In the hdrnet_ops.py, there are code about hdrnet_ops.so. But this project doesn't provide the hdrnet_ops.so. Can you offer the file or provide the way to fix it?
path = tf.resource_loader.get_path_to_datafile( os.path.join(path, 'lib', 'hdrnet_ops.so'))

Hi,
On what axis to do l2 norm and on what axis to use cosine distance from tf ?
Thanks!

Hello, thank you for your work.
I want to know whether the PSNR and SSIM results in table 2 in the original paper is tested on the RGB channel or Lab space as it is mentioned in HDRNet. In many previous works, they always use 4500 images for training, 250 images for validation and the randomly-chosen images for test on the MIT-Adobe Five5K dataset while in your model there are 500 images for test. I want to know if you use the same setting when re-train others' network.
Looking forward for your reply!

Hi

Thank you for spending time reading this issue. I am wondering whether the Bilateral grid based upsampling is the bilateral_slice_apply operation is hdrnet?

I am also wondering whether the code is run on tensorflow 1.1 and cuda 8.0.....

It seems like the hdrnet can only run on tensorflow 1.1 and cuda 8.0

This is a very good paper and I look forward to releasing the code as soon as possible.

Hi

I am just wondering anyone managed to compile the hdrnet ops code for the bilateral grid upsampling operation? I have a hard time compiling it since it always gives bugs about the bilateral grid operation.

Thank you so much and best regards

Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
ops/bilateral_slice.cu.cc:19:10: fatal error: third_party/eigen3/unsupported/Eigen/CXX11/Tensor: No such file or directory
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
Makefile:31: recipe for target 'build/bilateral_slice.cu.o' failed
make: *** [build/bilateral_slice.cu.o] Error 1

I've been reporting errors: ValueError: Invalid shape for image array

Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so

https://groups.csail.mit.edu/graphics/hdrnet/data/hdrnet.pdf

I don't understand Reconstruction Loss very well. Could you please help me understand it？

Hi, from the paper you have proposed a new dataset with 3000 images. Could you please share the download url?

I have tried 'make' many times，but there're still endless errors which I can't figure out. I'm a green hand in ML and DL, thank you.

Hello, I read your paper carefully, but I didn't understand the meaning of smoothness loss formula. How does it guarantee local smoothness? Why do we do logarithmic operations on images? Could you explain it for me? Thank you very much.

I construct loss function—color loss by myself, but I don't know whether it's correct or not. And it's confused me that the total loss is reduced first and increased when training. So can you release the training code so that I can know where is the problem in my training code?

Hi, which Adobe5k images did you use in your training dataset? How did you preprocess the adobe5k images? Many thanks, Sean

Hi, you did good work!

But I have trouble with loss weight, the L2-loss(reconstruction loss) finally converge to about 1000 for 16512512*3 [0,1],but the TV-loss calculated from groundtruth illumination map (get by input/gt image) and input is about 10^6, so we cannot place 1 for L2-loss and 2 for Tv-loss.

Any advise?

hi, RT.
Can you share the codes of color loss ?

Thanks for sharing your excellent work. Any plan for publishing the training code? Thanks

你好，请问能提供一下hdrnet_ops.so文件吗？

also, the test set is not shared publicly in this link: https://drive.google.com/file/d/1HZnNgptNxjKJAhekz2K5yh0mW0yKIws2/view?usp=sharing

Why is there no training process? The loss is not described in the same way as the article.

I had a running error. It happened during loading the model parameters. The error message is listed below. Does anyone have similar problem?

Traceback (most recent call last):
File "main/run.py", line 154, in
main(args)
File "main/run.py", line 78, in main
model_params = utils.get_model_params(sess)
File "/home/tcl980/snli/Proj-ImgEnhance/DeepUPE/DeepUPE/main/utils.py", line 24, in get_model_params
model_params = sess.run(params_)
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.InvalidArgumentError: No OpKernel was registered to support Op 'BilateralSliceApply' with these attrs. Registered devices: [CPU], Registered kernels:
device='GPU'

 [[Node: train/inference/output/slice/BilateralSliceApply = BilateralSliceApply[has_offset=true, _device="/device:GPU:0"](train/inference/output/slice/Reshape, train/inference/guide/Squeeze, train_data/shuffle_batch)]]

Caused by op u'train/inference/output/slice/BilateralSliceApply', defined at:
File "main/run.py", line 154, in
main(args)
File "main/run.py", line 75, in main
tf.train.import_meta_graph(metapath)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1595, in import_meta_graph
**kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/meta_graph.py", line 499, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/importer.py", line 308, in import_graph_def
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()

InvalidArgumentError (see above for traceback): No OpKernel was registered to support Op 'BilateralSliceApply' with these attrs. Registered devices: [CPU], Registered kernels:
device='GPU'

 [[Node: train/inference/output/slice/BilateralSliceApply = BilateralSliceApply[has_offset=true, _device="/device:GPU:0"](train/inference/output/slice/Reshape, train/inference/guide/Squeeze, train_data/shuffle_batch)]]

hdrnet_ops.so 一座大山

Traceback (most recent call last):
File "./main/run.py", line 137, in
main(args)
File "./main/run.py", line 64, in main
if not hasattr(models, model_params['model_name']):
TypeError: hasattr(): attribute name must be string

Hi, thank you for your work. I wonder how to compute the color loss mentioned in your paper. I couldn't find any specific codes computing color loss.

一名研一学生，学习图像处理，对该方法很感兴趣，故咨询MATLAB代码，谢谢。

How many trainable parameters does your model have?

when i run it with :
CUDA_VISIBLE_DEVICES=2 python ./main/run.py checkpoints '/data2/xxx/data/data_DarkFace/image_train' '/data2/xxx/data/data_DarkFace/image_train_dupe_enhance'

i get
Traceback (most recent call last):
File "./main/run.py", line 15, in
import main.models as models
ModuleNotFoundError: No module named 'main'

Hello, I appreciate your contribution. I used your pre-training model to test the FIVEK data set, and found that the output is darker than label, and the HDR effect is not obvious. Why is that? Can you release some test results? I want to compare them and verify what caused the problem.

Hello @wangruixing, that's a great method!

I've successfully build makefile like tutorial in readme, and i face a probblem like this when running file run.py
tensorflow.python.framework.errors_impl.NotFoundError: /media/4A76601876600753/DeepUPE/main/lib/hdrnet_ops.so: failed to map segment from shared object

Currently using python 2.7, ubuntu 18.04, tensorflow 1.14 and CUDA 10.

Does anyone can help?

Thanks

here is the error:
(env_DeepUPE) fudan@fudan-DGX-Station:~/Desktop/wsl/DeepUPE$ python main/run.py checkpoints /home/fudan/Desktop/wsl/DeepUPE/input/o.jpg /home/fudan/Desktop/wsl/DeepUPE/output/
Traceback (most recent call last):
File "main/run.py", line 30, in
import models as models
File "/home/fudan/Desktop/wsl/DeepUPE/main/models.py", line 21, in
from layers import (conv, fc, bilateral_slice_apply)
File "/home/fudan/Desktop/wsl/DeepUPE/main/layers.py", line 20, in
import hdrnet_ops
File "/home/fudan/Desktop/wsl/DeepUPE/main/hdrnet_ops.py", line 28, in
_hdrnet = tf.load_op_library('/home/fudan/Desktop/wsl/DeepUPE/main/lib/hdrnet_ops.so')
File "/home/fudan/Desktop/wsl/DeepUPE/env_DeepUPE/lib/python3.5/site-packages/tensorflow/python/framework/load_library.py", line 56, in load_op_library
lib_handle = py_tf.TF_LoadLibrary(library_filename)
tensorflow.python.framework.errors_impl.NotFoundError: /home/fudan/Desktop/wsl/DeepUPE/main/lib/hdrnet_ops.so: undefined symbol: _ZTIN10tensorflow8OpKernelE

Hi, I just use makefile to compile your *.cc files but meet some compile problem.

My cuda version is 10.0 . Do you meet problem like that? Thanks.

Why not use the default PNSR setting?
The other methods are also calculated by your Metric?

Hi, I am wondering whether I can ask something about the bilinear grid upsampling layer?

The original bilinear grid upsampling layer in the HDRNET seems to take a single-channel guide map and the feature map. Here it seems like it take the input image and the feature map(? not sure if I understand it right). Is it implemented in the manner as the HDRnet?

Also, I am wondering whether this bilateral grid upsampling layer actually help with the learning of the image to illumination mapping? Or it is just to apply the learned local affine transform to the high-res image?

Thank you so much and best regards

'''
input_files = [os.path.join(dirname, 'input',f) for f in flist]
print(input_files[0])
output_files = [os.path.join(dirname, 'output', f) for f in flist]
reflectance_files = [os.path.join(dirname, 'reflectance', f) for f in flist]
'''
What is the reflectance_files，which is different from HDRnet. The training data only need input and label, I didn't understand what reflectance was here. Shouldn't it be an intermediate result? Does it need this reflectance for training?

I'm sorry I didn't understand. If it's convenient for you, could you help me? Thank you.