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133 lines
4.5 KiB
133 lines
4.5 KiB
5 years ago
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# YOLOV3-DarkNet53 Example
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## Description
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This is an example of training YOLOV3-DarkNet53 with COCO2014 dataset in MindSpore.
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## Requirements
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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- Download the dataset COCO2014.
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> Unzip the COCO2014 dataset to any path you want, the folder should include train and eval dataset as follows:
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```
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.
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└─dataset
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├─train2014
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├─val2014
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└─annotations
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```
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## Structure
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```shell
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.
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└─yolov3_darknet53
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├─README.md
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├─scripts
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├─run_standalone_train.sh # launch standalone training(1p)
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├─run_distribute_train.sh # launch distributed training(8p)
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└─run_eval.sh # launch evaluating
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├─src
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├─config.py # parameter configuration
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├─darknet.py # backbone of network
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├─distributed_sampler.py # iterator of dataset
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├─initializer.py # initializer of parameters
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├─logger.py # log function
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├─loss.py # loss function
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├─lr_scheduler.py # generate learning rate
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├─transforms.py # Preprocess data
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├─util.py # util function
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├─yolo.py # yolov3 network
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├─yolo_dataset.py # create dataset for YOLOV3
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├─eval.py # eval net
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└─train.py # train net
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```
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## Running the example
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### Train
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#### Usage
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```
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# distributed training
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sh run_distribute_train.sh [DATASET_PATH] [PRETRAINED_BACKBONE] [MINDSPORE_HCCL_CONFIG_PATH]
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# standalone training
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sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_BACKBONE]
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```
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#### Launch
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```bash
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# distributed training example(8p)
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sh run_distribute_train.sh dataset/coco2014 backbone/backbone.ckpt rank_table_8p.json
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# standalone training example(1p)
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sh run_standalone_train.sh dataset/coco2014 backbone/backbone.ckpt
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```
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> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
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#### Result
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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.
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```
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# distribute training result(8p)
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epoch[0], iter[0], loss:14623.384766, 1.23 imgs/sec, lr:7.812499825377017e-05
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epoch[0], iter[100], loss:1486.253051, 15.01 imgs/sec, lr:0.007890624925494194
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epoch[0], iter[200], loss:288.579535, 490.41 imgs/sec, lr:0.015703124925494194
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epoch[0], iter[300], loss:153.136754, 531.99 imgs/sec, lr:0.023515624925494194
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epoch[1], iter[400], loss:106.429322, 405.14 imgs/sec, lr:0.03132812678813934
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...
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epoch[318], iter[102000], loss:34.135306, 431.06 imgs/sec, lr:9.63797629083274e-06
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epoch[319], iter[102100], loss:35.652469, 449.52 imgs/sec, lr:2.409552052995423e-06
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epoch[319], iter[102200], loss:34.652273, 384.02 imgs/sec, lr:2.409552052995423e-06
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epoch[319], iter[102300], loss:35.430038, 423.49 imgs/sec, lr:2.409552052995423e-06
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...
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```
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### Infer
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#### Usage
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```
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# infer
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sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]
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```
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#### Launch
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```bash
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# infer with checkpoint
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sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
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```
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> checkpoint can be produced in training process.
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#### Result
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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.
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```
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=============coco eval reulst=========
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.322
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
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```
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