From 666d91267810f5d57b831dd7df888ce4e4298798 Mon Sep 17 00:00:00 2001
From: Tingquan Gao <gaotingquan@baidu.com>
Date: Thu, 17 Jun 2021 20:56:03 +0800
Subject: [PATCH] Update HubServing (#870)

* Update HubServing

* Fix the relative path

* Update doc of HubServing
---
 deploy/hubserving/clas/module.py       |   64 +-
 deploy/hubserving/clas/params.py       |   48 +-
 deploy/hubserving/clas/test.py         |   35 -
 deploy/hubserving/readme.md            |   64 +-
 deploy/hubserving/test_hubserving.py   |   82 +-
 deploy/python/predict_cls.py           |   16 +-
 deploy/python/preprocess.py            |    2 +-
 deploy/utils/__init__.py               |    1 +
 deploy/utils/config.py                 |    4 +-
 deploy/utils/encode_decode.py          |   31 +
 deploy/utils/imagenet1k_label_list.txt | 1000 ++++++++++++++++++++++++
 11 files changed, 1207 insertions(+), 140 deletions(-)
 delete mode 100644 deploy/hubserving/clas/test.py
 create mode 100644 deploy/utils/encode_decode.py
 create mode 100644 deploy/utils/imagenet1k_label_list.txt

diff --git a/deploy/hubserving/clas/module.py b/deploy/hubserving/clas/module.py
index 113ab14ad..dd8bf5df6 100644
--- a/deploy/hubserving/clas/module.py
+++ b/deploy/hubserving/clas/module.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,15 +18,14 @@ sys.path.insert(0, ".")
 
 import time
 
-from paddlehub.utils.log import logger
-from paddlehub.module.module import moduleinfo, serving
-import cv2
 import numpy as np
 import paddle.nn as nn
+from paddlehub.module.module import moduleinfo, serving
 
-from tools.infer.predict import Predictor
-from tools.infer.utils import b64_to_np, postprocess
-from deploy.hubserving.clas.params import read_params
+from hubserving.clas.params import get_default_confg
+from python.predict_cls import ClsPredictor
+from utils import config
+from utils.encode_decode import b64_to_np
 
 
 @moduleinfo(
@@ -41,19 +40,24 @@ class ClasSystem(nn.Layer):
         """
         initialize with the necessary elements
         """
-        cfg = read_params()
+        self._config = self._load_config(
+            use_gpu=use_gpu, enable_mkldnn=enable_mkldnn)
+        self.cls_predictor = ClsPredictor(self._config)
+
+    def _load_config(self, use_gpu=None, enable_mkldnn=None):
+        cfg = get_default_confg()
+        cfg = config.AttrDict(cfg)
+        config.create_attr_dict(cfg)
         if use_gpu is not None:
-            cfg.use_gpu = use_gpu
+            cfg.Global.use_gpu = use_gpu
         if enable_mkldnn is not None:
-            cfg.enable_mkldnn = enable_mkldnn
-        cfg.hubserving = True
+            cfg.Global.enable_mkldnn = enable_mkldnn
         cfg.enable_benchmark = False
-        self.args = cfg
-        if cfg.use_gpu:
+        if cfg.Global.use_gpu:
             try:
                 _places = os.environ["CUDA_VISIBLE_DEVICES"]
                 int(_places[0])
-                print("Use GPU, GPU Memery:{}".format(cfg.gpu_mem))
+                print("Use GPU, GPU Memery:{}".format(cfg.Global.gpu_mem))
                 print("CUDA_VISIBLE_DEVICES: ", _places)
             except:
                 raise RuntimeError(
@@ -62,24 +66,36 @@ class ClasSystem(nn.Layer):
         else:
             print("Use CPU")
             print("Enable MKL-DNN") if enable_mkldnn else None
-        self.predictor = Predictor(self.args)
+        return cfg
 
-    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, inputs):
+        if not isinstance(inputs, list):
+            raise Exception(
+                "The input data is inconsistent with expectations.")
 
         starttime = time.time()
-        batch_outputs = self.predictor.predict(batch_input_data)
+        outputs = self.cls_predictor.predict(inputs)
         elapse = time.time() - starttime
-        batch_result_list = postprocess(batch_outputs, top_k)
-        return {"prediction": batch_result_list, "elapse": elapse}
+        preds = self.cls_predictor.postprocess(outputs)
+        return {"prediction": preds, "elapse": elapse}
 
     @serving
-    def serving_method(self, images, revert_params, **kwargs):
+    def serving_method(self, images, revert_params):
         """
         Run as a service.
         """
         input_data = b64_to_np(images, revert_params)
-        results = self.predict(batch_input_data=input_data, **kwargs)
+        results = self.predict(inputs=list(input_data))
         return results
+
+
+if __name__ == "__main__":
+    import cv2
+    import paddlehub as hub
+
+    module = hub.Module(name="clas_system")
+    img_path = "./hubserving/ILSVRC2012_val_00006666.JPEG"
+    img = cv2.imread(img_path)[:, :, ::-1]
+    img = cv2.resize(img, (224, 224)).transpose((2, 0, 1))
+    res = module.predict([img.astype(np.float32)])
+    print("The returned result of {}: {}".format(img_path, res))
diff --git a/deploy/hubserving/clas/params.py b/deploy/hubserving/clas/params.py
index 83224f597..358d116d3 100644
--- a/deploy/hubserving/clas/params.py
+++ b/deploy/hubserving/clas/params.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,28 +17,24 @@ from __future__ import division
 from __future__ import print_function
 
