[CondGAN] added architecture, trainer and script for Conditional GAN

bob/learn/pytorch/architectures/ConditionalGAN.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 ConditionalGAN_generator(nn.Module):
+  """
+  Class defining the Conditional GAN generator.
+
+  **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(ConditionalGAN_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 ConditionalGAN_discriminator(nn.Module):
+  """
+  Class defining the Conditional GAN discriminator.
+
+  **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(ConditionalGAN_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.BatchNorm2d(ndf * 2),
+      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.BatchNorm2d(ndf * 4),
+      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.BatchNorm2d(ndf * 8),
+      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/scripts/train_conditionalgan_multipie.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+
+""" Train a Conditional GAN 
+
+Usage:
+  %(prog)s [--noise-dim=<int>] [--conditional-dim=<int>] 
+           [--batch-size=<int>] [--epochs=<int>] [--sample=<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, --epochs=<int>            The number of training epochs [default: 100] 
+  -s, --sample=<int>            Save generated images at every 'sample' batch iteration [default: 100000000000] 
+  -o, --output-dir=<path>       Dir to save the logs, models and images [default: ./cgan-multipie/] 
+  -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 ConditionalGAN_generator as cgenerator
+from bob.learn.pytorch.architectures import ConditionalGAN_discriminator as cdiscriminator
+from bob.learn.pytorch.trainers import ConditionalGANTrainer as ctrainer
+
+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'])
+  epochs = int(args['--epochs'])
+  sample = int(args['--sample'])
+  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 = ctrainer(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_epochs=epochs, output_dir=output_dir)

bob/learn/pytorch/trainers/ConditionalGANTrainer.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+import numpy
+import time
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.autograd import Variable
+import torchvision.utils as vutils
+
+import bob.core
+logger = bob.core.log.setup("bob.learn.pytorch")
+
+class ConditionalGANTrainer(object):
+  """
+  Class to train a Conditional GAN
+
+  **Parameters**
+
+  generator: pytorch nn.Module
+    The generator network
+
+  discriminator: pytorch nn.Module
+    The discriminator network
+
+  image_size: list
+    The size of the images in this format: [channels,height, width]
+
+  batch_size: int
+    The size of your minibatch
+
+  noise_dim: int
+    The dimension of the noise (input to the generator)
+
+  conditional_dim: int
+    The dimension of the conditioning variable
+
+  use_gpu: boolean
+    If you would like to use the gpu
+
+  verbosity_level: int
+    The level of verbosity output to stdout
+  """
+  def __init__(self, netG, netD, image_size, batch_size=64, noise_dim=100, conditional_dim=13, use_gpu=False, verbosity_level=2):
+   
+    bob.core.log.set_verbosity_level(logger, verbosity_level)
+    
+    self.netG = netG
+    self.netD = netD
+    self.image_size = image_size
+    self.batch_size = batch_size
+    self.noise_dim = noise_dim 
+    self.conditional_dim = conditional_dim 
+    self.use_gpu = use_gpu
+
+    # fixed conditional noise - used to generate samples (one for each value of the conditional variable)
+    self.fixed_noise = torch.FloatTensor(self.conditional_dim, noise_dim, 1, 1).normal_(0, 1)
+    self.fixed_one_hot = torch.FloatTensor(self.conditional_dim, self.conditional_dim, 1, 1).zero_()
+    for k in range(self.conditional_dim):
+      self.fixed_one_hot[k, k] = 1
+    
+    # TODO: figuring out the CPU/GPU thing - Guillaume HEUSCH, 17-11-2017
+    self.fixed_noise = Variable(self.fixed_noise)
+    self.fixed_one_hot = Variable(self.fixed_one_hot)
+
+    # binary cross-entropy loss
+    self.criterion = nn.BCELoss()
+
+    # move stuff to GPU if needed
+    if self.use_gpu:
+      self.netD.cuda()
+      self.netG.cuda()
+      self.criterion.cuda()
+
+
+  def train(self, dataloader, n_epochs=10, learning_rate=0.0002, beta1=0.5, output_dir='out'):
+    """
+    Function that performs the training.
