Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"

Related tags

Deep Learning ambient-gan
Overview

AmbientGAN: Generative models from lossy measurements

This repository provides code to reproduce results from the paper AmbientGAN: Generative models from lossy measurements.

The training setup is as in the following diagram:

Here are a few example results:

Measured Baseline AmbientGAN (ours)

Few more samples from AmbientGAN models trained with 1-D projections:

Pad-Rotate-Project Pad-Rotate-Project-theta

The rest of the README describes how to reproduce the results.

Requirements

  • Python 2.7
  • Tensorflow >= 1.4.0
  • matplotlib
  • scipy
  • numpy
  • cvxpy
  • scikit-learn
  • tqdm
  • opencv-python
  • pandas

For pip installation, use $ pip install -r requirements.txt

Get the data

  • MNIST data is automatically downloaded
  • Get the celebA dataset here and put the jpeg files in ./data/celebA/
  • Get the CIFAR-10 python data from here and put it in ./data/cifar10/cifar-10-batches-py/*

Get inference models

We need inference models for computing the inception score.

  • For MNIST, you can train your own by

    cd ./src/mnist/inf
python train.py

    [TODO]: Provide a pretrained model.

  • Inception model for use with CIFAR-10 is automatically downloaded.

Create experiment scripts

Run ./create_scripts/create_scripts.sh

This will create scripts for all the experiments in the paper.

[Optional] If you want to run only a subset of experiments you can define the grid in ./create_scripts/DATASET_NAME/grid_*.sh or if you wish to tweak a lot of parameters, you can change ./create_scripts/DATASET_NAME/base_script.sh. Then run ./create_scripts/create_scripts.sh as above to create the corresponding scripts (remember to remove any previous files from ./scripts/)

Run experiments

We provide scripts to train on multiple GPUs in parallel. For example, if you wish to use 4 GPUs, you can run: ./run_scripts/run_sequentially_parallel.sh "0 1 2 3"

This will start 4 GNU screens. Each program within the screen will attempt to acquire and run experiments from ./scripts/, one at a time. Each experiment run will save samples, checkpoints, etc. to ./results/.

See results as you train

Samples

You can see samples for each experiment in ./results/samples/EXPT_DIR/

EXPT_DIR is defined based on the hyperparameters of the experiment. See ./src/commons/dir_def.py to see how this is done.

Quantitative plots

Run

python src/aggregator_mnist.py
python src/aggregator_cifar.py

This will create pickle files in ./results/ with the relevant data in a Pandas dataframe.

Now use the ipython notebooks ./plotting_mnist.ipynb and ./plotting_cifar.ipynb to get the relevant plots. The generated plots are also saved to ./results/plots/ (make sure this directory exists)

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Comments
  • MEASUREMENT MODELS to be differentiable?

    MEASUREMENT MODELS to be differentiable?

    Hi, In your paper, It's said "We additionally require f(θ)to be differentiable with respect to its inputs for all θ，I don't what its mean.

    It is necessary to derivate D(fθ(G(Z))) by myself? In my problem, I want to add a rician noise to Generative image? I don't know how to add this derivate process to the train.

    I review your code, I don't see how to deal with this derivate process, seveal measurement models are quite complex, is it necessary to derivate explicitly?

    Another question, if I can obtain the noise data in experiment as the lossy measurements, is it reasonable to add the noise to Generative image just as the role of f(θ), but the derivate compute may be complex.

    opened by duancaohui 1
  • Troubles trying to reproduce the results

    Troubles trying to reproduce the results

    Hi, My name is Alejandro Barredo Arrieta and I am trying reproduce your implentation for a research study I am conducting. I am trying to run your steps for the cifar dataset and found a couple of problems. I do not know if are only problems on my side or are actually problems on the implementation:

    1. inception_score.py - line 112 - logits = tf.matmul(tf.squeeze(pool3), w) throws the error - > tensor of rank 2 needed for matmul and [, xxx] was given. So I changed it to: logits = tf.matmul(tf.reshape(tf.squeeze(pool3), [1, -1]), w)

    2. for the second error, I do not know what is causing it, so I hope you can help. Running the generated scripts it throws: Traceback (most recent call last): File "src/cifar/gan_cifar_resnet.py", line 260, in <module> AMB_HPARAMS = amb_setup() File "src/cifar/gan_cifar_resnet.py", line 247, in amb_setup hparams = amb_hparams_def.get_hparams(args) File "/home/xai/Documentos/AmbientGAN/src/cifar/amb_hparams_def.py", line 7, in get_hparams hparams = tf.contrib.training.HParams( File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/site-packages/tensorflow/python/util/lazy_loader.py", line 53, in __getattr__ module = self._load() File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/site-packages/tensorflow/python/util/lazy_loader.py", line 42, in _load module = importlib.import_module(self.__name__) File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/importlib/__init__.py", line 37, in import_module __import__(name) File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/site-packages/tensorflow/contrib/__init__.py", line 47, in <module> from tensorflow.contrib import image File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/site-packages/tensorflow/contrib/image/__init__.py", line 70, in <module> from tensorflow.contrib.image.python.ops.single_image_random_dot_stereograms import single_image_random_dot_stereograms File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/site-packages/tensorflow/contrib/image/python/ops/single_image_random_dot_stereograms.py", line 27, in <module> "_single_image_random_dot_stereograms.so")) File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/site-packages/tensorflow/contrib/util/loader.py", line 56, in load_op_library ret = load_library.load_op_library(path) File "/home/xai/anaconda3/envs/AmbientGAN/lib/python2.7/site-packages/tensorflow/python/framework/load_library.py", line 73, in load_op_library exec(wrappers, module.__dict__) File "<string>", line 27 def single_image_random_dot_stereograms(depth_values, hidden_surface_removal=True, convergence_dots_size=8, dots_per_inch=72, eye_separation=2,5, mu=0,333299994, normalize=True, normalize_max=-100, normalize_min=100, border_level=0, number_colors=256, output_image_shape=[1024, 768, 1], output_data_window=[1022, 757], name=None): Thanks in advance for posting your code.

    opened by sakur33 0
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Ashish Bora
Ashish Bora
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