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zhanghuiyao
2d839e589d
|
1 year ago | |
|---|---|---|
| ascend310_infer | 1 year ago | |
| config | 1 year ago | |
| mindyolo | 1 year ago | |
| scripts | 1 year ago | |
| tools | 1 year ago | |
| README.md | 1 year ago | |
| export.py | 1 year ago | |
| postprocess.py | 1 year ago | |
| preprocess.py | 1 year ago | |
| run_check.py | 1 year ago | |
| test.py | 1 year ago | |
| train.py | 1 year ago | |
README.md
Contents
- YOLOv7 Description
- Dataset
- Environment Requirements
- Quick Start
- Script Description
- Model Description
- ModelZoo Homepage
YOLOv7 Description
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. Moreover, we train YOLOv7 only on MS COCO dataset from scratch without using any other datasets or pre-trained weights.
Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696, 2022.
Dataset
Dataset used: COCO2017
Download MS COCO dataset images (train, val, test) and labels.
-
Prepare Dataset
- Use the following script to download or manually refer to the above link to download
bash scripts/get_coco.sh-
The directory structure is as follows, the name of directory and file is user define:
├── dataset ├── coco ├── annotations │ ├─ instances_train2017.json │ └─ instances_val2017.json ├── images │ ├─train2017 │ │ ├─picture1.jpg │ │ ├─ ... │ │ └─picturen.jpg │ └─ val2017 │ ├─picture1.jpg │ ├─ ... │ └─picturen.jpg ├── labels │ ├─train2017 │ │ ├─label1.txt │ │ ├─ ... │ │ └─labeln.txt │ └─ val2017 │ ├─label1.txt │ ├─ ... │ └─labeln.txt ├── train2017.txt ├── val2017.txt └── test-dev2017.txt
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
- For more information, please check the resources below：
Quick Start
-
After installing MindSpore via the official website, you can start training and evaluation as follows:
-
Prepare the hccl_8p.json files, before run network.
-
Genatating hccl_8p.json, Run the script of hccl_tools.py.
The following parameter "[0-8)" indicates that the hccl_8p.json file of cards 0 to 7 is generated.python hccl_tools.py --device_num "[0,8)"
-
-
Specify the dataset path in config/data/coco.yaml
-
Run on local
#run training example(1p) by python command python train.py \ --config=./config/yolov7/net/yolov7.yaml \ --device_target="Ascend" \ --recompute=True \ --recompute_layers=5 \ --batch_size=16 \ --epochs=300 > log.txt 2>&1 & # standalone training example(1p) by shell script (Training with a single scale) bash run_standalone_train_ascend.sh 0 # For Ascend device, distributed training example(8p) by shell script (Training with multi scale) bash run_distribute_train_ascend.sh /path_to/hccl_8p.json # run evaluation by python command python test.py \ --config=./config/yolov7/net/yolov7.yaml \ --weights=/path_to/yolov7.ckpt \ --img_size=640 > log.txt 2>&1 & # run evaluation by shell script bash run_standalone_test_ascend.sh /path_to/yolov7.ckpt 0 -
Run on ModelArts
# Run with modelarts # (1) Perform a or b. # a. Set the default value of the "enable_modelarts" to "True" on args.py file. # Set the default value of the "data_dir" to "/cache/data/" on args.py file. # Set the default value of the "data_url" to "s3://dir_to_your_dataset/" on args.py file. (notes: No setting is required on the openi platform.) # Set the default value of the "train_url" to "s3://dir_to_your_output/" on args.py file. (notes: No setting is required on the openi platform.) # Set other parameters on args.py file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "data_dir=/cache/data/" on the website UI interface. # Add "data_url" and "train_url" on the website UI interface. (notes: No setting is required on the openi platform.) # Add other parameters on the website UI interface. # (2) Upload or copy your trained model to S3 bucket. (notes: No setting is required when train.) # (3) Upload a zip dataset to S3 bucket. (notes: you could also upload the origin dataset, but it can be so slow.) # (4) Set the code directory to "/path/yolov7" on the website UI interface. # (5) Set the startup file to "train.py" or "test.py" on the website UI interface. # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. (notes: No setting is required on the openi platform.) # (7) Create your job. #
Script Description
Script and Sample Code
└─yolov7
├─README.md
├─scripts
├─run_distribute_train_ascend.sh # launch distributed training(8p) in ascend
├─run_standalone_train_ascend.sh # launch standalone training(1p) in ascend
└─run_standalone_test_ascend.sh # launch evaluating in ascend
├─ascend310_infer
├─inc
├─utils.h
├─src
├─main.cc
├─utils.cc
├─build.sh
├─CMakeLists.txt
├─config
├─data
├─coco.yaml
├─yolov7
├─hyp
├─hyp.scratch.p5.yaml
├─hyp.scratch.p6.yaml
├─hyp.scratch.tiny.yaml
├─net
├─yolov7.yaml
├─...
