support bs>1 (#651)

* support bs>1

MANIFEST.in

 include LICENSE.txt
 include README.md
+include docs/en/whl_en.md
 
 recursive-include tools/infer utils.py predict.py
-recursive-include ppcls/utils imagenet1k_label_list.txt
\ No newline at end of file
+recursive-include ppcls/utils logger.py
+recursive-include ppcls/utils imagenet1k_label_list.txt

deploy/hubserving/clas/module.py

@@ -24,8 +24,8 @@ import cv2
 import numpy as np
 import paddle.nn as nn
 
-import tools.infer.predict as paddle_predict
-from tools.infer.utils import Base64ToCV2, create_paddle_predictor
+from tools.infer.predict import Predictor
+from tools.infer.utils import b64_to_np, postprocess
 from deploy.hubserving.clas.params import read_params
 
 
@@ -62,65 +62,24 @@ class ClasSystem(nn.Layer):
         else:
             print("Use CPU")
             print("Enable MKL-DNN") if enable_mkldnn else None
-        self.predictor = create_paddle_predictor(self.args)
+        self.predictor = Predictor(self.args)
 
-    def read_images(self, paths=[]):
-        images = []
-        for img_path in paths:
-            assert os.path.isfile(
-                img_path), "The {} isn't a valid file.".format(img_path)
-            img = cv2.imread(img_path)
-            if img is None:
-                logger.info("error in loading image:{}".format(img_path))
-                continue
-            img = img[:, :, ::-1]
-            images.append(img)
-        return images
+    def predict(self, batch_input_data, top_k=1):
+        assert isinstance(
+            batch_input_data,
+            np.ndarray), "The input data is inconsistent with expectations."
 
-    def predict(self, images=[], paths=[], top_k=1):
-        """
-        
-        Args:
-            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
-            paths (list[str]): The paths of images. If paths not images
-        Returns:
-            res (list): The result of chinese texts and save path of images.
-        """
-
-        if images != [] and isinstance(images, list) and paths == []:
-            predicted_data = images
-        elif images == [] and isinstance(paths, list) and paths != []:
-            predicted_data = self.read_images(paths)
-        else:
-            raise TypeError(
-                "The input data is inconsistent with expectations.")
-
-        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
-
-        all_results = []
-        for img in predicted_data:
-            if img is None:
-                logger.info("error in loading image")
-                all_results.append([])
-                continue
-
-            self.args.image_file = img
-            self.args.top_k = top_k
-
-            starttime = time.time()
-            classes, scores = paddle_predict.predict(self.args, self.predictor)
-            elapse = time.time() - starttime
-
-            logger.info("Predict time: {}".format(elapse))
-            all_results.append([classes.tolist(), scores.tolist(), elapse])
-        return all_results
+        starttime = time.time()
+        batch_outputs = self.predictor.predict(batch_input_data)
+        elapse = time.time() - starttime
+        batch_result_list = postprocess(batch_outputs, top_k)
+        return {"prediction": batch_result_list, "elapse": elapse}
 
     @serving
-    def serving_method(self, images, **kwargs):
+    def serving_method(self, images, revert_params, **kwargs):
         """
         Run as a service.
         """
-        to_cv2 = Base64ToCV2()
-        images_decode = [to_cv2(image) for image in images]
-        results = self.predict(images_decode, **kwargs)
+        input_data = b64_to_np(images, revert_params)
+        results = self.predict(batch_input_data=input_data, **kwargs)
         return results

deploy/hubserving/clas/test.py

@@ -12,11 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import sys
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
+import argparse
+import numpy as np
+import cv2
 import paddlehub as hub
+from tools.infer.utils import preprocess
+
+args = argparse.Namespace(resize_short=256, resize=224, normalize=True)
+
+img_path_list = ["./deploy/hubserving/ILSVRC2012_val_00006666.JPEG", ]
 
-image_path = ["./deploy/hubserving/ILSVRC2012_val_00006666.JPEG", ]
-top_k = 5
 module = hub.Module(name="clas_system")
-res = module.predict(paths=image_path, top_k=top_k)
-for i, image in enumerate(image_path):
-    print("The returned result of {}: {}".format(image, res[i]))
+for i, img_path in enumerate(img_path_list):
+    img = cv2.imread(img_path)[:, :, ::-1]
+    img = preprocess(img, args)
+    batch_input_data = np.expand_dims(img, axis=0)
+    res = module.predict(batch_input_data)
+    print("The returned result of {}: {}".format(img_path, res))

deploy/lite/readme.md

@@ -28,8 +28,10 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
       |Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/Android/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.CV_ON.tar.gz)|
       |iOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios.armv7.with_extra.CV_ON.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.6.1/iOS/inference_lite_lib.ios64.armv8.with_extra.CV_ON.tar.gz)|
 
-      注：1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/release_lib.html#android-toolchain-gcc)下载的预测库，
-      注意选择`with_extra=ON，with_cv=ON`的下载链接。2. 如果使用量化的模型部署在端侧，建议使用Paddle-Lite develop分支编译预测库。
+      **注**：
+      1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，
+      注意选择`with_extra=ON，with_cv=ON`的下载链接。
+      2. 如果使用量化的模型部署在端侧，建议使用Paddle-Lite develop分支编译预测库。
 
 2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
 ```shell

deploy/lite/readme_en.md

@@ -31,7 +31,7 @@ For the detailed compilation directions of different development environments, p
 
 **NOTE**:
 
-1. If you download the inference library from [Paddle-Lite official document](https://paddle-lite.readthedocs.io/zh/latest/user_guides/release_lib.html#android-toolchain-gcc), please choose `with_extra=ON` , `with_cv=ON` .
+1. If you download the inference library from [Paddle-Lite official document](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc), please choose `with_extra=ON` , `with_cv=ON` .
 
 2. It is recommended to build inference library using [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) develop branch if you want to deploy the [quantitative](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README_en.md) model to mobile phones. Please refer to the [link](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2) for more detailed information about compiling.
 

docs/en/tutorials/quick_start_en.md

@@ -116,12 +116,7 @@ python3 tools/infer/infer.py \
 The output is as follows. Top-5 class ids and their scores are printed.
 
