Merge pull request #569 from guru4elephant/imagenet_web_service_example

update readme and image_rpc_client

python/examples/imagenet/README.md

@@ -15,34 +15,35 @@ sh get_model.sh
 pip install paddle_serving_app
 ```
 
-### HTTP Infer
+### HTTP Service
 
 launch server side
 ```
-python image_classification_service.py ResNet50_vd_model workdir 9393 #cpu inference service
+python resnet50_web_service.py ResNet50_vd_model cpu 9696 #cpu inference service
 ```
 ```
-python image_classification_service_gpu.py ResNet50_vd_model workdir 9393 #gpu inference service
+python resnet50_web_service.py ResNet50_vd_model gpu 9696 #gpu inference service
 ```
 
 
 client send inference request
 ```
-python image_http_client.py
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9696/image/prediction
 ```
-### RPC Infer
+
+### RPC Service
 
 launch server side
 ```
-python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9393 #cpu inference service
+python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu inference service
 ```
 
 ```
-python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9393 --gpu_ids 0 #gpu inference service
+python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu inference service
 ```
 
 client send inference request
 ```
 python image_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
 ```
-*the port of server side in this example is 9393, the sample data used by client side is in the folder ./data. These parameter can be modified in practice*
+*the port of server side in this example is 9696

python/examples/imagenet/README_CN.md

@@ -15,34 +15,35 @@ sh get_model.sh
 pip install paddle_serving_app
 ```
 
-### 执行HTTP预测服务
+### HTTP服务
 
 启动server端
 ```
-python image_classification_service.py ResNet50_vd_model workdir 9393 #cpu预测服务
+python image_classification_service.py ResNet50_vd_model cpu 9696 #cpu预测服务
 ```
 ```
-python image_classification_service_gpu.py ResNet50_vd_model workdir 9393 #gpu预测服务
+python image_classification_service.py ResNet50_vd_model gpu 9696 #gpu预测服务
 ```
 
 
-client端进行预测
+发送HTTP POST请求
 ```
-python image_http_client.py
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9696/image/prediction
 ```
-### 执行RPC预测服务
+
+### RPC服务
 
 启动server端
 ```
-python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9393 #cpu预测服务
+python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu预测服务
 ```
 
 ```
-python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9393 --gpu_ids 0 #gpu预测服务
+python -m paddle_serving_server_gpu.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu预测服务
 ```
 
 client端进行预测
 ```
 python image_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
 ```
-*server端示例中服务端口为9393端口，client端示例中数据来自./data文件夹，server端地址为本地9393端口，可根据实际情况更改脚本。*
+*server端示例中服务端口为9696端口

python/examples/imagenet/image_classification_service_gpu.py

-# 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 sys
-import cv2
-import base64
-import numpy as np
-from paddle_serving_app import ImageReader
-from paddle_serving_server_gpu.web_service import WebService
-
-
-class ImageService(WebService):
-    def preprocess(self, feed={}, fetch=[]):
-        reader = ImageReader()
-        feed_batch = []
-        for ins in feed:
-            if "image" not in ins:
-                raise ("feed data error!")
-            sample = base64.b64decode(ins["image"])
-            img = reader.process_image(sample)
-            feed_batch.append({"image": img})
-        return feed_batch, fetch
-
-
-image_service = ImageService(name="image")
-image_service.load_model_config(sys.argv[1])
-image_service.set_gpus("0,1")
-image_service.prepare_server(
-    workdir=sys.argv[2], port=int(sys.argv[3]), device="gpu")
-image_service.run_server()
-image_service.run_flask()

python/examples/imagenet/image_http_client.py

-# 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 requests
-import base64
-import json
-import time
-import os
-import sys
-
-py_version = sys.version_info[0]
-
-
-def predict(image_path, server):
-    if py_version == 2:
-        image = base64.b64encode(open(image_path).read())
-    else:
-        image = base64.b64encode(open(image_path, "rb").read()).decode("utf-8")
-    req = json.dumps({"feed": [{"image": image}], "fetch": ["score"]})
-    r = requests.post(
-        server, data=req, headers={"Content-Type": "application/json"})
-    try:
-        print(r.json()["result"]["score"])
-    except ValueError:
-        print(r.text)
-    return r
-
-
-if __name__ == "__main__":
-    server = "http://127.0.0.1:9393/image/prediction"
-    image_list = os.listdir("./image_data/n01440764/")
-    start = time.time()
-    for img in image_list:
-        image_file = "./image_data/n01440764/" + img
-        res = predict(image_file, server)
-    end = time.time()
-    print(end - start)

