sgan.py

import argparse
import os
import numpy as np
import math
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("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--num_classes", type=int, default=10, help="number of classes for dataset")
parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=1, 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)

import jittor as jt
from jittor import init
from jittor import nn

def weights_init_normal(m):
    classname = m.__class__.__name__
    if (classname.find('Conv') != (- 1)):
        init.gauss_(m.weight, mean=0.0, std=0.02)
    elif (classname.find('BatchNorm') != (- 1)):
        init.gauss_(m.weight, mean=1.0, std=0.02)
        init.constant_(m.bias, value=0.0)

class Generator(nn.Module):

    def __init__(self):
        super(Generator, self).__init__()
        self.label_emb = nn.Embedding(opt.num_classes, opt.latent_dim)
        self.init_size = (opt.img_size // 4)
        self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, (128 * (self.init_size ** 2))))
        self.conv_blocks = nn.Sequential(nn.BatchNorm(128), nn.Upsample(scale_factor=2), nn.Conv(128, 128, 3, stride=1, padding=1), nn.BatchNorm(128, eps=0.8), nn.LeakyReLU(scale=0.2), nn.Upsample(scale_factor=2), nn.Conv(128, 64, 3, stride=1, padding=1), nn.BatchNorm(64, eps=0.8), nn.LeakyReLU(scale=0.2), nn.Conv(64, opt.channels, 3, stride=1, padding=1), nn.Tanh())

        for m in self.modules():
            weights_init_normal(m)

    def execute(self, noise):
        out = self.l1(noise)
        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.Conv(in_filters, out_filters, 3, stride=2, padding=1), nn.LeakyReLU(scale=0.2), nn.Dropout(p=0.25)]
            if bn:
                block.append(nn.BatchNorm(out_filters, eps=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))
        ds_size = (opt.img_size // (2 ** 4))
        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.num_classes + 1)), nn.Softmax())

        for m in self.modules():
            weights_init_normal(m)

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

import cv2
def save_image(img, path, nrow=10, padding=5):
    N,C,W,H = img.shape
    if (N%nrow!=0):
        print("N%nrow!=0")
        return
    ncol=int(N/nrow)
    img_all = []
    for i in range(ncol):
        img_ = []
        for j in range(nrow):
            img_.append(img[i*nrow+j])
            img_.append(np.zeros((C,W,padding)))
        img_all.append(np.concatenate(img_, 2))
        img_all.append(np.zeros((C,padding,img_all[0].shape[2])))
    img = np.concatenate(img_all, 1)
    img = np.concatenate([np.zeros((C,padding,img.shape[2])), img], 1)
    img = np.concatenate([np.zeros((C,img.shape[1],padding)), img], 2)
    min_=img.min()
    max_=img.max()
    img=(img-min_)/(max_-min_)*255
    img=img.transpose((1,2,0))
    if C==3:
        img = img[:,:,::-1]
    cv2.imwrite(path,img)


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

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

# Configure data loader
from jittor.dataset.mnist import MNIST
import jittor.transform as transform

transform = transform.Compose([
    transform.Resize(opt.img_size),
    transform.Gray(),
    transform.ImageNormalize(mean=[0.5], std=[0.5]),
])
dataloader = MNIST(train=True, transform=transform).set_attrs(batch_size=opt.batch_size, shuffle=True)

# Optimizers
optimizer_G = nn.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = nn.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

# ----------
#  Training
# ----------
for epoch in range(opt.n_epochs):
    for i, (imgs, labels) in enumerate(dataloader):

        batch_size = imgs.shape[0]

        # Adversarial ground truths
        valid = jt.ones((batch_size, 1)).float32().stop_grad()
        fake = jt.zeros((batch_size, 1)).float32().stop_grad()
        fake_aux_gt = jt.zeros((batch_size, 1)).float32().stop_grad() * opt.num_classes

        # Configure input
        real_imgs = jt.array(imgs).float32()
        labels = jt.array(labels).float32()

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

        # Sample noise and labels as generator input
        z = jt.array(np.random.normal(0, 1, (batch_size, opt.latent_dim))).float32()

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

        # Loss measures generator's ability to fool the discriminator
        validity, _ = discriminator(gen_imgs)
        g_loss = adversarial_loss(validity, valid)

        optimizer_G.step(g_loss)

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

        # 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, fake_aux_gt)) / 2

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

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

        optimizer_D.step(d_loss)

        print(
            "[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]"
            % (epoch, opt.n_epochs, i, len(dataloader), d_loss.data[0], 100 * d_acc, g_loss.data[0])
        )

        batches_done = epoch * len(dataloader) + i
        if batches_done % opt.sample_interval == 0:
            save_image(gen_imgs.numpy(), "images/%d.png" % batches_done, nrow=8)