 ```
-Current image file: docs/images/quick_start/flowers102/image_06739.jpg
-    top1, class id: 0, probability: 0.5129
-    top2, class id: 50, probability: 0.0671
-    top3, class id: 18, probability: 0.0377
-    top4, class id: 82, probability: 0.0238
-    top5, class id: 54, probability: 0.0231
+File:image_06739.jpg, Top-5 result: class id(s): [0, 96, 18, 50, 51], score(s): [0.79, 0.02, 0.01, 0.01, 0.01]
 ```
 
 * Note: Results are different for different models, so you might get different results for the command.

docs/en/whl_en.md

@@ -6,7 +6,7 @@
 
 install by pypi
 ```bash
-pip install paddleclas==2.0.0rc2
+pip install paddleclas==2.0.0rc3
 ```
 
 build own whl package and install
@@ -27,7 +27,7 @@ pip3 install dist/paddleclas-x.x.x-py3-none-any.whl
 
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False,use_tensorrt=False)
+clas = PaddleClas(model_name='ResNet50', top_k=5)
 image_file='docs/images/whl/demo.jpg'
 result=clas.predict(image_file)
 print(result)
@@ -35,25 +35,28 @@ print(result)
 
 ```
     >>> result
-    [{'filename': '/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg', 'class_ids': [8], 'scores': [0.9796774], 'label_names': ['hen']}]
+    [{'class_ids': array([ 8,  7, 86, 82, 80]), 'scores': array([9.7967714e-01, 2.0280687e-02, 2.7053760e-05, 6.1860351e-06,
+       2.6378802e-06], dtype=float32), 'label_names': ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], 'filename': 'docs/images/whl/demo.jpg'}
 ```
 
 * Using command line interactive programming
 ```bash
-paddleclas --model_name='ResNet50' --image_file='docs/images/whl/demo.jpg'
+paddleclas --model_name=ResNet50 --top_k=5 --image_file='docs/images/whl/demo.jpg'
 ```
 
 ```
     >>> result
-    **********/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg**********
-    [{'filename': '/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg', 'class_ids': [8], 'scores': [0.9796774], 'label_names': ['hen']}]
+    **********docs/images/whl/demo.jpg**********
+    filename: docs/images/whl/demo.jpg; class id: 8, 7, 86, 82, 80; scores: 0.9797, 0.0203, 0.0000, 0.0000, 0.0000; label: hen, cock, partridge, ruffed grouse, partridge, Bonasa umbellus, black grouse
+    Predict complete!
 ```
 
 ### 2. Definition of Parameters
 * model_name(str): model's name. If not assigning `model_file`and`params_file`, you can assign this param. If using inference model based on ImageNet1k provided by Paddle, set as default='ResNet50'.
-* image_file(str): image's path. Support assigning single local image, internet image and folder containing series of images. Also Support numpy.ndarray.
+* image_file(str or numpy.ndarray): image's path. Support assigning single local image, internet image and folder containing series of images. Also Support numpy.ndarray, the channel order is [B, G, R].
 * use_gpu(bool): Whether to use GPU or not, defalut=False。
 * use_tensorrt(bool): whether to open tensorrt or not. Using it can greatly promote predict preformance, default=False.
+* is_preprocessed(bool): Assign the image data has been preprocessed or not when the image_file is numpy.ndarray.
 * resize_short(int): resize the minima between height and width into resize_short(int), default=256
 * resize(int): resize image into resize(int), default=224.
 * normalize(bool): whether normalize image or not, default=True.
@@ -84,80 +87,75 @@ paddleclas -h
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_file='user-specified model path',
-    params_file='parmas path', use_gpu=False, use_tensorrt=False)
-image_file = ''
+clas = PaddleClas(model_file='the path of model file',
+    params_file='the path of params file')
+image_file = 'docs/images/whl/demo.jpg'
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_file='user-specified model path' --params_file='parmas path' --image_file='image path'
+paddleclas --model_file='user-specified model path' --params_file='parmas path' --image_file='docs/images/whl/demo.jpg'
 ```
 
-* Use inference model which PaddlePaddle provides to predict, you need to choose one of model when initializing PaddleClas to assign `model_name`. You may not assign `model_file` , and the model you chosen will be download in `BASE_INFERENCE_MODEL_DIR` ,which will be saved in folder named by `model_name`,avoiding overlay different inference model.
+* Use inference model which PaddlePaddle provides to predict, you need to choose one of model proviede by PaddleClas to assign `model_name`. So there's no need to assign `model_file`. And the model you chosen will be download in `~/.paddleclas/`, which will be saved in folder named by `model_name`.
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False)
-image_file = ''
+clas = PaddleClas(model_name='ResNet50')
+image_file = 'docs/images/whl/demo.jpg'
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path'
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/demo.jpg'
 ```
 
-* You can assign input as format`np.ndarray` which has been preprocessed `--image_file=np.ndarray`.
+* You can assign input as format `numpy.ndarray` which has been preprocessed `image_file=np.ndarray`. Note that the image data must be three channel. If need To preprocess the image, the image channels order must be [B, G, R].
 
 ###### python
 ```python
+import cv2
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False)
-image_file =np.ndarray # image_file 可指定为前缀是https的网络图片，也可指定为本地图片
+clas = PaddleClas(model_name='ResNet50')
+image_file = cv2.imread("docs/images/whl/demo.jpg")
 result=clas.predict(image_file)
 ```
 
-###### bash
-```bash
-paddleclas --model_name='ResNet50' --image_file=np.ndarray
-```
-
-
-* You can assign `image_file` as a folder path containing series of images, also can assign `top_k`.
+* You can assign `image_file` as a folder path containing series of images.
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False,top_k=5)
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_name='ResNet50')
+image_file = 'docs/images/whl/' # it can be image_file folder path which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path' --top_k=5
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/'
 ```
 
-* You can assign `--pre_label_image=True`, `--pre_label_out_idr= './output_pre_label/'`.Then images will be copied into folder named by top-1 class_id.
+* You can assign `--pre_label_image=True`, `--pre_label_out_idr= './output_pre_label/'`. Then images will be copied into folder named by top-1 class_id.
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False,top_k=5, pre_label_image=True,pre_label_out_idr='./output_pre_label/')
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_name='ResNet50', pre_label_image=True, pre_label_out_idr='./output_pre_label/')
+image_file = 'docs/images/whl/' # it can be image_file folder path which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path' --top_k=5 --pre_label_image=True --pre_label_out_idr='./output_pre_label/'
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/' --pre_label_image=True --pre_label_out_idr='./output_pre_label/'
 ```
 
 * You can assign `--label_name_path` as your own label_dict_file, format should be as(class_id<space>class_name<\n>).
@@ -169,32 +167,32 @@ paddleclas --model_name='ResNet50' --image_file='image path' --top_k=5 --pre_lab
 ......
 ```
 
