NVlabs/noise2noise代码（二）训练集的更改

逃离我推掉我的手 2022-04-23 03:38 350阅读 0赞

**背景**：为了更改运行通相应代码，需要先在小数据集上运行代码方便实现。

**目的：**在小数据集上运行代码。

**目录**

一、生成训练集代码

1.1 命令行与输入参数

1.2 输入图像

1.3 转换与存入

1.4 输出相关信息

二、一个版本原因的错误（已调通可不看）

2.1 版本导致的报错

2.2 解决方法

三、生成训练集

3.1 训练集与测试集的更改

3.2 命令行

3.3 validate set下载

四、训练

4.1 网络训练

4.2 validate

--------------------

# 一、生成训练集代码 #

**dataset\_tool\_tf.py**此代码用于将原始数据集生成tfrecord格式的文件给网络训练。

## 1.1 命令行与输入参数 ##

相应命令行：

# This should run through roughly 50K images and output a file called `datasets/imagenet_val_raw.tfrecords`.
    python dataset_tool_tf.py 
    --input-dir "<path_to_imagenet>/ILSVRC2012_img_val" 
    --out=datasets/imagenet_val_raw.tfrecords

或者

python dataset_tool_tf.py 
    --input-dir datasets/BSDS300-images/BSDS300/images/train 
    --out=datasets/bsd300.tfrecords

命令行涉及两个参数，--input-dir输入的数据集的位置 ,--out输出的tfrecords格式文档的位置。

def main():
        parser = argparse.ArgumentParser(
            description='Convert a set of image files into a TensorFlow tfrecords training set.',
            epilog=examples,
            formatter_class=argparse.RawDescriptionHelpFormatter
        )
        parser.add_argument("--input-dir", help="Directory containing ImageNet images")
        parser.add_argument("--out", help="Filename of the output tfrecords file")
        args = parser.parse_args()
    
        if args.input_dir is None:
            print ('Must specify input file directory with --input-dir')
            sys.exit(1)
        if args.out is None:
            print ('Must specify output filename with --out')
            sys.exit(1)

## 1.2 输入图像 ##

print ('Loading image list from %s' % args.input_dir)
        images = sorted(glob.glob(os.path.join(args.input_dir, '*.JPEG')))
        images += sorted(glob.glob(os.path.join(args.input_dir, '*.jpg')))
        images += sorted(glob.glob(os.path.join(args.input_dir, '*.png')))
        np.random.RandomState(0x1234f00d).shuffle(images)

所有JPEG jpg png格式的图片存入images

## 1.3 转换与存入 ##

#------Convert the data into tfrecords--------------------------
        outdir = os.path.dirname(args.out)
        os.makedirs(outdir, exist_ok=True)
        writer = tf.python_io.TFRecordWriter(args.out)
        for (idx, imgname) in enumerate(images):
            print (idx, imgname)
            image = load_image(imgname)
            feature = {
              'shape': shape_feature(image.shape),
              'data': bytes_feature(tf.compat.as_bytes(image.tostring()))
            }
            example = tf.train.Example(features=tf.train.Features(feature=feature))
            writer.write(example.SerializeToString())

## 1.4 输出相关信息 ##

print ('Dataset statistics:')
        print ('  Formats:')
        for key in format_stats:
            print ('    %s: %d images' % (key, format_stats[key]))
        print ('  width,height buckets:')
        for key in size_stats:
            print ('    %s: %d images' % (key, size_stats[key]))
        writer.close()

根据此信息得出，只要图像在相应文件夹之中，多少等等并不重要。我们可以将相应的数据集改小。

# 二、一个版本原因的错误（已调通可不看） #

BSD300文件夹拷出来，然后train文件夹删除部分照片，留20张，test文件夹留10张。

部分图片生成tfrecord的命令行：

python dataset\_tool\_tf.py --input-dir datasets/part\_BSDS300/images/train --out=datasets/part\_bsd300.tfrecords

所有图片生成的代码：

python dataset\_tool\_tf.py --input-dir datasets/BSDS300/images/train --out=datasets/bsd300.tfrecords

## 2.1 版本导致的报错 ##

jcx@smart-dsp:~/Desktop/xxr2019/NVlabs_noise2noise$ python dataset_tool_tf.py --input-dir datasets/part_BSDS300/images/train --out=datasets/part_bsd300.tfrecords
    Loading image list from datasets/part_BSDS300/images/train
    Traceback (most recent call last):
      File "dataset_tool_tf.py", line 94, in <module>
        main()
      File "dataset_tool_tf.py", line 70, in main
        os.makedirs(outdir, exist_ok=True)
    TypeError: makedirs() got an unexpected keyword argument 'exist_ok'

