[architectures, scripts] added architecture for Wasserstein with GP and script to train

bob/learn/pytorch/architectures/WassersteinCGAN.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+
+import torch
+import torch.nn as nn
+
+def weights_init(m):
+  """
+  Weights initialization
+  
+  **Parameters**
+  
+  m: 
+    The model
+  """
+  classname = m.__class__.__name__
+  if classname.find('Conv') != -1:
+    m.weight.data.normal_(0.0, 0.02)
+  elif classname.find('BatchNorm') != -1:
+    m.weight.data.normal_(1.0, 0.02)
+    m.bias.data.fill_(0)
+
+
+class WCGAN_generator(nn.Module):
+  """
+  Class defining the Conditional GAN generator for the Improved Wasserstein training.
+
+  **Parameters**
+
+  noise_dim: int
+    The dimension of the noise.
+
+  conditional_dim: int
+    The dimension of the conditioning variable.
+
+  channels: int
+    The number of channels in the input image (default: 3).
+
+  ngpu: int
+    The number of GPU (default: 1)
+  """ 
+  def __init__(self, noise_dim, conditional_dim, channels=3, ngpu=1):
+    
+    super(WCGAN_generator, self).__init__()
+    self.ngpu = ngpu
+    self.conditional_dim = conditional_dim
+    
+    # output dimension
+    ngf = 64
+
+    self.main = nn.Sequential(
+      # input is Z, going into a convolution
+      nn.ConvTranspose2d((noise_dim + conditional_dim), ngf * 8, 4, 1, 0, bias=False),
+      nn.BatchNorm2d(ngf * 8),
+      nn.ReLU(True),
+      # state size. (ngf*8) x 4 x 4
+      nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ngf * 4),
+      nn.ReLU(True),
+      # state size. (ngf*4) x 8 x 8
+      nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ngf * 2),
+      nn.ReLU(True),
+      # state size. (ngf*2) x 16 x 16
+      nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ngf),
+      nn.ReLU(True),
+      # state size. (ngf) x 32 x 32
+      nn.ConvTranspose2d(ngf, channels, 4, 2, 1, bias=False),
+      nn.Tanh()
+      # state size. (nc) x 64 x 64
+    )
+
+  def forward(self, z, y):
+    """
+      Forward function for the generator.
+
+    **Parameters**
+
+    z: pyTorch Variable
+      The minibatch of noise.
+
+    y: pyTorch Variable
+      The conditional one hot encoded vector for the minibatch.
+    """
+    generator_input = torch.cat((z, y), 1)
+    if isinstance(generator_input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+      output = nn.parallel.data_parallel(self.main, generator_input, range(self.ngpu))
+    else:
+      output = self.main(generator_input)
+    return output
+
+
+class WCGAN_discriminator(nn.Module):
+  """
+  Class defining the Conditional GAN discriminator for the Improved Wasserstein training.
+  (no batch normalization)
+
+  **Parameters**
+
+  conditional_dim: int
+    The dimension of the conditioning variable.
+
+  channels: int
+    The number of channels in the input image (default: 3).
+
+  ngpu: int
+    The number of GPU (default: 1)
+  """ 
+  def __init__(self, conditional_dim, channels=3, ngpu=1):
+    
+    super(WCGAN_discriminator, self).__init__()
+    self.conditional_dim = conditional_dim
+    self.ngpu = ngpu
+    
+    # input dimension
+    ndf = 64
+       
+    self.main = nn.Sequential(
+      # input is (nc) x 64 x 64
+      nn.Conv2d((channels + conditional_dim), ndf, 4, 2, 1, bias=False),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf) x 32 x 32
+      nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf*2) x 16 x 16
+      nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf*4) x 8 x 8
+      nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf*8) x 4 x 4
+      nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
+      nn.Sigmoid()
+    )
+  
+
+  def forward(self, images, y):
+    """
+      Forward function for the discriminator.
+
+    **Parameters**
+
+    images: pyTorch Variable
+      The minibatch of input images.
+
+    y: pyTorch Variable
+      The corresponding conditional feature maps.
+    """
+    input_discriminator = torch.cat((images, y), 1)
+    if isinstance(input_discriminator.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+      output = nn.parallel.data_parallel(self.main, input_discriminator, range(self.ngpu))
+    else:
+      output = self.main(input_discriminator)
+    return output.view(-1, 1).squeeze(1)

bob/learn/pytorch/architectures/__init__.py

@@ -4,6 +4,9 @@ from .DCGAN import DCGAN_discriminator
 from .ConditionalGAN import ConditionalGAN_generator
 from .ConditionalGAN import ConditionalGAN_discriminator
 
+from .WassersteinCGAN import WCGAN_generator
+from .WassersteinCGAN import WCGAN_discriminator
+
 from .DCGAN import weights_init
 