 
-class Config(object):
-    pass
-
-
-def read_params():
-    cfg = Config()
-
-    cfg.model_file = "./inference/cls_infer.pdmodel"
-    cfg.params_file = "./inference/cls_infer.pdiparams"
-    cfg.batch_size = 1
-    cfg.use_gpu = False
-    cfg.enable_mkldnn = False
-    cfg.ir_optim = True
-    cfg.gpu_mem = 8000
-    cfg.use_fp16 = False
-    cfg.use_tensorrt = False
-    cfg.cpu_num_threads = 10
-    cfg.enable_profile = False
-
-    # params for preprocess
-    cfg.resize_short = 256
-    cfg.resize = 224
-    cfg.normalize = True
-
-    return cfg
+def get_default_confg():
+    return {
+        'Global': {
+            "inference_model_dir": "../inference/",
+            "batch_size": 1,
+            'use_gpu': False,
+            'use_fp16': False,
+            'enable_mkldnn': False,
+            'cpu_num_threads': 1,
+            'use_tensorrt': False,
+            'ir_optim': False,
+            "gpu_mem": 8000,
+            'enable_profile': False,
+            "enable_benchmark": False
+        },
+        'PostProcess': {
+            'name': 'Topk',
+            'topk': 5,
+            'class_id_map_file': './utils/imagenet1k_label_list.txt'
+        }
+    }
\ No newline at end of file
diff --git a/deploy/hubserving/clas/test.py b/deploy/hubserving/clas/test.py
deleted file mode 100644
index 975260eeb..000000000
--- a/deploy/hubserving/clas/test.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# 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", ]
-
-module = hub.Module(name="clas_system")
-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))
diff --git a/deploy/hubserving/readme.md b/deploy/hubserving/readme.md
index 2045bbdc3..2a981a2aa 100644
--- a/deploy/hubserving/readme.md
+++ b/deploy/hubserving/readme.md
@@ -4,7 +4,7 @@
 
 hubserving服务部署配置服务包`clas`下包含3个必选文件，目录如下：
 ```
-deploy/hubserving/clas/
+hubserving/clas/
   └─  __init__.py    空文件，必选
   └─  config.json    配置文件，可选，使用配置启动服务时作为参数传入
   └─  module.py      主模块，必选，包含服务的完整逻辑
@@ -21,16 +21,16 @@ pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/s
 ### 2. 下载推理模型
 安装服务模块前，需要准备推理模型并放到正确路径，默认模型路径为：
 ```
-分类推理模型结构文件：./inference/cls_infer.pdmodel
-分类推理模型权重文件：./inference/cls_infer.pdiparams
+分类推理模型结构文件：PaddleClas/inference/inference.pdmodel
+分类推理模型权重文件：PaddleClas/inference/inference.pdiparams
 ```  
 
 **注意**：
-* 模型路径可在`./PaddleClas/deploy/hubserving/clas/params.py`中查看和修改。
+* 模型文件路径可在`PaddleClas/deploy/hubserving/clas/params.py`中查看和修改：
   ```python
-  cfg.model_file = "./inference/cls_infer.pdmodel"
-  cfg.params_file = "./inference/cls_infer.pdiparams"
+  "inference_model_dir": "../inference/"
   ```
+  需要注意，模型文件（包括.pdmodel与.pdiparams）名称必须为`inference`。
 * 我们也提供了大量基于ImageNet-1k数据集的预训练模型，模型列表及下载地址详见[模型库概览](../../docs/zh_CN/models/models_intro.md)，也可以使用自己训练转换好的模型。
 
 ### 3. 安装服务模块
@@ -38,14 +38,17 @@ pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/s
 
 * 在Linux环境下，安装示例如下：
 ```shell
-# 安装服务模块：  
-hub install deploy/hubserving/clas/
+cd PaddleClas/deploy
+# 安装服务模块：
+hub install hubserving/clas/
 ```
 
 * 在Windows环境下(文件夹的分隔符为`\`)，安装示例如下：
+
 ```shell
+cd PaddleClas\deploy
 # 安装服务模块：  
-hub install deploy\hubserving\clas\
+hub install hubserving\clas\
 ```
 
 ### 4. 启动服务
@@ -59,7 +62,6 @@ $ hub serving start --modules Module1==Version1 \
 ```  
 
 **参数：**  
-
 |参数|用途|  
 |-|-|  
 |--modules/-m| [**必选**] PaddleHub Serving预安装模型，以多个Module==Version键值对的形式列出<br>*`当不指定Version时，默认选择最新版本`*|  
@@ -108,30 +110,32 @@ $ hub serving start --modules Module1==Version1 \
 
 如，使用GPU 3号卡启动串联服务：  
 ```shell
+cd PaddleClas/deploy
 export CUDA_VISIBLE_DEVICES=3
-hub serving start -c deploy/hubserving/clas/config.json
+hub serving start -c hubserving/clas/config.json
 ```  
 
 ## 发送预测请求
 配置好服务端，可使用以下命令发送预测请求，获取预测结果:  
 
-```python tools/test_hubserving.py server_url image_path```  
+```shell
+cd PaddleClas/deploy
+python hubserving/test_hubserving.py server_url image_path
+```  
 
 需要给脚本传递2个必须参数：
 - **server_url**：服务地址，格式为  
 `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。
-- **top_k**：[**可选**] 返回前 `top_k` 个 `score` ，默认为 `1`。
 - **batch_size**：[**可选**] 以`batch_size`大小为单位进行预测，默认为`1`。
-- **resize_short**：[**可选**] 将图像等比例缩放到最短边为`resize_short`，默认为`256`。
-- **resize**：[**可选**] 将图像resize到`resize * resize`尺寸，默认为`224`。
-- **normalize**：[**可选**] 是否对图像进行normalize处理，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`。
 
 
 访问示例：  
-```python tools/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./deploy/hubserving/ILSVRC2012_val_00006666.JPEG --top_k 5```
+```shell
+python hubserving/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./hubserving/ILSVRC2012_val_00006666.JPEG --batch_size 8
+```
 
 ### 返回结果格式说明
 返回结果为列表（list），包含top-k个分类结果，以及对应的得分，还有此图片预测耗时，具体如下：
@@ -143,7 +147,7 @@ list: 返回结果
    └─ float: 该图分类耗时，单位秒
 ```
 
-**说明：** 如果需要增加、删除、修改返回字段，可在相应模块的`module.py`文件中进行修改，完整流程参考下一节自定义修改服务模块。
+**说明：** 如果需要增加、删除、修改返回字段，可对相应模块进行修改，完整流程参考下一节自定义修改服务模块。
 
 ## 自定义修改服务模块
 如果需要修改服务逻辑，你一般需要操作以下步骤：  
@@ -151,16 +155,30 @@ list: 返回结果
 - 1、 停止服务  
 ```hub serving stop --port/-p XXXX```  
 
-- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。  
-  例如，例如需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`cfg.model_file`和`cfg.params_file`。
-
-  修改并安装（`hub install deploy/hubserving/clas/`）完成后，在进行部署前，可通过`python deploy/hubserving/clas/test.py`测试已安装服务模块。
+- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。`module.py`修改后需要重新安装（`hub install hubserving/clas/`）并部署。在进行部署前，可通过`python hubserving/clas/module.py`测试已安装服务模块。
 
 - 3、 卸载旧服务包  
 ```hub uninstall clas_system```  
 
 - 4、 安装修改后的新服务包  
-```hub install deploy/hubserving/clas/```  
+```hub install hubserving/clas/```  
 
 - 5、重新启动服务  
 ```hub serving start -m clas_system```  
+
+**注意**：
+常用参数可在[params.py](./clas/params.py)中修改：
+  * 更换模型，需要修改模型文件路径参数:
+    ```python
+    "inference_model_dir":
+    ```
+  * 更改后处理时返回的`top-k`结果数量：
+    ```python
+    'topk':
+    ```
+  * 更改后处理时的lable与class id对应映射文件：
+    ```python
+    'class_id_map_file':
+    ```
+
+为了避免不必要的延时以及能够以batch_size进行预测，数据预处理逻辑（包括resize、crop等操作）在客户端完成，因此需要在[test_hubserving.py](./test_hubserving.py#L35-L52)中修改。
diff --git a/deploy/hubserving/test_hubserving.py b/deploy/hubserving/test_hubserving.py
index 41f1287c7..00f3db0e2 100644
--- a/deploy/hubserving/test_hubserving.py
+++ b/deploy/hubserving/test_hubserving.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,29 +15,54 @@
 import os
 import sys
 __dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__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 argparse
+
+import numpy as np
+import cv2
+
+from utils import logger
+from utils.get_image_list import get_image_list
+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 main(args):
     image_path_list = get_image_list(args.image_file)
     headers = {"Content-type": "application/json"}
+    preprocess_ops = create_operators(preprocess_config)
 
     cnt = 0
     predict_time = 0
     all_score = 0.0
     start_time = time.time()
 
-    batch_input_list = []
+    img_data_list = []
     img_name_list = []
     cnt = 0
     for idx, img_path in enumerate(image_path_list):
@@ -48,22 +73,23 @@ def main(args):
                 format(img_path))
             continue
         else:
-            img = img[:, :, ::-1]
-            data = preprocess(img, args)
-            batch_input_list.append(data)
+            for ops in preprocess_ops:
+                img = ops(img)
+            img = np.array(img)
+            img_data_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_path_list):
-            batch_input = np.array(batch_input_list)
-            b64str, revert_shape = np_to_b64(batch_input)
+            inputs = np.array(img_data_list)
+            b64str, revert_shape = np_to_b64(inputs)
             data = {
                 "images": b64str,
                 "revert_params": {
                     "shape": revert_shape,
-                    "dtype": str(batch_input.dtype)
-                },
-                "top_k": args.top_k
+                    "dtype": str(inputs.dtype)
+                }
             }
             try:
                 r = requests.post(
@@ -80,24 +106,25 @@ def main(args):
                 continue
             else:
                 results = r.json()["results"]
-                batch_result_list = results["prediction"]
+                preds = results["prediction"]
                 elapse = results["elapse"]
 
-                cnt += len(batch_result_list)
+                cnt += len(preds)
                 predict_time += elapse
 
-                for number, result_list in enumerate(batch_result_list):
+                for number, result_list in enumerate(preds):
                     all_score += result_list["scores"][0]
                     result_str = ""
-                    for i in range(len(result_list["clas_ids"])):
+                    for i in range(len(result_list["class_ids"])):
                         result_str += "{}: {:.2f}\t".format(
-                            result_list["clas_ids"][i],
+                            result_list["class_ids"][i],
                             result_list["scores"][i])
-                    logger.info("File:{}, The top-{} result(s): {}".format(
-                        img_name_list[number], args.top_k, result_str))
+
+                    logger.info("File:{}, The result(s): {}".format(
+                        img_name_list[number], result_str))
 
             finally:
-                batch_input_list = []
+                img_data_list = []
                 img_name_list = []
 
     total_time = time.time() - start_time
@@ -109,5 +136,10 @@ def main(args):
 
 
 if __name__ == '__main__':
-    args = parse_args()
+    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()
+
     main(args)
diff --git a/deploy/python/predict_cls.py b/deploy/python/predict_cls.py
index bc658b39c..19730801d 100644
--- a/deploy/python/predict_cls.py
+++ b/deploy/python/predict_cls.py
@@ -24,16 +24,22 @@ from utils import logger
 from utils import config
 from utils.predictor import Predictor
 from utils.get_image_list import get_image_list
-from preprocess import create_operators
-from postprocess import build_postprocess
+from python.preprocess import create_operators
+from python.postprocess import build_postprocess
 
 
 class ClsPredictor(Predictor):
     def __init__(self, config):
         super().__init__(config["Global"])
-        self.preprocess_ops = create_operators(config["PreProcess"][
-            "transform_ops"])
-        self.postprocess = build_postprocess(config["PostProcess"])
+
+        self.preprocess_ops = []
+        self.postprocess = None
+        if "PreProcess" in config:
+            if "transform_ops" in config["PreProcess"]:
+                self.preprocess_ops = create_operators(config["PreProcess"][
+                    "transform_ops"])
+        if "PostProcess" in config:
+            self.postprocess = build_postprocess(config["PostProcess"])
 
     def predict(self, images):
         input_names = self.paddle_predictor.get_input_names()
diff --git a/deploy/python/preprocess.py b/deploy/python/preprocess.py
index 2f21d63d2..b24b757a5 100644
--- a/deploy/python/preprocess.py
+++ b/deploy/python/preprocess.py
@@ -26,7 +26,7 @@ import cv2
 import numpy as np
 import importlib
 
