add imagenet demo

python/examples/imagenet/image_classifcation_service.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.
+
+from paddle_serving_server.plugin_service import PluginService
+import sys
+import cv2
+import base64
+from PIL import Image
+from StringIO import StringIO
+import numpy as np
+from image_server import start_serving
+
+
+class ImageService(PluginService):
+    def set_param(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
+
+    def preprocess(self, feed={}, fetch=[]):
+        self.set_param()
+        if "image" not in feed:
+            raise ("feed data error!")
+        sample = base64.b64decode(feed["image"])
+        img = self.process_image(sample)
+        res_feed = {}
+        res_feed["image"] = img.reshape(-1)
+        return res_feed, fetch
+
+
+image_service = ImageService(name="image", model=sys.argv[1], port=9291)
+image_service.start_service()

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 sys
+import cv2
+import json
+import numpy as np
+
+image = open("./to_longteng/n01440764/n01440764_12362.JPEG").read()
+image = base64.b64encode(image)
+
+req = {}
+req["image"] = image
+req["fetch"] = ["score"]
+
+req = json.dumps(req)
+url = "http://127.0.0.1:9291/image/prediction"
+headers = {"Content-Type": "application/json"}
+r = requests.post(url, data=req, headers=headers)
+score = r.json()["score"]
+score = np.array(score)
+print("max score : {} class {}".format(np.max(score), np.argmax(score)))

python/examples/imagenet/image_server.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 os
+import sys
+from paddle_serving_server import OpMaker
+from paddle_serving_server import OpSeqMaker
+from paddle_serving_server import Server
+
+
+def start_serving():
+    op_maker = OpMaker()
+
+    read_op = op_maker.create('general_reader')
+    general_infer_op = op_maker.create('general_infer')
+    general_response_op = op_maker.create('general_response')
+
+    op_seq_maker = OpSeqMaker()
+    op_seq_maker.add_op(read_op)
+    op_seq_maker.add_op(general_infer_op)
+    op_seq_maker.add_op(general_response_op)
+
+    server = Server()
+    server.set_op_sequence(op_seq_maker.get_op_sequence())
+    server.set_num_threads(24)
+
+    server.load_model_config(sys.argv[1])
+    port = int(sys.argv[2])
+    server.prepare_server(workdir="work_dir1", port=port, device="cpu")
+    server.run_server()
+
+
+if __name__ == "__main__":
+    start_serving()