dinglinhe
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| mindspore_hub_conf.py | ||
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README.md
Contents
- Contents
YOLOv3-DarkNet53 Description
You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLOv3 is extremely fast and accurate.
Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections. YOLOv3 use a totally different approach. It apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.
YOLOv3 uses a few tricks to improve training and increase performance, including: multi-scale predictions, a better backbone classifier, and more. The full details are in the paper!
Paper: YOLOv3: An Incremental Improvement. Joseph Redmon, Ali Farhadi, University of Washington
Model Architecture
YOLOv3 use DarkNet53 for performing feature extraction, which is a hybrid approach between the network used in YOLOv2, Darknet-19, and that newfangled residual network stuff. DarkNet53 uses successive 3 × 3 and 1 × 1 convolutional layers and has some shortcut connections as well and is significantly larger. It has 53 convolutional layers.
Dataset
Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
Dataset used: COCO2014
-
Dataset size: 19G, 123,287 images, 80 object categories.
- Train：13G, 82,783 images
- Val：6G, 40,504 images
- Annotations: 241M, Train/Val annotations
-
The directory structure is as follows.
├── dataset ├── coco2014 ├── annotations │ ├─ train.json │ └─ val.json ├─ train │ ├─picture1.jpg │ ├─ ... │ └─picturen.jpg └─ val ├─picture1.jpg ├─ ... └─picturen.jpg
Environment Requirements
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU 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 in as follows. If running on GPU, please add
--device_target=GPUin the python command or use the "_gpu" shell script ("xxx_gpu.sh"). - Prepare the backbone_darknet53.ckpt and hccl_8p.json files, before run network.
-
Pretrained_backbone can use src/convert_weight.py, convert darknet53.conv.74 to mindspore ckpt.
python convert_weight.py --input_file ./darknet53.conv.74darknet53.conv.74 can get from download . you can use command in linux os.
wget https://pjreddie.com/media/files/darknet53.conv.74 -
Genatating hccl_8p.json, Run the script of model_zoo/utils/hccl_tools/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)"
-
# The parameter of training_shape define image shape for network, default is "".
# It means use 10 kinds of shape as input shape, or it can be set some kind of shape.
# run training example(1p) by python command.
python train.py \
--data_dir=./dataset/coco2014 \
--pretrained_backbone=darknet53_backbone.ckpt \
--is_distributed=0 \
--lr=0.001 \
--loss_scale=1024 \
--weight_decay=0.016 \
--T_max=320 \
--max_epoch=320 \
--warmup_epochs=4 \
--training_shape=416 \
--lr_scheduler=cosine_annealing > log.txt 2>&1 &
# standalone training example(1p) by shell script
sh run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
# For Ascend device, distributed training example(8p) by shell script
sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
# For GPU device, distributed training example(8p) by shell script
sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
# run evaluation by python command
python eval.py \
--data_dir=./dataset/coco2014 \
--pretrained=yolov3.ckpt \
--testing_shape=416 > log.txt 2>&1 &
# run evaluation by shell script
sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
Script Description
Script and Sample Code
.
└─yolov3_darknet53
├─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
├─run_standalone_train_gpu.sh # launch standalone training(1p) in gpu
├─run_distribute_train_gpu.sh # launch distributed training(8p) in gpu
└─run_eval_gpu.sh # launch evaluating in gpu
├─src
├─__init__.py # python init file
├─config.py # parameter configuration
├─darknet.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 # yolov3 network
├─yolo_dataset.py # create dataset for YOLOV3
├─eval.py # eval net
└─train.py # train net
Script Parameters
Major parameters in train.py as follow.
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: 32.
--pretrained_backbone PRETRAINED_BACKBONE
The ckpt file of DarkNet53. Default: "".
--resume_yolov3 RESUME_YOLOV3
The ckpt file of YOLOv3, which used to fine tune.
Default: ""
--lr_scheduler LR_SCHEDULER
Learning rate scheduler, options: exponential,
cosine_annealing. Default: exponential
--lr LR Learning rate. Default: 0.001
--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/coco2014 \
--pretrained_backbone=darknet53_backbone.ckpt \
--is_distributed=0 \
--lr=0.001 \
--loss_scale=1024 \
--weight_decay=0.016 \
--T_max=320 \
--max_epoch=320 \
--warmup_epochs=4 \
--training_shape=416 \
--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. If running on GPU, please add --device_target=GPU in the python command.
