Merge branch 'master' into add-buildin-orchestration

LICENSE

@@ -98,6 +98,9 @@ Copyright (c) 2014-2019 Anthon van der Neut, Ruamel bvba
 6. jieba 0.39
 Copyright (c) 2013 Sun Junyi
 
+7. opencv-python 4.0.0
+Copyright (c) 2016-2018 Olli-Pekka Heinisuo and contributors
+
 
 
 

gnes/cli/parser.py

@@ -183,6 +183,10 @@ def set_grpc_frontend_parser(parser=None):
     _set_grpc_parser(parser)
     parser.add_argument('--max_concurrency', type=int, default=10,
                         help='maximum concurrent client allowed')
+    parser.add_argument('--max_send_size', type=int, default=100,
+                        help='maximum send size for grpc server in (M)')
+    parser.add_argument('--max_receive_size', type=int, default=100,
+                        help='maximum receive size for grpc server in (M)')
     return parser
 
 

gnes/encoder/__init__.py

@@ -39,6 +39,7 @@
     'PipelineEncoder': 'base',
     'HashEncoder': 'numeric.hash',
     'BasePytorchEncoder': 'image.base',
+    'TFInceptionEncoder': 'image.inception',
 }
 
 register_all_class(_cls2file_map)

gnes/encoder/image/inception.py

@@ -0,0 +1,70 @@
+#  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.
+
+from typing import List
+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)

gnes/encoder/image/inception_cores/inception_utils.py

@@ -0,0 +1,82 @@
+# Copyright 2016 The TensorFlow 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.
+# ==============================================================================
+"""Contains common code shared by all inception models.
+
+Usage of arg scope:
+  with slim.arg_scope(inception_arg_scope()):
+    logits, end_points = inception.inception_v3(images, num_classes,
+                                                is_training=is_training)
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def inception_arg_scope(weight_decay=0.00004,
+                        use_batch_norm=True,
+                        batch_norm_decay=0.9997,
+                        batch_norm_epsilon=0.001,
+                        activation_fn=tf.nn.relu,
+                        batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
+                        batch_norm_scale=False):
+  """Defines the default arg scope for inception models.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
+    batch_norm_decay: Decay for batch norm moving average.
+    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+      in batch norm.
+    activation_fn: Activation function for conv2d.
+    batch_norm_updates_collections: Collection for the update ops for
+      batch norm.
+    batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
+      activations in the batch normalization layer.
+
+  Returns:
+    An `arg_scope` to use for the inception models.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      # collection containing update_ops.
+      'updates_collections': batch_norm_updates_collections,
+      # use fused batch norm if possible.
+      'fused': None,
+      'scale': batch_norm_scale,
+  }
+  if use_batch_norm:
+    normalizer_fn = slim.batch_norm
+    normalizer_params = batch_norm_params
+  else:
+    normalizer_fn = None
+    normalizer_params = {}
+  # Set weight_decay for weights in Conv and FC layers.
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      weights_regularizer=slim.l2_regularizer(weight_decay)):
+    with slim.arg_scope(
+        [slim.conv2d],
+        weights_initializer=slim.variance_scaling_initializer(),
+        activation_fn=activation_fn,
+        normalizer_fn=normalizer_fn,
+        normalizer_params=normalizer_params) as sc:
+      return sc