Paddle记录
SOLOV2-Paddle版本
- 前言
- 一、solov2
- 二、执行步骤
-
- 1.构建数据集
- 2.编写配置文件
- 3.train
- 4. export model
- 4. deploy
- 总结
前言
运行 PaddleDetection 版本的 solov2
一、solov2
深度学习示例分割模型，据说是很牛的存在：
二、执行步骤
1.构建数据集
本人对标注工具并没有太多考究，目前使用的是商汤的 labelBee，在使用中感觉挺轻便的，所以就一直用着了，至于输出标注文件格式比较少，也是可以接受的，大不了在用 py 写一系列的脚本即可；
个人新建的项目如下图所示：
标注完需要把标注信息输出出来，选择 export 后弹出此窗口，选择比较通用的 coco即可；
保存的 json 文件内容如下:
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[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.编写配置文件
配置文件比较多，好在逻辑足够清晰，鉴于是自己修改，所以先把源文件 copy 一份，下面简单解析下会用到的几个文件，以solov2_r50_fpn_3x_coco为例：
主文件：定义了学习率，训练模型保存路径，epoch， bs等
其中，train 的 dataset 相关的设置都在 test_coco_instance.yml文件中
【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
数据配置文件，主要包含， train/test 相关数据路径问题，若只改变数据源，就该这个文件就好
[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
还有一个鸡肋文件：定义 is_use_gpu 等信息
【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
设置好参数，就是最耗时间且最不需要动脑子的 train 了，一句话就好：
python tools/train.py -c ./configs/solov2/test_solov2_r50_fpn_3x_coco.yml
漫长的过程，在 train 之初，可能会遇到 GPU显存等问题，见招拆招就好
比如，我出现的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)
解决方案就是找到 batch_size，将此值降低就好，奈何我的 1650有点 low，很残酷，只能把 batch_size 设为 1 才行；
正常运行时：
[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
train结束后会在目标目录下生成一系列的 pd 文件，如下图所示：
这些文件都是中间变量，因此，要将其中有效的模型文件*.pdparams生成可以 部署的 pdiparams 文件，例如：
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
测试下 model 的检测结果如何：需要指定 model_dir 和 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
总结
至此，基于 PaddleDetection 的 SOLOv2 从训练部署的过程已形成闭环，可以进行下一步的优化及功能修改了。