!18648 fix eval of yolov5
Merge pull request !18648 from zhouyaqiang0/master
This commit is contained in:
Gitee
commit
e07f1e3a98
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@ -371,7 +371,7 @@ YOLOv5 on 118K images(The annotation and data format must be the same as coco201
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| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G |
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| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G |
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| uploaded Date | 5/14/2021 (month/day/year) |
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| uploaded Date | 5/14/2021 (month/day/year) |
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| MindSpore Version | 1.0.0-alpha |
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| MindSpore Version | 1.0.0-alpha |
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| Dataset | 11.8K images |
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| Dataset | 118K images |
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| Training Parameters | epoch=320, batch_size=8, lr=0.01, momentum=0.9 |
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| Training Parameters | epoch=320, batch_size=8, lr=0.01, momentum=0.9 |
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| Optimizer | Momentum |
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| Optimizer | Momentum |
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| Loss Function | Sigmoid Cross Entropy with logits, Giou Loss |
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| Loss Function | Sigmoid Cross Entropy with logits, Giou Loss |
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@ -0,0 +1,365 @@
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# 目录
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- [YOLOv5说明](#yolov5说明)
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- [模型架构](#模型架构)
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- [数据集](#数据集)
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- [环境要求](#环境要求)
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- [快速入门](#快速入门)
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- [脚本说明](#脚本说明)
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- [脚本和示例代码](#脚本和示例代码)
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- [脚本参数](#脚本参数)
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- [训练过程](#训练过程)
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- [训练](#训练)
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- [测试过程](#测试过程)
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- [测试](#测试)
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- [评估过程](#评估过程)
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- [评估](#评估)
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- [转换过程](#转换过程)
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- [转换](#转换)
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- [模型说明](#模型说明)
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- [性能](#性能)
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- [评估性能](#评估性能)
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- [推理性能](#推理性能)
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- [ModelZoo主页](#modelzoo主页)
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# [YOLOv5描述](#目录)
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YOLOv5作为先进的检测器，它比所有可用的替代检测器更快（FPS）并且更准确（MS COCO AP50 ... 95和AP50）。
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本文已经验证了大量的特征，并选择使用这些特征来提高分类和检测的精度。
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这些特性可以作为未来研究和开发的最佳实践。
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[代码](https://github.com/ultralytics/yolov5)
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# [模型架构](#目录)
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选择CSP Focus主干、SPP附加模块、PANet路径聚合网络和YOLOv5（基于锚点）头作为YOLOv5架构。
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# [数据集](#目录)
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支持的数据集：[MS COCO]或与MS COCO格式相同的数据集
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支持的标注：[MS COCO]或与MS COCO相同格式的标注
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- 目录结构如下，由用户定义目录和文件的名称：
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```shell
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©À©¤©¤ dataset
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©À©¤©¤ YOLOv5
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©À©¤©¤ annotations
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©¦ ©À©¤ train.json
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©¦ ©¸©¤ val.json
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©À©¤ images
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©À©¤ train
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©¦ ©¸©¤images
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©¦ ©À©¤picture1.jpg
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©¦ ©À©¤ ...
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©¦ ©¸©¤picturen.jpg
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©¸©¤ val
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©¸©¤images
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©À©¤picture1.jpg
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©À©¤ ...