-from det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
+from python.det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
 
 
 def create_operators(params):
diff --git a/deploy/utils/__init__.py b/deploy/utils/__init__.py
index e8de1257a..baf14a9ee 100644
--- a/deploy/utils/__init__.py
+++ b/deploy/utils/__init__.py
@@ -2,3 +2,4 @@ from . import logger
 from . import config
 from . import get_image_list
 from . import predictor
+from . import encode_decode
\ No newline at end of file
diff --git a/deploy/utils/config.py b/deploy/utils/config.py
index 33c704c8d..11141105b 100644
--- a/deploy/utils/config.py
+++ b/deploy/utils/config.py
@@ -1,4 +1,4 @@
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,7 +16,9 @@ import os
 import copy
 import argparse
 import yaml
+
 from utils import logger
+
 __all__ = ['get_config']
 
 
diff --git a/deploy/utils/encode_decode.py b/deploy/utils/encode_decode.py
new file mode 100644
index 000000000..d76a529f1
--- /dev/null
+++ b/deploy/utils/encode_decode.py
@@ -0,0 +1,31 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import base64
+
+import numpy as np
+
+
+def np_to_b64(images):
+    img_str = base64.b64encode(images).decode('utf8')
+    return img_str, images.shape
+
+
+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
\ No newline at end of file
diff --git a/deploy/utils/imagenet1k_label_list.txt b/deploy/utils/imagenet1k_label_list.txt
new file mode 100644
index 000000000..376e18021
--- /dev/null
+++ b/deploy/utils/imagenet1k_label_list.txt
@@ -0,0 +1,1000 @@
+0 tench, Tinca tinca
+1 goldfish, Carassius auratus
+2 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+3 tiger shark, Galeocerdo cuvieri
+4 hammerhead, hammerhead shark
+5 electric ray, crampfish, numbfish, torpedo
+6 stingray
+7 cock
+8 hen
+9 ostrich, Struthio camelus
+10 brambling, Fringilla montifringilla
+11 goldfinch, Carduelis carduelis
+12 house finch, linnet, Carpodacus mexicanus
+13 junco, snowbird
+14 indigo bunting, indigo finch, indigo bird, Passerina cyanea
+15 robin, American robin, Turdus migratorius
+16 bulbul
+17 jay
+18 magpie
+19 chickadee
+20 water ouzel, dipper
+21 kite
+22 bald eagle, American eagle, Haliaeetus leucocephalus
+23 vulture
+24 great grey owl, great gray owl, Strix nebulosa
+25 European fire salamander, Salamandra salamandra
+26 common newt, Triturus vulgaris
+27 eft
+28 spotted salamander, Ambystoma maculatum
+29 axolotl, mud puppy, Ambystoma mexicanum
+30 bullfrog, Rana catesbeiana
+31 tree frog, tree-frog
+32 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+33 loggerhead, loggerhead turtle, Caretta caretta
+34 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+35 mud turtle
+36 terrapin
+37 box turtle, box tortoise
+38 banded gecko
+39 common iguana, iguana, Iguana iguana
+40 American chameleon, anole, Anolis carolinensis
+41 whiptail, whiptail lizard
+42 agama
+43 frilled lizard, Chlamydosaurus kingi
+44 alligator lizard
+45 Gila monster, Heloderma suspectum
+46 green lizard, Lacerta viridis
+47 African chameleon, Chamaeleo chamaeleon
+48 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+49 African crocodile, Nile crocodile, Crocodylus niloticus
+50 American alligator, Alligator mississipiensis
+51 triceratops
+52 thunder snake, worm snake, Carphophis amoenus
+53 ringneck snake, ring-necked snake, ring snake
+54 hognose snake, puff adder, sand viper
+55 green snake, grass snake
+56 king snake, kingsnake
+57 garter snake, grass snake
+58 water snake
+59 vine snake
+60 night snake, Hypsiglena torquata
+61 boa constrictor, Constrictor constrictor
+62 rock python, rock snake, Python sebae
+63 Indian cobra, Naja naja
+64 green mamba
+65 sea snake
+66 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+67 diamondback, diamondback rattlesnake, Crotalus adamanteus
+68 sidewinder, horned rattlesnake, Crotalus cerastes
+69 trilobite
+70 harvestman, daddy longlegs, Phalangium opilio
+71 scorpion
+72 black and gold garden spider, Argiope aurantia
+73 barn spider, Araneus cavaticus
+74 garden spider, Aranea diademata
+75 black widow, Latrodectus mactans
+76 tarantula
+77 wolf spider, hunting spider
+78 tick
+79 centipede
+80 black grouse
+81 ptarmigan
+82 ruffed grouse, partridge, Bonasa umbellus
+83 prairie chicken, prairie grouse, prairie fowl
+84 peacock
+85 quail
+86 partridge
+87 African grey, African gray, Psittacus erithacus
+88 macaw
+89 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+90 lorikeet
+91 coucal
+92 bee eater
+93 hornbill
+94 hummingbird
+95 jacamar
+96 toucan
+97 drake
+98 red-breasted merganser, Mergus serrator
+99 goose
+100 black swan, Cygnus atratus
+101 tusker
+102 echidna, spiny anteater, anteater
+103 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+104 wallaby, brush kangaroo
+105 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+106 wombat
+107 jellyfish
+108 sea anemone, anemone
+109 brain coral
+110 flatworm, platyhelminth
+111 nematode, nematode worm, roundworm
+112 conch
+113 snail
+114 slug
+115 sea slug, nudibranch
+116 chiton, coat-of-mail shell, sea cradle, polyplacophore
+117 chambered nautilus, pearly nautilus, nautilus
+118 Dungeness crab, Cancer magister
+119 rock crab, Cancer irroratus
+120 fiddler crab
+121 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+122 American lobster, Northern lobster, Maine lobster, Homarus americanus
+123 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+124 crayfish, crawfish, crawdad, crawdaddy