├─network
├─__init__.py
├─common.py
├─loss.py
├─yolo.py
├─utils
├─__init__.py
├─all_finite.py
├─augumentation.py
├─autoanchor.py
├─callback.py
├─checkpoint_fuse.py
├─dataset.py
├─general.py
├─metrics.py
├─modelarts.py
├─optimizer.py
├─plots.py
├─pth2ckpt.py
├─args.py
├─config.py
├─scripts
├─get_coco.sh
├─run_distribute_train_ascend.sh
├─run_standalone_train_ascend.sh
├─run_standalone_test_ascend.sh
├─run_infer_310.sh
├─postprocess.py
├─preprocess.py
├─export.py
├─test.py
└─train.py
Script Parameters
Major parameters config/yolov7/hyp/hyp.scratch.p5.yaml as follows:
Major parameters config/args.py as follows:
optional arguments:
--device_target device where the code will be implemented: "Ascend", default is "Ascend"
--ms_strategy train strategy, default is "StaticShape".
--ms_amp_level amp level, default O0.
--batch_size BATCH_SIZE
Batch size for Training. Default: 128.
--ms_loss_scaler train loss scaler, default is "static".
--weights The ckpt file of YOLOv7, which used to fine tune. default is "".
--config model and hyperparameters yaml path
Training Process
Training
For Ascend device, standalone training example(1p) by shell script
bash run_standalone_train.sh 0
python train.py \
--config=./config/yolov7/net/yolov7.yaml \
--recompute=True \
--recompute_layers=5 \
--batch_size=16 \
--epochs=300 > 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.
Distributed Training
For Ascend device, distributed training example(8p) by shell script
bash run_distribute_train_ascend.sh /path_to/hccl_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, dynamic shape)
...
Epoch 300/1, Step 924/800, size (640, 640), loss: 23.1380, lbox: 0.0639, lobj: 0.0299, lcls: 0.0870, cur_lr: [0.00043236, 0.00043236, 0.07160841], step time: 1225.23 ms
Epoch 300/1, Step 924/801, size (640, 640), loss: 23.2474, lbox: 0.0634, lobj: 0.0321, lcls: 0.0861, cur_lr: [0.00043290, 0.00043290, 0.07157287], step time: 1278.61 ms
Epoch 300/1, Step 924/802, size (640, 640), loss: 20.8814, lbox: 0.0652, lobj: 0.0255, lcls: 0.0725, cur_lr: [0.00043344, 0.00043344, 0.07153734], step time: 1291.87 ms
Epoch 300/1, Step 924/803, size (640, 640), loss: 22.0405, lbox: 0.0646, lobj: 0.0225, lcls: 0.0851, cur_lr: [0.00043398, 0.00043398, 0.07150180], step time: 1310.95 ms
Epoch 300/1, Step 924/804, size (640, 640), loss: 22.1711, lbox: 0.0634, lobj: 0.0269, lcls: 0.0828, cur_lr: [0.00043452, 0.00043452, 0.07146627], step time: 1261.18 ms
Epoch 300/1, Step 924/805, size (640, 640), loss: 22.3260, lbox: 0.0650, lobj: 0.0303, lcls: 0.0791, cur_lr: [0.00043506, 0.00043506, 0.07143074], step time: 1266.44 ms
Epoch 300/1, Step 924/806, size (640, 640), loss: 21.9784, lbox: 0.0654, lobj: 0.0239, lcls: 0.0824, cur_lr: [0.00043561, 0.00043561, 0.07139520], step time: 1274.14 ms
Epoch 300/1, Step 924/807, size (640, 640), loss: 22.1859, lbox: 0.0635, lobj: 0.0241, lcls: 0.0858, cur_lr: [0.00043615, 0.00043615, 0.07135967], step time: 1251.04 ms
Epoch 300/1, Step 924/808, size (640, 640), loss: 22.4195, lbox: 0.0658, lobj: 0.0246, lcls: 0.0847, cur_lr: [0.00043669, 0.00043669, 0.07132413], step time: 1266.10 ms
Epoch 300/1, Step 924/809, size (640, 640), loss: 23.9537, lbox: 0.0656, lobj: 0.0349, lcls: 0.0867, cur_lr: [0.00043723, 0.00043723, 0.07128860], step time: 1261.55 ms
Epoch 300/1, Step 924/810, size (640, 640), loss: 21.0179, lbox: 0.0643, lobj: 0.0221, lcls: 0.0778, cur_lr: [0.00043777, 0.00043777, 0.07125307], step time: 1280.73 ms
