from typing import List
import numpy as np
from PIL import Image
from ..base import BaseVideoEncoder
from ...helper import batching, get_first_available_gpu
[docs]class InceptionVideoEncoder(BaseVideoEncoder):
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 tensorflow as tf
from ..image.inception_cores.inception_v4 import inception_v4
from ..image.inception_cores.inception_utils import inception_arg_scope
import os
os.environ['CUDA_VISIBLE_DEVICES'] = str(get_first_available_gpu())
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, data: List['np.ndarray'], *args,
**kwargs) -> List['np.ndarray']:
v_len = [len(v) for v in data]
pos_start = [0] + [sum(v_len[:i + 1]) for i in range(len(v_len) - 1)]
pos_end = [sum(v_len[:i + 1]) for i in range(len(v_len))]
_resize = lambda x: np.array(Image.fromarray(x).resize((self.inception_size_x, self.inception_size_y)),
dtype=np.float32) * 2 / 255. - 1.
images = [_resize(im) for v in data for im in v]
@batching
def _encode(self, data):
_, end_points_ = self.sess.run((self.logits, self.end_points),
feed_dict={self.inputs: data})
return end_points_[self.select_layer]
encodes = _encode(self, images).astype(np.float32)
return [encodes[s:e].copy() for s, e in zip(pos_start, pos_end)]