-* If you use inference model that Paddle provides, you do not need assign `label_name_path`. Program will take `ppcls/utils/imagenet1k_label_list.txt` as defaults. If you hope using your own training model, you can provide `label_name_path` outputing 'label_name' and scores, otherwise no 'label_name' in output information.
+* If you use inference model that PaddleClas provides, you do not need assign `label_name_path`. Program will take `ppcls/utils/imagenet1k_label_list.txt` as defaults. If you hope using your own training model, you can provide `label_name_path` outputing 'label_name' and scores, otherwise no 'label_name' in output information.
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_file= './inference.pdmodel',params_file = './inference.pdiparams',label_name_path='./ppcls/utils/imagenet1k_label_list.txt',use_gpu=False)
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_file= 'the path of model file', params_file = 'the path of params file', label_name_path='./ppcls/utils/imagenet1k_label_list.txt')
+image_file = 'docs/images/whl/demo.jpg' # it can be image_file folder path which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_file= './inference.pdmodel' --params_file = './inference.pdiparams' --image_file='image path' --label_name_path='./ppcls/utils/imagenet1k_label_list.txt'
+paddleclas --model_file='the path of model file' --params_file='the path of params file' --image_file='docs/images/whl/demo.jpg' --label_name_path='./ppcls/utils/imagenet1k_label_list.txt'
 ```
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False)
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_name='ResNet50')
+image_file = 'docs/images/whl/' # it can be directory which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path'
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/'
 ```

docs/zh_CN/tutorials/quick_start.md

@@ -105,18 +105,14 @@ python3 tools/infer/infer.py \
     -i docs/images/quick_start/flowers102/image_06739.jpg \
     --model=ResNet50_vd \
     --pretrained_model="output/ResNet50_vd/best_model/ppcls" \
-    --class_num=102
+    --class_num=102 \
+    --top_k=5
 ```
 
 最终可以得到如下结果，打印出了Top-5对应的class id以及score。
 
 ```
-Current image file: docs/images/quick_start/flowers102/image_06739.jpg
-	top1, class id: 0, probability: 0.5129
-	top2, class id: 50, probability: 0.0671
-	top3, class id: 18, probability: 0.0377
-	top4, class id: 82, probability: 0.0238
-	top5, class id: 54, probability: 0.0231
+File:image_06739.jpg, Top-5 result: class id(s): [0, 96, 18, 50, 51], score(s): [0.79, 0.02, 0.01, 0.01, 0.01]
 ```
 
 * 注意：这里每个模型的训练结果都不相同，因此结果可能稍有不同。

docs/zh_CN/whl.md

@@ -6,13 +6,13 @@
 
 pip安装
 ```bash
-pip install paddleclas==2.0.0rc2
+pip install paddleclas==2.0.0rc3
 ```
 
 本地构建并安装
 ```bash
 python3 setup.py bdist_wheel
-pip3 install dist/paddleclas-x.x.x-py3-none-any.whl # x.x.x是paddleclas的版本号
+pip3 install dist/paddleclas-x.x.x-py3-none-any.whl # x.x.x是paddleclas的版本号，默认为0.0.0
 ```
 ### 1. 快速开始
 * 指定`image_file='docs/images/whl/demo.jpg'`,使用Paddle提供的inference model,`model_name='ResNet50'`, 使用图片`docs/images/whl/demo.jpg`。
@@ -25,7 +25,7 @@ pip3 install dist/paddleclas-x.x.x-py3-none-any.whl # x.x.x是paddleclas的版
 
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False,use_tensorrt=False)
+clas = PaddleClas(model_name='ResNet50', top_k=5)
 image_file='docs/images/whl/demo.jpg'
 result=clas.predict(image_file)
 print(result)
@@ -33,35 +33,39 @@ print(result)
 
 ```
     >>> result
-    [{'filename': '/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg', 'class_ids': [8], 'scores': [0.9796774], 'label_names': ['hen']}]
+    [{'class_ids': array([ 8,  7, 86, 82, 80]), 'scores': array([9.7967714e-01, 2.0280687e-02, 2.7053760e-05, 6.1860351e-06,
+       2.6378802e-06], dtype=float32), 'label_names': ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'black grouse'], 'filename': 'docs/images/whl/demo.jpg'}]
 ```
 
 * 使用命令行式交互方法。直接获得结果。
 ```bash
-paddleclas --model_name='ResNet50' --image_file='docs/images/whl/demo.jpg'
+paddleclas --model_name=ResNet50 --top_k=5 --image_file='docs/images/whl/demo.jpg'
 ```
 
 ```
     >>> result
-    **********/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg**********
-    [{'filename': '/Users/mac/Downloads/PaddleClas/docs/images/whl/demo.jpg', 'class_ids': [8], 'scores': [0.9796774], 'label_names': ['hen']}]
+    **********docs/images/whl/demo.jpg**********
+    filename: docs/images/whl/demo.jpg; class id: 8, 7, 86, 82, 80; scores: 0.9797, 0.0203, 0.0000, 0.0000, 0.0000; label: hen, cock, partridge, ruffed grouse, partridge, Bonasa umbellus, black grouse
+    Predict complete!
 ```
 
 ### 2. 参数解释
+以下参数可在命令行交互使用时通过参数指定，或在Python代码中实例化PaddleClas对象时作为构造函数的参数使用。
 * model_name(str): 模型名称，没有指定自定义的model_file和params_file时，可以指定该参数，使用PaddleClas提供的基于ImageNet1k的inference model，默认值为ResNet50。
-* image_file(str): 图像地址，支持指定单一图像的路径或图像的网址进行预测，支持指定包含图像的文件夹路径，支持经过预处理的np.ndarray形式输入。
+* image_file(str or numpy.ndarray): 图像地址，支持指定单一图像的路径或图像的网址进行预测，支持指定包含图像的文件夹路径，支持numpy.ndarray格式的三通道图像数据，且通道顺序为[B, G, R]。
 * use_gpu(bool): 是否使用GPU，如果使用，指定为True。默认为False。
 * use_tensorrt(bool): 是否开启TensorRT预测，可提升GPU预测性能，需要使用带TensorRT的预测库。当使用TensorRT推理加速，指定为True。默认为False。
+* is_preprocessed(bool): 当image_file为numpy.ndarray格式的图像数据时，图像数据是否已经过预处理。如果该参数为True，则不再对image_file数据进行预处理，否则将转换通道顺序后，按照resize_short，resize，normalize参数对图像进行预处理。默认值为False。
 * resize_short(int): 将图像的高宽二者中小的值，调整到指定的resize_short值，大的值按比例放大。默认为256。
 * resize(int): 将图像裁剪到指定的resize值大小，默认224。
 * normalize(bool): 是否对图像数据归一化，默认True。
-* batch_size(int): 预测时每个batch的样本数，默认为1。
+* batch_size(int): 预测时每个batch的样本数量，默认为1。
 * model_file(str): 模型.pdmodel的路径，若不指定该参数，需要指定model_name，获得下载的模型。
-* params_file(str): 模型参数.pdiparams的路径，若不与model_file指定，则需要指定model_name,以获得下载的模型。
+* params_file(str): 模型参数.pdiparams的路径，若不指定，则需要指定model_name,以获得下载的模型。
 * ir_optim(bool): 是否开启IR优化，默认为True。
 * gpu_mem(int): 使用的GPU显存大小，默认为8000。
 * enable_profile(bool): 是否开启profile功能，默认False。
-* top_k(int): 指定的topk，预测的前k个类别和对应的分类概率，默认为1。
+* top_k(int): 指定的topk，打印（返回）预测结果的前k个类别和对应的分类概率，默认为1。
 * enable_mkldnn(bool): 是否开启MKLDNN，默认False。
 * cpu_num_threads(int): 指定cpu线程数，默认设置为10。
 * label_name_path(str): 指定一个表示所有的label name的文件路径。当用户使用自己训练的模型，可指定这一参数，打印结果时可以显示图像对应的类名称。若用户使用Paddle提供的inference model，则可不指定该参数，使用imagenet1k的label_name，默认为空字符串。
@@ -84,84 +88,79 @@ paddleclas -h
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_file='user-specified model path',
-    params_file='parmas path', use_gpu=False, use_tensorrt=False)
-image_file = '' # image_file 可指定为前缀是https的网络图片，也可指定为本地图片
+clas = PaddleClas(model_file='the path of model file',
+    params_file='the path of params file')
+image_file = 'docs/images/whl/demo.jpg' # image_file 可指定为前缀是https的网络图片，也可指定为本地图片
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_file='user-specified model path' --params_file='parmas path' --image_file='image path'
+paddleclas --model_file='user-specified model path' --params_file='parmas path' --image_file='docs/images/whl/demo.jpg'
 ```
 
