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zhouyaqiang d5eddee94b add yolov5		2021-06-01 14:09:04 +08:00
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scripts	add yolov5	2021-06-01 14:09:04 +08:00
src	add yolov5	2021-06-01 14:09:04 +08:00
README.md	add yolov5	2021-06-01 14:09:04 +08:00
eval.py	add yolov5	2021-06-01 14:09:04 +08:00
export.py	add yolov5	2021-06-01 14:09:04 +08:00
mindspore_hub_conf.py	add yolov5	2021-06-01 14:09:04 +08:00
train.py	add yolov5	2021-06-01 14:09:04 +08:00

README.md

YOLOv5 Description
Model Architecture
Dataset
Environment Requirements
Quick Start
Script Description
Model Description
- Performance
  - Evaluation Performance
  - Inference Performance
ModelZoo Homepage

YOLOv5 Description

YOLOv5 is a state-of-the-art detector which is faster (FPS) and more accurate (MS COCO AP50...95 and AP50) than all available alternative detectors. YOLOv5 has verified a large number of features, and selected for use such of them for improving the accuracy of both the classifier and the detector. These features can be used as best-practice for future studies and developments.

Code

Model Architecture

YOLOv5 choose CSP with Focus backbone, SPP additional module, PANet path-aggregation neck, and YOLOv5 (anchor based) head as the architecture of YOLOv5.

Dataset

Dataset support: [MS COCO] or datasetd with the same format as MS COCO Annotation support: [MS COCO] or annotation as the same format as MS COCO

The directory structure is as follows, the name of directory and file is user define:

    ├── dataset
        ├── YOLOv5
            ├── annotations
            │   ├─ train.json
            │   └─ val.json
            ├─ images
                ├─ train
                │    └─images
                │       ├─picture1.jpg
                │       ├─ ...
                │       └─picturen.jpg
                └─ val
                    └─images
                        ├─picture1.jpg
                        ├─ ...
                        └─picturen.jpg

we suggest user to use MS COCO dataset to experience our model, other datasets need to use the same format as MS COCO.

Environment Requirements

Hardware（Ascend）
- Prepare hardware environment with Ascend processor.
Framework
- MindSpore
For more information, please check the resources below：
- MindSpore tutorials
- MindSpore API

Quick Start

After installing MindSpore via the official website, you can start training and evaluation as follows:

# The parameter of training_shape define image shape for network, default is [640, 640],

#run training example(1p) by python command
python train.py \
    --data_dir=./dataset/xxx \
    --is_distributed=0 \
    --lr=0.01 \
    --T_max=320 \
    --max_epoch=320 \
    --warmup_epochs=4 \
    --training_shape=640 \
    --lr_scheduler=cosine_annealing > log.txt 2>&1 &

# standalone training example(1p) by shell script
sh run_standalone_train.sh dataset/xxx

# For Ascend device, distributed training example(8p) by shell script
sh run_distribute_train.sh dataset/xxx rank_table_8p.json

# run evaluation by python command
python eval.py \
    --data_dir=./dataset/xxx \
    --pretrained=yolov5.ckpt \
    --testing_shape=640 > log.txt 2>&1 &

# run evaluation by shell script
sh run_eval.sh dataset/xxx checkpoint/xxx.ckpt

Script Description

Script and Sample Code

└─yolov5
  ├─README.md
  ├─mindspore_hub_conf.md             # config for mindspore hub
  ├─scripts
    ├─run_standalone_train.sh         # launch standalone training(1p) in ascend
    ├─run_distribute_train.sh         # launch distributed training(8p) in ascend
    └─run_eval.sh                     # launch evaluating in ascend
  ├─src
    ├─__init__.py                     # python init file
    ├─config.py                       # parameter configuration
    ├─yolov5_backbone.py              # backbone of network
    ├─distributed_sampler.py          # iterator of dataset
    ├─initializer.py                  # initializer of parameters
    ├─logger.py                       # log function
    ├─loss.py                         # loss function
    ├─lr_scheduler.py                 # generate learning rate
    ├─transforms.py                   # Preprocess data
    ├─util.py                         # util function
    ├─yolo.py                         # yolov5 network
    ├─yolo_dataset.py                 # create dataset for YOLOV5

