mindspore/model_zoo/official/cv/yolov3_darknet53_quant

YOLOV3-DarkNet53-Quant Example

Description

This is an example of training YOLOV3-DarkNet53-Quant with COCO2014 dataset in MindSpore.

Requirements

• Install MindSpore.

• Download the dataset COCO2014.

Unzip the COCO2014 dataset to any path you want, the folder should include train and eval dataset as follows:

.
└─dataset
    ├─train2014
    ├─val2014
    └─annotations

Structure

.
└─yolov3_darknet53_quant      
  ├─README.md
  ├─scripts      
    ├─run_standalone_train.sh         # launch standalone training(1p)
    ├─run_distribute_train.sh         # launch distributed training(8p)
    └─run_eval.sh                     # launch evaluating
  ├─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

Running the example

Train

Usage

# distributed training
sh run_distribute_train.sh [DATASET_PATH] [RESUME_YOLOV3] [MINDSPORE_HCCL_CONFIG_PATH]
 
# standalone training
sh run_standalone_train.sh [DATASET_PATH] [RESUME_YOLOV3]

Launch

# distributed training example(8p)
sh run_distribute_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_102400.ckpt rank_table_8p.json

# standalone training example(1p)
sh run_standalone_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_102400.ckpt

About rank_table.json, you can refer to the distributed training tutorial.

Result

Training result will be stored in the scripts path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in log.txt.

# distribute training result(8p)
epoch[0], iter[0], loss:483.341675, 0.31 imgs/sec, lr:0.0
epoch[0], iter[100], loss:55.690952, 3.46 imgs/sec, lr:0.0
epoch[0], iter[200], loss:54.045728, 126.54 imgs/sec, lr:0.0
epoch[0], iter[300], loss:48.771608, 133.04 imgs/sec, lr:0.0
epoch[0], iter[400], loss:48.486769, 139.69 imgs/sec, lr:0.0
epoch[0], iter[500], loss:48.649275, 143.29 imgs/sec, lr:0.0
epoch[0], iter[600], loss:44.731309, 144.03 imgs/sec, lr:0.0
epoch[1], iter[700], loss:43.037023, 136.08 imgs/sec, lr:0.0
epoch[1], iter[800], loss:41.514788, 132.94 imgs/sec, lr:0.0

…
epoch[133], iter[85700], loss:33.326716, 136.14 imgs/sec, lr:6.497331924038008e-06
epoch[133], iter[85800], loss:34.968744, 136.76 imgs/sec, lr:6.497331924038008e-06
epoch[134], iter[85900], loss:35.868543, 137.08 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86000], loss:35.740817, 139.49 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86100], loss:34.600463, 141.47 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86200], loss:36.641916, 137.91 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86300], loss:32.819769, 138.17 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86400], loss:35.603033, 142.23 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86500], loss:34.303755, 145.18 imgs/sec, lr:1.6245529650404933e-06
...

Infer

Usage

# infer
sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer with checkpoint
sh run_eval.sh dataset/coco2014/ checkpoint/0-135.ckpt

checkpoint can be produced in training process.

Result

Inference result will be stored in the scripts path, whose folder name is "eval". Under this, you can find result like the followings in log.txt.

=============coco eval reulst=========
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.310
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.531
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.322
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.326
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.260
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.402
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.429
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.232
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558