Siggraph 2017
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Michael Gharbi Jiawen Chen Jonathan T. Barron Samuel W. Hasinoff Fredo Durand
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No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 18.04.2 LTS
Release: 18.04
Codename: bionic
CUDA Version 10.0.130
cuDNN Version 7.5.1
gcc (Ubuntu 5.5.0-12ubuntu1) 5.5.0 20171010
TF 1.13.1
To install the Python dependencies, run:
cd hdrnet
pip install -r requirements.txt
Our network requires a custom Tensorflow operator to "slice" in the bilateral grid. To build it, run:
cd hdrnet
make
To build the benchmarking code, run:
cd benchmark
make
Note that the benchmarking code requires a frozen and optimized model. Use
hdrnet/bin/scripts/optimize_graph.py
and hdrnet/bin/freeze.py to produce these
.
To build the Android demo, see dedicated section below.
Run the test suite to make sure the BilateralSlice operator works correctly:
cd hdrnet
py.test test
We provide a set of pretrained models. One of these is included in the repo
(see pretrained_models/local_laplacian_sample
). To download the rest of them
run:
cd pretrained_models
./download.py
To train a model, run the following command:
./hdrnet/bin/train.py <checkpoint_dir> <path/to_training_data/filelist.txt>
Example:
./drnet/bin/train.py --learning_rate 1e-4 --batch_size 1 --model_name HDRNetPointwiseNNGuide\
--output_resolution 256 256 --luma_bins 8 --spatial_bin 16 --guide_complexity 16 --channel_multiplier 2\
--fliplr --rotate --net_input_size 128 ./ch /data/DATASETS/Retouche/webds/filelist.txt
Look at sample_data/identity/
for a typical structure of the training data folder.
You can monitor the training process using Tensorboard:
tensorboard --logdir <checkpoint_dir>
To run a trained model on a novel image (or set of images), use:
./hdrnet/bin/run.py <checkpoint_dir> <path/to_eval_data> <output_dir>
To prepare a model for use on mobile, freeze the graph, and optimize the network:
./hdrnet/bin/freeze_graph.py <checkpoint_dir>
./hdrnet/bin/scripts/optimize_graph.sh <checkpoint_dir>
You will need to change the ${TF_BASE}
environment variable in ./hdrnet/bin/scripts/optimize_graph.sh
and compile the necessary tensorflow command line tools for this (automated in the script).
I don't think that it will be posted.
-
The BilateralSliceApply operation is GPU only at this point. We do not plan on releasing a CPU implementation.
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The provided pre-trained models were updated from an older version and might slightly differ from the models used for evaluation in the paper.
-
The pre-trained HDR+ model expects as input a specially formatted 16-bit linear input. In summary, starting from Bayer RAW:
- Subtract black level.
- Apply white balance channel gains.
- Demosaic to RGB.
- Apply lens shading correction (aka vignetting correction).
Our Android demo approximates this by undoing the RGB->YUV conversion and white balance, and tone mapping performed by the Qualcomm SOC. It results in slightly different colors than that on the test set. If you run our HDR+ model on an sRGB input, it may produce uncanny colors.