AC_GAN.py

# From https://github.com/eriklindernoren/PyTorch-GAN

import argparse
import os
import numpy as np
import math

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

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

from testmodel import *


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


def load_my_classifier():
    print("Loading model...")
    model = load_resnet(18)
    model = change_model(model, 0, 30)
    model = model.cuda()

    model.load_state_dict(torch.load("cover_final/64 w relu/model64"))
    print("Loaded !")

    return model

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("BatchNorm2d") != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)


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

        self.label_emb = nn.Embedding(opt.n_classes, opt.latent_dim)

        self.init_size = opt.img_size // 4  # Initial size before upsampling
        self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128 * self.init_size ** 2))

        self.conv_blocks = nn.Sequential(
            nn.BatchNorm2d(128),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(128, 128, 3, stride=1, padding=1),
            nn.BatchNorm2d(128, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(128, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, opt.channels, 3, stride=1, padding=1),
            nn.Tanh(),
        )

    def forward(self, noise, labels):
        gen_input = torch.mul(self.label_emb(labels), noise)
        out = self.l1(gen_input)
        out = out.view(out.shape[0], 128, self.init_size, self.init_size)
        img = self.conv_blocks(out)
        return img


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

        def discriminator_block(in_filters, out_filters, bn=True):
            """Returns layers of each discriminator block"""
            block = [nn.Conv2d(in_filters, out_filters, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)]
            if bn:
                block.append(nn.BatchNorm2d(out_filters, 0.8))
            return block

        self.conv_blocks = nn.Sequential(
            *discriminator_block(opt.channels, 16, bn=False),
            *discriminator_block(16, 32),
            *discriminator_block(32, 64),
            *discriminator_block(64, 128),
        )

        # The height and width of downsampled image
        ds_size = opt.img_size // 2 ** 4

        # Output layers
        self.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())
        # self.aux_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, opt.n_classes), nn.Softmax())

        self.my_aux = load_my_classifier()

    def forward(self, img):
        out = self.conv_blocks(img)
        out = out.view(out.shape[0], -1)
        validity = self.adv_layer(out)
        # label = self.aux_layer(out)

        label = torch.exp(self.my_aux(img))

        return validity, label


if __name__ == "__main__":
    os.makedirs("gan_images", exist_ok=True)

    parser = argparse.ArgumentParser()
    parser.add_argument("--n_epochs", type=int, default=50, help="number of epochs of training")
    parser.add_argument("--batch_size", type=int, default=32, 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=4, help="number of cpu threads to use during batch generation")
    parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
    parser.add_argument("--n_classes", type=int, default=30, help="number of classes for dataset")
    parser.add_argument("--img_size", type=int, default=224, help="size of each image dimension")
    parser.add_argument("--channels", type=int, default=3, help="number of image channels")
    parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
    opt = parser.parse_args()
    print(opt)

    # Loss functions
    adversarial_loss = torch.nn.BCELoss()
    auxiliary_loss = torch.nn.CrossEntropyLoss()

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

    if cuda:
        generator.cuda()
        discriminator.cuda()
        adversarial_loss.cuda()
        auxiliary_loss.cuda()

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

    # Configure data loader
    cover_path = "dataset/covers"
    csv_path = "dataset/book30-listing-train.csv"

    data_transforms = transforms.Compose(
                [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])

    image_dataset = BookDataset(csv_path, cover_path, transform=data_transforms)

    dataloader = torch.utils.data.DataLoader(image_dataset, batch_size=opt.batch_size,
                                                shuffle=True, num_workers=2, pin_memory=False)

    class_names = image_dataset.classes

    # Optimizers
    optimizer_G = torch.optim.Adam(generator.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
    LongTensor2 = torch.cuda.LongTensor if cuda else torch.LongTensor


    def sample_image(n_row, batches_done):
        """Saves a grid of generated digits ranging from 0 to n_classes"""
        
        # Sample noise
        z = Variable(FloatTensor(np.random.normal(0, 1, (10 * 3, opt.latent_dim))))
        # Get labels ranging from 0 to n_classes for n rows
        # labels = np.array([num for _ in range(n_row) for num in range(30)])
        # labels = Variable(LongTensor(labels))

        labels2 = np.array([num for _ in range(3) for num in range(10)])
        labels2 = Variable(LongTensor2(labels2))

        gen_img = generator(z, labels2)
        save_image(gen_img.data, "gan_images/%d.png" % batches_done, nrow=10, normalize=True)


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

    for epoch in range(opt.n_epochs):
        lastPrint = 0
        progress = 0
        start = time.time()
        for i, (imgs, labels) in enumerate(dataloader):

            batch_size = imgs.shape[0]

            # Adversarial ground truths
            valid = Variable(FloatTensor(batch_size, 1).fill_(1.0), requires_grad=False)
            fake = Variable(FloatTensor(batch_size, 1).fill_(0.0), requires_grad=False)

            # Configure input
            real_imgs = Variable(imgs.type(FloatTensor))
            labels = Variable(labels.type(LongTensor))

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

            optimizer_G.zero_grad()

            # Sample noise and labels as generator input
            z = Variable(FloatTensor(np.random.normal(0, 1, (batch_size, opt.latent_dim))))
            gen_labels = Variable(LongTensor(np.random.randint(0, opt.n_classes, batch_size)))

            # Generate a batch of images
            gen_imgs = generator(z, gen_labels)

            # Loss measures generator's ability to fool the discriminator
            validity, pred_label = discriminator(gen_imgs)
            g_loss = 0.5 * (adversarial_loss(validity, valid) + auxiliary_loss(pred_label, gen_labels))

            g_loss.backward()
            optimizer_G.step()

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

            optimizer_D.zero_grad()

            # Loss for real images
            real_pred, real_aux = discriminator(real_imgs)
            d_real_loss = (adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2

            # Loss for fake images
            fake_pred, fake_aux = discriminator(gen_imgs.detach())
            d_fake_loss = (adversarial_loss(fake_pred, fake) + auxiliary_loss(fake_aux, gen_labels)) / 2

            # Total discriminator loss
            d_loss = (d_real_loss + d_fake_loss) / 2

            # Calculate discriminator accuracy
            pred = np.concatenate([real_aux.data.cpu().numpy(), fake_aux.data.cpu().numpy()], axis=0)
            gt = np.concatenate([labels.data.cpu().numpy(), gen_labels.data.cpu().numpy()], axis=0)
            d_acc = np.mean(np.argmax(pred, axis=1) == gt)

            d_loss.backward()
            optimizer_D.step()

            progress += 1 / len(dataloader) * 100
            if(progress > 10 + lastPrint) or lastPrint == 0:
                lastPrint = progress
                elapsed = time.time() - start
                print(
                    "[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f][Time : %dm%ds]"
                    % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), 100 * d_acc, g_loss.item(), elapsed // 60, elapsed % 60)
                )
            batches_done = epoch * len(dataloader) + i
            if batches_done % opt.sample_interval == 0:
                sample_image(n_row=30, batches_done=batches_done)