版权声明：转载注明出处：邢翔瑞的技术博客https://blog.csdn.net/weixin_36474809 https://blog.csdn.net/weixin_36474809/article/details/87919252
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目录
一、更改迭代次数
原始迭代次数较大，所以运行一次耗时较久，不易调试，我们将迭代次数改小以便调试。直接在main下面的def train(args)中更改就可以。
1.1定义位置
config.py之中，这里将其值改小就可以取得减少程序运行时间的效果。
if __name__ == "__main__":
def train(args):
if args:
n2n = args.noise2noise if 'noise2noise' in args else True
train_config.noise2noise = n2n
if 'long_train' in args and args.long_train:
#train_config.iteration_count = 500000
train_config.iteration_count = 500
#train_config.eval_interval = 5000
train_config.eval_interval = 50
train_config.ramp_down_perc = 0.5train_config初始化位置
train_config = dnnlib.EasyDict(
iteration_count=300000,
eval_interval=1000,
minibatch_size=4,
run_func_name="train.train",
learning_rate=0.0003,
ramp_down_perc=0.3,
noise=gaussian_noise_config,
# noise=poisson_noise_config,
noise2noise=True,
train_tfrecords='datasets/imagenet_val_raw.tfrecords',
validation_config=default_validation_config
)可以看出将其定义为dnnlib.EasyDict格式的数据。类似于字典，但比字典更易使用。
EasyDict定义位置
train_config与validate_config都运用了dnnlib.EasyDict作为初始化，此class的位置为dnnlib/util.py
class EasyDict(dict):
"""Convenience class that behaves like a dict but allows access with the attribute syntax."""
def __getattr__(self, name: str) -> Any:
try:
return self[name]
except KeyError:
raise AttributeError(name)
def __setattr__(self, name: str, value: Any) -> None:
self[name] = value
def __delattr__(self, name: str) -> None:
del self[name]1.2 更改迭代次数的方法
迭代次数分别改为,评估与输出的interval，和输出的斜率。
train_config.iteration_count = 100
train_config.eval_interval = 10
train_config.ramp_down_perc = 0.5迭代次数设为500时，最终PSNR=27.34，时间为2m28s
迭代次数为100时，最终PSNR=23.42，时间为1m48s
二、网络结构
2.1 原始网络结构与代码解析
原始网络可以看作一个unet
skips = [x]
n = x
n = conv_lr('enc_conv0', n, 48)
n = conv_lr('enc_conv1', n, 48)
n = maxpool2d(n)
skips.append(n)
n = conv_lr('enc_conv2', n, 48)
n = maxpool2d(n)
skips.append(n)
n = conv_lr('enc_conv3', n, 48)
n = maxpool2d(n)
skips.append(n)
n = conv_lr('enc_conv4', n, 48)
n = maxpool2d(n)
skips.append(n)
n = conv_lr('enc_conv5', n, 48)
n = maxpool2d(n)
n = conv_lr('enc_conv6', n, 48)
#-----------------------------------------------
n = upscale2d(n)
n = tf.concat([n, skips.pop()], axis=1)
n = conv_lr('dec_conv5', n, 96)
n = conv_lr('dec_conv5b', n, 96)
n = upscale2d(n)
n = tf.concat([n, skips.pop()], axis=1)
n = conv_lr('dec_conv4', n, 96)
n = conv_lr('dec_conv4b', n, 96)
n = upscale2d(n)
n = tf.concat([n, skips.pop()], axis=1)
n = conv_lr('dec_conv3', n, 96)
n = conv_lr('dec_conv3b', n, 96)
n = upscale2d(n)
n = tf.concat([n, skips.pop()], axis=1)
n = conv_lr('dec_conv2', n, 96)
n = conv_lr('dec_conv2b', n, 96)
n = upscale2d(n)
n = tf.concat([n, skips.pop()], axis=1)
n = conv_lr('dec_conv1a', n, 64)
n = conv_lr('dec_conv1b', n, 32)
n = conv('dec_conv1', n, 3, gain=1.0)结构如下，相应数值和尺寸需要按照上面代码进行变化。
基本可以确定autoencoder之中确定的网络结构。
2.2 训练时autoencoder的调用
直接在autoencoder之中加输出的信息
(n2n) jcx@smart-dsp:~/Desktop/NVlabs_noise2noise$ CUDA_VISIBLE_DEVICES=1 python config.py train --train-tfrecords=datasets/part_bsd300.tfrecords --long-train=false --noise=gaussian
----------train in config.py
----------Iteration count is 100 and eval_interval is 10
{'iteration_count': 100, 'eval_interval': 10, 'minibatch_size': 4, 'run_func_name': 'train.train', 'learning_rate': 0.0003, 'ramp_down_perc': 0.5, 'noise': {'func_name': 'train.AugmentGaussian', 'train_stddev_rng_range': (0.0, 50.0), 'validation_stddev': 25.0}, 'noise2noise': True, 'train_tfrecords': 'datasets/part_bsd300.tfrecords', 'validation_config': {'dataset_dir': 'datasets/kodak'}}
----------submit_run in submit.py
----------submit_config.submit_target in {SubmitTarget.LOCAL},create new dir to run
Creating the run dir: results/00006-autoencoder-n2n
----------_populate_run_dir function in submit.py. Copying files to the run dir
----------run_wrapper function in submit.py
dnnlib: Running train.train() on localhost...
----------train in train.py
----------Setting up dataset source from datasets/part_bsd300.tfrecords
----------net = tflib.Network(**config.net_config) in train.py
-------------autoencoder in network.py
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
----------Building TensorFlow graph...
-------------autoencoder in network.py
----------train_step = opt.apply_updates() in train.py
----------Training...