+125 hermit crab
+126 isopod
+127 white stork, Ciconia ciconia
+128 black stork, Ciconia nigra
+129 spoonbill
+130 flamingo
+131 little blue heron, Egretta caerulea
+132 American egret, great white heron, Egretta albus
+133 bittern
+134 crane
+135 limpkin, Aramus pictus
+136 European gallinule, Porphyrio porphyrio
+137 American coot, marsh hen, mud hen, water hen, Fulica americana
+138 bustard
+139 ruddy turnstone, Arenaria interpres
+140 red-backed sandpiper, dunlin, Erolia alpina
+141 redshank, Tringa totanus
+142 dowitcher
+143 oystercatcher, oyster catcher
+144 pelican
+145 king penguin, Aptenodytes patagonica
+146 albatross, mollymawk
+147 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+148 killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+149 dugong, Dugong dugon
+150 sea lion
+151 Chihuahua
+152 Japanese spaniel
+153 Maltese dog, Maltese terrier, Maltese
+154 Pekinese, Pekingese, Peke
+155 Shih-Tzu
+156 Blenheim spaniel
+157 papillon
+158 toy terrier
+159 Rhodesian ridgeback
+160 Afghan hound, Afghan
+161 basset, basset hound
+162 beagle
+163 bloodhound, sleuthhound
+164 bluetick
+165 black-and-tan coonhound
+166 Walker hound, Walker foxhound
+167 English foxhound
+168 redbone
+169 borzoi, Russian wolfhound
+170 Irish wolfhound
+171 Italian greyhound
+172 whippet
+173 Ibizan hound, Ibizan Podenco
+174 Norwegian elkhound, elkhound
+175 otterhound, otter hound
+176 Saluki, gazelle hound
+177 Scottish deerhound, deerhound
+178 Weimaraner
+179 Staffordshire bullterrier, Staffordshire bull terrier
+180 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+181 Bedlington terrier
+182 Border terrier
+183 Kerry blue terrier
+184 Irish terrier
+185 Norfolk terrier
+186 Norwich terrier
+187 Yorkshire terrier
+188 wire-haired fox terrier
+189 Lakeland terrier
+190 Sealyham terrier, Sealyham
+191 Airedale, Airedale terrier
+192 cairn, cairn terrier
+193 Australian terrier
+194 Dandie Dinmont, Dandie Dinmont terrier
+195 Boston bull, Boston terrier
+196 miniature schnauzer
+197 giant schnauzer
+198 standard schnauzer
+199 Scotch terrier, Scottish terrier, Scottie
+200 Tibetan terrier, chrysanthemum dog
+201 silky terrier, Sydney silky
+202 soft-coated wheaten terrier
+203 West Highland white terrier
+204 Lhasa, Lhasa apso
+205 flat-coated retriever
+206 curly-coated retriever
+207 golden retriever
+208 Labrador retriever
+209 Chesapeake Bay retriever
+210 German short-haired pointer
+211 vizsla, Hungarian pointer
+212 English setter
+213 Irish setter, red setter
+214 Gordon setter
+215 Brittany spaniel
+216 clumber, clumber spaniel
+217 English springer, English springer spaniel
+218 Welsh springer spaniel
+219 cocker spaniel, English cocker spaniel, cocker
+220 Sussex spaniel
+221 Irish water spaniel
+222 kuvasz
+223 schipperke
+224 groenendael
+225 malinois
+226 briard
+227 kelpie
+228 komondor
+229 Old English sheepdog, bobtail
+230 Shetland sheepdog, Shetland sheep dog, Shetland
+231 collie
+232 Border collie
+233 Bouvier des Flandres, Bouviers des Flandres
+234 Rottweiler
+235 German shepherd, German shepherd dog, German police dog, alsatian
+236 Doberman, Doberman pinscher
+237 miniature pinscher
+238 Greater Swiss Mountain dog
+239 Bernese mountain dog
+240 Appenzeller
+241 EntleBucher
+242 boxer
+243 bull mastiff
+244 Tibetan mastiff
+245 French bulldog
+246 Great Dane
+247 Saint Bernard, St Bernard
+248 Eskimo dog, husky
+249 malamute, malemute, Alaskan malamute
+250 Siberian husky
+251 dalmatian, coach dog, carriage dog
+252 affenpinscher, monkey pinscher, monkey dog
+253 basenji
+254 pug, pug-dog
+255 Leonberg
+256 Newfoundland, Newfoundland dog
+257 Great Pyrenees
+258 Samoyed, Samoyede
+259 Pomeranian
+260 chow, chow chow
+261 keeshond
+262 Brabancon griffon
+263 Pembroke, Pembroke Welsh corgi
+264 Cardigan, Cardigan Welsh corgi
+265 toy poodle
+266 miniature poodle
+267 standard poodle
+268 Mexican hairless
+269 timber wolf, grey wolf, gray wolf, Canis lupus
+270 white wolf, Arctic wolf, Canis lupus tundrarum
+271 red wolf, maned wolf, Canis rufus, Canis niger
+272 coyote, prairie wolf, brush wolf, Canis latrans
+273 dingo, warrigal, warragal, Canis dingo
+274 dhole, Cuon alpinus
+275 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+276 hyena, hyaena
+277 red fox, Vulpes vulpes
+278 kit fox, Vulpes macrotis
+279 Arctic fox, white fox, Alopex lagopus
+280 grey fox, gray fox, Urocyon cinereoargenteus
+281 tabby, tabby cat
+282 tiger cat
+283 Persian cat
+284 Siamese cat, Siamese
+285 Egyptian cat
+286 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+287 lynx, catamount
+288 leopard, Panthera pardus
+289 snow leopard, ounce, Panthera uncia
+290 jaguar, panther, Panthera onca, Felis onca
+291 lion, king of beasts, Panthera leo
+292 tiger, Panthera tigris
+293 cheetah, chetah, Acinonyx jubatus
+294 brown bear, bruin, Ursus arctos
+295 American black bear, black bear, Ursus americanus, Euarctos americanus
+296 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+297 sloth bear, Melursus ursinus, Ursus ursinus
+298 mongoose
+299 meerkat, mierkat
+300 tiger beetle
+301 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+302 ground beetle, carabid beetle
+303 long-horned beetle, longicorn, longicorn beetle
+304 leaf beetle, chrysomelid
+305 dung beetle
+306 rhinoceros beetle
+307 weevil
+308 fly
+309 bee
+310 ant, emmet, pismire
+311 grasshopper, hopper
+312 cricket
+313 walking stick, walkingstick, stick insect
+314 cockroach, roach
