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README.md

@@ -1,73 +1,113 @@
-## Compare GAN code.
-
-This is the code that was used in "Are GANs Created Equal? A Large-Scale Study"
-paper (https://arxiv.org/abs/1711.10337) and in "The GAN Landscape: Losses,
-Architectures, Regularization, and Normalization"
-(https://arxiv.org/abs/1807.04720).
-
-If you want to see the version used only in the first paper - please see the
-*v1* branch of this repository.
-
-## Pre-trained models
-
-The pre-trained models are available on TensorFlow Hub. Please see
-[this colab](https://colab.research.google.com/github/google/compare_gan/blob/master/compare_gan/src/tfhub_models.ipynb)
-for an example how to use them.
-
-### Best hyperparameters
-
-This repository also contains the values for the best hyperparameters for
-different combinations of models, regularizations and penalties. You can see
-them in `generate_tasks_lib.py` file and train using
-`--experiment=best_models_sndcgan`
-
-### Installation:
-
-To install, run:
-
-```shell
-python -m pip install -e . --user
-```
-
-After installing, make sure to run
-
-```shell
-compare_gan_prepare_datasets.sh
-```
-
-It will download all the necessary datasets and frozen TF graphs. By default it
-will store them in `/tmp/datasets`.
-
-WARNING: by default this script only downloads and installs small datasets - it
-doesn't download celebaHQ or lsun bedrooms.
-
-*   **Lsun bedrooms dataset**: If you want to install lsun-bedrooms you need to
-    run t2t-datagen yourself (this dataset will take couple hours to download
-    and unpack).
-
-*   **CelebaHQ dataset**: currently it is not available in tensor2tensor. Please
-    use the
-    [ProgressiveGAN github](https://github.com/tkarras/progressive_growing_of_gans)
-    for instructions on how to prepare it.
-
-### Running
-
-compare_gan has two binaries:
-
-*   `generate_tasks` - that creates a list of files with parameters to execute
-*   `run_one_task` - that executes a given task, both training and evaluation,
-    and stores results in the CSV file.
-
-```shell
-# Create tasks for experiment "test" in directory /tmp/results. See "src/generate_tasks_lib.py" to see other possible experiments.
-compare_gan_generate_tasks --workdir=/tmp/results --experiment=test
-
-# Run task 0 (training and eval)
-compare_gan_run_one_task --workdir=/tmp/results --task_num=0 --dataset_root=/tmp/datasets
-
-# Run task 1 (training and eval)
-compare_gan_run_one_task --workdir=/tmp/results --task_num=1 --dataset_root=/tmp/datasets
-```
-
-Results (all computed metrics) will be stored in
-`/tmp/results/TASK_NUM/scores.csv`.
+# Compare GAN
+
+This repository offers TensorFlow implementations for many components related to
+**Generative Adversarial Networks**:
+
+*   losses (such non-saturating GAN, least-squares GAN, and WGAN),
+*   penalties (such as the gradient penalty),
+*   normalization techniques (such as spectral normalization, batch
+    normalization, and layer normalization),
+*   neural architectures (BigGAN, ResNet, DCGAN), and
+*   evaluation metrics (FID score, Inception Score, precision-recall, and KID
+    score).