-* 用户使用PaddlePaddle训练好的inference model来预测，用户需要使用，初始化打印的模型的其中一个，并指定给`model_name`。
-用户可以不指定`model_file`,模型会自动下载到当前目录,并保存在以`model_name`命名的文件夹中，避免下载不同模型的覆盖问题。
+* 用户使用PaddlePaddle训练好的inference model来预测，并通过参数`model_name`指定。
+此时无需指定`model_file`,模型会根据`model_name`自动下载指定模型到当前目录,并保存在目录`~/.paddleclas/`下以`model_name`命名的文件夹中。
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False)
-image_file = '' # image_file 可指定为前缀是https的网络图片，也可指定为本地图片
+clas = PaddleClas(model_name='ResNet50')
+image_file = 'docs/images/whl/demo.jpg' # image_file 可指定为前缀是https的网络图片，也可指定为本地图片
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path'
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/demo.jpg'
 ```
 
-* 用户可以使用经过预处理的np.ndarray格式`--image_file=np.ndarray`。
+* 用户可以使用numpy.ndarray格式的图像数据，并通过参数`image_file`指定。注意该图像数据必须为三通道图像数据。如需对图像进行预处理，则图像通道顺序必须为[B, G, R]。
 
 ###### python
 ```python
+import cv2
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False)
-image_file =np.ndarray # image_file 可指定为前缀是https的网络图片，也可指定为本地图片
+clas = PaddleClas(model_name='ResNet50')
+image_file = cv2.imread("docs/images/whl/demo.jpg")
 result=clas.predict(image_file)
 ```
 
-###### bash
-```bash
-paddleclas --model_name='ResNet50' --image_file=np.ndarray
-```
-
-
-* 用户可以将`image_file`指定为包含图片的文件夹路径，可以指定`top_k`参数
+* 用户可以将`image_file`指定为包含图片的文件夹路径。
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False,top_k=5)
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_name='ResNet50')
+image_file = 'docs/images/whl/' # it can be image_file folder path which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path' --top_k=5
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/'
 ```
 
-* 用户可以指定`--pre_label_image=True`, `--pre_label_out_idr= './output_pre_label/'`，将图片复制到，以其top1对应的类别命名的文件夹中。
+* 用户可以指定`pre_label_image=True`, `pre_label_out_idr='./output_pre_label/'`，将图片按其top1预测结果保存到`pre_label_out_dir`目录下对应类别的文件夹中。
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False, use_tensorrt=False,top_k=5, pre_label_image=True,pre_label_out_idr='./output_pre_label/')
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_name='ResNet50', pre_label_image=True,pre_label_out_idr='./output_pre_label/')
+image_file = 'docs/images/whl/' # it can be image_file folder path which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path' --top_k=5 --pre_label_image=True --pre_label_out_idr='./output_pre_label/'
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/' --pre_label_image=True --pre_label_out_idr='./output_pre_label/'
 ```
 
-* 用户可以指定`--label_name_path`,作为用户自己训练模型的`label_dict_file`,格式应为(class_id<space>class_name<\n>)
+* 用户可以通过参数`label_name_path`指定模型的`label_dict_file`文件路径，文件内容格式应为(class_id<space>class_name<\n>)，例如：
 