Epoch 300/1, Step 924/811, size (640, 640), loss: 22.2355, lbox: 0.0600, lobj: 0.0308, lcls: 0.0829, cur_lr: [0.00043831, 0.00043831, 0.07121753], step time: 1254.24 ms
...
Evaluation Process
Weight average
After training, you can average the weight of the saved checkpoint to obtain higher accuracy.
# Average the weight of the last 80 checkpoints, fuse epoch 221 to 300 checkpoints.
python utils/checkpoint_fuse.py --num 80 --start 221 --base_name /path_to_weights/yolov7
Valid
python test.py \
--config=./config/yolov7/net/yolov7.yaml \
--weights=/path_to/yolov7.ckpt \
--img_size=640 > log.txt 2>&1 &
OR
bash run_standalone_test_ascend.sh /path_to/yolov7.ckpt 0
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 ] = 0.511
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.555
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.349
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.660
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.634
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.684
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.738
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.824
Convert Process
Convert
If you want to infer the network on Ascend 310, you should convert the model to MINDIR:
python export.py --weights [CKPT_PATH] --file_format [FILE_FORMAT]
The weights parameter is required, FILE_FORMAT should be in ["AIR", "ONNX", "MINDIR"]
Inference Process
Before inference, please refer to MindSpore Inference with C++ Deployment Guide to set environment variables.
Usage
Before performing inference, the mindir file must be exported by export script on the 910 environment.
Current batch_Size can only be set to 1.
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DEVICE_ID]
result
Inference result is saved in current path, you can find result like this in acc.log file.
# =============coco eval reulst=========
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.511
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.555
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.349
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.660
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.381
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.634
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.684
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.738
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.824
Model Description
Performance
Train Performance
| Parameters | YOLOv7 |
|---|---|
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G; System, Euleros 2.8; |
| uploaded Date | 12/06/2022 (month/day/year) |
| MindSpore Version | 1.9.0 |
| Dataset | coco2017 |
| Training Parameters | epoch=300, batch_size=128, lrf=0.01, warmup_epochs=20 |
| Optimizer | Momentum |
| Loss Function | Sigmoid Cross Entropy with logits, Ciou Loss |
| outputs | boxes and labels |
| Loss | 5-10 |
| Speed | 8p 98FPS |
| Total time | 100h |
| Checkpoint for Fine tuning | about 150M (.ckpt file) |
| Scripts | https://gitee.com/mindspore/models/tree/master/official/cv/YOLOv7 |
Evaluation Performance
| Parameters | YOLOv7 |
|---|---|
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G |
| uploaded Date | 12/06/2022 (month/day/year) |
| MindSpore Version | 1.9.0 |
| Dataset | coco2017 |
| batch_size | 1 |
| outputs | box position and sorces, and probability |
| Accuracy | mAP: 51.1% |
| Model for inference | about 150M (.ckpt file) |
Description of Random Situation
In train.py, we set the seed inside set_seed function.
ModelZoo Homepage
Please check the official homepage.