Re-implement CycleGAN in Tensorlayer

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:

Image text

The discriminator process:

Image text

Result Improvement

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

data augmentation:

Image text

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

Image text

Resize convolution (Remove Checkerboard Artifacts):

Image text

Image text

Final Results:

Image text

Image text

Reference:

Comments
  • Difference from original code

    Difference from original code

    HI very nice implemented cyclegan I have a few questions...

    1. What does "Resize Convolution" mean?
    2. 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

    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?

    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.

    About the imagepool.

    opened by Zardinality 0
  • Error in main.py?

    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?

    Where are datasets shown in readme?

    opened by Zardinality 7
Releases(0.1)
PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

PyTorch Realtime Multi-Person Pose Estimation This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-P

Dave Fang 157 Nov 12, 2022
Analysing poker data from home games with friends

Poker Game Analysis Analysing poker data from home games with friends. Not a lot of data is collected, so this project is primarily focussed on descri

Stavros Karmaniolos 1 Oct 15, 2022
Deeplab-resnet-101 in Pytorch with Jaccard loss

Deeplab-resnet-101 Pytorch with Lovász hinge loss Train deeplab-resnet-101 with binary Jaccard loss surrogate, the Lovász hinge, as described in http:

Maxim Berman 95 Apr 15, 2022
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

This repository is the official PyTorch implementation of Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

hippopmonkey 4 Dec 11, 2022
AI that generate music

PianoGPT ai that generate music try it here https://share.streamlit.io/annasajkh/pianogpt/main/main.py or here https://huggingface.co/spaces/Annas/Pia

Annas 28 Nov 27, 2022
DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching Python (Pytorch) and Matlab (MatConvNet) implementations of our paper DFM: A Performance Baselin

143 Jan 02, 2023
Rlmm blender toolkit - A set of tools to streamline level generation in UDK straight from Blender

rlmm_blender_toolkit A set of tools to streamline level generation in UDK straig

Rocket League Mapmaking 0 Jan 15, 2022
[ICCV 2021 Oral] NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo

NerfingMVS Project Page | Paper | Video | Data NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo Yi Wei, Shaohui

Yi Wei 369 Dec 24, 2022
Official implementation of FCL-taco2: Fast, Controllable and Lightweight version of Tacotron2 @ ICASSP 2021

FCL-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech synthesis (ICASSP 2021) Paper | Demo Block diagram of FCL-taco2, where the decode

Disong Wang 39 Sep 28, 2022
Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.

6D Rotation Representation for Unconstrained Head Pose Estimation (Pytorch) Paper Thorsten Hempel and Ahmed A. Abdelrahman and Ayoub Al-Hamadi, "6D Ro

Thorsten Hempel 284 Dec 23, 2022
Learning to Map Large-scale Sparse Graphs on Memristive Crossbar

Release of AutoGMap:Learning to Map Large-scale Sparse Graphs on Memristive Crossbar For reproduction of our searched model, the Ubuntu OS is recommen

2 Aug 23, 2022
Enabling dynamic analysis of Legacy Embedded Systems in full emulated environment

PENecro This project is based on "Enabling dynamic analysis of Legacy Embedded Systems in full emulated environment", published on hardwear.io USA 202

Ta-Lun Yen 10 May 17, 2022
Code for Understanding Pooling in Graph Neural Networks

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

Daniele Grattarola 37 Dec 13, 2022
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

Class-balanced-loss-pytorch Pytorch implementation of the paper Class-Balanced Loss Based on Effective Number of Samples presented at CVPR'19. Yin Cui

Vandit Jain 697 Dec 29, 2022
Using image super resolution models with vapoursynth and speeding them up with TensorRT

vs-RealEsrganAnime-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Also a docker image since

4 Aug 23, 2022
Point detection through multi-instance deep heatmap regression for sutures in endoscopy

Suture detection PyTorch This repo contains the reference implementation of suture detection model in PyTorch for the paper Point detection through mu

artificial intelligence in the area of cardiovascular healthcare 3 Jul 16, 2022
Neuralnetwork - Basic Multilayer Perceptron Neural Network for deep learning

Neural Network Just a basic Neural Network module Usage Example Importing Module

andreecy 0 Nov 01, 2022
null

DeformingThings4D dataset Video | Paper DeformingThings4D is an synthetic dataset containing 1,972 animation sequences spanning 31 categories of human

208 Jan 03, 2023
This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

Microsoft 1.3k Dec 26, 2022