出现此错误，原来作者要求版本为python 3.6 ，这样报错的版本为python 2.7.6

jcx@smart-dsp:~/Desktop/xxr2019/NVlabs_noise2noise$ python
    Python 2.7.6 (default, Oct 26 2016, 20:30:19)
    [GCC 4.8.4] on linux2
    Type "help", "copyright", "credits" or "license" for more information.
    >>> quit()

需要按照要求在服务器上安装Anaconda并且配置环境

python3的版本输入的时候，报错为tensorflow配置不对。

jcx@smart-dsp:~/Desktop/xxr2019/NVlabs_noise2noise$ python3 dataset_tool_tf.py --input-dir datasets/part_BSDS300/images/train --out=datasets/part_bsd300.tfrecords
    Traceback (most recent call last):
      File "dataset_tool_tf.py", line 12, in <module>
        import tensorflow as tf
    ImportError: No module named 'tensorflow'
    jcx@smart-dsp:~/Desktop/xxr2019/NVlabs_noise2noise$ python3
    Python 3.5.2 (default, May 23 2017, 10:15:40)
    [GCC 5.4.1 20160904] on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>> quit()

## 2.2 解决方法 ##

虚拟环境中用Anaconda安装显卡CUDA驱动与CUDA运行版本匹配[  https://blog.csdn.net/weixin\_36474809/article/details/87820314][https_blog.csdn.net_weixin_36474809_article_details_87820314]

运用Anaconda对python 3.6与tensorflow-gpu与pip环境配置[  https://blog.csdn.net/weixin\_36474809/article/details/87714182][https_blog.csdn.net_weixin_36474809_article_details_87714182]

Ubuntu14.04安装Anaconda3-2018.12-x86\_64[  https://blog.csdn.net/weixin\_36474809/article/details/87804903][https_blog.csdn.net_weixin_36474809_article_details_87804903]

解决之后tensorboard依然有问题,但不影响我们的实验

(n2n) jcx@smart-dsp:~$ tensorboard
    Traceback (most recent call last):
      File "/home/jcx/.conda/envs/n2n/bin/tensorboard", line 11, in <module>
        sys.exit(run_main())
      File "/home/jcx/.conda/envs/n2n/lib/python3.6/site-packages/tensorboard/main.py", line 36, in run_main
        tf.app.run(main)
      File "/home/jcx/.conda/envs/n2n/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 126, in run
        _sys.exit(main(argv))
      File "/home/jcx/.conda/envs/n2n/lib/python3.6/site-packages/tensorboard/main.py", line 45, in main
        default.get_assets_zip_provider())
      File "/home/jcx/.conda/envs/n2n/lib/python3.6/site-packages/tensorboard/program.py", line 166, in main
        tb = create_tb_app(plugins, assets_zip_provider)
      File "/home/jcx/.conda/envs/n2n/lib/python3.6/site-packages/tensorboard/program.py", line 190, in create_tb_app
        raise ValueError('A logdir must be specified when db is not specified. '
    ValueError: A logdir must be specified when db is not specified. Run `tensorboard --help` for details and examples.

# 三、生成训练集 #

dataset\_tool\_tf.py此代码用于将图片生成训练集，供后面网络训练使用。

## 3.1 训练集与测试集的更改 ##

BSD300文件夹拷出来，然后train文件夹删除部分照片，留10张，test文件夹留5张。

但是，这样相应的文件夹/datasets/part\_BSDS300/之中的文件

iids\_test.txt和iids\_train.txt中的名称暂时没有更改。

## 3.2 命令行 ##

部分图片生成tfrecord的命令行：

python dataset\_tool\_tf.py --input-dir datasets/part\_BSDS300/images/train --out=datasets/part\_bsd300.tfrecords

所有图片生成的代码（暂时不用）：

python dataset\_tool\_tf.py --input-dir datasets/BSDS300/images/train --out=datasets/bsd300.tfrecords

只运用部分生成相应的数据集后：

。。。
    17 datasets/part_BSDS300/images/train/43083.jpg
    18 datasets/part_BSDS300/images/train/60079.jpg
    19 datasets/part_BSDS300/images/train/16052.jpg
    Dataset statistics:
      Formats:
        RGB: 20 images
      width,height buckets:
        >= 256x256: 20 images