-------------autoencoder in network.py
-------------autoencoder in network.py
-------------autoencoder in network.py
Average PSNR: 10.03
iter 0 time 5s sec/eval 0.0 sec/iter 0.00 maintenance 5.1
Average PSNR: 20.21
iter 10 time 1m 13s sec/eval 3.5 sec/iter 0.35 maintenance 64.6
Average PSNR: 21.51
iter 20 time 1m 24s sec/eval 2.1 sec/iter 0.21 maintenance 8.5
Average PSNR: 22.28
iter 30 time 1m 34s sec/eval 2.1 sec/iter 0.21 maintenance 8.5
Average PSNR: 22.71
iter 40 time 1m 45s sec/eval 2.0 sec/iter 0.20 maintenance 8.2
Average PSNR: 22.94
iter 50 time 1m 55s sec/eval 2.1 sec/iter 0.21 maintenance 8.5
Average PSNR: 23.36
iter 60 time 2m 06s sec/eval 2.1 sec/iter 0.21 maintenance 8.5
Average PSNR: 23.31
iter 70 time 2m 16s sec/eval 2.0 sec/iter 0.20 maintenance 8.4
Average PSNR: 23.38
iter 80 time 2m 27s sec/eval 1.9 sec/iter 0.19 maintenance 8.7
Average PSNR: 23.47
iter 90 time 2m 37s sec/eval 2.0 sec/iter 0.20 maintenance 8.2
Elapsed time: 2m 47s
dnnlib: Finished train.train() in 2m 48s.
----------try in run wrapper finally handle _finished.txt三、训练函数嵌套关系
3.1 config.py 到submit_run
config.py之中是主函数，训练过程中，函数的作用是加入相应参数之后，运行train函数
submit_config.run_desc = desc + args.desc
if args.run_dir_root is not None:
submit_config.run_dir_root = args.run_dir_root
if args.command is not None:
args.func(args)
else:
# Train if no subcommand was given
train(args)train函数中关键语句为
dnnlib.submission.submit.submit_run(submit_config, **train_config)其中输入参数，train_config与submit_config就是上面设置的相应参数.其中，train_config是一系列与训练相关的代码，submit_config在submit.py之中，用于实现将运行目录及结果写入等等操作。
#train config
train_config = dnnlib.EasyDict(
iteration_count=300000,
eval_interval=1000,
minibatch_size=4,
run_func_name="train.train",
learning_rate=0.0003,
ramp_down_perc=0.3,
noise=gaussian_noise_config,
# noise=poisson_noise_config,
noise2noise=True,
train_tfrecords='datasets/imagenet_val_raw.tfrecords',
validation_config=default_validation_config
)
#submit config
submit_config = dnnlib.SubmitConfig()
submit_config.run_dir_root = 'results'
submit_config.run_dir_ignore += ['datasets', 'results']
desc = "autoencoder"3.2 submit.py 中的run_wrapper
submit_run之中,创建相应文件夹，在文件夹之中运行该运行的函数，核心语句为run_wrapper函数
run_wrapper函数中用于运行封装好的函数
核心语句：
try:
print("dnnlib: Running {0}() on {1}...".format(submit_config.run_func_name, submit_config.host_name))
start_time = time.time()
util.call_func_by_name(func_name=submit_config.run_func_name, submit_config=submit_config, **submit_config.run_func_kwargs)
print("dnnlib: Finished {0}() in {1}.".format(submit_config.run_func_name, util.format_time(time.time() - start_time)))
except:
if is_local:
raise
else:
traceback.print_exc()
log_src = os.path.join(submit_config.run_dir, "log.txt")
log_dst = os.path.join(get_path_from_template(submit_config.run_dir_root), "{0}-error.txt".format(submit_config.run_name))
shutil.copyfile(log_src, log_dst)
finally:
open(os.path.join(submit_config.run_dir, "_finished.txt"), "w").close()其中，就是运用call_func_by_name函数进行函数的调用与实现
util.call_func_by_name(func_name=submit_config.run_func_name, submit_config=submit_config, **submit_config.run_func_kwargs)3.3 调用train.py
util.call_func_by_name(func_name=submit_config.run_func_name, submit_config=submit_config, **submit_config.run_func_kwargs)
通过call_func_by_name这个函数调用相应的函数，
def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
"""Finds the python object with the given name and calls it as a function."""
assert func_name is not None
func_obj = get_obj_by_name(func_name)
assert callable(func_obj)
return func_obj(*args, **kwargs)3.4打印出信息
通过打印出信息验证了嵌套及调用关系
----------train in config.py
----------Iteration count is 100 and eval_interval is 10
{'iteration_count': 100, 'eval_interval': 10, 'minibatch_size': 4, 'run_func_name': 'train.train', 'learning_rate': 0.0003, 'ramp_down_perc': 0.5, 'noise': {'func_name': 'train.AugmentGaussian', 'train_stddev_rng_range': (0.0, 50.0), 'validation_stddev': 25.0}, 'noise2noise': True, 'train_tfrecords': 'datasets/part_bsd300.tfrecords', 'validation_config': {'dataset_dir': 'datasets/kodak'}}
----------submit_run in submit.py
----------submit_config.submit_target in {SubmitTarget.LOCAL},create new dir to run
Creating the run dir: results/00503-autoencoder-n2n
----------_populate_run_dir function in submit.py. Copying files to the run dir
----------run_wrapper function in submit.py
dnnlib: Running train.train() on localhost...
---------------train function in train.py
四、训练函数
train.py用于进行函数的训练
4.1 输入参数及类型
def train(
submit_config: dnnlib.SubmitConfig,
iteration_count: int,
eval_interval: int,
minibatch_size: int,
learning_rate: float,
ramp_down_perc: float,
noise: dict,
validation_config: dict,
train_tfrecords: str,
noise2noise: bool):