autofinetune-cv.md

# PaddleHub 超参优化（Auto Fine-tune）——CV图像分类任务


使用PaddleHub Auto Fine-tune必须准备两个文件，并且这两个文件需要按照指定的格式书写。这两个文件分别是需要Fine-tune的python脚本finetuee.py和需要优化的超参数信息yaml文件hparam.yaml。

以Fine-tune图像分类任务为例，我们展示如何利用PaddleHub Auto Finetune进行超参优化。

以下是待优化超参数的yaml文件hparam.yaml，包含需要搜素的超参名字、类型、范围等信息。其中类型只支持float和int类型
```
param_list:
- name : learning_rate
  init_value : 0.001
  type : float
  lower_than : 0.05
  greater_than : 0.00005
- name : batch_size
  init_value : 12
  type : int
  lower_than : 20
  greater_than : 10
```

以下是图像分类的finetunee.py

```python
# coding:utf-8
import argparse
import os
import ast
import shutil

import paddle.fluid as fluid
import paddlehub as hub
import numpy as np

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--num_epoch",          type=int,               default=1,                         help="Number of epoches for fine-tuning.")
parser.add_argument("--use_gpu",            type=ast.literal_eval,  default=True,                      help="Whether use GPU for fine-tuning.")
parser.add_argument("--checkpoint_dir",     type=str,               default=None,                      help="Path to save log data.")
parser.add_argument("--batch_size",         type=int,               default=16,                        help="Total examples' number in batch for training.")
parser.add_argument("--saved_params_dir",   type=str,               default="",                        help="Directory for saving model")
parser.add_argument("--learning_rate",      type=float,             default=1e-4,                      help="learning_rate.")
parser.add_argument("--model_path",         type=str,               default="",                        help="load model path")
# yapf: enable.


def is_path_valid(path):
    if path == "":
        return False
    path = os.path.abspath(path)
    dirname = os.path.dirname(path)
    if not os.path.exists(dirname):
        os.mkdir(dirname)
    return True

def finetune(args):
    module = hub.Module(name="resnet_v2_50_imagenet")
    input_dict, output_dict, program = module.context(trainable=True)

    dataset = hub.dataset.Flowers()

    data_reader = hub.reader.ImageClassificationReader(
        image_width=module.get_expected_image_width(),
        image_height=module.get_expected_image_height(),
        images_mean=module.get_pretrained_images_mean(),
        images_std=module.get_pretrained_images_std(),
        dataset=dataset)

    feature_map = output_dict["feature_map"]

    img = input_dict["image"]
    feed_list = [img.name]

    # Select finetune strategy, setup config and finetune
    strategy = hub.DefaultFinetuneStrategy(
        learning_rate=args.learning_rate)

    config = hub.RunConfig(
        use_cuda=True,
        num_epoch=args.num_epoch,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=strategy)

    task = hub.ImageClassifierTask(
        data_reader=data_reader,
        feed_list=feed_list,
        feature=feature_map,
        num_classes=dataset.num_labels,
        config=config)

    # Load model from the defined model path or not
    if args.model_path != "":
        with task.phase_guard(phase="train"):
            task.init_if_necessary()
            task.load_parameters(args.model_path)
            logger.info("PaddleHub has loaded model from %s" % args.model_path)


    task.finetune()
    run_states = task.eval()
    eval_avg_score, eval_avg_loss, eval_run_speed = task._calculate_metrics(run_states)

    # Move ckpt/best_model to the defined saved parameters directory
    best_model_dir = os.path.join(config.checkpoint_dir, "best_model")
    if is_path_valid(args.saved_params_dir) and os.path.exists(best_model_dir):
        shutil.copytree(best_model_dir, args.saved_params_dir)
        shutil.rmtree(config.checkpoint_dir)

    print("AutoFinetuneEval"+"\t"+str(float(eval_avg_score["acc"])))


if __name__ == "__main__":
    args = parser.parse_args()
    finetune(args)
```