363 eta: 0:47:52 batch_cost: 1.3299 data_cost: 0.0003 ips: 0.7520 images/s
[09/07 06:23:29] ppdet.engine INFO: Epoch: [0] [20/60] learning_rate: 0.000200 loss_ins: 2.995760 loss_cate: 0.649032 loss: 3.645120 eta: 0:19:30 batch_cost: 0.5079 data_cost: 0.0002 ips: 1.9688 images/s
[09/07 06:23:39] ppdet.engine INFO: Epoch: [0] [40/60] learning_rate: 0.000400 loss_ins: 2.998715 loss_cate: 1.043475 loss: 3.999994 eta: 0:18:34 batch_cost: 0.5033 data_cost: 0.0002 ips: 1.9870 images/s
[09/07 06:23:52] ppdet.utils.checkpoint INFO: Save checkpoint: output/test_solov2_r50_fpn_3x_coco
[09/07 06:23:53] ppdet.engine INFO: Epoch: [1] [ 0/60] learning_rate: 0.000600 loss_ins: 2.996527 loss_cate: 0.800405 loss: 3.752607 eta: 0:18:11 batch_cost: 0.5073 data_cost: 0.0002 ips: 1.9713 images/s
[09/07 06:24:03] ppdet.engine INFO: Epoch: [1] [20/60] learning_rate: 0.000800 loss_ins: 2.996997 loss_cate: 0.415354 loss: 3.412265 eta: 0:17:49 batch_cost: 0.4970 data_cost: 0.0002 ips: 2.0123 images/s

4. export model

trainAfter completion, a series of files will be generated in the target directory pd, as shown in the figure below: insert image description here
These files are all intermediate variables, so the effective model files among them *.pdparamsshould be generated into deployable pdiparamsfiles, for example:

python tools/export_model.py -c configs/solov2/test_solov2_r50_fpn_3x_coco.yml --output_dir=./inference_model-0907 -o weights=output/test_solov2_r50_fpn_3x_coco/model_final

4. deploy

What is the detection result under the test model: Need to specify model_dirand image_file:

python deploy/python/infer.py --model_dir=./inference_model-0907/test_solov2_r50_fpn_3x_coco --image_file=./dataset/coco/battery/test/images/Image_20220901173956628.jpg --device gpu

Summarize

So far, the process of training deployment based on PaddleDetectionhas SOLOv2formed a closed loop, and the next step of optimization and function modification can be carried out.

Paddle records

SOLOV2-Paddle version