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©¸©¤picturen.jpg
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```
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建议用户使用MS COCO数据集来体验模型，
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其他数据集需要使用与MS COCO相同的格式。
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# [环境要求](#目录)
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- 硬件 Ascend
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- 使用Ascend处理器准备硬件环境。
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- 框架
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- [MindSpore](https://cmc-szv.clouddragon.huawei.com/cmcversion/index/search?searchKey=Do-MindSpore%20V100R001C00B622)
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- 更多关于Mindspore的信息，请查看以下资源：
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- [MindSpore教程](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
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# [快速入门](#目录)
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通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
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``` shell
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# training_shape参数定义网络图像形状，默认为[640, 640]。
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```
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```shell
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# python命令执行训练示例（1卡）
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python train.py \
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--data_dir=./dataset/xxx \
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--is_distributed=0 \
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--lr=0.01 \
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--T_max=320 \
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--max_epoch=320 \
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--warmup_epochs=4 \
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--training_shape=640 \
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--lr_scheduler=cosine_annealing > log.txt 2>&1 &
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```
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```shell
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# shell脚本单机训练示例（1卡）
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sh run_standalone_train.sh dataset/xxx
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```
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```shell
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# 对于Ascend设备，使用shell脚本分布式训练示例（8卡）
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sh run_distribute_train.sh dataset/xxx rank_table_8p.json
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```
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```python
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# 使用python命令评估
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python eval.py \
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--data_dir=./dataset/xxx \
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--pretrained=yolov5.ckpt \
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--testing_shape=640 > log.txt 2>&1 &
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```
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```python
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# shell脚本执行评估
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sh run_eval.sh dataset/xxx checkpoint/xxx.ckpt
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```
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# [脚本说明](#目录)
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## [脚本和示例代码](#目录)
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```python
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©¸©¤yolov5
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©À©¤README.md
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©À©¤mindspore_hub_conf.md # Mindspore Hub配置
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©À©¤scripts
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©À©¤run_standalone_train.sh # 在Ascend中启动单机训练（1卡）
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©À©¤run_distribute_train.sh # 在Ascend中启动分布式训练（8卡）
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©¸©¤run_eval.sh # 在Ascend中启动评估
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©À©¤src
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©À©¤__init__.py # Python初始化文件
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©À©¤config.py # 参数配置
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©À©¤yolov5_backbone.py # 网络骨干
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©À©¤distributed_sampler.py # 数据集迭代器
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©À©¤initializer.py # 参数初始化器
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©À©¤logger.py # 日志函数
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©À©¤loss.py # 损失函数
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©À©¤lr_scheduler.py # 生成学习率
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©À©¤transforms.py # 预处理数据
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©À©¤util.py # 工具函数
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©À©¤yolo.py # YOLOv5网络
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©À©¤yolo_dataset.py # 为YOLOv5创建数据集
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©À©¤eval.py # 评估验证结果
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©À©¤export.py # 将MindSpore模型转换为AIR模型
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©¸©¤train.py # 训练网络
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```
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## [脚本参数](#目录)
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train.py中主要参数如下：
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```shell
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可选参数：
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-h, --help 显示此帮助消息并退出
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--device_target 实现代码的设备：“Ascend”（默认值）|“GPU”
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--data_dir DATA_DIR 训练数据集目录
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--per_batch_size PER_BATCH_SIZE
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训练的批处理大小。 默认值：8。
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--pretrained_backbone PRETRAINED_BACKBONE
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YOLOv5主干文件。 默认值：""。
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--resume_yolov5 RESUME_YOLOV5
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YOLOv5的ckpt文件，用于微调。
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默认值：""
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--lr_scheduler LR_SCHEDULER
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学习率调度器，取值选项：exponential，
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cosine_annealing。 默认值：exponential
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--lr LR 学习率。 默认值：0.01
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--lr_epochs LR_EPOCHS
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LR变化轮次，用“,”分隔。
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默认值：220,250
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--lr_gamma LR_GAMMA 将LR降低一个exponential lr_scheduler因子。
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默认值：0.1
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--eta_min ETA_MIN cosine_annealing调度器中的eta_min。 默认值：0
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--T_max T_MAX cosine_annealing调度器中的T-max。 默认值：320
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--max_epoch MAX_EPOCH
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训练模型的最大轮次数。 默认值：320
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--warmup_epochs WARMUP_EPOCHS
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热身轮次。 默认值：0
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--weight_decay WEIGHT_DECAY
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权重衰减因子。 默认值：0.0005