+315 mantis, mantid
+316 cicada, cicala
+317 leafhopper
+318 lacewing, lacewing fly
+319 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+320 damselfly
+321 admiral
+322 ringlet, ringlet butterfly
+323 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+324 cabbage butterfly
+325 sulphur butterfly, sulfur butterfly
+326 lycaenid, lycaenid butterfly
+327 starfish, sea star
+328 sea urchin
+329 sea cucumber, holothurian
+330 wood rabbit, cottontail, cottontail rabbit
+331 hare
+332 Angora, Angora rabbit
+333 hamster
+334 porcupine, hedgehog
+335 fox squirrel, eastern fox squirrel, Sciurus niger
+336 marmot
+337 beaver
+338 guinea pig, Cavia cobaya
+339 sorrel
+340 zebra
+341 hog, pig, grunter, squealer, Sus scrofa
+342 wild boar, boar, Sus scrofa
+343 warthog
+344 hippopotamus, hippo, river horse, Hippopotamus amphibius
+345 ox
+346 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+347 bison
+348 ram, tup
+349 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+350 ibex, Capra ibex
+351 hartebeest
+352 impala, Aepyceros melampus
+353 gazelle
+354 Arabian camel, dromedary, Camelus dromedarius
+355 llama
+356 weasel
+357 mink
+358 polecat, fitch, foulmart, foumart, Mustela putorius
+359 black-footed ferret, ferret, Mustela nigripes
+360 otter
+361 skunk, polecat, wood pussy
+362 badger
+363 armadillo
+364 three-toed sloth, ai, Bradypus tridactylus
+365 orangutan, orang, orangutang, Pongo pygmaeus
+366 gorilla, Gorilla gorilla
+367 chimpanzee, chimp, Pan troglodytes
+368 gibbon, Hylobates lar
+369 siamang, Hylobates syndactylus, Symphalangus syndactylus
+370 guenon, guenon monkey
+371 patas, hussar monkey, Erythrocebus patas
+372 baboon
+373 macaque
+374 langur
+375 colobus, colobus monkey
+376 proboscis monkey, Nasalis larvatus
+377 marmoset
+378 capuchin, ringtail, Cebus capucinus
+379 howler monkey, howler
+380 titi, titi monkey
+381 spider monkey, Ateles geoffroyi
+382 squirrel monkey, Saimiri sciureus
+383 Madagascar cat, ring-tailed lemur, Lemur catta
+384 indri, indris, Indri indri, Indri brevicaudatus
+385 Indian elephant, Elephas maximus
+386 African elephant, Loxodonta africana
+387 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+388 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+389 barracouta, snoek
+390 eel
+391 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+392 rock beauty, Holocanthus tricolor
+393 anemone fish
+394 sturgeon
+395 gar, garfish, garpike, billfish, Lepisosteus osseus
+396 lionfish
+397 puffer, pufferfish, blowfish, globefish
+398 abacus
+399 abaya
+400 academic gown, academic robe, judge's robe
+401 accordion, piano accordion, squeeze box
+402 acoustic guitar
+403 aircraft carrier, carrier, flattop, attack aircraft carrier
+404 airliner
+405 airship, dirigible
+406 altar
+407 ambulance
+408 amphibian, amphibious vehicle
+409 analog clock
+410 apiary, bee house
+411 apron
+412 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+413 assault rifle, assault gun
+414 backpack, back pack, knapsack, packsack, rucksack, haversack
+415 bakery, bakeshop, bakehouse
+416 balance beam, beam
+417 balloon
+418 ballpoint, ballpoint pen, ballpen, Biro
+419 Band Aid
+420 banjo
+421 bannister, banister, balustrade, balusters, handrail
+422 barbell
+423 barber chair
+424 barbershop
+425 barn
+426 barometer
+427 barrel, cask
+428 barrow, garden cart, lawn cart, wheelbarrow
+429 baseball
+430 basketball
+431 bassinet
+432 bassoon
+433 bathing cap, swimming cap
+434 bath towel
+435 bathtub, bathing tub, bath, tub
+436 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+437 beacon, lighthouse, beacon light, pharos
+438 beaker
+439 bearskin, busby, shako
+440 beer bottle
+441 beer glass
+442 bell cote, bell cot
+443 bib
+444 bicycle-built-for-two, tandem bicycle, tandem
+445 bikini, two-piece
+446 binder, ring-binder
+447 binoculars, field glasses, opera glasses
+448 birdhouse
+449 boathouse
+450 bobsled, bobsleigh, bob
+451 bolo tie, bolo, bola tie, bola
+452 bonnet, poke bonnet
+453 bookcase
+454 bookshop, bookstore, bookstall
+455 bottlecap
+456 bow
+457 bow tie, bow-tie, bowtie
+458 brass, memorial tablet, plaque
+459 brassiere, bra, bandeau
+460 breakwater, groin, groyne, mole, bulwark, seawall, jetty
+461 breastplate, aegis, egis
+462 broom
+463 bucket, pail
+464 buckle
+465 bulletproof vest
+466 bullet train, bullet
+467 butcher shop, meat market
+468 cab, hack, taxi, taxicab
+469 caldron, cauldron
+470 candle, taper, wax light
+471 cannon
+472 canoe
+473 can opener, tin opener
+474 cardigan
+475 car mirror
+476 carousel, carrousel, merry-go-round, roundabout, whirligig
+477 carpenter's kit, tool kit
+478 carton
+479 car wheel
+480 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+481 cassette
+482 cassette player
+483 castle
+484 catamaran
+485 CD player
+486 cello, violoncello
+487 cellular telephone, cellular phone, cellphone, cell, mobile phone
+488 chain
+489 chainlink fence
+490 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+491 chain saw, chainsaw
+492 chest
+493 chiffonier, commode
+494 chime, bell, gong
+495 china cabinet, china closet
+496 Christmas stocking
+497 church, church building
+498 cinema, movie theater, movie theatre, movie house, picture palace
+499 cleaver, meat cleaver, chopper
+500 cliff dwelling
+501 cloak
+502 clog, geta, patten, sabot
+503 cocktail shaker
+504 coffee mug
+505 coffeepot
+506 coil, spiral, volute, whorl, helix
+507 combination lock
+508 computer keyboard, keypad
