kaonashi-tyc / zi2zi Goto Github PK

Learning Chinese Character style with conditional GAN

Home Page: https://kaonashi-tyc.github.io/2017/04/06/zi2zi.html

License: Apache License 2.0

Python 100.00%

chinese-characters deep-learning deep-neural-networks deeplearning generative-adversarial-networks machine-learning pix2pix style-transfer tensorflow

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felixgao jdc08161063 gongqingyi-github amzhanghan nwohaibi chagge benjamesbabala mylearning2017 dreadlord1984 tracyliang18 iamhuskar allensmile roszcz pittacus splendor-kill shuidong franter666 uetchy zffffff adrianhust couragelfyang mvpduncan orchestor hudawei996 kjeanclaude eideo rubythonode alvinahayward gxlcliqi hoopsoul naru-hiyoko sjas hello-world-zsp chenjingcheng codeaudit russellcloud liupeng89 nieshaoshuai swayfreeda stevenlol studymachinelearning angice parety junxu-ai little1tow meshiguge dengcy028 takuma7 ustcjerry nanfengpo mengmengbai falconjhc clscy daodaofr revo-future yangerkun eternallovelin billjuncaipeng cmc1023 dbc148 gepettolab winnerineast leezqcst chongzhe rongchangzhao justfouw andyhx chaoshangcs guitaryourself vvvtreasure fanxuedong6 jasvixban taichu012 vermuz winterstar milesqli haolongjie lianzhouhui dingman081130 weiliangxiao dd-var shinrai2 yjzhang96 jeffreychan123 gherao zhangxd12 chuanglee yiqinggit tracycuiyc jianchi8 cyxuanwater feiward ran337287 yrz2015 hymicrobusai yangxue0827 lcg22 sjang42 pecannbu sungjunyoung

zi2zi's Issues

Can i use a new model to get the same effect, such as VAE

The result of experiment is surprising ,so i have an question: can i use an alternative way to get the same effect, such as VAE. because my recent research is VAEs, and VAE is also a generative model., I wanna try it .
could you give me any advice? Thank you!!

I cannt find codes about train

so sad about how to know the trains

这个项目好厉害。顺便推荐一个Scrapy的项目

源码地址

Run train.py How can I modify the epoch ?

I have modify :
def train(self, lr=0.0002, epoch=4, schedule=10, resume=True, flip_labels=False,
freeze_encoder=False, fine_tune=None, sample_steps=50, checkpoint_steps=500):
g_vars, d_vars = self.retrieve_trainable_vars(freeze_encoder=freeze_encoder)
input_handle, loss_handle, _, summary_handle = self.retrieve_handles()
but dont work ?

and other problem is :fail to restore model experiment/checkpoint/experiment_0_batch_16
what can I do for it ?

Can the program work for English Characters?

I am wondering can this program transform the English Characters between different styles. Take the following two images for an example:

B

A

According to your rich experience in this field, can this program tranform from image A to image B or B to A?
Thank you.

where is "embedding" being trained?

Hi, thanks for sharing nice work.

I guess embedding of each font should be trained somewhere.
init_embedding() creates the embedding variable under scope "embedding" but I cannot find it being used in any optimizer.
d_optimizer trains variables with prefix "d_" and g_optimizer trains "g_" where none of both includes "embedding" or "E".

Could you provide some explanation?

imageio errors

when i tried to train,some errors arised.thanks for your help.
Traceback (most recent call last):
File "train.py", line 8, in
from model.unet import UNet
File "/mnt/data/zi2zi-master/model/unet.py", line 12, in
from .dataset import TrainDataProvider, InjectDataProvider, NeverEndingLoopingProvider
File "/mnt/data/zi2zi-master/model/dataset.py", line 8, in
from .utils import pad_seq, bytes_to_file,
File "/mnt/data/zi2zi-master/model/utils.py", line 8, in
import imageio
ImportError: No module named imageio

infer.py: error: unrecognized arguments: ./result/val.obj

What should be the format of the input argument 'source_obj'? Thanks!

I got an error as the title when running the line as below.
python infer.py --model_dir=./experiments/checkpoint/experiment_0_batch_16/
--batch_size=16
--source_obj= ./result/val.obj
--embedding_ids=0
--save_dir=./result/

Does zi2zi generate an style-transferred OTF file?

Hi,
After reading your instructions, i'm a little confused about the final output of the infer execution.
Would it be an OTF file (with num of characters)
or just another image file that showing the style-transferred characters?

Is the testing char appeared during training

Hi, I'm playing with your code and it looks great. Meanwhile I'm following cycleGAN to do style transform too. I'd like to ask you several some questions, your reply is appreciated.

Is the char for testing appear in training? Are they memorized by the model?
How do you train 27 different fonts at same time? It seems that the image generating only supports 2 types of font.
How are the Gaussian noises mapped to the font type? Is the category loss predicting the font type?
Your work is trained using pairs of images. Can I view it as a semi-supervised approach?
If I add noises(Gaussian) to the background of images, can your algorithm tolerate these noises?

Long may the sun shine! 🌞

question about the font2img

After run the font2img.py, I can't find the output(the images from the ttf). Then I can't set the image_directories in the next step.
My terminal input:python font2img.py --src_font=why.ttf --dst_font=tgt.ttf --charset=CN --sample_count=1000 --sample_dir=./font --label=0 --filter=1 --shuffle=1
My terminal output:filter hashes -> -863928754147917952

How much graphics memory do you need for training? Can you give me a minimum formulation?

Can you share board of loss changing?

TKS

infer error when use my model

@kaonashi-tyc

i trained my model use

python font2img.py --src_font=simsun.ttf --dst_font=钟齐山文丰手写体.ttf --charset=top_3000_simplified.txt --sample_count=3000 --sample_dir=dir3 --label=0 --filter=1 --shuffle=1
python package.py --dir=dir3 --save_dir=save_dir3 --split_ratio=0.1
python train.py --experiment_dir=experiment --experiment_id=0 --batch_size=50 --lr=0.001 --epoch=100 --sample_steps=60 --schedule=20 --L1_penalty=100 --Lconst_penalty=15

in the experiment/checkpoint/experiment_0_batch_50 dir , why it has "checkpoint" ,"unet.model-4500.data-00000-of-00001","unet.model-4500.index", "unet.model-4500.meta", "unet.model-5000.data-00000-of-00001", "unet.model-5000.index", "unet.model-5000.meta", "unet.model-5500.data-00000-of-00001", "unet.model-5500.index", "unet.model-5500.meta", 10 files?

I download your model ,it only has 4 files.

