remove old web service and reader file

python/examples/imagenet/image_classification_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.web_service import WebService
-import sys
-import cv2
-import base64
-import numpy as np
-from paddle_serving_app import ImageReader
-
-
-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.prepare_server(
-    workdir=sys.argv[2], port=int(sys.argv[3]), device="cpu")
-image_service.run_server()
-image_service.run_flask()

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_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