Question

我一直在使用CycleGAN进行图像到图像的翻译，并且代码正在运行。但是后来我的学校更新了我们的HPC，我不知道为什么该代码不再起作用。读入的图像会产生一些NaN值，因此该程序将引发异常，但是我无法找到为什么某些值被读为NaN而不是其他值。

我什至尝试了原始horse2zebra数据集，该数据集是我使用代码的存储库。就像我说的那样，它曾经可以工作，但现在不再起作用。存储库位于https://github.com/LynnHo/CycleGAN-Tensorflow-PyTorch/blob/master/README.md

这是进行初始图像调用的地方

''' data '''
a_img_paths = glob('./datasets/' + dataset + '/trainA/*.jpg')
b_img_paths = glob('./datasets/' + dataset + '/trainB/*.jpg')
a_data_pool = data.ImageData(sess, a_img_paths, batch_size, 
load_size=load_size, crop_size=crop_size)
b_data_pool = data.ImageData(sess, b_img_paths, batch_size, 
load_size=load_size, crop_size=crop_size)

a_test_img_paths = glob('./datasets/' + dataset + '/testA/*.jpg')
b_test_img_paths = glob('./datasets/' + dataset + '/testB/*.jpg')
a_test_pool = data.ImageData(sess, a_test_img_paths, batch_size, 
load_size=load_size, crop_size=crop_size)
b_test_pool = data.ImageData(sess, b_test_img_paths, batch_size, 
load_size=load_size, crop_size=crop_size)

这些是读取图像的功能

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf


class ImageData:

def __init__(self,
             session,
             image_paths,
             batch_size,
             load_size=286,
             crop_size=256,
             channels=3,
             prefetch_batch=2,
             drop_remainder=True,
             num_threads=16,
             shuffle=True,
             buffer_size=4096,
             repeat=-1):

    self._sess = session
    self._img_batch = ImageData._image_batch(image_paths,
                                             batch_size,
                                             load_size,
                                             crop_size,
                                             channels,
                                             prefetch_batch,
                                             drop_remainder,
                                             num_threads,
                                             shuffle,
                                             buffer_size,
                                             repeat)
    self._img_num = len(image_paths)

def __len__(self):
    return self._img_num

def batch(self):
    return self._sess.run(self._img_batch)

@staticmethod
def _image_batch(image_paths,
                 batch_size,
                 load_size=286,
                 crop_size=256,
                 channels=3,
                 prefetch_batch=2,
                 drop_remainder=True,
                 num_threads=16,
                 shuffle=True,
                 buffer_size=4096,
                 repeat=-1):
    def _parse_func(path):
        img = tf.read_file(path)
        img = tf.image.decode_jpeg(img, channels=channels)
        img = tf.image.random_flip_left_right(img)
        img = tf.image.resize_images(img, [load_size, load_size])
        img = (img - tf.reduce_min(img)) / (tf.reduce_max(img) - tf.reduce_min(img))
        img = tf.random_crop(img, [crop_size, crop_size, channels])
        img = img * 2 - 1
        return img

    dataset = tf.data.Dataset.from_tensor_slices(image_paths)

    dataset = dataset.map(_parse_func, num_parallel_calls=num_threads)

    if shuffle:
        dataset = dataset.shuffle(buffer_size)

    if drop_remainder:
        dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
    else:
        dataset = dataset.batch(batch_size)

    dataset = dataset.repeat(repeat).prefetch(prefetch_batch)

    return dataset.make_one_shot_iterator().get_next()

Tensorflow无法正确读取图像。获取一些值和一些NaN

0 个答案: