pixelda.py

import argparse
import os
import numpy as np
import math
import itertools

import torchvision.transforms as transforms
from torchvision.utils import save_image

from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable

from mnistm import MNISTM

import torch.nn as nn
import torch.nn.functional as F
import torch

os.makedirs('images', exist_ok=True)

parser = argparse.ArgumentParser()
parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training')
parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
parser.add_argument('--n_residual_blocks', type=int, default=6, help='number of residual blocks in generator')
parser.add_argument('--latent_dim', type=int, default=10, help='dimensionality of the noise input')
parser.add_argument('--img_size', type=int, default=32, help='size of each image dimension')
parser.add_argument('--channels', type=int, default=3, help='number of image channels')
parser.add_argument('--n_classes', type=int, default=10, help='number of classes in the dataset')
parser.add_argument('--sample_interval', type=int, default=300, help='interval betwen image samples')
opt = parser.parse_args()
print(opt)

# Calculate output of image discriminator (PatchGAN)
patch = int(opt.img_size / 2**4)
patch = (1, patch, patch)

cuda = True if torch.cuda.is_available() else False

def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)

class ResidualBlock(nn.Module):
    def __init__(self, in_features=64, out_features=64):
        super(ResidualBlock, self).__init__()

        self.block = nn.Sequential(
            nn.Conv2d(in_features, in_features, 3, 1, 1),
            nn.BatchNorm2d(in_features),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_features, in_features, 3, 1, 1),
            nn.BatchNorm2d(in_features)
        )

    def forward(self, x):
        return x + self.block(x)

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        # Fully-connected layer which constructs image channel shaped output from noise
        self.fc = nn.Linear(opt.latent_dim, opt.channels*opt.img_size**2)

        self.l1 = nn.Sequential(nn.Conv2d(opt.channels*2, 64, 3, 1, 1), nn.ReLU(inplace=True))

        resblocks = []
        for _ in range(opt.n_residual_blocks):
            resblocks.append(ResidualBlock())
        self.resblocks = nn.Sequential(*resblocks)

        self.l2 = nn.Sequential(nn.Conv2d(64, opt.channels, 3, 1, 1), nn.Tanh())


    def forward(self, img, z):
        gen_input = torch.cat((img, self.fc(z).view(*img.shape)), 1)
        out = self.l1(gen_input)
        out = self.resblocks(out)
        img_ = self.l2(out)

        return img_

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        def block(in_features, out_features, normalization=True):
            """Discriminator block"""
            layers = [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
                        nn.LeakyReLU(0.2, inplace=True) ]
            if normalization:
                layers.append(nn.InstanceNorm2d(out_features))
            return layers

        self.model = nn.Sequential(
            *block(opt.channels, 64, normalization=False),
            *block(64, 128),
            *block(128, 256),
            *block(256, 512),
            nn.Conv2d(512, 1, 3, 1, 1)
        )

    def forward(self, img):
        validity = self.model(img)

        return validity


class Classifier(nn.Module):
    def __init__(self):
        super(Classifier, self).__init__()

        def block(in_features, out_features, normalization=True):
            """Classifier block"""
            layers = [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
                        nn.LeakyReLU(0.2, inplace=True) ]
            if normalization:
                layers.append(nn.InstanceNorm2d(out_features))
            return layers

        self.model = nn.Sequential(
            *block(opt.channels, 64, normalization=False),
            *block(64, 128),
            *block(128, 256),
            *block(256, 512)
        )

        input_size = opt.img_size // 2**4
        self.output_layer = nn.Sequential(
            nn.Linear(512*input_size**2, opt.n_classes),
            nn.Softmax()
        )

    def forward(self, img):
        feature_repr = self.model(img)
        feature_repr = feature_repr.view(feature_repr.size(0), -1)
        label = self.output_layer(feature_repr)
        return label

# Loss function
adversarial_loss = torch.nn.MSELoss()
task_loss = torch.nn.CrossEntropyLoss()

# Loss weights
lambda_adv =  1
lambda_task = 0.1

# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
classifier = Classifier()

if cuda:
    generator.cuda()
    discriminator.cuda()
    classifier.cuda()
    adversarial_loss.cuda()
    task_loss.cuda()

# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
classifier.apply(weights_init_normal)