+509 confectionery, confectionary, candy store
+510 container ship, containership, container vessel
+511 convertible
+512 corkscrew, bottle screw
+513 cornet, horn, trumpet, trump
+514 cowboy boot
+515 cowboy hat, ten-gallon hat
+516 cradle
+517 crane
+518 crash helmet
+519 crate
+520 crib, cot
+521 Crock Pot
+522 croquet ball
+523 crutch
+524 cuirass
+525 dam, dike, dyke
+526 desk
+527 desktop computer
+528 dial telephone, dial phone
+529 diaper, nappy, napkin
+530 digital clock
+531 digital watch
+532 dining table, board
+533 dishrag, dishcloth
+534 dishwasher, dish washer, dishwashing machine
+535 disk brake, disc brake
+536 dock, dockage, docking facility
+537 dogsled, dog sled, dog sleigh
+538 dome
+539 doormat, welcome mat
+540 drilling platform, offshore rig
+541 drum, membranophone, tympan
+542 drumstick
+543 dumbbell
+544 Dutch oven
+545 electric fan, blower
+546 electric guitar
+547 electric locomotive
+548 entertainment center
+549 envelope
+550 espresso maker
+551 face powder
+552 feather boa, boa
+553 file, file cabinet, filing cabinet
+554 fireboat
+555 fire engine, fire truck
+556 fire screen, fireguard
+557 flagpole, flagstaff
+558 flute, transverse flute
+559 folding chair
+560 football helmet
+561 forklift
+562 fountain
+563 fountain pen
+564 four-poster
+565 freight car
+566 French horn, horn
+567 frying pan, frypan, skillet
+568 fur coat
+569 garbage truck, dustcart
+570 gasmask, respirator, gas helmet
+571 gas pump, gasoline pump, petrol pump, island dispenser
+572 goblet
+573 go-kart
+574 golf ball
+575 golfcart, golf cart
+576 gondola
+577 gong, tam-tam
+578 gown
+579 grand piano, grand
+580 greenhouse, nursery, glasshouse
+581 grille, radiator grille
+582 grocery store, grocery, food market, market
+583 guillotine
+584 hair slide
+585 hair spray
+586 half track
+587 hammer
+588 hamper
+589 hand blower, blow dryer, blow drier, hair dryer, hair drier
+590 hand-held computer, hand-held microcomputer
+591 handkerchief, hankie, hanky, hankey
+592 hard disc, hard disk, fixed disk
+593 harmonica, mouth organ, harp, mouth harp
+594 harp
+595 harvester, reaper
+596 hatchet
+597 holster
+598 home theater, home theatre
+599 honeycomb
+600 hook, claw
+601 hoopskirt, crinoline
+602 horizontal bar, high bar
+603 horse cart, horse-cart
+604 hourglass
+605 iPod
+606 iron, smoothing iron
+607 jack-o'-lantern
+608 jean, blue jean, denim
+609 jeep, landrover
+610 jersey, T-shirt, tee shirt
+611 jigsaw puzzle
+612 jinrikisha, ricksha, rickshaw
+613 joystick
+614 kimono
+615 knee pad
+616 knot
+617 lab coat, laboratory coat
+618 ladle
+619 lampshade, lamp shade
+620 laptop, laptop computer
+621 lawn mower, mower
+622 lens cap, lens cover
+623 letter opener, paper knife, paperknife
+624 library
+625 lifeboat
+626 lighter, light, igniter, ignitor
+627 limousine, limo
+628 liner, ocean liner
+629 lipstick, lip rouge
+630 Loafer
+631 lotion
+632 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+633 loupe, jeweler's loupe
+634 lumbermill, sawmill
+635 magnetic compass
+636 mailbag, postbag
+637 mailbox, letter box
+638 maillot
+639 maillot, tank suit
+640 manhole cover
+641 maraca
+642 marimba, xylophone
+643 mask
+644 matchstick
+645 maypole
+646 maze, labyrinth
+647 measuring cup
+648 medicine chest, medicine cabinet
+649 megalith, megalithic structure
+650 microphone, mike
+651 microwave, microwave oven
+652 military uniform
+653 milk can
+654 minibus
+655 miniskirt, mini
+656 minivan
+657 missile
+658 mitten
+659 mixing bowl
+660 mobile home, manufactured home
+661 Model T
+662 modem
+663 monastery
+664 monitor
+665 moped
+666 mortar
+667 mortarboard
+668 mosque
+669 mosquito net
+670 motor scooter, scooter
+671 mountain bike, all-terrain bike, off-roader
+672 mountain tent
+673 mouse, computer mouse
+674 mousetrap
+675 moving van
+676 muzzle
+677 nail
+678 neck brace
+679 necklace
+680 nipple
+681 notebook, notebook computer
+682 obelisk
+683 oboe, hautboy, hautbois
+684 ocarina, sweet potato
+685 odometer, hodometer, mileometer, milometer
+686 oil filter
+687 organ, pipe organ
+688 oscilloscope, scope, cathode-ray oscilloscope, CRO
+689 overskirt
+690 oxcart
+691 oxygen mask
+692 packet
+693 paddle, boat paddle
+694 paddlewheel, paddle wheel
+695 padlock
+696 paintbrush
+697 pajama, pyjama, pj's, jammies
+698 palace
+699 panpipe, pandean pipe, syrinx
+700 paper towel
+701 parachute, chute
+702 parallel bars, bars
+703 park bench
+704 parking meter
+705 passenger car, coach, carriage
+706 patio, terrace
+707 pay-phone, pay-station
+708 pedestal, plinth, footstall
+709 pencil box, pencil case
+710 pencil sharpener
+711 perfume, essence
+712 Petri dish
+713 photocopier
+714 pick, plectrum, plectron
+715 pickelhaube
+716 picket fence, paling
+717 pickup, pickup truck
+718 pier
+719 piggy bank, penny bank
+720 pill bottle
+721 pillow
+722 ping-pong ball
+723 pinwheel
+724 pirate, pirate ship
+725 pitcher, ewer
+726 plane, carpenter's plane, woodworking plane
+727 planetarium
+728 plastic bag
+729 plate rack
+730 plow, plough
+731 plunger, plumber's helper
+732 Polaroid camera, Polaroid Land camera
+733 pole
+734 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+735 poncho
+736 pool table, billiard table, snooker table
+737 pop bottle, soda bottle
+738 pot, flowerpot
+739 potter's wheel
+740 power drill
+741 prayer rug, prayer mat
+742 printer
+743 prison, prison house
+744 projectile, missile
+745 projector
+746 puck, hockey puck
+747 punching bag, punch bag, punching ball, punchball