 ```
 0 tench, Tinca tinca
@@ -177,27 +176,27 @@ paddleclas --model_name='ResNet50' --image_file='image path' --top_k=5 --pre_lab
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_file= './inference.pdmodel',params_file = './inference.pdiparams',label_name_path='./ppcls/utils/imagenet1k_label_list.txt',use_gpu=False)
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_file='the path of model file', params_file ='the path of params file', label_name_path='./ppcls/utils/imagenet1k_label_list.txt')
+image_file = 'docs/images/whl/demo.jpg' # it can be image_file folder path which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_file= './inference.pdmodel' --params_file = './inference.pdiparams' --image_file='image path' --label_name_path='./ppcls/utils/imagenet1k_label_list.txt'
+paddleclas --model_file='the path of model file' --params_file='the path of params file' --image_file='docs/images/whl/demo.jpg' --label_name_path='./ppcls/utils/imagenet1k_label_list.txt'
 ```
 
 ###### python
 ```python
 from paddleclas import PaddleClas
-clas = PaddleClas(model_name='ResNet50',use_gpu=False)
-image_file = '' # it can be image_file folder path which contains all of images you want to predict.
+clas = PaddleClas(model_name='ResNet50')
+image_file = 'docs/images/whl/' # it can be image_file folder path which contains all of images you want to predict.
 result=clas.predict(image_file)
 print(result)
 ```
 
 ###### bash
 ```bash
-paddleclas --model_name='ResNet50' --image_file='image path'
+paddleclas --model_name='ResNet50' --image_file='docs/images/whl/'
 ```

paddleclas.py

@@ -30,14 +30,17 @@ import os
 import sys
 __dir__ = os.path.dirname(__file__)
 sys.path.append(os.path.join(__dir__, ''))
+import argparse
+import shutil
 
 import cv2
 import numpy as np
 import tarfile
 import requests
 from tqdm import tqdm
-import tools.infer.utils as utils
-import shutil
+from tools.infer.utils import get_image_list, preprocess, save_prelabel_results
+from tools.infer.predict import Predictor
+
 __all__ = ['PaddleClas']
 BASE_DIR = os.path.expanduser("~/.paddleclas/")
 BASE_INFERENCE_MODEL_DIR = os.path.join(BASE_DIR, 'inference_model')
@@ -98,7 +101,9 @@ def download_with_progressbar(url, save_path):
             file.write(data)
     progress_bar.close()
     if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
-        raise Exception("Something went wrong while downloading models")
+        raise Exception(
+            "Something went wrong while downloading model/image from {}".
+            format(url))
 
 
 def maybe_download(model_storage_directory, url):
@@ -130,20 +135,14 @@ def maybe_download(model_storage_directory, url):
         os.remove(tmp_path)
 
 
-def save_prelabel_results(class_id, input_filepath, output_idr):
-    output_dir = os.path.join(output_idr, str(class_id))
-    if not os.path.isdir(output_dir):
-        os.makedirs(output_dir)
-    shutil.copy(input_filepath, output_dir)
-
-
 def load_label_name_dict(path):
-    result = {}
     if not os.path.exists(path):
         print(
-            'Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!'
+            "Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!"
         )
+        return None
     else:
+        result = {}
         for line in open(path, 'r'):
             partition = line.split('\n')[0].partition(' ')
             try:
@@ -155,8 +154,6 @@ def load_label_name_dict(path):
 
 
 def parse_args(mMain=True, add_help=True):
-    import argparse
-
     def str2bool(v):
         return v.lower() in ("true", "t", "1")
 
@@ -186,9 +183,7 @@ def parse_args(mMain=True, add_help=True):
         parser.add_argument("--enable_profile", type=str2bool, default=False)
         parser.add_argument("--top_k", type=int, default=1)
         parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
-        parser.add_argument("--enable_benchmark", type=str2bool, default=False)
         parser.add_argument("--cpu_num_threads", type=int, default=10)
-        parser.add_argument("--hubserving", type=str2bool, default=False)
 
         # parameters for pre-label the images
         parser.add_argument("--label_name_path", type=str, default='')
@@ -207,6 +202,7 @@ def parse_args(mMain=True, add_help=True):
             use_gpu=False,
             use_fp16=False,
             use_tensorrt=False,
+            is_preprocessed=False,
             resize_short=256,
             resize=224,
             normalize=True,
@@ -218,9 +214,7 @@ def parse_args(mMain=True, add_help=True):
             enable_profile=False,
             top_k=1,
             enable_mkldnn=False,
-            enable_benchmark=False,
             cpu_num_threads=10,
-            hubserving=False,
             label_name_path='',
             pre_label_image=False,
             pre_label_out_idr=None)
@@ -231,7 +225,6 @@ class PaddleClas(object):
           format(model_names), '\n')
 
     def __init__(self, **kwargs):
-
         process_params = parse_args(mMain=False, add_help=False)
         process_params.__dict__.update(**kwargs)
 
@@ -270,76 +263,96 @@ class PaddleClas(object):
             process_params.label_name_path)
 
         self.args = process_params
-        self.predictor = utils.create_paddle_predictor(process_params)
-
-    def predict(self, img):
+        self.predictor = Predictor(process_params)
+
+    def postprocess(self, output):
+        output = output.flatten()
+        classes = np.argpartition(output, -self.args.top_k)[-self.args.top_k:]
+        class_ids = classes[np.argsort(-output[classes])]
+        scores = output[class_ids]
+        label_names = [self.label_name_dict[c]
+                       for c in class_ids] if self.label_name_dict else []
+        return {
+            "class_ids": class_ids,
+            "scores": scores,
+            "label_names": label_names
+        }
+
+    def predict(self, input_data):
         """
         predict label of img with paddleclas
         Args:
-            img: input image for clas, support single image , internet url, folder path containing series of images
+            input_data(string, NumPy.ndarray): image to be classified, support:
+                string: local path of image file, internet URL, directory containing series of images;
+                NumPy.ndarray: preprocessed image data that has 3 channels and accords with [C, H, W], or raw image data that has 3 channels and accords with [H, W, C]
         Returns:
-            dict：{image_name: "", class_id: [], scores: [], label_names: []}，if label name path == None，label_names will be empty.
+            dict: {image_name: "", class_id: [], scores: [], label_names: []}，if label name path == None，label_names will be empty.
         """
-        assert isinstance(img, (str, np.ndarray))
-
-        input_names = self.predictor.get_input_names()
-        input_tensor = self.predictor.get_input_handle(input_names[0])
-
-        output_names = self.predictor.get_output_names()
-        output_tensor = self.predictor.get_output_handle(output_names[0])
-        if isinstance(img, str):
+        if isinstance(input_data, np.ndarray):
+            if not self.args.is_preprocessed:
+                input_data = input_data[:, :, ::-1]
+                input_data = preprocess(input_data, self.args)
+            input_data = np.expand_dims(input_data, axis=0)
+            batch_outputs = self.predictor.predict(input_data)
+            result = {"filename": "image"}
+            result.update(self.postprocess(batch_outputs[0]))
+            return result
+        elif isinstance(input_data, str):
+            input_path = input_data
             # download internet image