  ├─eval.py                           # evaluate val results
  ├─export.py                         # convert mindspore model to air model
  └─train.py                          # train net

Script Parameters

Major parameters train.py as follows:

optional arguments:
  -h, --help            show this help message and exit
  --device_target       device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
  --data_dir DATA_DIR   Train dataset directory.
  --per_batch_size PER_BATCH_SIZE
                        Batch size for Training. Default: 8.
  --pretrained_backbone PRETRAINED_BACKBONE
                        The backbone file of yolov5. Default: "".
  --resume_yolov5 RESUME_YOLOV5
                        The ckpt file of YOLOv5, which used to fine tune.
                        Default: ""
  --lr_scheduler LR_SCHEDULER
                        Learning rate scheduler, options: exponential,
                        cosine_annealing. Default: exponential
  --lr LR               Learning rate. Default: 0.01
  --lr_epochs LR_EPOCHS
                        Epoch of changing of lr changing, split with ",".
                        Default: 220,250
  --lr_gamma LR_GAMMA   Decrease lr by a factor of exponential lr_scheduler.
                        Default: 0.1
  --eta_min ETA_MIN     Eta_min in cosine_annealing scheduler. Default: 0
  --T_max T_MAX         T-max in cosine_annealing scheduler. Default: 320
  --max_epoch MAX_EPOCH
                        Max epoch num to train the model. Default: 320
  --warmup_epochs WARMUP_EPOCHS
                        Warmup epochs. Default: 0
  --weight_decay WEIGHT_DECAY
                        Weight decay factor. Default: 0.0005
  --momentum MOMENTUM   Momentum. Default: 0.9
  --loss_scale LOSS_SCALE
                        Static loss scale. Default: 1024
  --label_smooth LABEL_SMOOTH
                        Whether to use label smooth in CE. Default:0
  --label_smooth_factor LABEL_SMOOTH_FACTOR
                        Smooth strength of original one-hot. Default: 0.1
  --log_interval LOG_INTERVAL
                        Logging interval steps. Default: 100
  --ckpt_path CKPT_PATH
                        Checkpoint save location. Default: outputs/
  --ckpt_interval CKPT_INTERVAL
                        Save checkpoint interval. Default: None
  --is_save_on_master IS_SAVE_ON_MASTER
                        Save ckpt on master or all rank, 1 for master, 0 for
                        all ranks. Default: 1
  --is_distributed IS_DISTRIBUTED
                        Distribute train or not, 1 for yes, 0 for no. Default:
                        1
  --rank RANK           Local rank of distributed. Default: 0
  --group_size GROUP_SIZE
                        World size of device. Default: 1
  --need_profiler NEED_PROFILER
                        Whether use profiler. 0 for no, 1 for yes. Default: 0
  --training_shape TRAINING_SHAPE
                        Fix training shape. Default: ""
  --resize_rate RESIZE_RATE
                        Resize rate for multi-scale training. Default: None

Training Process

Training

python train.py \
    --data_dir=/dataset/xxx \
    --is_distributed=0 \
    --lr=0.01 \
    --T_max=320 \
    --max_epoch=320 \
    --warmup_epochs=4 \
    --training_shape=640 \
    --lr_scheduler=cosine_annealing > log.txt 2>&1 &

The python command above will run in the background, you can view the results through the file log.txt.

After training, you'll get some checkpoint files under the outputs folder by default. The loss value will be achieved as follows:

# grep "loss:" train/log.txt
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
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
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
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
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
2021-05-13 20:51:29,359:INFO:epoch[0], iter[600], loss:loss:294.734397, fps:70.37 imgs/sec, lr:0.00010250374907627702
2021-05-13 20:51:40,634:INFO:epoch[0], iter[700], loss:loss:281.497078, fps:70.98 imgs/sec, lr:0.00011955928493989632
2021-05-13 20:51:52,307:INFO:epoch[0], iter[800], loss:loss:264.300707, fps:68.54 imgs/sec, lr:0.0001366148208035156
2021-05-13 20:52:05,479:INFO:epoch[0], iter[900], loss:loss:261.971103, fps:60.76 imgs/sec, lr:0.0001536703493911773
2021-05-13 20:52:17,362:INFO:epoch[0], iter[1000], loss:loss:264.591175, fps:67.33 imgs/sec, lr:0.00017072587797883898
...

