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metric learning tutorial

Data Preparation

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

metric learning support cifar and imagenet(raw and tfrecord) format data.

Imagenet format

You can also use your self-defined data which follows imagenet format, you should provide a root directory which condatains images for metric learning 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 metric learning, you can refer to configs/metric_learning/imagenet_resnet50_1000kid_jpg.py for more configuration details.

Local & PAI-DSW

Training

Single gpu:

python -m torch.distributed.launch \
        --nproc_per_node=1 \
        --master_port=${PORT} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR} \
        --launcher pytorch --fp16

Multi gpus:

python -m torch.distributed.launch \
        --nproc_per_node=${GPUS} \
        --master_port=${PORT} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR} \
        --launcher pytorch --fp16
Arguments

  • NUM_GPUS: number of gpus

  • CONFIG_PATH: the config file path of a metric learning 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
CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port=29500 tools/train.py configs/metric_learning/imagenet_resnet50_1000kid_jpg.py --work_dir ~/projects/work_dirs/ --launcher pytorch --fp16
```

multi gpu training
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=29500 tools/train.py configs/metric_learning/imagenet_resnet50_1000kid_jpg.py --work_dir ~/projects/work_dirs/ --launcher pytorch --fp16
```

training using python api
```python
import easycv.tools

import os
# config_path can be a local file or http url
config_path = 'configs/metric_learning/imagenet_resnet50_1000kid_jpg.py'
easycv.tools.train(config_path, gpus=8, fp16=False, master_port=29527)
```

Evaluation

Single gpu:

python -m torch.distributed.launch \
        --nproc_per_node=1 \
        --master_port=${PORT} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR} \
        --launcher pytorch --fp16
		--eval

Multi gpus:

python -m torch.distributed.launch \
        --nproc_per_node=${GPUS} \
        --master_port=${PORT} \
		${CONFIG_PATH} \
		--work_dir ${WORK_DIR} \
        --launcher pytorch --fp16
		--eval
Arguments

  • CONFIG_PATH: the config file path of a metric learning method

  • NUM_GPUS: number of gpus

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

Examples:

single gpu evaluation
```shell
CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port=29500 tools/train.py configs/metric_learning/imagenet_resnet50_1000kid_jpg.py --work_dir ~/projects/work_dirs/ --launcher pytorch --fp16 --eval
```

multi-gpu evaluation

```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=29500 tools/train.py configs/metric_learning/imagenet_resnet50_1000kid_jpg.py --work_dir ~/projects/work_dirs/ --launcher pytorch --fp16 --eval
```

evaluation using python api
```python
import easycv.tools

import os
os.environ['CUDA_VISIBLE_DEVICES']='3,4,5,6'
config_path = 'configs/metric_learning/imagenet_resnet50_1000kid_jpg'
checkpoint_path = 'work_dirs/metric_learning/imagenet_resnet50_1000kid_jpg/epoch_90.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_resnet50_1000kid_jpg.py
model = dict(
    ...
    backbone=dict(
        ...
        norm_cfg=dict(type='BN')), # SyncBN --> BN
    ...)
```

```shell
python tools/export.py configs/metric_learning/imagenet_resnet50_1000kid_jpg \
    work_dirs/metric_learning/imagenet_resnet50_1000kid_jpg/epoch_90.pth \
    work_dirs/metric_learning/imagenet_resnet50_1000kid_jpg/epoch_90_export.pth
```

or using python api
```python
import easycv.tools

config_path = './imagenet_resnet50_1000kid_jpg.py'
checkpoint_path = 'oss://pai-vision-data-hz/pretrained_models/easycv/imagenet_resnet50_1000kid_jpg/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/metric_learning/imagenet_resnet50_1000kid_jpg/epoch_90_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)
```