Adding doc, adding a timer, fixing bugs.

README.md

@@ -3,7 +3,7 @@
 A GAN toolbox for researchers and developers with:
 - Progressive Growing of GAN(PGAN): https://arxiv.org/pdf/1710.10196.pdf
 - DCGAN: https://arxiv.org/pdf/1511.06434.pdf
-- To come: StyleGAN https://arxiv.org/abs/1812.04948
+- StyleGAN https://arxiv.org/abs/1812.04948
 
 <img src="illustration.png" alt="illustration">
 Picture: Generated samples from GANs trained on celebaHQ, fashionGen, DTD.
@@ -48,6 +48,21 @@ pip install -r requirements.txt
  - DTD: https://www.robots.ox.ac.uk/~vgg/data/dtd/
  - CIFAR10: http://www.cs.toronto.edu/~kriz/cifar.html
 
+For a quick start with CelebAHQ, you might:
+```
+git clone https://github.com/nperraud/download-celebA-HQ.git
+cd download-celebA-HQ
+conda create -n celebaHQ python=3
+source activate celebaHQ
+conda install jpeg=8d tqdm requests pillow==3.1.1 urllib3 numpy cryptography scipy
+pip install opencv-python==3.4.0.12 cryptography==2.1.4
+sudo apt-get install p7zip-full
+python download_celebA.py ./
+python download_celebA_HQ.py ./
+python make_HQ_images.py ./
+export PATH_TO_CELEBAHQ=`readlink -f ./celebA-HQ/512`
+```
+
 ## Quick training
 
 The datasets.py script allows you to prepare your datasets and build their corresponding configuration files.
@@ -64,8 +79,9 @@ And wait for a few days. Your checkpoints will be dumped in output_networks/cele
 For celebaHQ:
 
 ```
-python datasets.py celebaHQ $PATH_TO_CELEBAHQ -o $OUTPUT_DATASET - f
-python train.py PGAN -c config_celebaHQ.json --restart -n celebaHQ
+python datasets.py celebaHQ $PATH_TO_CELEBAHQ -o $OUTPUT_DATASET - f  # Prepare the dataset and build the configuration file.
+python train.py PGAN -c config_celebaHQ.json --restart -n celebaHQ  # Train.
+python eval.py inception -n celebaHQ -m PGAN  # If you want to check the inception score.
 ```
 
 Your checkpoints will be dumped in output_networks/celebaHQ. You should get 1024x1024 generations at the end.
@@ -130,7 +146,8 @@ Where:
 
 1 - MODEL_NAME is the name of the model you want to run. Currently, two models are available:
     - PGAN(progressive growing of gan)
-    - PPGAN(decoupled version of PGAN)
+    - DCGAN
+    - StyleGAN
 
 2 - CONFIGURATION_FILE(mandatory): path to a training configuration file. This file is a json file containing at least a pathDB entry with the path to the training dataset. See below for more informations about this file.
 
@@ -209,19 +226,19 @@ You need to use the eval.py script.
 
 You can generate more images from an existing checkpoint using:
 ```
-python eval.py visualization -n $modelName -m $modelType
+python eval.py visualization -n $runName -m $modelName
 ```
 
-Where modelType is in [PGAN, PPGAN, DCGAN] and modelName is the name given to your model. This script will load the last checkpoint detected at testNets/$modelName. If you want to load a specific iteration, please call:
+Where modelName is in [PGAN, StyleGAN, DCGAN] and runName is the name given to your run (trained model). This script will load the last checkpoint detected at output_networks/$modelName. If you want to load a specific iteration, please call:
 
 ```
-python eval.py visualization -n $modelName -m $modelType -s $SCALE -i $ITER
+python eval.py visualization -n $runName -m $modelName -s $SCALE -i $ITER
 ```
 
 If your model is conditioned, you can ask the visualizer to print out some conditioned generations. For example:
 
 ```
-python eval.py visualization -n $modelName -m $modelType --Class T_SHIRT
+python eval.py visualization -n $runName -m $modelName --Class T_SHIRT
 ```
 
 Will plot a batch of T_SHIRTS in visdom. Please use the option - -showLabels to see all the available labels for your model.
@@ -231,16 +248,21 @@ Will plot a batch of T_SHIRTS in visdom. Please use the option - -showLabels to
 To save a randomly generated fake dataset from a checkpoint please use:
 
 ```
-python eval.py visualization -n $modelName -m $modelType --save_dataset $PATH_TO_THE_OUTPUT_DATASET --size_dataset $SIZE_OF_THE_OUTPUT
+python eval.py visualization -n $runName -m $modelName --save_dataset $PATH_TO_THE_OUTPUT_DATASET --size_dataset $SIZE_OF_THE_OUTPUT
 ```
 
 ### SWD metric
 
 Using the same kind of configuration file as above, just launch:
-
 ```
-python eval.py laplacian_SWD -c $CONFIGURATION_FILE -n $modelName -m $modelType
+python eval.py laplacian_SWD -c $CONFIGURATION_FILE -n $runName -m $modelName
 ```
+for the SWD score, to be maximized, or for the inception score:
+```
+python eval.py inception -c $CONFIGURATION_FILE -n $runName -m $modelName
+```
+also to be maximized (see https://hal.inria.fr/hal-01850447/document for a discussion).
+
 
 Where $CONFIGURATION_FILE is the training configuration file called by train.py (see above): it must contains a "pathDB" field pointing to path to the dataset's directory. For example, if you followed the instruction of the Quick Training section to launch a training session on celebaHQ your configuration file will be config_celebaHQ.json.
 
@@ -252,6 +274,7 @@ You can add optional arguments:
 
 ### Inspirational generation
 
+An inspirational generation consists in generation with your GAN an image which looks like a given input image.
 To make an inspirational generation, you first need to build a feature extractor:
 
 ```
@@ -261,14 +284,14 @@ python save_feature_extractor.py {vgg16, vgg19} $PATH_TO_THE_OUTPUT_FEATURE_EXTR
 Then run your model:
 
 ```
-python eval.py inspirational_generation -n $modelName -m $modelType --inputImage $pathTotheInputImage -f $PATH_TO_THE_OUTPUT_FEATURE_EXTRACTOR
+python eval.py inspirational_generation -n $runName -m $modelName --inputImage $pathTotheInputImage -f $PATH_TO_THE_OUTPUT_FEATURE_EXTRACTOR
 ```
 
 ### I have generated my metrics. How can i plot them on visdom ?
 
 Just run
 ```
-python eval.py metric_plot  -n $modelName
+python eval.py metric_plot  -n $runName
 ```
 
 ## LICENSE

models/eval/inspirational_generation.py

@@ -71,7 +71,7 @@ def updateParser(parser):
     parser.add_argument('--weights', type=float, dest='weights',
                         nargs='*', help="Weight of each classifier. Default \
                         value is one. If specified, the number of weights must\
-                        match the number of feature exatrcators.")
+                        match the number of feature extractors.")
     parser.add_argument('--gradient_descent', help='gradient descent',
                         action='store_true')
     parser.add_argument('--random_search', help='Random search',

train.py

@@ -30,7 +30,7 @@ def getTrainer(name):
     parser = argparse.ArgumentParser(description='Testing script')
     parser.add_argument('model_name', type=str,
                         help='Name of the model to launch, available models are\
-                        PGAN and PPGAN. To get all possible option for a model\
+                        PGAN and DCGAN and StyleGAN. To get all possible option for a model\
                          please run train.py $MODEL_NAME -overrides')
     parser.add_argument('--no_vis', help=' Disable all visualizations',
                         action='store_true')