-            if img.startswith('http'):
+            if input_path.startswith('http'):
                 if not os.path.exists(BASE_IMAGES_DIR):
                     os.makedirs(BASE_IMAGES_DIR)
-                image_path = os.path.join(BASE_IMAGES_DIR, 'tmp.jpg')
-                download_with_progressbar(img, image_path)
+                file_path = os.path.join(BASE_IMAGES_DIR, 'tmp.jpg')
+                download_with_progressbar(input_path, file_path)
                 print("Current using image from Internet:{}, renamed as: {}".
-                      format(img, image_path))
-                img = image_path
-            image_list = utils.get_image_list(img)
+                      format(input_path, file_path))
+                input_path = file_path
+            image_list = get_image_list(input_path)
+
+            total_result = []
+            batch_input_list = []
+            img_path_list = []
+            cnt = 0
+            for idx, img_path in enumerate(image_list):
+                img = cv2.imread(img_path)
+                if img is None:
+                    print(
+                        "Warning: Image file failed to read and has been skipped. The path: {}".
+                        format(img_path))
+                    continue
+                else:
+                    img = img[:, :, ::-1]
+                    data = preprocess(img, self.args)
+                    batch_input_list.append(data)
+                    img_path_list.append(img_path)
+                    cnt += 1
+
+                if cnt % self.args.batch_size == 0 or (idx + 1
+                                                       ) == len(image_list):
+                    batch_outputs = self.predictor.predict(
+                        np.array(batch_input_list))
+                    for number, output in enumerate(batch_outputs):
+                        result = {"filename": img_path_list[number]}
+                        result.update(self.postprocess(output))
+
+                        result_str = "top-{} result: {}".format(
+                            self.args.top_k, result)
+                        print(result_str)
+
+                        total_result.append(result)
+                        if self.args.pre_label_image:
+                            save_prelabel_results(result["class_ids"][0],
+                                                  img_path_list[number],
+                                                  self.args.pre_label_out_idr)
+                    batch_input_list = []
+                    img_path_list = []
+            return total_result
         else:
-            if isinstance(img, np.ndarray):
-                image_list = [img]
-            else:
-                print('Please input legal image!')
-
-        total_result = []
-        for filename in image_list:
-            if isinstance(filename, str):
-                image = cv2.imread(filename)[:, :, ::-1]
-                assert image is not None, "Error in loading image: {}".format(
-                    filename)
-                inputs = utils.preprocess(image, self.args)
-                inputs = np.expand_dims(
-                    inputs, axis=0).repeat(
-                        1, axis=0).copy()
-            else:
-                inputs = filename
-
-            input_tensor.copy_from_cpu(inputs)
-
-            self.predictor.run()
-
-            outputs = output_tensor.copy_to_cpu()
-            classes, scores = utils.postprocess(outputs, self.args)
-            label_names = []
-            if len(self.label_name_dict) != 0:
-                label_names = [self.label_name_dict[c] for c in classes]
-            result = {
-                "filename": filename if isinstance(filename, str) else 'image',
-                "class_ids": classes.tolist(),
-                "scores": scores.tolist(),
-                "label_names": label_names,
-            }
-            total_result.append(result)
-            if self.args.pre_label_image:
-                save_prelabel_results(classes[0], filename,
-                                      self.args.pre_label_out_idr)
-                print("\tSaving prelabel results in {}".format(
-                    os.path.join(self.args.pre_label_out_idr, str(classes[
-                        0]))))
-        return total_result
+            print(
+                "Error: Please input legal image! The type of image supported by PaddleClas are: NumPy.ndarray and string of local path or Ineternet URL"
+            )
+            return []
 
 
 def main():
@@ -347,9 +360,9 @@ def main():
     args = parse_args(mMain=True)
     clas_engine = PaddleClas(**(args.__dict__))
     print('{}{}{}'.format('*' * 10, args.image_file, '*' * 10))
-    result = clas_engine.predict(args.image_file)
-    if result is not None:
-        print(result)
+    total_result = clas_engine.predict(args.image_file)
+
+    print("Predict complete!")
 
 
 if __name__ == '__main__':

tools/infer/infer.py

@@ -14,7 +14,6 @@
 
 import numpy as np
 import cv2
-import shutil
 import os
 import sys
 
@@ -26,61 +25,60 @@ sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 
 from ppcls.utils.save_load import load_dygraph_pretrain
+from ppcls.utils import logger
 from ppcls.modeling import architectures
-import utils
-from utils import get_image_list
-
-
-def postprocess(outputs, topk=5):
-    output = outputs[0]
-    prob = np.array(output).flatten()
-    index = prob.argsort(axis=0)[-topk:][::-1].astype('int32')
-    return zip(index, prob[index])
-
-
-def save_prelabel_results(class_id, input_filepath, output_idr):
-    output_dir = os.path.join(output_idr, str(class_id))
-    if not os.path.isdir(output_dir):
-        os.makedirs(output_dir)
-    shutil.copy(input_filepath, output_dir)
+from utils import parse_args, get_image_list, preprocess, postprocess, save_prelabel_results
 
 
 def main():
-    args = utils.parse_args()
+    args = parse_args()
     # assign the place
     place = paddle.set_device('gpu' if args.use_gpu else 'cpu')
 
     net = architectures.__dict__[args.model](class_dim=args.class_num)
     load_dygraph_pretrain(net, args.pretrained_model, args.load_static_weights)
     image_list = get_image_list(args.image_file)
-    for idx, filename in enumerate(image_list):
-        img = cv2.imread(filename)[:, :, ::-1]
-        data = utils.preprocess(img, args)
-        data = np.expand_dims(data, axis=0)
-        data = paddle.to_tensor(data)
-        net.eval()
-        outputs = net(data)
-        if args.model == "GoogLeNet":
-            outputs = outputs[0]
-        outputs = F.softmax(outputs)
-        outputs = outputs.numpy()
-        probs = postprocess(outputs)
-
-        top1_class_id = 0
-        rank = 1
-        print("Current image file: {}".format(filename))
-        for idx, prob in probs:
-            print("\ttop{:d}, class id: {:d}, probability: {:.4f}".format(
-                rank, idx, prob))
-            if rank == 1:
-                top1_class_id = idx
-            rank += 1
-
-        if args.pre_label_image:
-            save_prelabel_results(top1_class_id, filename,
-                                  args.pre_label_out_idr)
-
-    return
+    batch_input_list = []
+    img_path_list = []
+    cnt = 0
+    for idx, img_path in enumerate(image_list):
+        img = cv2.imread(img_path)
+        if img is None:
+            logger.warning(
+                "Image file failed to read and has been skipped. The path: {}".
+                format(img_path))
+            continue
+        else:
+            img = img[:, :, ::-1]
+            data = preprocess(img, args)
+            batch_input_list.append(data)
+            img_path_list.append(img_path)
+            cnt += 1
+
+        if cnt % args.batch_size == 0 or (idx + 1) == len(image_list):
+            batch_tensor = paddle.to_tensor(batch_input_list)
+            net.eval()
+            batch_outputs = net(batch_tensor)
+            if args.model == "GoogLeNet":
+                batch_outputs = batch_outputs[0]
+            batch_outputs = F.softmax(batch_outputs)
+            batch_outputs = batch_outputs.numpy()
+            batch_result_list = postprocess(batch_outputs, args.top_k)
+
+            for number, result_dict in enumerate(batch_result_list):
+                filename = img_path_list[number].split("/")[-1]
+                clas_ids = result_dict["clas_ids"]
+                scores_str = "[{}]".format(", ".join("{:.2f}".format(
+                    r) for r in result_dict["scores"]))
+                print("File:{}, Top-{} result: class id(s): {}, score(s): {}".
+                      format(filename, args.top_k, clas_ids, scores_str))
+
+                if args.pre_label_image:
+                    save_prelabel_results(clas_ids[0], img_path_list[number],
+                                          args.pre_label_out_idr)
+
+            batch_input_list = []
+            img_path_list = []
 
 
 if __name__ == "__main__":

tools/infer/predict.py

@@ -18,60 +18,76 @@ import time
 
 import sys
 sys.path.insert(0, ".")
-import tools.infer.utils as utils
-from tools.infer.utils import get_image_list
-
-
-def predict(args, predictor):
-    input_names = predictor.get_input_names()
-    input_tensor = predictor.get_input_handle(input_names[0])
-
-    output_names = predictor.get_output_names()
-    output_tensor = predictor.get_output_handle(output_names[0])
-
-    test_num = 500
-    test_time = 0.0
-    if not args.enable_benchmark:
-        # for PaddleHubServing
-        if args.hubserving:
-            img_list = [args.image_file]
-        # for predict only
-        else:
-            img_list = get_image_list(args.image_file)
-
-        for idx, img_name in enumerate(img_list):
-            if not args.hubserving:
-                img = cv2.imread(img_name)[:, :, ::-1]
-                assert img is not None, "Error in loading image: {}".format(
-                    img_name)
+from ppcls.utils import logger
+from tools.infer.utils import parse_args, get_image_list, create_paddle_predictor, preprocess, postprocess
+
+
+class Predictor(object):
+    def __init__(self, args):
+        # HALF precission predict only work when using tensorrt
+        if args.use_fp16 is True:
+            assert args.use_tensorrt is True
+        self.args = args
+
+        self.paddle_predictor = create_paddle_predictor(args)
+        input_names = self.paddle_predictor.get_input_names()
+        self.input_tensor = self.paddle_predictor.get_input_handle(input_names[
+            0])
+
+        output_names = self.paddle_predictor.get_output_names()
+        self.output_tensor = self.paddle_predictor.get_output_handle(
+            output_names[0])
+
+    def predict(self, batch_input):
+        self.input_tensor.copy_from_cpu(batch_input)
+        self.paddle_predictor.run()
+        batch_output = self.output_tensor.copy_to_cpu()
+        return batch_output
+
+    def normal_predict(self):
+        image_list = get_image_list(self.args.image_file)
+        batch_input_list = []
+        img_name_list = []
+        cnt = 0
+        for idx, img_path in enumerate(image_list):
+            img = cv2.imread(img_path)
+            if img is None:
+                logger.warning(
+                    "Image file failed to read and has been skipped. The path: {}".
+                    format(img_path))
+                continue
             else:
-                img = img_name
-            inputs = utils.preprocess(img, args)
-            inputs = np.expand_dims(
-                inputs, axis=0).repeat(
-                    args.batch_size, axis=0).copy()
-            input_tensor.copy_from_cpu(inputs)
-
-            predictor.run()
-
-            output = output_tensor.copy_to_cpu()
-            classes, scores = utils.postprocess(output, args)
-            if args.hubserving:
-                return classes, scores
-            print("Current image file: {}".format(img_name))
-            print("\ttop-1 class: {0}".format(classes[0]))
-            print("\ttop-1 score: {0}".format(scores[0]))
-    else:
+                img = img[:, :, ::-1]
+                img = preprocess(img, args)
+                batch_input_list.append(img)
+                img_name = img_path.split("/")[-1]
+                img_name_list.append(img_name)
+                cnt += 1
+
+            if cnt % args.batch_size == 0 or (idx + 1) == len(image_list):
+                batch_outputs = self.predict(np.array(batch_input_list))
+                batch_result_list = postprocess(batch_outputs, self.args.top_k)
+
+                for number, result_dict in enumerate(batch_result_list):
+                    filename = img_name_list[number]
+                    clas_ids = result_dict["clas_ids"]
+                    scores_str = "[{}]".format(", ".join("{:.2f}".format(
+                        r) for r in result_dict["scores"]))
+                    print(
+                        "File:{}, Top-{} result: class id(s): {}, score(s): {}".
+                        format(filename, self.args.top_k, clas_ids,
+                               scores_str))
+                batch_input_list = []
+                img_name_list = []
+
+    def benchmark_predict(self):
+        test_num = 500
+        test_time = 0.0
         for i in range(0, test_num + 10):
             inputs = np.random.rand(args.batch_size, 3, 224,
                                     224).astype(np.float32)
             start_time = time.time()
-            input_tensor.copy_from_cpu(inputs)
-
-            predictor.run()
-
-            output = output_tensor.copy_to_cpu()
-            output = output.flatten()
+            batch_output = self.predict(inputs).flatten()
             if i >= 10:
                 test_time += time.time() - start_time
             time.sleep(0.01)  # sleep for T4 GPU
@@ -83,19 +99,11 @@ def predict(args, predictor):
             / test_num))
 
 
-def main(args):
+if __name__ == "__main__":
+    args = parse_args()
+    predictor = Predictor(args)
     if not args.enable_benchmark:
-        assert args.batch_size == 1
+        predictor.normal_predict()
     else:
         assert args.model is not None
-    # HALF precission predict only work when using tensorrt
-    if args.use_fp16 is True:
-        assert args.use_tensorrt is True
-
-    predictor = utils.create_paddle_predictor(args)
-    predict(args, predictor)
-
-
-if __name__ == "__main__":
-    args = utils.parse_args()
-    main(args)
+        predictor.benchmark_predict()

tools/infer/utils.py

@@ -14,6 +14,8 @@
 
 import os
 import argparse
+import base64
+import shutil
 import cv2
 import numpy as np
 
@@ -68,6 +70,9 @@ def parse_args():
         help="Whether to pre-label the images using the loaded weights")
     parser.add_argument("--pre_label_out_idr", type=str, default=None)
 
+    # parameters for test hubserving
+    parser.add_argument("--server_url", type=str)
+
     return parser.parse_args()
 
 
@@ -119,12 +124,18 @@ def preprocess(img, args):
     return img
 
 
-def postprocess(output, args):
-    output = output.flatten()
-    classes = np.argpartition(output, -args.top_k)[-args.top_k:]
-    classes = classes[np.argsort(-output[classes])]
-    scores = output[classes]
-    return classes, scores
+def postprocess(batch_outputs, topk=5):
+    batch_results = []
+    for probs in batch_outputs:
+        results = []
+        index = probs.argsort(axis=0)[-topk:][::-1].astype("int32")
+        clas_id_list = []
+        score_list = []
+        for i in index:
+            clas_id_list.append(i.item())
+            score_list.append(probs[i].item())
+        batch_results.append({"clas_ids": clas_id_list, "scores": score_list})
+    return batch_results
 
 
 def get_image_list(img_file):
@@ -144,6 +155,13 @@ def get_image_list(img_file):
     return imgs_lists
 
 
+def save_prelabel_results(class_id, input_file_path, output_dir):
+    output_dir = os.path.join(output_dir, str(class_id))
+    if not os.path.isdir(output_dir):
+        os.makedirs(output_dir)
+    shutil.copy(input_file_path, output_dir)
+
+
 class ResizeImage(object):
     def __init__(self, resize_short=None):
         self.resize_short = resize_short
@@ -197,13 +215,15 @@ class ToTensor(object):
         return img
 
 
-class Base64ToCV2(object):
-    def __init__(self):
-        pass
+def b64_to_np(b64str, revert_params):
+    shape = revert_params["shape"]
+    dtype = revert_params["dtype"]
+    dtype = getattr(np, dtype) if isinstance(str, type(dtype)) else dtype
+    data = base64.b64decode(b64str.encode('utf8'))
+    data = np.fromstring(data, dtype).reshape(shape)
+    return data
+
 
-    def __call__(self, b64str):
-        import base64
-        data = base64.b64decode(b64str.encode('utf8'))
-        data = np.fromstring(data, np.uint8)
-        data = cv2.imdecode(data, cv2.IMREAD_COLOR)[:, :, ::-1]
-        return data
+def np_to_b64(images):
+    img_str = base64.b64encode(images).decode('utf8')
+    return img_str, images.shape

tools/test_hubserving.py

@@ -18,85 +18,95 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
 
+from tools.infer.utils import parse_args, get_image_list, preprocess, np_to_b64
 from ppcls.utils import logger
+import numpy as np
 import cv2
 import time
 import requests
 import json
 import base64
-import imghdr
 
 
-def get_image_file_list(img_file):
-    imgs_lists = []
-    if img_file is None or not os.path.exists(img_file):
-        raise Exception("not found any img file in {}".format(img_file))
-
-    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
-    if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
-        imgs_lists.append(img_file)
-    elif os.path.isdir(img_file):
-        for single_file in os.listdir(img_file):
-            file_path = os.path.join(img_file, single_file)
-            if imghdr.what(file_path) in img_end:
-                imgs_lists.append(file_path)
-    if len(imgs_lists) == 0:
-        raise Exception("not found any img file in {}".format(img_file))
-    return imgs_lists
-
-
-def cv2_to_base64(image):
-    return base64.b64encode(image).decode('utf8')
-
-
-def main(url, image_path, top_k=1):
-    image_file_list = get_image_file_list(image_path)
+def main(args):
+    image_path_list = get_image_list(args.image_file)
     headers = {"Content-type": "application/json"}
+
     cnt = 0
-    total_time = 0
-    all_acc = 0.0
+    predict_time = 0
+    all_score = 0.0
+    start_time = time.time()
 
-    for image_file in image_file_list:
-        file_str = image_file.split('/')[-1]
-        img = open(image_file, 'rb').read()
+    batch_input_list = []
+    img_name_list = []
+    cnt = 0
+    for idx, img_path in enumerate(image_path_list):
+        img = cv2.imread(img_path)
         if img is None:
-            logger.error("Loading image:{} failed".format(image_file))
+            logger.warning(
+                "Image file failed to read and has been skipped. The path: {}".
+                format(img_path))
             continue
-        data = {'images': [cv2_to_base64(img)], 'top_k': top_k}
-
-        try:
-            r = requests.post(url=url, headers=headers, data=json.dumps(data))
-            r.raise_for_status()
-        except Exception as e:
-            logger.error("File:{}, {}".format(file_str, e))
-            continue
-        if r.json()['status'] != '000':
-            logger.error(
-                "File:{}, The parameters returned by the server are: {}".
-                format(file_str, r.json()['msg']))
-            continue
-        res = r.json()["results"][0]
-        classes, scores, elapse = res
-        all_acc += scores[0]
-        total_time += elapse
-        cnt += 1
-
-        scores = map(lambda x: round(x, 5), scores)
-        results = dict(zip(classes, scores))
-
-        message = "No.{}, File:{}, The top-{} result(s):{}, Time cost:{:.3f}".format(
-            cnt, file_str, top_k, results, elapse)
-        logger.info(message)
-
-    logger.info("The average time cost: {}".format(float(total_time) / cnt))
-    logger.info("The average top-1 score: {}".format(float(all_acc) / cnt))
+        else:
+            img = img[:, :, ::-1]
+            data = preprocess(img, args)
+            batch_input_list.append(data)
+            img_name = img_path.split('/')[-1]
+            img_name_list.append(img_name)
+            cnt += 1
+        if cnt % args.batch_size == 0 or (idx + 1) == len(image_path_list):
+            batch_input = np.array(batch_input_list)
+            b64str, revert_shape = np_to_b64(batch_input)
+            data = {
+                "images": b64str,
+                "revert_params": {
+                    "shape": revert_shape,
+                    "dtype": str(batch_input.dtype)
+                },
+                "top_k": args.top_k
+            }
+            try:
+                r = requests.post(
+                    url=args.server_url,
+                    headers=headers,
+                    data=json.dumps(data))
+                r.raise_for_status
+                if r.json()["status"] != "000":
+                    msg = r.json()["msg"]
+                    raise Exception(msg)
+            except Exception as e:
+                logger.error("{}, in file(s): {} etc.".format(e, img_name_list[
+                    0]))
+                continue
+            else:
+                results = r.json()["results"]
+                batch_result_list = results["prediction"]
+                elapse = results["elapse"]
+
+                cnt += len(batch_result_list)
+                predict_time += elapse
+
+                for number, result_list in enumerate(batch_result_list):
+                    all_score += result_list[0]["score"]
+                    result_str = ", ".join([
+                        "{}: {:.2f}".format(r["cls_id"], r["score"])
+                        for r in result_list
+                    ])
+                    logger.info("File:{}, The top-{} result(s): {}".format(
+                        img_name_list[number], args.top_k, result_str))
+
+            finally:
+                batch_input_list = []
+                img_name_list = []
+
+    total_time = time.time() - start_time
+    logger.info("The average time of prediction cost: {:.3f} s/image".format(
+        predict_time / cnt))
+    logger.info("The average time cost: {:.3f} s/image".format(total_time /
+                                                               cnt))
+    logger.info("The average top-1 score: {:.3f}".format(all_score / cnt))
 
 
 if __name__ == '__main__':
-    if len(sys.argv) != 3 and len(sys.argv) != 4:
-        logger.info("Usage: %s server_url image_path" % sys.argv[0])
-    else:
-        server_url = sys.argv[1]
-        image_path = sys.argv[2]
-        top_k = int(sys.argv[3]) if len(sys.argv) == 4 else 1
-        main(server_url, image_path, top_k)
+    args = parse_args()
+    main(args)