+748 purse
+749 quill, quill pen
+750 quilt, comforter, comfort, puff
+751 racer, race car, racing car
+752 racket, racquet
+753 radiator
+754 radio, wireless
+755 radio telescope, radio reflector
+756 rain barrel
+757 recreational vehicle, RV, R.V.
+758 reel
+759 reflex camera
+760 refrigerator, icebox
+761 remote control, remote
+762 restaurant, eating house, eating place, eatery
+763 revolver, six-gun, six-shooter
+764 rifle
+765 rocking chair, rocker
+766 rotisserie
+767 rubber eraser, rubber, pencil eraser
+768 rugby ball
+769 rule, ruler
+770 running shoe
+771 safe
+772 safety pin
+773 saltshaker, salt shaker
+774 sandal
+775 sarong
+776 sax, saxophone
+777 scabbard
+778 scale, weighing machine
+779 school bus
+780 schooner
+781 scoreboard
+782 screen, CRT screen
+783 screw
+784 screwdriver
+785 seat belt, seatbelt
+786 sewing machine
+787 shield, buckler
+788 shoe shop, shoe-shop, shoe store
+789 shoji
+790 shopping basket
+791 shopping cart
+792 shovel
+793 shower cap
+794 shower curtain
+795 ski
+796 ski mask
+797 sleeping bag
+798 slide rule, slipstick
+799 sliding door
+800 slot, one-armed bandit
+801 snorkel
+802 snowmobile
+803 snowplow, snowplough
+804 soap dispenser
+805 soccer ball
+806 sock
+807 solar dish, solar collector, solar furnace
+808 sombrero
+809 soup bowl
+810 space bar
+811 space heater
+812 space shuttle
+813 spatula
+814 speedboat
+815 spider web, spider's web
+816 spindle
+817 sports car, sport car
+818 spotlight, spot
+819 stage
+820 steam locomotive
+821 steel arch bridge
+822 steel drum
+823 stethoscope
+824 stole
+825 stone wall
+826 stopwatch, stop watch
+827 stove
+828 strainer
+829 streetcar, tram, tramcar, trolley, trolley car
+830 stretcher
+831 studio couch, day bed
+832 stupa, tope
+833 submarine, pigboat, sub, U-boat
+834 suit, suit of clothes
+835 sundial
+836 sunglass
+837 sunglasses, dark glasses, shades
+838 sunscreen, sunblock, sun blocker
+839 suspension bridge
+840 swab, swob, mop
+841 sweatshirt
+842 swimming trunks, bathing trunks
+843 swing
+844 switch, electric switch, electrical switch
+845 syringe
+846 table lamp
+847 tank, army tank, armored combat vehicle, armoured combat vehicle
+848 tape player
+849 teapot
+850 teddy, teddy bear
+851 television, television system
+852 tennis ball
+853 thatch, thatched roof
+854 theater curtain, theatre curtain
+855 thimble
+856 thresher, thrasher, threshing machine
+857 throne
+858 tile roof
+859 toaster
+860 tobacco shop, tobacconist shop, tobacconist
+861 toilet seat
+862 torch
+863 totem pole
+864 tow truck, tow car, wrecker
+865 toyshop
+866 tractor
+867 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+868 tray
+869 trench coat
+870 tricycle, trike, velocipede
+871 trimaran
+872 tripod
+873 triumphal arch
+874 trolleybus, trolley coach, trackless trolley
+875 trombone
+876 tub, vat
+877 turnstile
+878 typewriter keyboard
+879 umbrella
+880 unicycle, monocycle
+881 upright, upright piano
+882 vacuum, vacuum cleaner
+883 vase
+884 vault
+885 velvet
+886 vending machine
+887 vestment
+888 viaduct
+889 violin, fiddle
+890 volleyball
+891 waffle iron
+892 wall clock
+893 wallet, billfold, notecase, pocketbook
+894 wardrobe, closet, press
+895 warplane, military plane
+896 washbasin, handbasin, washbowl, lavabo, wash-hand basin
+897 washer, automatic washer, washing machine
+898 water bottle
+899 water jug
+900 water tower
+901 whiskey jug
+902 whistle
+903 wig
+904 window screen
+905 window shade
+906 Windsor tie
+907 wine bottle
+908 wing
+909 wok
+910 wooden spoon
+911 wool, woolen, woollen
+912 worm fence, snake fence, snake-rail fence, Virginia fence
+913 wreck
+914 yawl
+915 yurt
+916 web site, website, internet site, site
+917 comic book
+918 crossword puzzle, crossword
+919 street sign
+920 traffic light, traffic signal, stoplight
+921 book jacket, dust cover, dust jacket, dust wrapper
+922 menu
+923 plate
+924 guacamole
+925 consomme
+926 hot pot, hotpot
+927 trifle
+928 ice cream, icecream
+929 ice lolly, lolly, lollipop, popsicle
+930 French loaf
+931 bagel, beigel
+932 pretzel
+933 cheeseburger
+934 hotdog, hot dog, red hot
+935 mashed potato
+936 head cabbage
+937 broccoli
+938 cauliflower
+939 zucchini, courgette
+940 spaghetti squash
+941 acorn squash
+942 butternut squash
+943 cucumber, cuke
+944 artichoke, globe artichoke
+945 bell pepper
+946 cardoon
+947 mushroom
+948 Granny Smith
+949 strawberry
+950 orange
+951 lemon
+952 fig
+953 pineapple, ananas
+954 banana
+955 jackfruit, jak, jack
+956 custard apple
+957 pomegranate
+958 hay
+959 carbonara
+960 chocolate sauce, chocolate syrup
+961 dough
+962 meat loaf, meatloaf
+963 pizza, pizza pie
+964 potpie
+965 burrito
+966 red wine
+967 espresso
+968 cup
+969 eggnog
+970 alp
+971 bubble
+972 cliff, drop, drop-off
+973 coral reef
+974 geyser
+975 lakeside, lakeshore
+976 promontory, headland, head, foreland
+977 sandbar, sand bar
+978 seashore, coast, seacoast, sea-coast
+979 valley, vale
+980 volcano
+981 ballplayer, baseball player
+982 groom, bridegroom
+983 scuba diver
+984 rapeseed
+985 daisy
+986 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+987 corn
+988 acorn
+989 hip, rose hip, rosehip
+990 buckeye, horse chestnut, conker
+991 coral fungus
+992 agaric
+993 gyromitra
+994 stinkhorn, carrion fungus
+995 earthstar
+996 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+997 bolete
+998 ear, spike, capitulum
+999 toilet tissue, toilet paper, bathroom tissue