python/examples/imagenet/image_reader.py

-# 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 cv2
-import numpy as np
-
-
-class ImageReader():
-    def __init__(self):
-        self.image_mean = [0.485, 0.456, 0.406]
-        self.image_std = [0.229, 0.224, 0.225]
-        self.image_shape = [3, 224, 224]
-        self.resize_short_size = 256
-        self.interpolation = None
-
-    def resize_short(self, img, target_size, interpolation=None):
-        """resize image
-
-        Args:
-            img: image data
-            target_size: resize short target size
-            interpolation: interpolation mode
-
-        Returns:
-            resized image data
-        """
-        percent = float(target_size) / min(img.shape[0], img.shape[1])
-        resized_width = int(round(img.shape[1] * percent))
-        resized_height = int(round(img.shape[0] * percent))
-        if interpolation:
-            resized = cv2.resize(
-                img, (resized_width, resized_height),
-                interpolation=interpolation)
-        else:
-            resized = cv2.resize(img, (resized_width, resized_height))
-        return resized
-
-    def crop_image(self, img, target_size, center):
-        """crop image
-
-        Args:
-            img: images data
-            target_size: crop target size
-            center: crop mode
-
-        Returns:
-            img: cropped image data
-        """
-        height, width = img.shape[:2]
-        size = target_size
-        if center == True:
-            w_start = (width - size) // 2
-            h_start = (height - size) // 2
-        else:
-            w_start = np.random.randint(0, width - size + 1)
-            h_start = np.random.randint(0, height - size + 1)
-        w_end = w_start + size
-        h_end = h_start + size
-        img = img[h_start:h_end, w_start:w_end, :]
-        return img
-
-    def process_image(self, sample):
-        """ process_image """
-        mean = self.image_mean
-        std = self.image_std
-        crop_size = self.image_shape[1]
-
-        data = np.fromstring(sample, np.uint8)
-        img = cv2.imdecode(data, cv2.IMREAD_COLOR)
-
-        if img is None:
-            print("img is None, pass it.")
-            return None
-
-        if crop_size > 0:
-            target_size = self.resize_short_size
-            img = self.resize_short(
-                img, target_size, interpolation=self.interpolation)
-            img = self.crop_image(img, target_size=crop_size, center=True)
-
-        img = img[:, :, ::-1]
-
-        img = img.astype('float32').transpose((2, 0, 1)) / 255
-        img_mean = np.array(mean).reshape((3, 1, 1))
-        img_std = np.array(std).reshape((3, 1, 1))
-        img -= img_mean
-        img /= img_std
-        return img

python/examples/imagenet/image_rpc_client.py → python/examples/imagenet/resnet50_rpc_client.py

@@ -14,23 +14,35 @@
 
 import sys
 from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize
+from paddle_serving_app.reader import Sequential, URL2Image, Resize
+from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
 import time
 
 client = Client()
 client.load_client_config(sys.argv[1])
-client.connect(["127.0.0.1:9393"])
+client.connect(["127.0.0.1:9696"])
+
+label_dict = {}
+label_idx = 0
+with open("imagenet.label") as fin:
+    for line in fin:
+        label_dict[label_idx] = line.strip()
+        label_idx += 1
 
 seq = Sequential([
-    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
-    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
 ])
-print(seq)
 
 start = time.time()
-image_file = "daisy.jpg"
-for i in range(1000):
+image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
+for i in range(10):
     img = seq(image_file)
     fetch_map = client.predict(feed={"image": img}, fetch=["score"])
+    prob = max(fetch_map["score"][0])
+    label = label_dict[fetch_map["score"][0].tolist().index(prob)].strip(
+    ).replace(",", "")
+    print("prediction: {}, probability: {}".format(label, prob))
+
 end = time.time()
 print(end - start)

python/examples/imagenet/image_classification_service.py → python/examples/imagenet/resnet50_web_service.py

@@ -11,31 +11,62 @@
 # 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.
-
-from paddle_serving_server.web_service import WebService
 import sys
-import cv2
-import base64
-import numpy as np
-from paddle_serving_app import ImageReader
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize
+
+if len(sys.argv) != 4:
+    print("python resnet50_web_service.py model device port")
+    sys.exit(-1)
+
+device = sys.argv[2]
+
+if device == "cpu":
+    from paddle_serving_server.web_service import WebService
+else:
+    from paddle_serving_server_gpu.web_service import WebService
 
 
 class ImageService(WebService):
-    def preprocess(self, feed={}, fetch=[]):
-        reader = ImageReader()
+    def init_imagenet_setting(self):
+        self.seq = Sequential([
+            URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose(
+                (2, 0, 1)), Div(255), Normalize([0.485, 0.456, 0.406],
+                                                [0.229, 0.224, 0.225], True)
+        ])
+        self.label_dict = {}
+        label_idx = 0
+        with open("imagenet.label") as fin:
+            for line in fin:
+                self.label_dict[label_idx] = line.strip()
+                label_idx += 1
+
+    def preprocess(self, feed=[], fetch=[]):
         feed_batch = []
         for ins in feed:
             if "image" not in ins:
                 raise ("feed data error!")
-            sample = base64.b64decode(ins["image"])
-            img = reader.process_image(sample)
+            img = self.seq(ins["image"])
             feed_batch.append({"image": img})
         return feed_batch, fetch
 
+    def postprocess(self, feed=[], fetch=[], fetch_map={}):
+        score_list = fetch_map["score"]
+        result = {"label": [], "prob": []}
+        for score in score_list:
+            max_score = max(score)
+            result["label"].append(self.label_dict[score.index(max_score)]
+                                   .strip().replace(",", ""))
+            result["prob"].append(max_score)
+        return result
+
 
 image_service = ImageService(name="image")
 image_service.load_model_config(sys.argv[1])
+image_service.init_imagenet_setting()
+if device == "gpu":
+    image_service.set_gpus("0,1")
 image_service.prepare_server(
-    workdir=sys.argv[2], port=int(sys.argv[3]), device="cpu")
+    workdir="workdir", port=int(sys.argv[3]), device=device)
 image_service.run_server()
 image_service.run_flask()