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--momentum MOMENTUM 动量。 默认值：0.9
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--loss_scale LOSS_SCALE
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静态损失尺度。 默认值：1024
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--label_smooth LABEL_SMOOTH
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CE中是否使用标签平滑。 默认值：0
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--label_smooth_factor LABEL_SMOOTH_FACTOR
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原one-hot的光滑强度。 默认值：0.1
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--log_interval LOG_INTERVAL
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日志记录间隔步数。 默认值：100
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--ckpt_path CKPT_PATH
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Checkpoint保存位置。 默认值：outputs/
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--ckpt_interval CKPT_INTERVAL
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保存checkpoint间隔。 默认值：None
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--is_save_on_master IS_SAVE_ON_MASTER
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在master或all rank上保存ckpt，1代表master，0代表
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all ranks。 默认值：1
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--is_distributed IS_DISTRIBUTED
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是否分发训练，1代表是，0代表否。 默认值：
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1
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--rank RANK 分布式本地进程序号。 默认值：0
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--group_size GROUP_SIZE
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设备进程总数。 默认值：1
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--need_profiler NEED_PROFILER
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是否使用profiler。 0表示否，1表示是。 默认值：0
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--training_shape TRAINING_SHAPE
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恢复训练形状。 默认值：""
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--resize_rate RESIZE_RATE
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多尺度训练的缩放速率。 默认值：None
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```
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## [训练过程](#目录)
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### 训练
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```python
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python train.py \
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--data_dir=/dataset/xxx \
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--is_distributed=0 \
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--lr=0.01 \
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--T_max=320 \
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--max_epoch=320 \
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--warmup_epochs=4 \
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--training_shape=640 \
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--lr_scheduler=cosine_annealing > log.txt 2>&1 &
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```
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上述python命令将在后台运行，您可以通过log.txt文件查看结果。
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训练结束后，您可在默认输出文件夹下找到checkpoint文件。 得到如下损失值：
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```shell
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# grep "loss:" train/log.txt
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2021-05-13 20:50:25,617:INFO:epoch[0], iter[100], loss:loss:2648.764910, fps:61.59 imgs/sec, lr:1.7226087948074564e-05
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2021-05-13 20:50:39,821:INFO:epoch[0], iter[200], loss:loss:764.535622, fps:56.33 imgs/sec, lr:3.4281620173715055e-05
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2021-05-13 20:50:53,287:INFO:epoch[0], iter[300], loss:loss:494.950782, fps:59.47 imgs/sec, lr:5.1337152399355546e-05
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2021-05-13 20:51:06,138:INFO:epoch[0], iter[400], loss:loss:393.339678, fps:62.25 imgs/sec, lr:6.839268462499604e-05
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2021-05-13 20:51:17,985:INFO:epoch[0], iter[500], loss:loss:329.976604, fps:67.57 imgs/sec, lr:8.544822048861533e-05
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2021-05-13 20:51:29,359:INFO:epoch[0], iter[600], loss:loss:294.734397, fps:70.37 imgs/sec, lr:0.00010250374907627702
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2021-05-13 20:51:40,634:INFO:epoch[0], iter[700], loss:loss:281.497078, fps:70.98 imgs/sec, lr:0.00011955928493989632
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2021-05-13 20:51:52,307:INFO:epoch[0], iter[800], loss:loss:264.300707, fps:68.54 imgs/sec, lr:0.0001366148208035156
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2021-05-13 20:52:05,479:INFO:epoch[0], iter[900], loss:loss:261.971103, fps:60.76 imgs/sec, lr:0.0001536703493911773
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2021-05-13 20:52:17,362:INFO:epoch[0], iter[1000], loss:loss:264.591175, fps:67.33 imgs/sec, lr:0.00017072587797883898
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...
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```
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### 分布式训练
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|
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对于Ascend设备，使用shell脚本分布式训练示例（8卡）
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|
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```shell
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sh run_distribute_train.sh dataset/coco2017 rank_table_8p.json
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```
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上述shell脚本将在后台运行分布式训练。 您可以通过train_parallel[X]/log.txt文件查看结果。 得到如下损失值：
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```shell
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# 分布式训练示例（8卡）
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...
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2021-05-13 21:08:41,992:INFO:epoch[0], iter[600], loss:247.577421, fps:469.29 imgs/sec, lr:0.0001640283880988136
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2021-05-13 21:08:56,291:INFO:epoch[0], iter[700], loss:235.298894, fps:447.67 imgs/sec, lr:0.0001913209562189877
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2021-05-13 21:09:10,431:INFO:epoch[0], iter[800], loss:239.481037, fps:452.78 imgs/sec, lr:0.00021861353889107704
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2021-05-13 21:09:23,517:INFO:epoch[0], iter[900], loss:232.826709, fps:489.15 imgs/sec, lr:0.0002459061215631664
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2021-05-13 21:09:36,407:INFO:epoch[0], iter[1000], loss:224.734599, fps:496.65 imgs/sec, lr:0.0002731987042352557
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2021-05-13 21:09:49,072:INFO:epoch[0], iter[1100], loss:232.334771, fps:505.34 imgs/sec, lr:0.0003004912578035146
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2021-05-13 21:10:03,597:INFO:epoch[0], iter[1200], loss:242.001476, fps:440.69 imgs/sec, lr:0.00032778384047560394
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2021-05-13 21:10:18,237:INFO:epoch[0], iter[1300], loss:225.391021, fps:437.20 imgs/sec, lr:0.0003550764231476933
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2021-05-13 21:10:33,027:INFO:epoch[0], iter[1400], loss:228.738176, fps:432.76 imgs/sec, lr:0.0003823690058197826
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2021-05-13 21:10:47,424:INFO:epoch[0], iter[1500], loss:225.712950, fps:444.54 imgs/sec, lr:0.0004096615593880415
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2021-05-13 21:11:02,077:INFO:epoch[0], iter[1600], loss:221.249353, fps:436.77 imgs/sec, lr:0.00043695414206013083
|
||||||
|
2021-05-13 21:11:16,631:INFO:epoch[0], iter[1700], loss:222.449119, fps:439.89 imgs/sec, lr:0.00046424672473222017
|
||||||
|
...
