fix(encoder): add inception tf

gnes/encoder/image/inception.py

@@ -0,0 +1,71 @@
+#  Tencent is pleased to support the open source community by making GNES available.
+#
+#  Copyright (C) 2019 THL A29 Limited, a Tencent company. 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
+from typing import List, Callable
+import numpy as np
+from gnes.helper import batch_iterator
+from ..base import BaseImageEncoder
+from PIL import Image
+
+
+class TFInceptionEncoder(BaseImageEncoder):
+
+    def __init__(self, model_dir: str,
+                 batch_size: int = 64,
+                 select_layer: str = 'PreLogitsFlatten',
+                 use_gpu: bool = True,
+                 *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.model_dir = model_dir
+        self.batch_size = batch_size
+        self.select_layer = select_layer
+        self.use_gpu = use_gpu
+        self.inception_size_x = 299
+        self.inception_size_y = 299
+
+    def post_init(self):
+        import tensorflow as tf
+        from gnes.encoder.image.inception_cores.inception_v4 import inception_v4
+        from gnes.encoder.image.inception_cores.inception_utils import inception_arg_scope
+
+        arg_scope = inception_arg_scope()
+        inception_v4.default_image_size = self.inception_size_x
+        self.inputs = tf.placeholder(tf.float32, (None,
+                                                  self.inception_size_x,
+                                                  self.inception_size_y, 3))
+
+        with tf.contrib.slim.arg_scope(arg_scope):
+            self.logits, self.end_points = inception_v4(self.inputs,
+                                                        is_training=False,
+                                                        dropout_keep_prob=1.0)
+
+        config = tf.ConfigProto(log_device_placement=False)
+        if self.use_gpu:
+            config.gpu_options.allow_growth = True
+        self.sess = tf.Session(config=config)
+        self.saver = tf.train.Saver()
+        self.saver.restore(self.sess, self.model_dir)
+
+    def encode(self, img: List['np.ndarray'], *args, **kwargs) -> np.ndarray:
+        ret = []
+        img = [(np.array(Image.fromarray(im).resize((self.inception_size_x,
+                         self.inception_size_y)), dtype=np.float32) * 2 / 255. - 1.) for im in img]
+        for _im in batch_iterator(img, self.batch_size):
+            _, end_points_ = self.sess.run((self.logits, self.end_points),
+                                           feed_dict={self.inputs: _im})
+            ret.append(end_points_[self.select_layer])
+        return np.concatenate(ret, axis=0).astype(np.float32)