+
+The code is **configurable via [Gin](https://github.com/google/gin-config)** and
+runs on **GPU/TPU/CPUs**. Several research papers make use of this repository,
+including:
+
+1.  [Are GANs Created Equal? A Large-Scale Study](https://arxiv.org/abs/1711.10337)
+    [<font color="green">[Code]</font>](https://github.com/google/compare_gan/tree/v1)
+    \
+    Mario Lucic*, Karol Kurach*, Marcin Michalski, Sylvain Gelly, Olivier
+    Bousquet **[NeurIPS 2018]**
+
+2.  [The GAN Landscape: Losses, Architectures, Regularization, and Normalization](https://arxiv.org/abs/1807.04720)
+    [<font color="green">[Code]</font>](https://github.com/google/compare_gan/tree/v2)
+    \
+    Karol Kurach*, Mario Lucic*, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly
+    **[2018]**
+
+3.  [Assessing Generative Models via Precision and Recall](https://arxiv.org/abs/1806.00035)
+    [<font color="green">[Code]</font>](https://github.com/google/compare_gan/blob/560697ee213f91048c6b4231ab79fcdd9bf20381/compare_gan/src/prd_score.py)
+    \
+    Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain
+    Gelly **[NeurIPS 2018]**
+
+4.  [GILBO: One Metric to Measure Them All](https://arxiv.org/abs/1802.04874)
+    [<font color="green">[Code]</font>](https://github.com/google/compare_gan/blob/560697ee213f91048c6b4231ab79fcdd9bf20381/compare_gan/src/gilbo.py)
+    \
+    Alexander A. Alemi, Ian Fischer **[NeurIPS 2018]**
+
+5.  [A Case for Object Compositionality in Deep Generative Models of Images](https://arxiv.org/abs/1810.10340)
+    [<font color="green">[Code]</font>](https://github.com/google/compare_gan/tree/v2_multigan)
+    \
+    Sjoerd van Steenkiste, Karol Kurach, Sylvain Gelly **[2018]**
+
+6.  [On Self Modulation for Generative Adversarial Networks](https://arxiv.org/abs/1810.01365)
+    [<font color="green">[Code]</font>](https://github.com/google/compare_gan) \
+    Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly **[ICLR 2019]**
+
+7.  [Self-Supervised Generative Adversarial Networks](https://arxiv.org/abs/1811.11212)
+    [<font color="green">[Code]</font>](https://github.com/google/compare_gan) \
+    Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, Neil Houlsby **[CVPR
+    2019]**
+
+
+## Installation
+
+You can easily install the library and all necessary dependencies by running:
+`pip install -e .` from the `compare_gan/` folder.
+
+## Running experiments
+
+Simply run the `main.py` passing a `--model_dir` (this is where checkpoints are
+stored) and a `--gin_config` (defies which model on which data set and other
+options). We provide several example configurations in the `example_configs/`
+folder, namely:
+
+*   **dcgan_celeba64**: DCGAN architecture with non-saturating loss on CelebA
+    64x64px
+*   **resnet_cifar10**: ResNet architecture with non-saturating loss and
+    spectral normalization on CIFAR-10
+*   **resnet_lsun-bedroom128**: ResNet architecture with WGAN loss and gradient
+    penalty on LSUN-bedrooms 128x128px
+*   **sndcgan_celebahq128**: SN-DCGAN architecture with non-saturating loss and
+    spectral normalization on CelebA-HQ 128x128px
+*   **biggan_imagenet128**: BigGAN architecture with hinge loss and spectral
+    normalization on ImageNet 128x128px
+
+### Training and evaluation
+
+To see all available options please run `python main.py --help`. Main options:
+
+*   To **train** the model use `--schedule=train` (default). Training is resumed
+    from the last saved checkpoint.
+*   To **evaluate** all checkpoints use `--schedule=continuous_eval
+    --eval_every_steps=0`. To evaluate only checkpoints where the step size is
+    divisible by 5000, use `--schedule=continuous_eval --eval_every_steps=5000`.
+    By default, 3 averaging runs are used to estimate the Inception Score and
+    the FID score. Keep in mind that when running locally on a single GPU it may
+    not be possible to run training and evaluation simultaneously due to memory
+    constraints.
+*   To **train and evaluate** the model use `--schedule=eval_after_train
+    --eval_every_steps=0`.
+
+### Training on Cloud TPUs
+
+We recommend using the
+[ctpu tool](https://github.com/tensorflow/tpu/tree/master/tools/ctpu) to create
+a Cloud TPU and corresponding Compute Engine VM. We use v3-128 Cloud TPU v3 Pod
+for training models on ImageNet in 128x128 resolutions. You can use smaller
+slices if you reduce the batch size (`options.batch_size` in the Gin config) or
+model parameters. Keep in mind that the model quality might change. Before
+training make sure that the environment variable `TPU_NAME` is set. Running
+evaluation on TPUs is currently not supported. Use a VM with a single GPU
+instead.
