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#
# 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.
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from typing import List
import numpy as np
from ..base import BaseImageEncoder
from ...helper import batching, get_first_available_gpu
[docs]class TFInceptionEncoder(BaseImageEncoder):
batch_size = 64
def __init__(self, model_dir: str,
select_layer: str = 'PreLogitsFlatten',
*args, **kwargs):
super().__init__(*args, **kwargs)
self.model_dir = model_dir
self.select_layer = select_layer
self.inception_size_x = 299
self.inception_size_y = 299
[docs] def post_init(self):
import os
os.environ['CUDA_VISIBLE_DEVICES'] = str(get_first_available_gpu())
import tensorflow as tf
from .inception_cores.inception_v4 import inception_v4
from .inception_cores.inception_utils import inception_arg_scope
g = tf.Graph()
with g.as_default():
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.on_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)
[docs] def encode(self, img: List['np.ndarray'], *args, **kwargs) -> np.ndarray:
img = [(im * 2 / 255. - 1.) for im in img]
@batching
def _encode(_, data):
_, end_points_ = self.sess.run((self.logits, self.end_points),
feed_dict={self.inputs: data})
return end_points_[self.select_layer]
return _encode(self, img).astype(np.float32)