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)
## Setup
## 1. Training and Evaluation on Windows or CPU
**Setup PYTHONPATH：**
If training and evaluation are performed on Windows system or CPU, it is recommended to use the `tools/train_multi_platform.py` and `tools/eval_multi_platform.py` scripts.
## 1.1 Model training
After preparing the configuration file, The training process can be started in the following way.
```
python tools/train_multi_platform.py \
-c configs/ResNet/ResNet50.yaml \
-o model_save_dir=./output/ \
-o use_gpu=True
```
Among them, `-c` is used to specify the path of the configuration file, `-o` is used to specify the parameters needed to be modified or added, `-o model_save_dir=./output/` means to modify the `model_save_dir` in the configuration file to ` ./output/`. `-o use_gpu=True` means to use GPU for training. If you want to use the CPU for training, you need to set `use_gpu` to `False`.
Of course, you can also directly modify the configuration file to update the configuration. For specific configuration parameters, please refer to [Configuration Document](config.md).
* The output log examples are as follows:
* If mixup or cutmix is used in training, only loss, lr (learning rate) and training time of the minibatch will be printed in the log.
* If mixup or cutmix is not used during training, in addition to loss, lr (learning rate) and the training time of the minibatch, top-1 and top-k( The default is 5) will also be printed in the log.
Among them, `pretrained_model` is used to set the address to load the pretrained weights. When using it, you need to replace it with your own pretrained weights' path, or you can modify the path directly in the configuration file.
### 1.3 Resume Training
* If the training process is terminated for some reasons, you can also load the checkpoints to continue training.
```
python tools/train_multi_platform.py \
-c configs/ResNet/ResNet50.yaml \
-o checkpoints="./output/ResNet/0/ppcls"
```
The configuration file does not need to be modified. You only need to add the `checkpoints` parameter during training, which represents the path of the checkpoints. The parameter weights, earning rate, optimizer and other information will be loaded using this parameter.
### 1.4 Model evaluation
* The model evaluation process can be started as follows.
```bash
# PaddleClas use paddle.distributed.launch to start multi-cards and multiprocess training.
# Set FLAGS_selected_gpus to indicate GPU cards
python tools/eval_multi_platform.py \
-c ./configs/eval.yaml \
-o ARCHITECTURE.name="ResNet50_vd"\
-opretrained_model=path_to_pretrained_models
```
You can modify the `ARCHITECTURE.name` field and `pretrained_model` field in `configs/eval.yaml` to configure the evaluation model, and you also can update the configuration through the -o parameter.
**Note:** When loading the pretrained model, you need to specify the prefix of the pretrained model. For example, the pretrained model path is `output/ResNet50_vd/19`, and the pretrained model filename is `output/ResNet50_vd/19/ppcls.pdparams`, the parameter `pretrained_model` needs to be specified as `output/ResNet50_vd/19/ppcls`, PaddleClas will automatically fill in the `.pdparams` suffix.
### 2. Training and evaluation on Linux+GPU
If you want to run PaddleClas on Linux with GPU, it is highly recommended to use the model training and evaluation scripts provided by PaddleClas: `tools/train.py` and `tools/eval.py`.
### 2.1 Model training
After preparing the configuration file, The training process can be started in the following way.
```bash
# PaddleClas starts multi-card and multi-process training through launch
# Specify the GPU running card number by setting FLAGS_selected_gpus
or modify configuration directly to config fileds, please refer to [config](config.md) for more details.
Among them, `pretrained_model` is used to set the address to load the pretrained weights. When using it, you need to replace it with your own pretrained weights' path, or you can modify the path directly in the configuration file.
use visuldl to visulize training loss in the real time
* There contains a lot of examples of model finetuning in [The quick start tutorial](./quick_start_en.md). You can refer to this tutorial to finetune the model on a specific dataset.
* If the training process is terminated for some reasons, you can also load the checkpoints to continue training.
```
python -m paddle.distributed.launch \
--selected_gpus="0,1,2,3" \
tools/train.py \
-c configs/ResNet/ResNet50.yaml \
-o checkpoints="./output/ResNet/0/ppcls"
```
### finetune
The configuration file does not need to be modified. You only need to add the `checkpoints` parameter during training, which represents the path of the checkpoints. The parameter weights, learning rate, optimizer and other information will be loaded using this parameter.
* please refer to [Trial](./quick_start.md) for more details.
### 2.4 Model evaluation
### validation
* The model evaluation process can be started as follows.
```bash
python tools/eval.py \
python tools/eval_multi_platform.py \
-c ./configs/eval.yaml \
-o ARCHITECTURE.name="ResNet50_vd"\
-opretrained_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.
You can modify the `ARCHITECTURE.name` field and `pretrained_model` field in `configs/eval.yaml` to configure the evaluation model, and you also can update the configuration through the -o parameter.
**NOTE: ** when loading the pretrained model, should ignore the suffix ```.pdparams```
## 3. Model inference
## Predict
PaddlePaddle provides three ways to perform model inference. Next, how to use the inference engine to perforance model inference will be introduced.
PaddlePaddle supprot three predict interfaces
Use predicator interface to predict
First, export inference model
Firstly, you should export inference model using `tools/export_model.py`.
```bash
python tools/export_model.py \
...
...
@@ -90,7 +172,8 @@ python tools/export_model.py \
--output_path=save_inference_dir
```
Second, start predicator enginee：
Secondly, Inference engine can be started using the following commands.