|
||||||
|
```
|
||||||
|
|
||||||
|
## [评估过程](#目录)
|
||||||
|
|
||||||
|
### 验证
|
||||||
|
|
||||||
|
```python
|
||||||
|
python eval.py \
|
||||||
|
--data_dir=./dataset/coco2017 \
|
||||||
|
--pretrained=yolov5.ckpt \
|
||||||
|
--testing_shape=640 > log.txt 2>&1 &
|
||||||
|
OR
|
||||||
|
sh run_eval.sh dataset/coco2017 checkpoint/yolov5.ckpt
|
||||||
|
```
|
||||||
|
|
||||||
|
上述python命令将在后台运行。 您可以通过log.txt文件查看结果。 测试数据集的mAP如下：
|
||||||
|
|
||||||
|
```shell
|
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|
# log.txt
|
||||||
|
=============coco eval reulst=========
|
||||||
|
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.372
|
||||||
|
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.574
|
||||||
|
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.403
|
||||||
|
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.219
|
||||||
|
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.426
|
||||||
|
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480
|
||||||
|
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.302
|
||||||
|
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.504
|
||||||
|
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.560
|
||||||
|
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.399
|
||||||
|
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.619
|
||||||
|
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674
|
||||||
|
```
|
||||||
|
|
||||||
|
## [转换过程](#目录)
|
||||||
|
|
||||||
|
### 转换
|
||||||
|
|
||||||
|
如果您想推断Ascend 310上的网络，则应将模型转换为AIR：
|
||||||
|
|
||||||
|
```python
|
||||||
|
python export.py [BATCH_SIZE] [PRETRAINED_BACKBONE]
|
||||||
|
```
|
||||||
|
|
||||||
|
# [模型说明](#目录)
|
||||||
|
|
||||||
|
## [性能](#目录)
|
||||||
|
|
||||||
|
### 评估性能
|
||||||
|
|
||||||
|
YOLOv5应用于118000张图像上（标注和数据格式必须与COCO 2017相同）
|
||||||
|
|
||||||
|
|参数| YOLOv5s |
|
||||||
|
| -------------------------- | ----------------------------------------------------------- |
|
||||||
|
| 资源 | Ascend 910；CPU 2.60GHz，192核；内存：755G |
|
||||||
|
|上传日期| 2021年05月14日 |
|
||||||
|
| MindSpore版本|1.0.0-alpha|
|
||||||
|
|数据集|118000张图像|
|
||||||
|
|训练参数|epoch=320, batch_size=8, lr=0.01, momentum=0.9|
|
||||||
|
| 优化器 | Momentum |
|
||||||
|
|损失函数|Sigmoid Cross Entropy with logits, Giou Loss|
|
||||||
|
|输出|heatmaps |
|
||||||
|
| 损失 | 53 |
|
||||||
|
|速度| 1卡：55 img/s；8卡：440 img/s（shape=640）|
|
||||||
|
| 总时长 | 80小时 |
|
||||||
|
| 微调检查点 | 58M （.ckpt文件） |
|
||||||
|
|脚本| <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/> |
|
||||||
|
|
||||||
|
### 推理性能
|
||||||
|
|
||||||
|
YOLOv5应用于5000张图像上（标注和数据格式必须与COCO val 2017相同）
|
||||||
|
|
||||||
|
|参数| YOLOv5s |
|
||||||
|
| -------------------------- | ----------------------------------------------------------- |
|
||||||
|
| 资源 | Ascend 910；CPU 2.60GHz，192核；内存：755G |
|
||||||
|
|上传日期| 2021年05月14日 |
|
||||||
|
| MindSpore版本 | 1.2.0 |
|
||||||
|
|数据集|5000张图像|
|
||||||
|
|批处理大小|1|
|
||||||
|
|输出|边框位置和分数，以及概率|
|
||||||
|
|精度|map=36.8~37.2%（shape=640）|
|
||||||
|
|推理模型| 58M（.ckpt文件）|
|
||||||
|
|
||||||
|
# [随机情况说明](#目录)
|
||||||
|
|
||||||
|
在dataset.py中，我们设置了“create_dataset”函数内的种子。
|
||||||
|
在var_init.py中，我们设置了权重初始化的种子。
|
||||||
|
|
||||||
|
# [ModelZoo主页](#目录)
|
||||||
|
|
||||||
|
