utils_backdoor.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date    : 2018-11-05 11:30:01
# @Author  : Bolun Wang (bolunwang@cs.ucsb.edu)
# @Link    : http://cs.ucsb.edu/~bolunwang


import h5py
import numpy as np
import tensorflow.compat.v1 as tf
from keras.preprocessing import image


def dump_image(x, filename, format):
    img = image.array_to_img(x, scale=False)
    img.save(filename, format)
    return


def fix_gpu_memory(mem_fraction=1):
    import tensorflow.python.keras.backend as K
    
    gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=mem_fraction)
    tf_config = tf.compat.v1.ConfigProto(gpu_options=gpu_options)
    tf_config.gpu_options.allow_growth = True
    tf_config.log_device_placement = False
    tf_config.allow_soft_placement = True
    
    config=tf_config
    #sess = tf.compat.v1.Session(config=tf_config)
    with tf.compat.v1.Session() as sess:
        init_op = tf.compat.v1.global_variables_initializer()
        print(sess.run(init_op))
    K.set_session(sess)

    return sess


def load_dataset(data_filename, keys=None):
    ''' assume all datasets are numpy arrays '''
    dataset = {}
    with h5py.File(data_filename, 'r') as hf:
        if keys is None:
            for name in hf:
                dataset[name] = np.array(hf.get(name))
        else:
            for name in keys:
                dataset[name] = np.array(hf.get(name))

    return dataset