+
+    **Parameters**
+
+    dataloader: pytorch DataLoader
+      The dataloader for your data
+
+    n_epochs: int
+      The number of epochs you would like to train for
+
+    learning_rate: float
+      The learning rate for Adam optimizer
+
+    beta1: float
+      The beta1 for Adam optimizer
+
+    output_dir: path
+      The directory where you would like to output images and models
+    """
+    real_label = 1
+    fake_label = 0
+    
+    # setup optimizer
+    optimizerD = optim.Adam(self.netD.parameters(), lr=learning_rate, betas=(beta1, 0.999))
+    optimizerG = optim.Adam(self.netG.parameters(), lr=learning_rate, betas=(beta1, 0.999))
+    
+    for epoch in range(n_epochs):
+      for i, data in enumerate(dataloader, 0):
+     
+        start = time.time()
+
+        # get the data and pose labels
+        real_images = data['image']
+        poses = data['pose']
+
+        # WARNING: the last batch could be smaller than the provided size
+        batch_size = len(real_images)
+       
+        # create the Tensors with the right batch size
+        noise = torch.FloatTensor(batch_size, self.noise_dim, 1, 1).normal_(0, 1)
+        label = torch.FloatTensor(batch_size)
+
+        # create the one hot conditional vector (generator) and feature maps (discriminator)
+        one_hot_feature_maps = torch.FloatTensor(batch_size, self.conditional_dim, self.image_size[1], self.image_size[2]).zero_()
+        one_hot_vector = torch.FloatTensor(batch_size, self.conditional_dim, 1, 1).zero_()
+        for k in range(batch_size):
+          one_hot_feature_maps[k, poses[k], :, :] = 1
+          one_hot_vector[k, poses[k]] = 1
+        
+        # move stuff to GPU if needed
+        if self.use_gpu:
+          real_images = real_images.cuda()
+          label = label.cuda()
+          noise = noise.cuda()
+          one_hot_feature_maps = one_hot_feature_maps.cuda() 
+          one_hot_vector = one_hot_vector.cuda()
+
+        # =============
+        # DISCRIMINATOR 
+        # =============
+        self.netD.zero_grad()
+     
+        # === REAL DATA ===
+        label.resize_(batch_size).fill_(real_label)
+        imagev = Variable(real_images)
+        one_hot_fmv = Variable(one_hot_feature_maps)
+        labelv = Variable(label)
+        output_real = self.netD(imagev, one_hot_fmv)
+        errD_real = self.criterion(output_real, labelv)
+        errD_real.backward()
+
+        # === FAKE DATA ===
+        noisev = Variable(noise)
+        one_hot_vv = Variable(one_hot_vector)
+        fake = self.netG(noisev, one_hot_vv)
+        labelv = Variable(label.fill_(fake_label))
+        output_fake = self.netD(fake, one_hot_fmv)
+        errD_fake = self.criterion(output_fake, labelv)
+        errD_fake.backward(retain_graph=True)
+        
+        # perform optimization (i.e. update discriminator parameters)
+        errD = errD_real + errD_fake
+        optimizerD.step()
+
+        # =========
+        # GENERATOR 
+        # =========
+        self.netG.zero_grad()
+        labelv = Variable(label.fill_(real_label))  # fake labels are real for generator cost
+        output_generated = self.netD(fake, one_hot_fmv)
+        errG = self.criterion(output_generated, labelv)
+        errG.backward()
+        optimizerG.step()
+
+        end = time.time()
+        logger.info("[{}/{}][{}/{}] => Loss D = {} -- Loss G = {} (time spent: {})".format(epoch, n_epochs, i, len(dataloader), errD.data[0], errG.data[0], (end-start)))
+        
+      # save generated images at every epoch
+      # TODO: model moved to CPU and back and I don't really know why (expected CPU tensor error)
+      # To summarize:
+      # tried to move tensors, variables on the GPU -> does not work
+      # let the tensors on the CPU -> does not work
+      # => model has to be brought back to the CPU :/
+      self.netG = self.netG.cpu()
+      fake_examples = self.netG(self.fixed_noise, self.fixed_one_hot)
+      self.netG = self.netG.cuda()
+      vutils.save_image(fake_examples.data, '%s/fake_samples_epoch_%03d.png' % (output_dir, epoch), normalize=True)
+
+      # do checkpointing
+      torch.save(self.netG.state_dict(), '%s/netG_epoch_%d.pth' % (output_dir, epoch))
+      torch.save(self.netD.state_dict(), '%s/netD_epoch_%d.pth' % (output_dir, epoch))