请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。
|
||||||
|
|
@ -18,6 +18,7 @@ import argparse
|
||||||
import datetime
|
import datetime
|
||||||
import time
|
import time
|
||||||
import sys
|
import sys
|
||||||
|
import ast
|
||||||
from collections import defaultdict
|
from collections import defaultdict
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
@ -56,7 +57,7 @@ parser.add_argument('--nms_thresh', type=float, default=0.6, help='threshold for
|
||||||
parser.add_argument('--ann_file', type=str, default='', help='path to annotation')
|
parser.add_argument('--ann_file', type=str, default='', help='path to annotation')
|
||||||
parser.add_argument('--testing_shape', type=str, default='', help='shape for test ')
|
parser.add_argument('--testing_shape', type=str, default='', help='shape for test ')
|
||||||
parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
|
parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
|
||||||
parser.add_argument('--multi_label', type=ast.iteral_eval, default=True, help='whether to use multi label')
|
parser.add_argument('--multi_label', type=ast.literal_eval, default=True, help='whether to use multi label')
|
||||||
parser.add_argument('--multi_label_thresh', type=float, default=0.1, help='threshhold to throw low quality boxes')
|
parser.add_argument('--multi_label_thresh', type=float, default=0.1, help='threshhold to throw low quality boxes')
|
||||||
|
|
||||||
args, _ = parser.parse_known_args()
|
args, _ = parser.parse_known_args()
|
||||||
|
|
@ -115,7 +116,7 @@ class DetectionEngine:
|
||||||
for clsi in self.results[img_id]:
|
for clsi in self.results[img_id]:
|
||||||
dets = self.results[img_id][clsi]
|
dets = self.results[img_id][clsi]
|
||||||
dets = np.array(dets)
|
dets = np.array(dets)
|
||||||
keep_index = self._diou_nms(dets, thresh=nms_thresh)
|
keep_index = self._diou_nms(dets, thresh=self.nms_thresh)
|
||||||
|
|
||||||
keep_box = [{'image_id': int(img_id),
|
keep_box = [{'image_id': int(img_id),
|
||||||
'category_id': int(clsi),
|
'category_id': int(clsi),
|
||||||
|
|
|
||||||
|
|
@ -78,7 +78,7 @@ def parse_args(cloud_args=None):
|
||||||
# logging related
|
# logging related
|
||||||
parser.add_argument('--log_interval', type=int, default=100, help='Logging interval steps. Default: 100')
|
parser.add_argument('--log_interval', type=int, default=100, help='Logging interval steps. Default: 100')
|
||||||
parser.add_argument('--ckpt_path', type=str, default='outputs/', help='Checkpoint save location. Default: outputs/')
|
parser.add_argument('--ckpt_path', type=str, default='outputs/', help='Checkpoint save location. Default: outputs/')
|
||||||
parser.add_argument('--ckpt_interval', type=int, default=None, help='Save checkpoint interval. Default: None')
|
parser.add_argument('--ckpt_interval', type=int, default=10, help='Save checkpoint interval. Default: 10')
|
||||||
|
|
||||||
parser.add_argument('--is_save_on_master', type=int, default=1,
|
parser.add_argument('--is_save_on_master', type=int, default=1,
|
||||||
help='Save ckpt on master or all rank, 1 for master, 0 for all ranks. Default: 1')
|
help='Save ckpt on master or all rank, 1 for master, 0 for all ranks. Default: 1')
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue