From 83253af0ff3321dc20402273c0a7f3ea99e25a19 Mon Sep 17 00:00:00 2001 From: Tingquan Gao Date: Mon, 19 Jul 2021 19:23:23 +0800 Subject: [PATCH] [Cherry-pick] Some fixes in HubServing and whl. (#1056) * Fix params format * Add preprocess in HubServing * Fix printing prediction * Supplement some fixes --- deploy/hubserving/clas/params.py | 10 ++-- deploy/hubserving/readme.md | 8 ++- deploy/hubserving/readme_en.md | 8 ++- deploy/hubserving/test_hubserving.py | 84 +++++++++++++++++----------- paddleclas.py | 19 ++++--- 5 files changed, 82 insertions(+), 47 deletions(-) diff --git a/deploy/hubserving/clas/params.py b/deploy/hubserving/clas/params.py index 358d116d..9d9c0dda 100644 --- a/deploy/hubserving/clas/params.py +++ b/deploy/hubserving/clas/params.py @@ -33,8 +33,10 @@ def get_default_confg(): "enable_benchmark": False }, 'PostProcess': { - 'name': 'Topk', - 'topk': 5, - 'class_id_map_file': './utils/imagenet1k_label_list.txt' + 'main_indicator': 'Topk', + 'Topk': { + 'topk': 5, + 'class_id_map_file': './utils/imagenet1k_label_list.txt' + } } - } \ No newline at end of file + } diff --git a/deploy/hubserving/readme.md b/deploy/hubserving/readme.md index 2a981a2a..6b2b2dd4 100644 --- a/deploy/hubserving/readme.md +++ b/deploy/hubserving/readme.md @@ -15,7 +15,7 @@ hubserving/clas/ ### 1. 准备环境 ```shell # 安装paddlehub,请安装2.0版本 -pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple +pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple ``` ### 2. 下载推理模型 @@ -128,8 +128,12 @@ python hubserving/test_hubserving.py server_url image_path `http://[ip_address]:[port]/predict/[module_name]` - **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径。 - **batch_size**：[**可选**] 以`batch_size`大小为单位进行预测，默认为`1`。 +- **resize_short**：[**可选**] 预处理时，按短边调整大小，默认为`256`。 +- **crop_size**：[**可选**] 预处理时，居中裁剪的大小，默认为`224`。 +- **normalize**：[**可选**] 预处理时，是否进行`normalize`，默认为`True`。 +- **to_chw**：[**可选**] 预处理时，是否调整为`CHW`顺序，默认为`True`。 -**注意**：如果使用`Transformer`系列模型，如`DeiT_***_384`, `ViT_***_384`等，请注意模型的输入数据尺寸。需要指定`--resize_short=384 --resize=384`。 +**注意**：如果使用`Transformer`系列模型，如`DeiT_***_384`, `ViT_***_384`等，请注意模型的输入数据尺寸，需要指定`--resize_short=384 --crop_size=384`。 访问示例： diff --git a/deploy/hubserving/readme_en.md b/deploy/hubserving/readme_en.md index d7ad8840..bb0ddbd2 100644 --- a/deploy/hubserving/readme_en.md +++ b/deploy/hubserving/readme_en.md @@ -15,7 +15,7 @@ hubserving/clas/ ### 1. Prepare the environment ```shell # Install version 2.0 of PaddleHub -pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple +pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple ``` ### 2. Download inference model @@ -126,9 +126,13 @@ Two required parameters need to be passed to the script: `http://[ip_address]:[port]/predict/[module_name]` - **image_path**: Test image path, can be a single image path or an image directory path - **batch_size**: [**Optional**] batch_size. Default by `1`. +- **resize_short**: [**Optional**] In preprocessing, resize according to short size. Default by `256`。 +- **crop_size**: [**Optional**] In preprocessing, centor crop size. Default by `224`。 +- **normalize**: [**Optional**] In preprocessing, whether to do `normalize`. Default by `True`。 +- **to_chw**: [**Optional**] In preprocessing, whether to transpose to `CHW`. Default by `True`。 **Notice**: -If you want to use `Transformer series models`, such as `DeiT_***_384`, `ViT_***_384`, etc., please pay attention to the input size of model, and need to set `--resize_short=384`, `--resize=384`. +If you want to use `Transformer series models`, such as `DeiT_***_384`, `ViT_***_384`, etc., please pay attention to the input size of model, and need to set `--resize_short=384`, `--crop_size=384`. **Eg.** ```shell diff --git a/deploy/hubserving/test_hubserving.py b/deploy/hubserving/test_hubserving.py index c738cc53..e5d2acaf 100644 --- a/deploy/hubserving/test_hubserving.py +++ b/deploy/hubserving/test_hubserving.py @@ -32,30 +32,59 @@ from utils import config from utils.encode_decode import np_to_b64 from python.preprocess import create_operators -preprocess_config = [{ - 'ResizeImage': { - 'resize_short': 256 - } -}, { - 'CropImage': { - 'size': 224 - } -}, { - 'NormalizeImage': { - 'scale': 0.00392157, - 'mean': [0.485, 0.456, 0.406], - 'std': [0.229, 0.224, 0.225], - 'order': '' - } -}, { - 'ToCHWImage': None -}] + +def get_args(): + def str2bool(v): + return v.lower() in ("true", "t", "1") + + parser = argparse.ArgumentParser() + parser.add_argument("--server_url", type=str) + parser.add_argument("--image_file", type=str) + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--resize_short", type=int, default=256) + parser.add_argument("--crop_size", type=int, default=224) + parser.add_argument("--normalize", type=str2bool, default=True) + parser.add_argument("--to_chw", type=str2bool, default=True) + return parser.parse_args() + + +class PreprocessConfig(object): + def __init__(self, + resize_short=256, + crop_size=224, + normalize=True, + to_chw=True): + self.config = [{ + 'ResizeImage': { + 'resize_short': resize_short + } + }, { + 'CropImage': { + 'size': crop_size + } + }] + if normalize: + self.config.append({ + 'NormalizeImage': { + 'scale': 0.00392157, + 'mean': [0.485, 0.456, 0.406], + 'std': [0.229, 0.224, 0.225], + 'order': '' + } + }) + if to_chw: + self.config.append({'ToCHWImage': None}) + + def __call__(self): + return self.config def main(args): image_path_list = get_image_list(args.image_file) headers = {"Content-type": "application/json"} - preprocess_ops = create_operators(preprocess_config) + preprocess_ops = create_operators( + PreprocessConfig(args.resize_short, args.crop_size, args.normalize, + args.to_chw)()) cnt = 0 predict_time = 0 @@ -113,14 +142,10 @@ def main(args): for number, result_list in enumerate(preds): all_score += result_list["scores"][0] - result_str = "" - for i in range(len(result_list["class_ids"])): - result_str += "{}: {:.2f}\t".format( - result_list["class_ids"][i], - result_list["scores"][i]) - + pred_str = ", ".join( + [f"{k}: {result_list[k]}" for k in result_list]) logger.info( - f"File:{img_name_list[number]}, The result(s): {result_str}" + f"File:{img_name_list[number]}, The result(s): {pred_str}" ) finally: @@ -136,10 +161,5 @@ def main(args): if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--server_url", type=str) - parser.add_argument("--image_file", type=str) - parser.add_argument("--batch_size", type=int, default=1) - args = parser.parse_args() - + args = get_args() main(args) diff --git a/paddleclas.py b/paddleclas.py index 4c878691..d0bd6b14 100644 --- a/paddleclas.py +++ b/paddleclas.py @@ -279,8 +279,13 @@ def args_cfg(): "--save_dir", type=str, help="The directory to save prediction results as pre-label.") - parser.add_argument("--resize_short", type=int, default=256, help="") - parser.add_argument("--crop_size", type=int, default=224, help="") + parser.add_argument( + "--resize_short", + type=int, + default=256, + help="Resize according to short size.") + parser.add_argument( + "--crop_size", type=int, default=224, help="Centor crop size.") args = parser.parse_args() return vars(args) @@ -407,7 +412,7 @@ class PaddleClas(object): Args: model_name: The model name supported by PaddleClas, default by None. If specified, override config. inference_model_dir: The directory that contained model file and params file to be used, default by None. If specified, override config. - use_gpu: Wheather use GPU, default by None. If specified, override config. + use_gpu: Whether use GPU, default by None. If specified, override config. batch_size: The batch size to pridict, default by None. If specified, override config. topk: Return the top k prediction results with the highest score. """ @@ -459,7 +464,7 @@ class PaddleClas(object): Args: input_data (str | NumPy.array): The path of image, or the directory containing images, or the URL of image from Internet. - print_pred (bool, optional): Wheather print the prediction result. Defaults to False. + print_pred (bool, optional): Whether print the prediction result. Defaults to False. Raises: ImageTypeError: Illegal input_data. @@ -506,12 +511,12 @@ class PaddleClas(object): preds = self.cls_predictor.postprocess(outputs, img_path_list) if print_pred and preds: - for nu, pred in enumerate(preds): + for pred in preds: + filename = pred.pop("file_name") pred_str = ", ".join( [f"{k}: {pred[k]}" for k in pred]) print( - f"filename: {img_path_list[nu]}, top-{topk}, {pred_str}" - ) + f"filename: {filename}, top-{topk}, {pred_str}") img_list = [] img_path_list = [] -- GitLab