add autofinetune-cv demo

demo/autofinetune/README.md

+# PaddleHub超参优化——图像分类
+
+本示例展示如何利用PaddleHub超参优化Auto Finetune，得到一个效果较佳的超参数组合
+
+使用PaddleHub Auto Fine-tune需要准备两个指定格式的文件：待优化的超参数信息yaml文件hparam.yaml和需要Fine-tune的python脚本train.py
+
+以Fine-tune图像分类任务为例, 其中：
+
+## hparam.yaml
+
+hparam给出待搜索的超参名字、类型（int或者float，离散型和连续型的两种超参）、搜索范围等信息。
+通过这些信息构建了一个超参空间，PaddleHub将在这个空间内进行超参数的搜索，将搜索到的超参传入train.py获得评估效果，根据评估效果自动调整超参搜索方向，直到满足搜索次数。
+
+本示例中待优化超参数为learning_rate和batch_size。
+
+
+## img_cls.py
+
+以mobilenet为预训练模型，在flowers数据集上进行Finetune。
+
+
+`NOTE`: 关于PaddleHub超参优化详情参考[教程](https://github.com/PaddlePaddle/PaddleHub/blob/release/v1.2/tutorial/autofinetune.md)

demo/autofinetune/hparam.yaml

+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

demo/autofinetune/img_cls.py

+# coding:utf-8
+import argparse
+import os
+import ast
+import shutil
+
+import paddle.fluid as fluid
+import paddlehub as hub
+from paddlehub.common.logger import logger
+
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument(
+    "--epochs", type=int, default=5, 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.")
+
+# the name of hyperparameters to be searched should keep with hparam.py
+parser.add_argument(
+    "--batch_size",
+    type=int,
+    default=16,
+    help="Total examples' number in batch for training.")
+parser.add_argument(
+    "--learning_rate", type=float, default=1e-4, help="learning_rate.")
+
+# saved_params_dir and model_path are needed by auto finetune
+parser.add_argument(
+    "--saved_params_dir",
+    type=str,
+    default="",
+    help="Directory for saving model")
+parser.add_argument(
+    "--model_path", type=str, default="", help="load model path")
+
+
+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):
+    # Load Paddlehub resnet50 pretrained model
+    module = hub.Module(name="mobilenet_v2_imagenet")
+    input_dict, output_dict, program = module.context(trainable=True)
+
+    # Download dataset and use ImageClassificationReader to read dataset
+    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.epochs,
+        batch_size=args.batch_size,
+        checkpoint_dir=args.checkpoint_dir,
+        strategy=strategy)
+
+    # Construct transfer learning network
+    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)
+
+    # acc on dev will be used by auto finetune
+    print("AutoFinetuneEval" + "\t" + str(float(eval_avg_score["acc"])))
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    finetune(args)

demo/autofinetune/run_autofinetune.sh

+OUTPUT=result/
+
+hub autofinetune img_cls.py \
+    --param_file=hparam.yaml \
+    --cuda=['6'] \
+    --popsize=5 \
+    --round=10 \
+    --output_dir=${OUTPUT} \
+    --evaluate_choice=fulltrail \
+    --tuning_strategy=pshe2