 # gets sphinx autodoc done right - don't remove it

bob/learn/pytorch/scripts/train_wcgan_multipie.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+
+""" Train a Conditional GAN using Improved Wasserstein Training. 
+
+Usage:
+  %(prog)s [--noise-dim=<int>] [--conditional-dim=<int>] 
+           [--batch-size=<int>] [--iterations=<int>] 
+           [--output-dir=<path>] [--use-gpu] [--seed=<int>] [--verbose ...]
+
+Options:
+  -h, --help                    Show this screen.
+  -V, --version                 Show version.
+  -n, --noise-dim=<int>         The dimension of the noise [default: 100]
+  -c, --conditional-dim=<int>   The dimension of the conditional variable [default: 13]
+  -b, --batch-size=<int>        The size of your mini-batch [default: 64]
+  -e, --iterations=<int>        The number of training iterations [default: 100000] 
+  -o, --output-dir=<path>       Dir to save the logs, models and images [default: ./drgan-light-mpie-casia/] 
+  -g, --use-gpu                 Use the GPU 
+  -S, --seed=<int>              The random seed [default: 3] 
+  -v, --verbose                 Increase the verbosity (may appear multiple times).
+
+Example:
+
+  To run the training process 
+
+    $ %(prog)s --batch-size 64 --epochs 25 --output-dir drgan 
+
+See '%(prog)s --help' for more information.
+
+"""
+
+import os, sys
+import pkg_resources
+
+import bob.core
+logger = bob.core.log.setup("bob.learn.pytorch")
+
+from docopt import docopt
+
+version = pkg_resources.require('bob.learn.pytorch')[0].version
+
+import numpy
+import bob.io.base
+
+# torch
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+import torchvision.utils as vutils
+from torch.autograd import Variable
+
+# data and architecture from the package
+from bob.learn.pytorch.datasets import MultiPIEDataset
+from bob.learn.pytorch.datasets import RollChannels
+from bob.learn.pytorch.datasets import ToTensor
+from bob.learn.pytorch.datasets import Normalize
+
+from bob.learn.pytorch.architectures import weights_init
+
+from bob.learn.pytorch.architectures import WCGAN_generator as cgenerator
+from bob.learn.pytorch.architectures import WCGAN_discriminator as cdiscriminator
+from bob.learn.pytorch.trainers import IWCGANTrainer as iwtrainer
+
+def main(user_input=None):
+  
+  # Parse the command-line arguments
+  if user_input is not None:
+      arguments = user_input
+  else:
+      arguments = sys.argv[1:]
+
+  prog = os.path.basename(sys.argv[0])
+  completions = dict(prog=prog, version=version,)
+  args = docopt(__doc__ % completions,argv=arguments,version='Train conditional GAN (%s)' % version,)
+
+  # verbosity
+  verbosity_level = args['--verbose']
+  bob.core.log.set_verbosity_level(logger, verbosity_level)
+
+  # get the arguments
+  noise_dim = int(args['--noise-dim'])
+  conditional_dim = int(args['--conditional-dim'])
+  batch_size = int(args['--batch-size'])
+  iterations = int(args['--iterations'])
+  output_dir = str(args['--output-dir'])
+  seed = int(args['--seed'])
+  use_gpu = bool(args['--use-gpu'])
+
+  images_dir = os.path.join(output_dir, 'samples')
+  log_dir = os.path.join(output_dir, 'logs')
+  model_dir = os.path.join(output_dir, 'models')
+
+  # process on the arguments / options
+  torch.manual_seed(seed)
+  if use_gpu:
+    torch.cuda.manual_seed_all(seed)
+  if torch.cuda.is_available() and not use_gpu:
+    logger.warn("You have a CUDA device, so you should probably run with --use-gpu")
+  bob.io.base.create_directories_safe(images_dir)
+  bob.io.base.create_directories_safe(log_dir)
+  bob.io.base.create_directories_safe(images_dir)
+
+  # ============
+  # === DATA ===
+  # ============
+  
+  # WARNING with the transforms ... act on labels too, at some point, I may have to write my own
+  # Also, in 'ToTensor', there is a reshape performed from: HxWxC to CxHxW
+  face_dataset = MultiPIEDataset(#root_dir='/Users/guillaumeheusch/work/idiap/data/multipie-cropped-64x64', 
+                                 root_dir='/idiap/temp/heusch/data/multipie-cropped-64x64', 
+                                 frontal_only=False, 
+                                 transform=transforms.Compose([
+                                   RollChannels(), # bob to skimage:  
+                                   ToTensor(),
+                                   Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+                                   ])
+                                 )
+
+  dataloader = torch.utils.data.DataLoader(face_dataset, batch_size=batch_size, shuffle=True)
+  logger.info("There are {} training images".format(len(face_dataset))) 
+  
+  # ===============
+  # === NETWORK ===
+  # ===============
+  ngpu = 1 # usually we don't have more than one GPU
+  
+  generator = cgenerator(noise_dim, conditional_dim)
+  generator.apply(weights_init)
+  logger.info("Generator architecture: {}".format(generator))
+
+  discriminator = cdiscriminator(conditional_dim)
+  discriminator.apply(weights_init)
+  logger.info("Discriminator architecture: {}".format(discriminator))
+
+  # ===============
+  # === TRAINER ===
+  # ===============
+  trainer = iwtrainer(generator, discriminator, [3, 64, 64], batch_size=batch_size, noise_dim=noise_dim, conditional_dim=conditional_dim, use_gpu=use_gpu, verbosity_level=verbosity_level)
+  trainer.train(dataloader, n_iterations=iterations, output_dir=output_dir)

bob/learn/pytorch/trainers/__init__.py

 from .DCGANTrainer import DCGANTrainer
 from .ConditionalGANTrainer import ConditionalGANTrainer
+from .ImprovedWassersteinCGANTrainer import IWCGANTrainer 
 
 # gets sphinx autodoc done right - don't remove it
 __all__ = [_ for _ in dir() if not _.startswith('_')]