Source code for gnes.encoder.image.inception

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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)