Re-implement CycleGAN in Tensorlayer

Last update: Aug 15, 2022

Overview

CycleGAN_Tensorlayer

Re-implement CycleGAN in TensorLayer

Original CycleGAN
Improved CycleGAN with resize-convolution

Prerequisites:

TensorLayer
TensorFlow
Python

Run:

CUDA_VISIBLE_DEVICES=0 python main.py

(if datasets are collected by yourself, you can use dataset_clean.py or dataset_crop.py to pre-process images)

Theory:

The generator process:

The discriminator process:

Result Improvement

Data augmentation
Resize convolution[4]
Instance normalization[5]

data augmentation:

Instance normalization（comparision by original paper https://arxiv.org/abs/1607.08022）:

Resize convolution (Remove Checkerboard Artifacts):

Final Results:

Reference:

[1] Original Paper: https://arxiv.org/pdf/1703.10593.pdf
[2] Original implement in Torch: https://github.com/junyanz/CycleGAN/
[3] TensorLayer by HaoDong: https://github.com/zsdonghao/tensorlayer
[4] Resize Convolution: https://distill.pub/2016/deconv-checkerboard/
[5] Instance Normalization: https://arxiv.org/abs/1607.08022

Comments

Difference from original code
HI very nice implemented cyclegan I have a few questions...

What does "Resize Convolution" mean?

I wonder what is different from the original code of the author.
opened by taki0112 7
Color inversion, black image and nan in loss after ~20 epochs

I've tried to train the model on original summer2winter_yosemite dataset. After ~20 epochs all sample images turned completely black, and all all loss parameters turned to nan. However, the model continued to run for 30 more epochs regularly saving checkpoints until I stopped it.

I've also used another, my own dataset, and it ran correctly for 70 epochs at least, unfortunately the only result I had was color inversion of images. Any advice on changing training parameters (I used default)?

opened by victor-felicitas 0
How to change test output size?

Hi! It is a great implementation of Cyclegan, providing excellent results on Hiptensorflow and ROCm. However, I could not use it to generate test images of different from 256x256 sizes. How can I change that?

For now, I have trained the model on 256x256 images and try to test it on bigger ones. I tried adding two more flags to main.py: flags.DEFINE_integer("image_width", 420, "The size of image to use (will be center cropped) [256]") flags.DEFINE_integer("image_height", 420, "The size of image to use (will be center cropped) [256]")

Which I use later in Test section: test_A = tf.placeholder(tf.float32, [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, FLAGS.c_dim], name='test_x') test_B = tf.placeholder(tf.float32, [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, FLAGS.c_dim], name='test_y')

However, I always get error: Invalid argument: Conv2DSlowBackpropInput: Size of out_backprop doesn't match computed: actual = 105, computed = 64 Traceback (most recent call last): File "main.py", line 285, in tf.app.run() File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 44, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "main.py", line 281, in main test_cyclegan() File "main.py", line 262, in test_cyclegan fake_img = sess.run(net_g_logits, feed_dict={in_var: sample_image}) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 767, in run run_metadata_ptr) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 965, in _run feed_dict_string, options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1015, in _do_run target_list, options, run_metadata) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1035, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: Conv2DSlowBackpropInput: Size of out_backprop doesn't match computed: actual = 105, computed = 64 [[Node: gen_A2B/u64/conv2d_transpose = Conv2DBackpropInput[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](gen_A2B/u64/conv2d_transpose/output_shape, gen_A2B/u64/W_deconv2d/read, gen_A2B/b_residual_add/8)]]

Is there any way to choose output image size? Original Cyclegan has special option to choose it - how can i implement it? resize_or_crop = 'resize_and_crop', -- resizing/cropping strategy: resize_and_crop | crop | scale_width | scale_height

Any help would be appreciated!

opened by victor-felicitas 0
About the imagepool.

I noticed in https://github.com/luoxier/CycleGAN_Tensorlayer/blob/master/main.py#L88 you obtain the logit of image sampled from imagepool but do not use it, is that for some reason or just do not intend to implement it?

opened by Zardinality 0
Error in main.py?

Hi @zsdonghao @luoxier , Is there an error in your main.py: _, errGB2A = sess.run([g_b2a_optim, g_b2a_loss], feed_dict={real_A: batch_imgB, real_B: batch_imgB}) Does it should be: _, errGB2A = sess.run([g_b2a_optim, g_b2a_loss], feed_dict={real_A: batch_imgA, real_B: batch_imgB}) Could you please check it and let me know, thanks.

opened by yongqiangzhang1 2
Where are datasets shown in readme?

There are sunflower2daisy and leopard2tiger results shown in readme, but I don't find any clue about where to download them in code. In https://github.com/luoxier/CycleGAN_Tensorlayer/blob/master/main.py#L32 an optional value for dataset_dir is sunflower2daisy, where can I get it? The author of original paper doesn't seem to provide it.

opened by Zardinality 7

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Re-implement CycleGAN in Tensorlayer

Related tags

Overview

CycleGAN_Tensorlayer

Prerequisites:

Run:

Theory:

Result Improvement

data augmentation:

Instance normalization（comparision by original paper https://arxiv.org/abs/1607.08022）:

Resize convolution (Remove Checkerboard Artifacts):

Final Results:

Reference:

Comments

Difference from original code

Color inversion, black image and nan in loss after ~20 epochs

How to change test output size?

About the imagepool.

Error in main.py?

Where are datasets shown in readme?

Releases(0.1)

0.1(Sep 30, 2017)

Owner

Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”

Official repo for BMVC2021 paper ASFormer: Transformer for Action Segmentation

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Code for our paper Aspect Sentiment Quad Prediction as Paraphrase Generation in EMNLP 2021.

Discover hidden deepweb pages

Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

Official implementation for paper Knowledge Bridging for Empathetic Dialogue Generation (AAAI 2021).

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

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