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
2020-08-20 14:14:43,640:INFO:epoch[0], iter[0], loss:7809.262695, 0.15 imgs/sec, lr:9.746589057613164e-06
2020-08-20 14:15:05,142:INFO:epoch[0], iter[100], loss:2778.349033, 133.92 imgs/sec, lr:0.0009844054002314806
2020-08-20 14:15:31,796:INFO:epoch[0], iter[200], loss:535.517361, 130.54 imgs/sec, lr:0.0019590642768889666
...
The model checkpoint will be saved in outputs directory.
Distributed Training
For Ascend device, distributed training example(8p) by shell script
sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
For GPU device, distributed training example(8p) by shell script
sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
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)
epoch[0], iter[0], loss:14623.384766, 1.23 imgs/sec, lr:7.812499825377017e-07
epoch[0], iter[100], loss:746.253051, 22.01 imgs/sec, lr:7.890690624925494e-05
epoch[0], iter[200], loss:101.579535, 344.41 imgs/sec, lr:0.00015703124925494192
epoch[0], iter[300], loss:85.136754, 341.99 imgs/sec, lr:0.00023515624925494185
epoch[1], iter[400], loss:79.429322, 405.14 imgs/sec, lr:0.00031328126788139345
...
epoch[318], iter[102000], loss:30.504046, 458.03 imgs/sec, lr:9.63797575082026e-08
epoch[319], iter[102100], loss:31.599150, 341.08 imgs/sec, lr:2.409552052995423e-08
epoch[319], iter[102200], loss:31.652273, 372.57 imgs/sec, lr:2.409552052995423e-08
epoch[319], iter[102300], loss:31.952403, 496.02 imgs/sec, lr:2.409552052995423e-08
...
Evaluation Process
Evaluation
Before running the command below. If running on GPU, please add --device_target=GPU in the python command or use the "_gpu" shell script ("xxx_gpu.sh").
python eval.py \
--data_dir=./dataset/coco2014 \
--pretrained=yolov3.ckpt \
--testing_shape=416 > log.txt 2>&1 &
OR
sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.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:
This the standard format from pycocotools, you can refer to cocodataset for more detail.
# log.txt
=============coco eval reulst=========
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.322
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
Model Description
Performance
Evaluation Performance
| Parameters | YOLO | YOLO |
|---|---|---|
| Model Version | YOLOv3 | YOLOv3 |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | NV SMX2 V100-16G; CPU 2.10GHz, 96cores; Memory, 251G |
| uploaded Date | 09/15/2020 (month/day/year) | 09/02/2020 (month/day/year) |
| MindSpore Version | 1.1.1 | 1.1.1 |
| Dataset | COCO2014 | COCO2014 |
| Training Parameters | epoch=320, batch_size=32, lr=0.001, momentum=0.9 | epoch=320, batch_size=32, lr=0.1, momentum=0.9 |
| Optimizer | Momentum | Momentum |
| Loss Function | Sigmoid Cross Entropy with logits | Sigmoid Cross Entropy with logits |
| outputs | boxes and label | boxes and label |
| Loss | 34 | 34 |
| Speed | 1pc: 350 ms/step; | 1pc: 600 ms/step; |
| Total time | 8pc: 13 hours | 8pc: 18 hours(shape=416) |
| Parameters (M) | 62.1 | 62.1 |
| Checkpoint for Fine tuning | 474M (.ckpt file) | 474M (.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53 | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53 |
Inference Performance
| Parameters | YOLO | YOLO |
|---|---|---|
| Model Version | YOLOv3 | YOLOv3 |
| Resource | Ascend 910; OS Euler2.8 | NV SMX2 V100-16G |
| Uploaded Date | 09/15/2020 (month/day/year) | 08/20/2020 (month/day/year) |
| MindSpore Version | 1.1.1 | 1.1.1 |
| Dataset | COCO2014, 40,504 images | COCO2014, 40,504 images |
| batch_size | 1 | 1 |
| outputs | mAP | mAP |
| Accuracy | 8pcs: 31.1% | 8pcs: 29.7%~30.3% (shape=416) |
| Model for inference | 474M (.ckpt file) | 474M (.ckpt file) |
Description of Random Situation
There are random seeds in distributed_sampler.py, transforms.py, yolo_dataset.py files.
ModelZoo Homepage
Please check the official homepage.