+
+### Datasets
+
+Compare GAN uses [TensorFlow Datasets](https://www.tensorflow.org/datasets) and
+it will automatically download and prepare the data. For ImageNet you will need
+to download the archive yourself. For CelebAHq you need to download and prepare
+the images on your own. If you are using TPUs make sure to point the training
+script to your Google Storage Bucket (`--tfds_data_dir`).

compare_gan/architectures/abstract_arch.py

@@ -0,0 +1,127 @@
+# coding=utf-8
+# Copyright 2018 Google LLC & Hwalsuk Lee.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Defines interfaces for generator and discriminator networks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+from compare_gan import utils
+import gin
+import six
+import tensorflow as tf
+
+
+@gin.configurable("G", blacklist=["name", "image_shape"])
+@six.add_metaclass(abc.ABCMeta)
+class AbstractGenerator(object):
+  """Interface for generator architectures."""
+
+  def __init__(self,
+               name="generator",
+               image_shape=None,
+               batch_norm_fn=None,
+               spectral_norm=False):
+    """Constructor for all generator architectures.
+
+    Args:
+      name: Scope name of the generator.
+      image_shape: Image shape to be generated, [height, width, colors].
+      batch_norm_fn: Function for batch normalization or None.
+      spectral_norm: If True use spectral normalization for all weights.
+    """
+    self._name = name
+    self._image_shape = image_shape
+    self._batch_norm_fn = batch_norm_fn
+    self._spectral_norm = spectral_norm
+
+  def __call__(self, z, y, is_training, reuse=tf.AUTO_REUSE):
+    with tf.variable_scope(self._name, values=[z, y], reuse=reuse):
+      outputs = self.apply(z=z, y=y, is_training=is_training)
+    return outputs
+
+  def batch_norm(self, inputs, **kwargs):
+    if self._batch_norm_fn is None:
+      return inputs
+    args = kwargs.copy()
+    args["inputs"] = inputs
+    if "use_sn" not in args:
+      args["use_sn"] = self._spectral_norm
+    return utils.call_with_accepted_args(self._batch_norm_fn, **args)
+
+  @abc.abstractmethod
+  def apply(self, z, y, is_training):
+    """Apply the generator on a input.
+
+    Args:
+      z: `Tensor` of shape [batch_size, z_dim] with latent code.
+      y: `Tensor` of shape [batch_size, num_classes] with one hot encoded
+        labels.
+      is_training: Boolean, whether the architecture should be constructed for
+        training or inference.
+
+    Returns:
+      Generated images of shape [batch_size] + self.image_shape.
+    """
+
+
+@gin.configurable("D", blacklist=["name"])
+@six.add_metaclass(abc.ABCMeta)
+class AbstractDiscriminator(object):
+  """Interface for discriminator architectures."""
+
+  def __init__(self,
+               name="discriminator",
+               batch_norm_fn=None,
+               layer_norm=False,
+               spectral_norm=False):
+    self._name = name
+    self._batch_norm_fn = batch_norm_fn
+    self._layer_norm = layer_norm
+    self._spectral_norm = spectral_norm
+
+  def __call__(self, x, y, is_training, reuse=tf.AUTO_REUSE):
+    with tf.variable_scope(self._name, values=[x, y], reuse=reuse):
+      outputs = self.apply(x=x, y=y, is_training=is_training)
+    return outputs
+
+  def batch_norm(self, inputs, **kwargs):
+    if self._batch_norm_fn is None:
+      return inputs
+    args = kwargs.copy()
+    args["inputs"] = inputs
+    if "use_sn" not in args:
+      args["use_sn"] = self._spectral_norm
+    return utils.call_with_accepted_args(self._batch_norm_fn, **args)
+
+
+  @abc.abstractmethod
+  def apply(self, x, y, is_training):
+    """Apply the discriminator on a input.
+
+    Args:
+      x: `Tensor` of shape [batch_size, ?, ?, ?] with real or fake images.
+      y: `Tensor` of shape [batch_size, num_classes] with one hot encoded
+        labels.
+      is_training: Boolean, whether the architecture should be constructed for
+        training or inference.
+
+    Returns:
+      Tuple of 3 Tensors, the final prediction of the discriminator, the logits
+      before the final output activation function and logits form the second
+      last layer.
+    """