1.4. Getting Started¶
Please refer to Installation to setup environment at first, and prepare ImageNet1K data by following the instruction mentioned in the data
1.4.2. Training and validating¶
PaddleClas support tools/train.py and tools/eval.py to start training and validating.
1.4.2.1. Training¶
# PaddleClas use paddle.distributed.launch to start multi-cards and multiprocess training.
# Set FLAGS_selected_gpus to indicate GPU cards
python -m paddle.distributed.launch \
--selected_gpus="0,1,2,3" \
tools/train.py \
-c ./configs/ResNet/ResNet50_vd.yaml
- log:
epoch:0 train step:13 loss:7.9561 top1:0.0156 top5:0.1094 lr:0.100000 elapse:0.193
add -o params to update configuration
python -m paddle.distributed.launch \
--selected_gpus="0,1,2,3" \
tools/train.py \
-c ./configs/ResNet/ResNet50_vd.yaml \
-o use_mix=1 \
--vdl_dir=./scalar/
- log:
epoch:0 train step:522 loss:1.6330 lr:0.100000 elapse:0.210
or modify configuration directly to config fileds, please refer to config for more details.
use visuldl to visulize training loss in the real time
visualdl --logdir ./scalar --host <host_IP> --port <port_num>
1.4.2.3. validation¶
python tools/eval.py \
-c ./configs/eval.yaml \
-o ARCHITECTURE.name="ResNet50_vd" \
-o pretrained_model=path_to_pretrained_models
modify `configs/eval.yaml filed: `ARCHITECTURE.name` and filed: `pretrained_model` to config valid model or add -o params to update config directly.
**NOTE: ** when loading the pretrained model, should ignore the suffix ```.pdparams```
## Predict
PaddlePaddle supprot three predict interfaces
Use predicator interface to predict
First, export inference model
```bash
python tools/export_model.py \
--model=model_name \
--pretrained_model=pretrained_model_dir \
--output_path=save_inference_dir
Second, start predicator enginee：
python tools/infer/predict.py \
-m model_path \
-p params_path \
-i image path \
--use_gpu=1 \
--use_tensorrt=True
please refer to inference for more details.