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