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fix(encoder): add inception tf

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Larryjianfeng committed Jul 11, 2019
1 parent e486dd7 commit b480774a51f8ce5406295a44ae641fe18343d106
Showing with 71 additions and 0 deletions.
  1. +71 −0 gnes/encoder/image/inception.py
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# 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)

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