# Configure data loader
os.makedirs('../../data/mnist', exist_ok=True)
dataloader_A = torch.utils.data.DataLoader(
    datasets.MNIST('../../data/mnist', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.Resize(opt.img_size),
                       transforms.ToTensor(),
                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])),
    batch_size=opt.batch_size, shuffle=True)

os.makedirs('../../data/mnistm', exist_ok=True)
dataloader_B = torch.utils.data.DataLoader(
    MNISTM('../../data/mnistm', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.Resize(opt.img_size),
                       transforms.ToTensor(),
                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])),
    batch_size=opt.batch_size, shuffle=True)

# Optimizers
optimizer_G = torch.optim.Adam( itertools.chain(generator.parameters(), classifier.parameters()),
                                lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor

# ----------
#  Training
# ----------

# Keeps 100 accuracy measurements
task_performance = []
target_performance = []

for epoch in range(opt.n_epochs):
    for i, ((imgs_A, labels_A), (imgs_B, labels_B)) in enumerate(zip(dataloader_A, dataloader_B)):

        batch_size = imgs_A.size(0)

        # Adversarial ground truths
        valid = Variable(FloatTensor(batch_size, *patch).fill_(1.0), requires_grad=False)
        fake = Variable(FloatTensor(batch_size, *patch).fill_(0.0), requires_grad=False)

        # Configure input
        imgs_A      = Variable(imgs_A.type(FloatTensor).expand(batch_size, 3, opt.img_size, opt.img_size))
        labels_A    = Variable(labels_A.type(LongTensor))
        imgs_B      = Variable(imgs_B.type(FloatTensor))

        # -----------------
        #  Train Generator
        # -----------------

        optimizer_G.zero_grad()

        # Sample noise
        z = Variable(FloatTensor(np.random.uniform(-1, 1, (batch_size, opt.latent_dim))))

        # Generate a batch of images
        fake_B = generator(imgs_A, z)

        # Perform task on translated source image
        label_pred = classifier(fake_B)

        # Calculate the task loss
        task_loss_ =    (task_loss(label_pred, labels_A) + \
                        task_loss(classifier(imgs_A), labels_A)) / 2

        # Loss measures generator's ability to fool the discriminator
        g_loss =    lambda_adv * adversarial_loss(discriminator(fake_B), valid) + \
                    lambda_task * task_loss_

        g_loss.backward()
        optimizer_G.step()

        # ---------------------
        #  Train Discriminator
        # ---------------------

        optimizer_D.zero_grad()

        # Measure discriminator's ability to classify real from generated samples
        real_loss = adversarial_loss(discriminator(imgs_B), valid)
        fake_loss = adversarial_loss(discriminator(fake_B.detach()), fake)
        d_loss = (real_loss + fake_loss) / 2

        d_loss.backward()
        optimizer_D.step()

        # ---------------------------------------
        #  Evaluate Performance on target domain
        # ---------------------------------------

        # Evaluate performance on translated Domain A
        acc = np.mean(np.argmax(label_pred.data.cpu().numpy(), axis=1) == labels_A.data.cpu().numpy())
        task_performance.append(acc)
        if len(task_performance) > 100:
            task_performance.pop(0)

        # Evaluate performance on Domain B
        pred_B = classifier(imgs_B)
        target_acc = np.mean(np.argmax(pred_B.data.cpu().numpy(), axis=1) == labels_B.numpy())
        target_performance.append(target_acc)
        if len(target_performance) > 100:
            target_performance.pop(0)

        print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f] [CLF acc: %3d%% (%3d%%), target_acc: %3d%% (%3d%%)]" %
                                                            (epoch, opt.n_epochs,
                                                            i, len(dataloader_A),
                                                            d_loss.item(), g_loss.item(),
                                                            100*acc, 100*np.mean(task_performance),
                                                            100*target_acc, 100*np.mean(target_performance)))

        batches_done = len(dataloader_A) * epoch + i
        if batches_done % opt.sample_interval == 0:
            sample = torch.cat((imgs_A.data[:5], fake_B.data[:5], imgs_B.data[:5]), -2)
            save_image(sample, 'images/%d.png' % batches_done, nrow=int(math.sqrt(batch_size)), normalize=True)