Distributed Training

For Ascend device, distributed training example(8p) by shell script

sh run_distribute_train.sh dataset/coco2017 rank_table_8p.json

The above shell script will run distribute training in the background. You can view the results through the file train_parallel[X]/log.txt. The loss value will be achieved as follows:

# distribute training result(8p)
...
2021-05-13 21:08:41,992:INFO:epoch[0], iter[600], loss:247.577421, fps:469.29 imgs/sec, lr:0.0001640283880988136
2021-05-13 21:08:56,291:INFO:epoch[0], iter[700], loss:235.298894, fps:447.67 imgs/sec, lr:0.0001913209562189877
2021-05-13 21:09:10,431:INFO:epoch[0], iter[800], loss:239.481037, fps:452.78 imgs/sec, lr:0.00021861353889107704
2021-05-13 21:09:23,517:INFO:epoch[0], iter[900], loss:232.826709, fps:489.15 imgs/sec, lr:0.0002459061215631664
2021-05-13 21:09:36,407:INFO:epoch[0], iter[1000], loss:224.734599, fps:496.65 imgs/sec, lr:0.0002731987042352557
2021-05-13 21:09:49,072:INFO:epoch[0], iter[1100], loss:232.334771, fps:505.34 imgs/sec, lr:0.0003004912578035146
2021-05-13 21:10:03,597:INFO:epoch[0], iter[1200], loss:242.001476, fps:440.69 imgs/sec, lr:0.00032778384047560394
2021-05-13 21:10:18,237:INFO:epoch[0], iter[1300], loss:225.391021, fps:437.20 imgs/sec, lr:0.0003550764231476933
2021-05-13 21:10:33,027:INFO:epoch[0], iter[1400], loss:228.738176, fps:432.76 imgs/sec, lr:0.0003823690058197826
2021-05-13 21:10:47,424:INFO:epoch[0], iter[1500], loss:225.712950, fps:444.54 imgs/sec, lr:0.0004096615593880415
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
...

Evaluation Process

Valid

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

The above python command will run in the background. You can view the results through the file "log.txt". The mAP of the test dataset will be as follows:

# 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

Convert Process

Convert

If you want to infer the network on Ascend 310, you should convert the model to AIR:

python export.py [BATCH_SIZE] [PRETRAINED_BACKBONE]

Model Description

Performance

Evaluation Performance

YOLOv5 on 118K images(The annotation and data format must be the same as coco2017)

Parameters	YOLOv5s
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G
uploaded Date	5/14/2021 (month/day/year)
MindSpore Version	1.0.0-alpha
Dataset	11.8K images
Training Parameters	epoch=320, batch_size=8, lr=0.01, momentum=0.9
Optimizer	Momentum
Loss Function	Sigmoid Cross Entropy with logits, Giou Loss
outputs	heatmaps
Loss	53
Speed	1p 55 img/s 8p 440 img/s(shape=640)
Total time	80h
Checkpoint for Fine tuning	58M (.ckpt file)
Scripts	https://gitee.com/mindspore/mindspore/tree/master/model_zoo/

Inference Performance

YOLOv5 on 5K images(The annotation and data format must be the same as coco val2017 )

Parameters	YOLOv5s
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G
uploaded Date	5/14/2021 (month/day/year)
MindSpore Version	1.2.0
Dataset	5K images
batch_size	1
outputs	box position and sorces, and probability
Accuracy	map=36.8~37.2%(shape=640)
Model for inference	58M (.ckpt file)

Description of Random Situation

In dataset.py, we set the seed inside create_dataset function. In var_init.py, we set seed for weight initialization

ModelZoo Homepage

Please check the official homepage.

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