Is there any error in my train? My tensorflow is 1.3.0
(训练结果怎么有那么多文件？我下载的您的模型里面只有四个文件)

另一个问题：
我在使用自己训练的模型进行infer时，出现以下错误：

python infer.py --model_dir=font3 --batch_size=50 --source_obj=save_dir1/train.obj --embedding_ids=0 --save_dir=save_dir1
2017-10-13 08:27:21.597033: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-13 08:27:21.597060: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-13 08:27:21.597065: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-13 08:27:21.597069: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-13 08:27:21.597074: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-10-13 08:27:21.870330: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:02:00.0
Total memory: 10.91GiB
Free memory: 10.39GiB
2017-10-13 08:27:21.870368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-10-13 08:27:21.870376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-10-13 08:27:21.870386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0)
unpickled total 9 examples
examples -> 9
2017-10-13 08:27:23.749874: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [40,1,1,128] rhs shape= [1,1,1,128]
[[Node: save/Assign_6 = Assign[T=DT_FLOAT, _class=["loc:@embedding/E"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](embedding/E, save/RestoreV2_6/_189)]]
2017-10-13 08:27:23.750613: W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Assign requires shapes of both tensors to match. lhs shape= [40,1,1,128] rhs shape= [1,1,1,128]
[[Node: save/Assign_6 = Assign[T=DT_FLOAT, _class=["loc:@embedding/E"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](embedding/E, save/RestoreV2_6/_189)]]
Traceback (most recent call last):
File "infer.py", line 67, in
tf.app.run()
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "infer.py", line 47, in main
save_dir=args.save_dir)
File "/home/customer/zi2zi-master/model/unet.py", line 360, in infer
self.restore_model(saver, model_dir)
File "/home/customer/zi2zi-master/model/unet.py", line 305, in restore_model
saver.restore(self.sess, ckpt.model_checkpoint_path)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1560, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
options, run_metadata)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [40,1,1,128] rhs shape= [1,1,1,128]
[[Node: save/Assign_6 = Assign[T=DT_FLOAT, _class=["loc:@embedding/E"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](embedding/E, save/RestoreV2_6/_189)]]

Caused by op u'save/Assign_6', defined at:
File "infer.py", line 67, in
tf.app.run()
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "infer.py", line 47, in main
save_dir=args.save_dir)
File "/home/customer/zi2zi-master/model/unet.py", line 359, in infer
saver = tf.train.Saver(var_list=self.retrieve_generator_vars())
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1140, in init
self.build()
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1172, in build
filename=self._filename)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 688, in build
restore_sequentially, reshape)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 419, in _AddRestoreOps
assign_ops.append(saveable.restore(tensors, shapes))
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 155, in restore
self.op.get_shape().is_fully_defined())
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/state_ops.py", line 274, in assign
validate_shape=validate_shape)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_state_ops.py", line 43, in assign
use_locking=use_locking, name=name)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1204, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [40,1,1,128] rhs shape= [1,1,1,128]
[[Node: save/Assign_6 = Assign[T=DT_FLOAT, _class=["loc:@embedding/E"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](embedding/E, save/RestoreV2_6/_189)]]

这是tensorflow的版本问题吗？但是我在使用您的模型进行infer时是没有这种错误的。

Chinese Font Creation from Handwriting

Would it be plausible to use this method to create a font from one's handwriting? If I created a dataset of (2K-3K?) handwritten characters, would you expect it to learn my handwriting style and be able to produce arbitrary characters (my handwriting is pretty neat)? Has anyone tried this? Also, if you have any tips on how to accomplish this (e.g. how to integrate my own images into the workflow), that would be quite helpful!

Edit: after reading some of the recent papers on GANS, I assume this subsequent point is trivial, it should be the default behaviour already. [After learning one's handwriting style, I'm wondering if some part of the model is capable of receiving small amounts of randomness, so that instead of having a font in which every instance of a character is identical, you are able to create many subtly different versions of each characters.]

I think that if these two objectives could be accomplished, the results would be quite fascinating. Furthermore, one could experiment with mixing one's handwriting style with a standard font, or even mixing different people's handwriting styles!

embedding_num vs embedding_ids

Working with 83 fonts in english. The embedding_num by default is 40. During training, should the embedding_num be set to 83 because there are 83 fonts? What is embedding_num?

Why would embedding_num be bigger or smaller than embedding_ids?

How can set the numer of gpu when training?

Does the program have the options to set the number of gpu when training the model. Thank you.

font2img.py

python font2img.py --src_font=./fonts/STXINWEI.TTF,--dst_font=./fonts/smallfresh.otf,--charset=CN,--sample_count=1000,--sample_dir=./fonts/newfonts,--label=0,--filter=1,--shuffle=1
error:error: the following arguments are required: --dst_font
I use python3.6

there is maybe something wrong in my train.py

@kaonashi-tyc

python package1.py --dir=dir2 --save_dir=save_dir2 --split_ratio=0
img dir2/0_0000.jpg 0
img dir2/0_0001.jpg 0
img dir2/0_0002.jpg 0
img dir2/0_0003.jpg 0
img dir2/0_0004.jpg 0
img dir2/0_0005.jpg 0
img dir2/0_0006.jpg 0
img dir2/0_0007.jpg 0
img dir2/0_0008.jpg 0
img dir2/0_0009.jpg 0
img dir2/0_0010.jpg 0
......
img dir2/0_2942.jpg 0
img dir2/0_2943.jpg 0
img dir2/0_2944.jpg 0
img dir2/0_2945.jpg 0
img dir2/0_2946.jpg 0
img dir2/0_2947.jpg 0
img dir2/0_2948.jpg 0
img dir2/0_2949.jpg 0
img dir2/0_2950.jpg 0
img dir2/0_2951.jpg 0

