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image classification tutorial

Data Preparation

To download the dataset, please refer to prepare_data.md.

Image classification support cifar and imagenet(raw and tfrecord) format data.

Cifar

To use Cifar data to train classification, you can refer to configs/classification/cifar10/swintiny_b64_5e_jpg.py for more configuration details.

Imagenet format

You can also use your self-defined data which follows imagenet format, you should provide a root directory which condatains images for classification training and a filelist which contains image path to the root directory. For example, the image root directory is as follows

images/ ├── 0001.jpg ├── 0002.jpg ├── 0003.jpg |... └── 9999.jpg

each line of the filelist consists of two parts, subpath to the image files starting from the image root directory, class label string for the corresponding image, which are seperated by space

0001.jpg label1 0002.jpg label2 0003.jpg label3 ... 9999.jpg label9999

To use Imagenet format data to train classification, you can refer to configs/classification/imagenet/imagenet_rn50_jpg.py for more configuration details.

Local & PAI-DSW

Training

Single gpu:

python tools/train.py \ ${CONFIG_PATH} \ --work_dir ${WORK_DIR}

Multi gpus:

bash tools/dist_train.sh \ ${NUM_GPUS} \ ${CONFIG_PATH} \ --work_dir ${WORK_DIR}
Arguments

  • NUM_GPUS: number of gpus

  • CONFIG_PATH: the config file path of a image classification method

  • WORK_DIR: your path to save models and logs

Examples:

Edit data_rootpath in the ${CONFIG_PATH} to your own data path.

single gpu training: ```shell python tools/train.py configs/classification/cifar10/swintiny_b64_5e_jpg.py --work_dir work_dirs/classification/cifar10/swintiny --fp16 ``` multi gpu training ```shell GPUS=8 bash tools/dist_train.sh configs/classification/cifar10/swintiny_b64_5e_jpg.py $GPUS --fp16 ``` training using python api ```python import easycv.tools import os # config_path can be a local file or http url config_path = 'configs/classification/cifar10/swintiny_b64_5e_jpg.py' easycv.tools.train(config_path, gpus=8, fp16=False, master_port=29527) ```

Evaluation

Single gpu:

python tools/eval.py \ ${CONFIG_PATH} \ ${CHECKPOINT} \ --eval

Multi gpus:

bash tools/dist_test.sh \ ${CONFIG_PATH} \ ${NUM_GPUS} \ ${CHECKPOINT} \ --eval
Arguments

  • CONFIG_PATH: the config file path of a image classification method

  • NUM_GPUS: number of gpus

  • CHECKPOINT: the checkpoint file named as epoch_*.pth

Examples:

single gpu evaluation ```shell python tools/eval.py configs/classification/cifar10/swintiny_b64_5e_jpg.py work_dirs/classification/cifar10/swintiny/epoch_350.pth --eval --fp16 ``` multi-gpu evaluation ```shell GPUS=8 bash tools/dist_test.sh configs/classification/cifar10/swintiny_b64_5e_jpg.py $GPUS work_dirs/classification/cifar10/swintiny/epoch_350.pth --eval --fp16 ``` evaluation using python api ```python import easycv.tools import os os.environ['CUDA_VISIBLE_DEVICES']='3,4,5,6' config_path = 'configs/classification/cifar10/swintiny_b64_5e_jpg.py' checkpoint_path = 'work_dirs/classification/cifar10/swintiny/epoch_350.pth' easycv.tools.eval(config_path, checkpoint_path, gpus=8) ```

Export model for inference

If SyncBN is configured, we should replace it with BN in config file ```python # imagenet_rn50.py model = dict( ... backbone=dict( ... norm_cfg=dict(type='BN')), # SyncBN --> BN ...) ``` ```shell python tools/export.py configs/classification/cifar10/swintiny_b64_5e_jpg.py \ work_dirs/classification/cifar10/swintiny/epoch_350.pth \ work_dirs/classification/cifar10/swintiny/epoch_350_export.pth ``` or using python api ```python import easycv.tools config_path = './imagenet_rn50.py' checkpoint_path = 'oss://pai-vision-data-hz/pretrained_models/easycv/resnet/resnet50.pth' export_path = './resnet50_export.pt' easycv.tools.export(config_path, checkpoint_path, export_path) ```

Inference

Download [test_image](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/cifar10/qince_data/predict/aeroplane_s_000004.png) ```python import cv2 from easycv.predictors.classifier import TorchClassifier output_ckpt = 'work_dirs/classification/cifar10/swintiny/epoch_350_export.pth' tcls = TorchClassifier(output_ckpt) img = cv2.imread('aeroplane_s_000004.png') # input image should be RGB order img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) output = tcls.predict([img]) print(output) ```
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