## 3.3 validate set下载 ##

命令行

python download_kodak.py --output-dir=datasets/kodak

代码

import os
    import sys
    import argparse
    
    from urllib.request import urlretrieve
    
    examples='''examples:
    
      python %(prog)s --output-dir=./tmp
    '''
    
    def main():
        parser = argparse.ArgumentParser(
            description='Download the Kodak dataset .PNG image files.',
            epilog=examples,
            formatter_class=argparse.RawDescriptionHelpFormatter
        )
        parser.add_argument("--output-dir", help="Directory where to save the Kodak dataset .PNGs")
        args = parser.parse_args()
    
        if args.output_dir is None:
            print ('Must specify output directory where to store tfrecords with --output-dir')
            sys.exit(1)
    
        os.makedirs(args.output_dir, exist_ok=True)
    
        for i in range(1, 25):
            imgname = 'kodim%02d.png' % i
            url = "http://r0k.us/graphics/kodak/kodak/" + imgname
            print ('Downloading', url)
            urlretrieve(url, os.path.join(args.output_dir, imgname))
        print ('Kodak validation set successfully downloaded.')
    
    if __name__ == "__main__":
        main()

运行结果，只有24张

Downloading http://r0k.us/graphics/kodak/kodak/kodim22.png
    Downloading http://r0k.us/graphics/kodak/kodak/kodim23.png
    Downloading http://r0k.us/graphics/kodak/kodak/kodim24.png
    Kodak validation set successfully downloaded.

# 四、训练 #

## 4.1 网络训练 ##

**注意:**一定要按照要求配置好相应的CUDA驱动版本，CUDA运行版本，TF版本等等。

**虚拟环境中用anaconda安装显卡CUDA驱动与CUDA运行版本匹配** [https://blog.csdn.net/weixin\_36474809/article/details/87820314][https_blog.csdn.net_weixin_36474809_article_details_87820314]

运用config.py函数进行训练，相应参数帮助为：

(n2n) jcx@smart-dsp:~/Desktop/xxr2019/NVlabs_noise2noise$ python config.py train --help
    usage: config.py train [-h] [--noise2noise [NOISE2NOISE]] [--noise NOISE]
                           [--long-train LONG_TRAIN]
                           [--train-tfrecords TRAIN_TFRECORDS]
    
    optional arguments:
      -h, --help            show this help message and exit
      --noise2noise [NOISE2NOISE]
                            Noise2noise (--noise2noise=true) or noise2clean
                            (--noise2noise=false). Default is noise2noise=true.
      --noise NOISE         Type of noise corruption (one of: gaussian, poisson)
      --long-train LONG_TRAIN
                            Train for a very long time (500k iterations or
                            500k*minibatch image)
      --train-tfrecords TRAIN_TFRECORDS
                            Filename of the training set tfrecords file

相应命令行

对于imagenet而言，相应的命令行为：

python config.py --desc='-test' train --train-tfrecords=datasets/imagenet_val_raw.tfrecords --long-train=true --noise=gaussian

后面查看源码看--desc=‘-test’ 表示什么，--train-tfrecords后面的表示训练集。

python config.py --desc='-test' train --train-tfrecords=datasets/part\_bsd300.tfrecords --long-train=false --noise=gaussian

python config.py train --train-tfrecords=datasets/part\_bsd300.tfrecords --noise=gaussian

相应网络结构

Setting up dataset source from datasets/part_bsd300.tfrecords
    
    autoencoder                 Params      OutputShape             WeightShape
    ---                         ---         ---                     ---
    x                           -           (?, 3, 256, 256)        -
    enc_conv0                   1344        (?, 48, 256, 256)       (3, 3, 3, 48)
    enc_conv1                   20784       (?, 48, 256, 256)       (3, 3, 48, 48)
    MaxPool                     -           (?, 48, 128, 128)       -
    enc_conv2                   20784       (?, 48, 128, 128)       (3, 3, 48, 48)
    MaxPool_1                   -           (?, 48, 64, 64)         -
    enc_conv3                   20784       (?, 48, 64, 64)         (3, 3, 48, 48)
    MaxPool_2                   -           (?, 48, 32, 32)         -
    enc_conv4                   20784       (?, 48, 32, 32)         (3, 3, 48, 48)
    MaxPool_3                   -           (?, 48, 16, 16)         -
    enc_conv5                   20784       (?, 48, 16, 16)         (3, 3, 48, 48)
    MaxPool_4                   -           (?, 48, 8, 8)           -
    enc_conv6                   20784       (?, 48, 8, 8)           (3, 3, 48, 48)
    Upscale2D                   -           (?, 48, 16, 16)         -
    dec_conv5                   83040       (?, 96, 16, 16)         (3, 3, 96, 96)
    dec_conv5b                  83040       (?, 96, 16, 16)         (3, 3, 96, 96)
    Upscale2D_1                 -           (?, 96, 32, 32)         -
    dec_conv4                   124512      (?, 96, 32, 32)         (3, 3, 144, 96)
    dec_conv4b                  83040       (?, 96, 32, 32)         (3, 3, 96, 96)
    Upscale2D_2                 -           (?, 96, 64, 64)         -
    dec_conv3                   124512      (?, 96, 64, 64)         (3, 3, 144, 96)
    dec_conv3b                  83040       (?, 96, 64, 64)         (3, 3, 96, 96)
    Upscale2D_3                 -           (?, 96, 128, 128)       -
    dec_conv2                   124512      (?, 96, 128, 128)       (3, 3, 144, 96)
    dec_conv2b                  83040       (?, 96, 128, 128)       (3, 3, 96, 96)
    Upscale2D_4                 -           (?, 96, 256, 256)       -
    dec_conv1a                  57088       (?, 64, 256, 256)       (3, 3, 99, 64)
    dec_conv1b                  18464       (?, 32, 256, 256)       (3, 3, 64, 32)
    dec_conv1                   867         (?, 3, 256, 256)        (3, 3, 32, 3)
    ---                         ---         ---                     ---
    Total                       991203

训练过程非常耗时。

Building TensorFlow graph...
    Training...
    Average PSNR: 6.56
    iter 0          time 4s           sec/eval 0.0     sec/iter 0.00    maintenance 4.4
    Average PSNR: 28.24
    iter 1000       time 2m 30s       sec/eval 117.5   sec/iter 0.12    maintenance 28.4
    Average PSNR: 29.85
    iter 2000       time 4m 33s       sec/eval 114.2   sec/iter 0.11    maintenance 8.2
    Average PSNR: 29.96
    iter 3000       time 6m 39s       sec/eval 115.8   sec/iter 0.12    maintenance 10.3
    。。。
    iter 297000     time 9h 24m 11s   sec/eval 99.3    sec/iter 0.10    maintenance 10.0
    Average PSNR: 28.58
    iter 298000     time 9h 26m 02s   sec/eval 100.6   sec/iter 0.10    maintenance 10.3
    Average PSNR: 28.58
    iter 299000     time 9h 27m 52s   sec/eval 99.1    sec/iter 0.10    maintenance 10.8
    Elapsed time: 9h 29m 43s
    dnnlib: Finished train.train() in 9h 29m 44s.

花费时间可能比较久，例如ImageNet作为训练集时，NVIDIA Titan V GPU花费了7.5小时。而我们用部分BSD300数据集，反而花费了更多时间。9h29m

每开始一次训练，都会在results/文件夹下，生成一个文件夹000xx-autoencoder-n2n。

训练完成时，会生成一个network\_final.pickle在results/000xx-autoencoder-n2n目录下。

## 4.2 validate ##

假如训练好的网络在下面目录下：

`results/00001-autoencoder-1gpu-L-n2n`. Here's how to run a set of images through this network:

python config.py validate --dataset-dir=datasets/kodak --network-snapshot=results/00001-autoencoder-1gpu-L-n2n/network\_final.

我们前一步的目录为results/00009-autoencoder-n2n/network\_final.pickle

python config.py validate --dataset-dir=datasets/kodak/ --network-snapshot=results/00009-autoencoder-n2n/network\_final.pickle

Average PSNR: 28.58
    dnnlib: Finished validation.validate() in 25s.

[https_blog.csdn.net_weixin_36474809_article_details_87820314]: https://blog.csdn.net/weixin_36474809/article/details/87820314
[https_blog.csdn.net_weixin_36474809_article_details_87714182]: https://blog.csdn.net/weixin_36474809/article/details/87714182
[https_blog.csdn.net_weixin_36474809_article_details_87804903]: https://blog.csdn.net/weixin_36474809/article/details/87804903