python train.py --experiment_dir=experiment --experiment_id=0 --batch_size=24 --lr=0.001 --epoch=10 --sample_steps=123 --schedule=20 --L1_penalty=100 --Lconst_penalty=15
2017-10-09 19:01:32.313597: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 19:01:32.313626: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 19:01:32.313631: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 19:01:32.313636: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 19:01:32.313640: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 19:01:32.537983: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:02:00.0
Total memory: 10.91GiB
Free memory: 10.18GiB
2017-10-09 19:01:32.538014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-10-09 19:01:32.538020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-10-09 19:01:32.538027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0)
processed 1000 examples
processed 2000 examples
unpickled total 2952 examples
unpickled total 0 examples
train examples -> 2952, val examples -> 0
fail to restore model experiment/checkpoint/experiment_0_batch_24
Epoch: [ 0], [ 0/ 123] time: 6.0433, d_loss: 10.14546, g_loss: 274.66415, category_loss: 0.00000, cheat_loss: 173.93898, const_loss: 7.20869, l1_loss: 93.51649, tv_loss: 0.00000
Epoch: [ 0], [ 1/ 123] time: 8.1327, d_loss: 146.35306, g_loss: 78.34413, category_loss: 0.00000, cheat_loss: 0.00000, const_loss: 0.12824, l1_loss: 78.21589, tv_loss: 0.00000
Epoch: [ 0], [ 2/ 123] time: 10.2258, d_loss: 181.37769, g_loss: 152.27695, category_loss: 0.00000, cheat_loss: 97.29399, const_loss: 0.06894, l1_loss: 54.91401, tv_loss: 0.00000
Epoch: [ 0], [ 3/ 123] time: 12.3139, d_loss: 60.48555, g_loss: 35.58843, category_loss: 0.00000, cheat_loss: 0.00000, const_loss: 0.04017, l1_loss: 35.54825, tv_loss: 0.00000
Epoch: [ 0], [ 4/ 123] time: 14.4027, d_loss: 68.51615, g_loss: 75.03025, category_loss: 0.00000, cheat_loss: 49.46065, const_loss: 0.06694, l1_loss: 25.50266, tv_loss: 0.00000
Epoch: [ 0], [ 5/ 123] time: 16.5009, d_loss: 16.37053, g_loss: 24.69693, category_loss: 0.00000, cheat_loss: 3.03605, const_loss: 0.07840, l1_loss: 21.58248, tv_loss: 0.00000
Epoch: [ 0], [ 6/ 123] time: 18.5991, d_loss: 14.45806, g_loss: 92.51143, category_loss: 0.00000, cheat_loss: 73.25694, const_loss: 0.09051, l1_loss: 19.16398, tv_loss: 0.00000
Epoch: [ 0], [ 7/ 123] time: 20.6916, d_loss: 39.45554, g_loss: 19.35016, category_loss: 0.00000, cheat_loss: 0.27510, const_loss: 0.23808, l1_loss: 18.83698, tv_loss: 0.00000
Epoch: [ 0], [ 8/ 123] time: 22.7883, d_loss: 47.10860, g_loss: 71.88419, category_loss: 0.00000, cheat_loss: 50.56374, const_loss: 0.10448, l1_loss: 21.21597, tv_loss: 0.00000
Epoch: [ 0], [ 9/ 123] time: 24.8894, d_loss: 34.83733, g_loss: 58.39854, category_loss: 0.00000, cheat_loss: 30.93460, const_loss: 0.70017, l1_loss: 26.76377, tv_loss: 0.00000
Epoch: [ 0], [ 10/ 123] time: 26.9899, d_loss: 103.12030, g_loss: 47.40792, category_loss: 0.00000, cheat_loss: 2.60304, const_loss: 19.21144, l1_loss: 25.59344, tv_loss: 0.00000
......
Epoch: [ 0], [ 121/ 123] time: 263.4440, d_loss: 33.96601, g_loss: 20.80124, category_loss: 0.00000, cheat_loss: 0.01192, const_loss: 0.69635, l1_loss: 20.09298, tv_loss: 0.00000
Epoch: [ 0], [ 122/ 123] time: 265.5618, d_loss: 24.81020, g_loss: 62.80609, category_loss: 0.00000, cheat_loss: 42.07758, const_loss: 0.16414, l1_loss: 20.56438, tv_loss: 0.00i000
^CTraceback (most recent call last):
File "train.py", line 62, in
tf.app.run()
File "/opt/software/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "train.py", line 58, in main
sample_steps=args.sample_steps, checkpoint_steps=args.checkpoint_steps)
File "/home/customer/zi2zi-master/model/unet.py", line 539, in train
self.validate_model(val_batch_iter, ei, counter)
File "/home/customer/zi2zi-master/model/unet.py", line 325, in validate_model
labels, images = next(val_iter)
File "/home/customer/zi2zi-master/model/dataset.py", line 101, in get_val_iter
val_batch_iter = get_batch_iter(val_examples, batch_size, augment=False)
File "/home/customer/zi2zi-master/model/dataset.py", line 39, in get_batch_iter
def process(img):
KeyboardInterrupt

in the train.py, it fail to restore model experiment/checkpoint/experiment_0_batch_24, what is wrong with it?

NVIDIA 2GB can I use?

Can I use 2GBNVIDIA run this process?

ValueError: Could not find a format to write the specified file in mode 'I'

Hi yc,
I encountered the following error doing interpolation with running infer.py, I've pip installed imageio and ffmpeg already, how come this error still exists? BTW, actually when i “dpkg -L imageio'' it says imageio is not installed. I'm not quite sure what is the problem:

Traceback (most recent call last):
File "infer.py", line 67, in
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "infer.py", line 62, in main
compile_frames_to_gif(args.save_dir, gif_path)
File "/home/Downloads/zi2zi-master/model/utils.py", line 76, in compile_frames_to_gif
imageio.mimsave(gif_file, images, duration=0.1)
File "/usr/local/lib/python2.7/dist-packages/imageio/core/functions.py", line 302, in mimwrite
writer = get_writer(uri, format, 'I', **kwargs)
File "/usr/local/lib/python2.7/dist-packages/imageio/core/functions.py", line 153, in get_writer
'in mode %r' % mode)
ValueError: Could not find a format to write the specified file in mode 'I'

Resuming training should not reset counters and training data iterator

When using the same data to resume the training process, the model is restored but the epoch and batch counters are reset and training data is fed from to training from the beginning. Would it be better to resume the exact epoch and batch when resume the training process.

生成背景是绿色

你好，我训练了之后打印出来的生成图背景是绿色的，可以请教一下原因吗？
未曾改过代码，邮箱联系一下吧，谢谢
[email protected]

preprocess command in readme "python preprocess.py" should be changed to "python font2img.py"

Need a way to run it in Windows

Tensorflow doesn't offer cp27 win32 binaries.

I didn't get this

Exception: no need to interpolate yourself unless you are a narcissist

IOError: [Errno 2] No such file or directory: 'dir/0_0000.jpg'

This is a SOLVED problem.

When running the preprocess command

python font2img.py --src_font=src.ttf
                   --dst_font=tgt.otf
                   --charset=CN 
                   --sample_count=1000
                   --sample_dir=dir
                   --label=0
                   --filter=1
                   --shuffle=1

An error occurred as title, which informs that the file is not exist by the code
[line 87 | font2img.py] e.save(os.path.join(sample_dir, "%d_%04d.jpg" % (label, count)))

This is not a bug of PIL(pillow). It is coursed by the command. If the sample_dir is not exits, the program will through an error rather than create the sample_dir.

In conclusion, BEFORE run the preprocess command, sample_dir should be mkdir MANUALLY.

How 2 finetuning with your font27

About the meaning and usage of some options

In "package.py", "train.obj" and "val.obj" are generated, but what is different? What are these used for? I can not understand these differences.
Please tell me about "schedule option" of python train.py. How should schedule be decided? By default (epoch = 40, schedule = 20) and schedule should be halved for epoch? I do not understand the meaning of "schedule".
How do you use "Pretrained Model" when doing "train.py"? I downloaded the "Pretrained Model" of the distributed "font 27", but I do not know how to use it. Is there an option command to use this when doing "train.py"? How do you specify checkpoints?

infer.py

is the infer.py is used to generate?

The final trained model

Hi, @kaonashi-tyc , can you supply one final trained model for directly testing?

image saving problem in font2img.py

hi @kaonashi-tyc ,
When I use font2img.py to generate jpg format images, I find the generated images have some noise around the strokes. I think it will degrade the training result more or less. Then after several experiments I find the problem lies in PIL image save function. Exactly in font2img.py line 87

e.save(os.path.join(sample_dir, "%d_%04d.jpg" % (label, count)))

I change this line by

e.save(os.path.join(sample_dir, "%d_%04d.jpg" % (label, count)), quality=100)

And the noise disappear.

Considering using a more explicit variable name

In the code:

class UNet(object):
    def __init__(self, experiment_dir=None, experiment_id=0, batch_size=16, input_width=256, output_width=256,
                 generator_dim=64, discriminator_dim=64, L1_penalty=100, Lconst_penalty=15, Ltv_penalty=0.0,
                 embedding_num=1, embedding_dim=128, input_filters=3, output_filters=3):

self.input_filters = input_filters
self.output_filters = output_filters

I think changing input_filters and output_filters names to input_channels and output_channels is more explicit.

how to train 27 different fonts as a single model?

As mentioned in your blog, u use SIMSUM font as source font , and transfer it to 27 different fonts. how can I train a model that contain 27 different font styles??

infer.py question

你好,有几个问题,希望能解答一下.
在测试的时候,输入图片的大小是固定的吗(我直接用fonts27测试的)? 并且如果一张图片中包含了多个文字的话,效果就会非常差,是不是一张图片包含的文字数目也有限制呢?

How can I get a gif image?

Fonts used during training

I understand that you can not share the fonts due to copyright issues. Could you comment which fonts did you use for the training?

Thank you

how to transfer my handwriting font into other style

when doing this step :
python font2img.py --src_font=src.ttf
--dst_font=tgt.otf
it generated 1000 random font image ,can i use my expected font to generate image?
can i use my handwriting font image and transfer it into my expect style?
thank you ！

运行font2img.py 时，使用的是下载的两个ttf字体和otf字体，生成的1000张图片中的字都是随机的，能不能选用我想要生成的字？此外，这个模型能不能把我自己手写的字拍成的图片转变为我期待的风格？
另外，我在运行train.py时，总是出错如下：
2017-05-08 14:20:19.059104: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.41GiB
2017-05-08 14:20:19.059115: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit: 1605828608
InUse: 1517696256
MaxInUse: 1605828608
NumAllocs: 353
MaxAllocSize: 134217728

2017-05-08 14:20:19.059144: W tensorflow/core/common_runtime/bfc_allocator.cc:277] xx*****************************************xx__*******************xx
2017-05-08 14:20:19.059163: W tensorflow/core/framework/op_kernel.cc:1152] Resource exhausted: OOM when allocating tensor with shape[5,5,512,1024]
Traceback (most recent call last):
File "train.py", line 62, in
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "train.py", line 58, in main
sample_steps=args.sample_steps, checkpoint_steps=args.checkpoint_steps)
File "/home/xsm/zi2zi-master/model/unet.py", line 465, in train
tf.global_variables_initializer().run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1552, in run
_run_using_default_session(self, feed_dict, self.graph, session)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3776, in _run_using_default_session
session.run(operation, feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 778, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 982, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1032, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[512]
[[Node: generator/g_d4_deconv/b/Assign = Assign[T=DT_FLOAT, _class=["loc:@generator/g_d4_deconv/b"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](generator/g_d4_deconv/b, generator/g_d4_deconv/b/Initializer/Const)]]

Caused by op u'generator/g_d4_deconv/b/Assign', defined at:
File "train.py", line 62, in
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "train.py", line 51, in main
model.build_model(is_training=True, inst_norm=args.inst_norm)
File "/home/xsm/zi2zi-master/model/unet.py", line 167, in build_model
inst_norm=inst_norm)
File "/home/xsm/zi2zi-master/model/unet.py", line 133, in generator
output = self.decoder(embedded, enc_layers, embedding_ids, inst_norm, is_training=is_training, reuse=reuse)
File "/home/xsm/zi2zi-master/model/unet.py", line 119, in decoder
d4 = decode_layer(d3, s16, self.generator_dim * 8, layer=4, enc_layer=encoding_layers["e4"])
File "/home/xsm/zi2zi-master/model/unet.py", line 98, in decode_layer
output_width, output_filters], scope="g_d%d_deconv" % layer)
File "/home/xsm/zi2zi-master/model/ops.py", line 35, in deconv2d
biases = tf.get_variable('b', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1049, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 948, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 356, in get_variable
validate_shape=validate_shape, use_resource=use_resource)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 341, in _true_getter
use_resource=use_resource)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 714, in _get_single_variable
validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 197, in init
expected_shape=expected_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 306, in _init_from_args
validate_shape=validate_shape).op
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/state_ops.py", line 270, in assign
validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_state_ops.py", line 47, in assign
use_locking=use_locking, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1228, in init
self._traceback = _extract_stack()

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[512]
[[Node: generator/g_d4_deconv/b/Assign = Assign[T=DT_FLOAT, _class=["loc:@generator/g_d4_deconv/b"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](generator/g_d4_deconv/b, generator/g_d4_deconv/b/Initializer/Const)]]

I would like to apply the project to Chinese calligraphy. I wonder if it is feasible?

For example: I have 3000 character pictures of a calligrapher, I am now looking for a ttf font as a source, with the calligrapher character pictures as the destination for training.
A little bit different from this project, it will appear, the same character, calligrapher will have a variety of writing.
Generated images with ttf font when generated.
Can you predict whether such training can achieve the desired result?
Thank you very much for the project. It was great!

如何利用作者的font27模型接着训练

由于自身GPU太菜，希望拿作者的预训练模型接着训练，但是放在experiment_0_batch_16文件夹下一只报错，是不是需要一些特殊的设置？我就是拿简单的两个ttf文件作为src.ttf 和 tgt.ttf，求教~谢谢

How to use the pretrained model?

Awesome Project! But i can not figure out how to the pretrained model. Should we put the pretrained model into the directory experiment/checkpoint/? Does it need rename?

pretained model how to train ?

Hello, I run your code. It is so amazing. I do not wanna start training, I would like to start from this 27-character (pretained model ) training a new style font. Please tell me how to do it specifically. p.s:Wasserstein GAN(WGAN) maybe is another better try.

train.py error

@kaonashi-tyc
in font2img, I use simsun as source font, 硬笔行书 as target font, use about 3000 characters in a txt file.

when i train it with
python train.py --experiment_dir=experiment --experiment_id=0 --batch_size=8 --lr=0.001 --epoch=20 --sample_steps=50 --schedule=20 --L1_penalty=100 --Lconst_penalty=15

the error follow: if the GPU of my computer is not enough, I think it may take more time to train but not a error. Why? Thanks a lot.

2017-09-20 21:04:15.919510: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-20 21:04:15.919538: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-20 21:04:15.919548: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-20 21:04:15.919556: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-20 21:04:15.919565: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-09-20 21:04:16.107879: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-20 21:04:16.108359: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] Found device 0 with properties:
name: GeForce 810A
major: 3 minor: 5 memoryClockRate (GHz) 0.758
pciBusID 0000:01:00.0
Total memory: 1.96GiB
Free memory: 1.74GiB
2017-09-20 21:04:16.108380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929] DMA: 0
2017-09-20 21:04:16.108386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:939] 0: Y
2017-09-20 21:04:16.108404: I tensorflow/core/common_runtime/gpu/gpu_device.cc:998] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 810A, pci bus id: 0000:01:00.0)
processed 1000 examples
processed 2000 examples
unpickled total 2983 examples
unpickled total 0 examples
train examples -> 2983, val examples -> 0
fail to restore model experiment/checkpoint/experiment_0_batch_8
2017-09-20 21:04:44.328082: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 16.00MiB. Current allocation summary follows.
2017-09-20 21:04:44.328123: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328134: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512): Total Chunks: 1, Chunks in use: 0 512B allocated for chunks. 4B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328144: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024): Total Chunks: 1, Chunks in use: 0 1.8KiB allocated for chunks. 4B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328152: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328159: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
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2017-09-20 21:04:44.328177: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328183: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328189: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328193: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328200: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144): Total Chunks: 1, Chunks in use: 0 495.5KiB allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328205: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328210: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576): Total Chunks: 1, Chunks in use: 0 1.56MiB allocated for chunks. 1.56MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328216: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152): Total Chunks: 2, Chunks in use: 0 5.12MiB allocated for chunks. 5.12MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328221: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304): Total Chunks: 1, Chunks in use: 0 6.50MiB allocated for chunks. 6.00MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328226: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328230: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328235: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328239: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328244: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328249: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.328254: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 16.00MiB was 16.00MiB, Chunk State:
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2017-09-20 21:04:44.361243: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c495a00 of size 38400
2017-09-20 21:04:44.361249: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c49f000 of size 38400
2017-09-20 21:04:44.361254: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8600 of size 256
2017-09-20 21:04:44.361260: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8700 of size 256
2017-09-20 21:04:44.361266: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8800 of size 512
2017-09-20 21:04:44.361271: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8a00 of size 19200
2017-09-20 21:04:44.361276: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4ad500 of size 819200
2017-09-20 21:04:44.361281: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c575500 of size 3276800
2017-09-20 21:04:44.361286: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c895500 of size 13107200
2017-09-20 21:04:44.361292: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45d515500 of size 26214400
2017-09-20 21:04:44.361297: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45ee15500 of size 32768000
2017-09-20 21:04:44.361304: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x460d55500 of size 53193472
2017-09-20 21:04:44.361310: I tensorflow/core/common_runtime/bfc_allocator.cc:693] Summary of in-use Chunks by size:
2017-09-20 21:04:44.361321: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 154 Chunks of size 256 totalling 38.5KiB
2017-09-20 21:04:44.361329: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 52 Chunks of size 512 totalling 26.0KiB
2017-09-20 21:04:44.361335: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 51 Chunks of size 1024 totalling 51.0KiB
2017-09-20 21:04:44.361341: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
2017-09-20 21:04:44.361347: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 170 Chunks of size 2048 totalling 340.0KiB
2017-09-20 21:04:44.361353: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4096 totalling 4.0KiB
2017-09-20 21:04:44.361360: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 19200 totalling 75.0KiB
2017-09-20 21:04:44.361366: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 38400 totalling 300.0KiB
2017-09-20 21:04:44.361372: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 65536 totalling 64.0KiB
2017-09-20 21:04:44.361378: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 262144 totalling 256.0KiB
2017-09-20 21:04:44.361384: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 6 Chunks of size 819200 totalling 4.69MiB
2017-09-20 21:04:44.361391: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 911872 totalling 890.5KiB
2017-09-20 21:04:44.361397: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1112832 totalling 1.06MiB
2017-09-20 21:04:44.361402: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 1638400 totalling 7.81MiB
2017-09-20 21:04:44.361409: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 7 Chunks of size 2097152 totalling 14.00MiB
2017-09-20 21:04:44.361415: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 3276800 totalling 25.00MiB
2017-09-20 21:04:44.361421: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4194304 totalling 4.00MiB
2017-09-20 21:04:44.361427: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 6291456 totalling 6.00MiB
2017-09-20 21:04:44.361433: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 6553600 totalling 18.75MiB
2017-09-20 21:04:44.361440: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 8973824 totalling 8.56MiB
2017-09-20 21:04:44.361445: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 9306112 totalling 8.88MiB
2017-09-20 21:04:44.361452: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 13107200 totalling 100.00MiB
2017-09-20 21:04:44.361458: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 16777216 totalling 16.00MiB
2017-09-20 21:04:44.361465: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 16 Chunks of size 26214400 totalling 400.00MiB
2017-09-20 21:04:44.361471: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 29953024 totalling 28.57MiB
2017-09-20 21:04:44.361477: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 32481280 totalling 30.98MiB
2017-09-20 21:04:44.361483: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 32768000 totalling 156.25MiB
2017-09-20 21:04:44.361489: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 40894464 totalling 39.00MiB
2017-09-20 21:04:44.361496: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 49020928 totalling 46.75MiB
2017-09-20 21:04:44.361502: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 12 Chunks of size 52428800 totalling 600.00MiB
2017-09-20 21:04:44.361508: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 53193472 totalling 50.73MiB
2017-09-20 21:04:44.361514: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.53GiB
2017-09-20 21:04:44.361523: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit: 1659568128
InUse: 1645230336
MaxInUse: 1652048384
NumAllocs: 567
MaxAllocSize: 53193472

2017-09-20 21:04:44.361556: W tensorflow/core/common_runtime/bfc_allocator.cc:277] ****************************************************************************************************
2017-09-20 21:04:44.361575: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 32.00MiB. Current allocation summary follows.
2017-09-20 21:04:44.361577: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
2017-09-20 21:04:44.361602: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361613: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512): Total Chunks: 1, Chunks in use: 0 512B allocated for chunks. 4B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361623: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024): Total Chunks: 1, Chunks in use: 0 1.8KiB allocated for chunks. 4B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361632: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361641: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361651: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361660: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361669: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361679: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361688: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361699: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144): Total Chunks: 1, Chunks in use: 0 495.5KiB allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361708: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361716: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576): Total Chunks: 1, Chunks in use: 0 1.56MiB allocated for chunks. 1.56MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361724: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152): Total Chunks: 2, Chunks in use: 0 5.12MiB allocated for chunks. 5.12MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361727: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.361734: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304): Total Chunks: 1, Chunks in use: 0 6.50MiB allocated for chunks. 6.00MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361743: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361751: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361759: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.361760: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361782: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361791: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361795: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.361800: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:44.361810: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 32.00MiB was 32.00MiB, Chunk State:
2017-09-20 21:04:44.361817: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060000 of size 1280
2017-09-20 21:04:44.361823: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060500 of size 256
2017-09-20 21:04:44.361823: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.361835: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060600 of size 256
2017-09-20 21:04:44.361861: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060700 of size 256
2017-09-20 21:04:44.361869: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060800 of size 256
2017-09-20 21:04:44.361874: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060900 of size 256
2017-09-20 21:04:44.361879: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060a00 of size 256
2017-09-20 21:04:44.361886: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060b00 of size 512
2017-09-20 21:04:44.361889: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.361892: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060d00 of size 512
2017-09-20 21:04:44.361908: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060f00 of size 512
2017-09-20 21:04:44.361913: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061100 of size 256
2017-09-20 21:04:44.361919: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061200 of size 256
2017-09-20 21:04:44.361926: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061300 of size 1024
2017-09-20 21:04:44.361926: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.361936: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061700 of size 1024
2017-09-20 21:04:44.361953: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061b00 of size 1024
2017-09-20 21:04:44.361958: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061f00 of size 256
2017-09-20 21:04:44.361961: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401062000 of size 256
2017-09-20 21:04:44.361965: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401062100 of size 2048
2017-09-20 21:04:44.361970: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401062900 of size 2048
2017-09-20 21:04:44.361971: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.361974: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063100 of size 2048
2017-09-20 21:04:44.361990: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063900 of size 256
2017-09-20 21:04:44.361995: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063a00 of size 256
2017-09-20 21:04:44.362001: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063b00 of size 2048
2017-09-20 21:04:44.362006: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.362007: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401064300 of size 2048
2017-09-20 21:04:44.362026: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401064b00 of size 2048
2017-09-20 21:04:44.362033: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065300 of size 256
2017-09-20 21:04:44.362040: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065400 of size 256
2017-09-20 21:04:44.362040: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.362053: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065500 of size 2048
2017-09-20 21:04:44.362068: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065d00 of size 2048
2017-09-20 21:04:44.362073: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066500 of size 2048
2017-09-20 21:04:44.362079: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066d00 of size 256
2017-09-20 21:04:44.362084: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066e00 of size 256
2017-09-20 21:04:44.362089: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066f00 of size 2048
2017-09-20 21:04:44.362095: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401067700 of size 2048
2017-09-20 21:04:44.362098: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.362100: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401067f00 of size 2048
2017-09-20 21:04:44.362111: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401068700 of size 256
2017-09-20 21:04:44.362117: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401068800 of size 256
2017-09-20 21:04:44.362123: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401068900 of size 2048
2017-09-20 21:04:44.362128: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401069100 of size 2048
2017-09-20 21:04:44.362130: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.362134: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401069900 of size 2048
2017-09-20 21:04:44.362144: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106a100 of size 256
2017-09-20 21:04:44.362149: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106a200 of size 256
2017-09-20 21:04:44.362155: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106a300 of size 2048
2017-09-20 21:04:44.362160: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106ab00 of size 2048
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2017-09-20 21:04:44.364959: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45d515500 of size 26214400
2017-09-20 21:04:44.364965: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45ee15500 of size 32768000
2017-09-20 21:04:44.364971: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x460d55500 of size 53193472
2017-09-20 21:04:44.364978: I tensorflow/core/common_runtime/bfc_allocator.cc:693] Summary of in-use Chunks by size:
2017-09-20 21:04:44.364988: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 154 Chunks of size 256 totalling 38.5KiB
2017-09-20 21:04:44.364996: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 52 Chunks of size 512 totalling 26.0KiB
2017-09-20 21:04:44.365003: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 51 Chunks of size 1024 totalling 51.0KiB
2017-09-20 21:04:44.365010: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
2017-09-20 21:04:44.365017: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 170 Chunks of size 2048 totalling 340.0KiB
2017-09-20 21:04:44.365024: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4096 totalling 4.0KiB
2017-09-20 21:04:44.365030: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 19200 totalling 75.0KiB
2017-09-20 21:04:44.365037: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 38400 totalling 300.0KiB
2017-09-20 21:04:44.365044: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 65536 totalling 64.0KiB
2017-09-20 21:04:44.365051: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 262144 totalling 256.0KiB
2017-09-20 21:04:44.365058: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 6 Chunks of size 819200 totalling 4.69MiB
2017-09-20 21:04:44.365065: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 911872 totalling 890.5KiB
2017-09-20 21:04:44.365071: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1112832 totalling 1.06MiB
2017-09-20 21:04:44.365078: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 1638400 totalling 7.81MiB
2017-09-20 21:04:44.365084: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 7 Chunks of size 2097152 totalling 14.00MiB
2017-09-20 21:04:44.365091: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 3276800 totalling 25.00MiB
2017-09-20 21:04:44.365098: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4194304 totalling 4.00MiB
2017-09-20 21:04:44.365104: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 6291456 totalling 6.00MiB
2017-09-20 21:04:44.365111: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 6553600 totalling 18.75MiB
2017-09-20 21:04:44.365118: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 8973824 totalling 8.56MiB
2017-09-20 21:04:44.365125: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 9306112 totalling 8.88MiB
2017-09-20 21:04:44.365132: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 13107200 totalling 100.00MiB
2017-09-20 21:04:44.365139: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 16777216 totalling 16.00MiB
2017-09-20 21:04:44.365146: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 16 Chunks of size 26214400 totalling 400.00MiB
2017-09-20 21:04:44.365153: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 29953024 totalling 28.57MiB
2017-09-20 21:04:44.365160: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 32481280 totalling 30.98MiB
2017-09-20 21:04:44.365166: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 32768000 totalling 156.25MiB
2017-09-20 21:04:44.365173: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 40894464 totalling 39.00MiB
2017-09-20 21:04:44.365180: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 49020928 totalling 46.75MiB
2017-09-20 21:04:44.365187: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 12 Chunks of size 52428800 totalling 600.00MiB
2017-09-20 21:04:44.365194: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 53193472 totalling 50.73MiB
2017-09-20 21:04:44.365200: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.53GiB
2017-09-20 21:04:44.365208: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit: 1659568128
InUse: 1645230336
MaxInUse: 1652048384
NumAllocs: 567
MaxAllocSize: 53193472

2017-09-20 21:04:44.365236: W tensorflow/core/common_runtime/bfc_allocator.cc:277] ****************************************************************************************************
2017-09-20 21:04:44.365250: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,128,128,64]
2017-09-20 21:04:44.362160: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365311: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365333: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365352: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365371: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365397: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365417: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365436: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365456: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365475: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365494: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365513: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:44.365531: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
2017-09-20 21:04:54.365421: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 32.00MiB. Current allocation summary follows.
2017-09-20 21:04:54.365461: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365469: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512): Total Chunks: 1, Chunks in use: 0 512B allocated for chunks. 4B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365479: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024): Total Chunks: 1, Chunks in use: 0 1.8KiB allocated for chunks. 4B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365489: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365499: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365507: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365515: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365524: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365532: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365541: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365551: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144): Total Chunks: 1, Chunks in use: 0 495.5KiB allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365558: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365564: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576): Total Chunks: 1, Chunks in use: 0 1.56MiB allocated for chunks. 1.56MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365571: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152): Total Chunks: 2, Chunks in use: 0 5.12MiB allocated for chunks. 5.12MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365577: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304): Total Chunks: 1, Chunks in use: 0 6.50MiB allocated for chunks. 6.00MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365583: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365588: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365594: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365599: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365605: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365614: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456): Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-09-20 21:04:54.365623: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 32.00MiB was 32.00MiB, Chunk State:
2017-09-20 21:04:54.365630: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060000 of size 1280
2017-09-20 21:04:54.365635: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060500 of size 256
2017-09-20 21:04:54.365641: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060600 of size 256
2017-09-20 21:04:54.365648: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060700 of size 256
2017-09-20 21:04:54.365653: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060800 of size 256
2017-09-20 21:04:54.365660: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060900 of size 256
2017-09-20 21:04:54.365665: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060a00 of size 256
2017-09-20 21:04:54.365672: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060b00 of size 512
2017-09-20 21:04:54.365678: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060d00 of size 512
2017-09-20 21:04:54.365684: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401060f00 of size 512
2017-09-20 21:04:54.365690: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061100 of size 256
2017-09-20 21:04:54.365696: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061200 of size 256
2017-09-20 21:04:54.365702: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061300 of size 1024
2017-09-20 21:04:54.365708: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061700 of size 1024
2017-09-20 21:04:54.365714: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061b00 of size 1024
2017-09-20 21:04:54.365720: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401061f00 of size 256
2017-09-20 21:04:54.365726: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401062000 of size 256
2017-09-20 21:04:54.365732: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401062100 of size 2048
2017-09-20 21:04:54.365738: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401062900 of size 2048
2017-09-20 21:04:54.365744: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063100 of size 2048
2017-09-20 21:04:54.365750: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063900 of size 256
2017-09-20 21:04:54.365756: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063a00 of size 256
2017-09-20 21:04:54.365762: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401063b00 of size 2048
2017-09-20 21:04:54.365768: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401064300 of size 2048
2017-09-20 21:04:54.365773: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401064b00 of size 2048
2017-09-20 21:04:54.365779: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065300 of size 256
2017-09-20 21:04:54.365785: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065400 of size 256
2017-09-20 21:04:54.365791: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065500 of size 2048
2017-09-20 21:04:54.365797: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401065d00 of size 2048
2017-09-20 21:04:54.365803: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066500 of size 2048
2017-09-20 21:04:54.365809: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066d00 of size 256
2017-09-20 21:04:54.365815: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066e00 of size 256
2017-09-20 21:04:54.365821: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401066f00 of size 2048
2017-09-20 21:04:54.365827: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401067700 of size 2048
2017-09-20 21:04:54.365833: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401067f00 of size 2048
2017-09-20 21:04:54.365839: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401068700 of size 256
2017-09-20 21:04:54.365845: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401068800 of size 256
2017-09-20 21:04:54.365851: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401068900 of size 2048
2017-09-20 21:04:54.365856: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401069100 of size 2048
2017-09-20 21:04:54.365863: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x401069900 of size 2048
2017-09-20 21:04:54.365868: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106a100 of size 256
2017-09-20 21:04:54.365875: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106a200 of size 256
2017-09-20 21:04:54.365880: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106a300 of size 2048
2017-09-20 21:04:54.365886: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106ab00 of size 2048
2017-09-20 21:04:54.365892: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106b300 of size 2048
2017-09-20 21:04:54.365898: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106bb00 of size 256
2017-09-20 21:04:54.365904: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106bc00 of size 256
2017-09-20 21:04:54.365910: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106bd00 of size 2048
2017-09-20 21:04:54.365916: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x40106c500 of size 2048
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2017-09-20 21:04:54.367992: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x42ecea600 of size 1638400
2017-09-20 21:04:54.367999: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x42ee88000 of size 512
2017-09-20 21:04:54.368005: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x42eed8200 of size 1792
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2017-09-20 21:04:54.368016: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x42ef55e00 of size 3276800
2017-09-20 21:04:54.368022: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x42f876a00 of size 6815744
2017-09-20 21:04:54.368028: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x4300f8300 of size 2097152
2017-09-20 21:04:54.368035: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x441860000 of size 2048
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2017-09-20 21:04:54.368082: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x441864000 of size 32768000
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2017-09-20 21:04:54.368142: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x4456e8000 of size 52428800
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2017-09-20 21:04:54.368308: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x4582f3000 of size 26214400
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2017-09-20 21:04:54.368320: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45b4f3000 of size 1024
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2017-09-20 21:04:54.368355: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45b4f4800 of size 6553600
2017-09-20 21:04:54.368361: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45bb34800 of size 6553600
2017-09-20 21:04:54.368367: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c174800 of size 512
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2017-09-20 21:04:54.368403: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c175400 of size 1638400
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2017-09-20 21:04:54.368415: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c495400 of size 256
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2017-09-20 21:04:54.368427: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c495600 of size 256
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2017-09-20 21:04:54.368451: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c495a00 of size 38400
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2017-09-20 21:04:54.368462: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8600 of size 256
2017-09-20 21:04:54.368468: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8700 of size 256
2017-09-20 21:04:54.368474: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8800 of size 512
2017-09-20 21:04:54.368480: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4a8a00 of size 19200
2017-09-20 21:04:54.368486: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c4ad500 of size 819200
2017-09-20 21:04:54.368492: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c575500 of size 3276800
2017-09-20 21:04:54.368497: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45c895500 of size 13107200
2017-09-20 21:04:54.368503: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45d515500 of size 26214400
2017-09-20 21:04:54.368510: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x45ee15500 of size 32768000
2017-09-20 21:04:54.368516: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x460d55500 of size 53193472
2017-09-20 21:04:54.368522: I tensorflow/core/common_runtime/bfc_allocator.cc:693] Summary of in-use Chunks by size:
2017-09-20 21:04:54.368530: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 154 Chunks of size 256 totalling 38.5KiB
2017-09-20 21:04:54.368537: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 52 Chunks of size 512 totalling 26.0KiB
2017-09-20 21:04:54.368544: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 51 Chunks of size 1024 totalling 51.0KiB
2017-09-20 21:04:54.368551: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
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2017-09-20 21:04:54.368564: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4096 totalling 4.0KiB
2017-09-20 21:04:54.368571: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 19200 totalling 75.0KiB
2017-09-20 21:04:54.368578: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 38400 totalling 300.0KiB
2017-09-20 21:04:54.368585: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 65536 totalling 64.0KiB
2017-09-20 21:04:54.368591: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 262144 totalling 256.0KiB
2017-09-20 21:04:54.368598: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 6 Chunks of size 819200 totalling 4.69MiB
2017-09-20 21:04:54.368605: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 911872 totalling 890.5KiB
2017-09-20 21:04:54.368611: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1112832 totalling 1.06MiB
2017-09-20 21:04:54.368618: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 1638400 totalling 7.81MiB
2017-09-20 21:04:54.368625: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 7 Chunks of size 2097152 totalling 14.00MiB
2017-09-20 21:04:54.368632: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 3276800 totalling 25.00MiB
2017-09-20 21:04:54.368638: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4194304 totalling 4.00MiB
2017-09-20 21:04:54.368644: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 6291456 totalling 6.00MiB
2017-09-20 21:04:54.368652: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 6553600 totalling 18.75MiB
2017-09-20 21:04:54.368658: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 8973824 totalling 8.56MiB
2017-09-20 21:04:54.368665: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 9306112 totalling 8.88MiB
2017-09-20 21:04:54.368671: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 13107200 totalling 100.00MiB
2017-09-20 21:04:54.368678: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 16777216 totalling 16.00MiB
2017-09-20 21:04:54.368685: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 16 Chunks of size 26214400 totalling 400.00MiB
2017-09-20 21:04:54.368692: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 29953024 totalling 28.57MiB
2017-09-20 21:04:54.368699: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 32481280 totalling 30.98MiB
2017-09-20 21:04:54.368706: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 32768000 totalling 156.25MiB
2017-09-20 21:04:54.368712: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 40894464 totalling 39.00MiB
2017-09-20 21:04:54.368719: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 49020928 totalling 46.75MiB
2017-09-20 21:04:54.368726: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 12 Chunks of size 52428800 totalling 600.00MiB
2017-09-20 21:04:54.368733: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 53193472 totalling 50.73MiB
2017-09-20 21:04:54.368739: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.53GiB
2017-09-20 21:04:54.368748: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit: 1659568128
InUse: 1645230336
MaxInUse: 1652048384
NumAllocs: 567
MaxAllocSize: 53193472

2017-09-20 21:04:54.368778: W tensorflow/core/common_runtime/bfc_allocator.cc:277] ****************************************************************************************************
2017-09-20 21:04:54.368795: W tensorflow/core/framework/op_kernel.cc:1169] Resource exhausted: OOM when allocating tensor with shape[8,128,128,64]
Traceback (most recent call last):
File "train.py", line 62, in
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "train.py", line 58, in main
sample_steps=args.sample_steps, checkpoint_steps=args.checkpoint_steps)
File "/home/xsm/zi2zi-master/model/unet.py", line 503, in train
learning_rate: current_lr
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 786, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 994, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1129, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1149, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
[[Node: Mean_4/_165 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_2023_Mean_4", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]]

Caused by op u'gradients/discriminator/Maximum_3_grad/zeros', defined at:
File "train.py", line 62, in
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "train.py", line 58, in main
sample_steps=args.sample_steps, checkpoint_steps=args.checkpoint_steps)
File "/home/xsm/zi2zi-master/model/unet.py", line 463, in train
d_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5).minimize(loss_handle.d_loss, var_list=d_vars)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 315, in minimize
grad_loss=grad_loss)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 386, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 562, in gradients
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 368, in _MaybeCompile
return grad_fn() # Exit early
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 562, in
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py", line 765, in _MaximumGrad
return _MaximumMinimumGrad(op, grad, math_ops.greater_equal)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py", line 752, in _MaximumMinimumGrad
zeros = array_ops.zeros(gradshape, gdtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1387, in zeros
output = fill(shape, constant(zero, dtype=dtype), name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1183, in fill
result = _op_def_lib.apply_op("Fill", dims=dims, value=value, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2340, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1232, in init
self._traceback = _extract_stack()

...which was originally created as op u'discriminator/Maximum_3', defined at:
File "train.py", line 62, in
tf.app.run()
[elided 0 identical lines from previous traceback]
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "train.py", line 51, in main
model.build_model(is_training=True, inst_norm=args.inst_norm)
File "/home/xsm/zi2zi-master/model/unet.py", line 173, in build_model
real_D, real_D_logits, real_category_logits = self.discriminator(real_AB, is_training=is_training, reuse=False)
File "/home/xsm/zi2zi-master/model/unet.py", line 146, in discriminator
is_training, scope="d_bn_3"))
File "/home/xsm/zi2zi-master/model/ops.py", line 42, in lrelu
return tf.maximum(x, leak * x)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1373, in maximum
result = _op_def_lib.apply_op("Maximum", x=x, y=y, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2340, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1232, in init
self._traceback = _extract_stack()

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[8,32,32,512]
[[Node: gradients/discriminator/Maximum_3_grad/zeros = Fill[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/discriminator/Maximum_3_grad/Shape_2, gradients/discriminator/Maximum_3_grad/zeros/Const)]]
[[Node: Mean_4/_165 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_2023_Mean_4", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]]

how much font data is needed?

awesome job!
I'm wondering how many kinds of fonts, characters are needed for training this model?
meanwhile, could you please share the dataset? thank you.

package.py: error: argument --split_ratio: invalid float value: '[0,1]'

Hi, I got the following message when running package.py :

package.py: error: argument --split_ratio: invalid float value: '[0,1]'

Any hints I could solve it? thanks!

why the input image of generator contains target text ?

the shape of input is (batch_size,256,256,6), the first 3 chanels is image of target font, why？I thought it would make generator cheat...
In my test, if set all value of first 3 chanels to zero. the image generated was not the same, although the difference was not big as I thought, that is not what we expect,right?

README.md question

In packaging process, README.md refer to me to execute this command

python package.py --dir=image_directories
                  --save_dir=binary_save_directory
                  --split_ratio=[0,1]

But, In implement, split_ratio option is float type, so Is it right to set split_ratio option to [0,1]?

Using well trained model data and replacing Simsun with other fonts, what's the effect?

I have downloaded the trained model data. I tried the Simsun to a variety of font generation, the effect is very good.

Also try to use other fonts instead of Simsun, to a variety of font generation, the effect is acceptable.

That is to say, the model data is robust and can accept various inputs to generate target fonts.

I wonder if you have tested this situation. What's the effect?

The pictures are in the right order

package.py
modify：
pickle_examples(sorted(glob.glob(os.path.join(args.dir, "*.jpg"))), train_path=train_path, val_path=val_path,
train_val_split=args.split_ratio)

so you can get a right order like this:
img dir/0_0000.jpg 0
img dir/0_0001.jpg 0
img dir/0_0002.jpg 0
img dir/0_0003.jpg 0
img dir/0_0004.jpg 0
img dir/0_0005.jpg 0
img dir/0_0006.jpg 0
img dir/0_0007.jpg 0
img dir/0_0008.jpg 0
img dir/0_0009.jpg 0
img dir/0_0010.jpg 0
img dir/0_0011.jpg 0
img dir/0_0012.jpg 0

font2img.py error

:~/zi2zi$ python font2img.py --src_font=src.ttf \

               --dst_font=tgt.otf \
               --charset=CN \
               --sample_count=1000 \
               --sample_dir=dir \
               --label=0 \
               --filter=1 \
               --shuffle=1

Traceback (most recent call last):
File "font2img.py", line 120, in
args.sample_count, args.sample_dir, args.label, args.filter)
File "font2img.py", line 72, in font2img
src_font = ImageFont.truetype(src, size=char_size)
File "/Users/Ru/anaconda/envs/gan/lib/python2.7/site-packages/PIL/ImageFont.py", line 238, in truetype
return FreeTypeFont(font, size, index, encoding)
File "/Users/Ru/anaconda/envs/gan/lib/python2.7/site-packages/PIL/ImageFont.py", line 127, in init
self.font = core.getfont(font, size, index, encoding)
IOError: cannot open resource

@kaonashi-tyc thank you!

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