dcgan.py

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Runs DCGAN training with differential privacy.

"""
from __future__ import print_function

import argparse
import os
import random

import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
from opacus import PrivacyEngine
from tqdm import tqdm


parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--data-root", required=True, help="path to dataset")
parser.add_argument(
    "--workers", type=int, help="number of data loading workers", default=2
)
parser.add_argument("--batch-size", type=int, default=64, help="input batch size")
parser.add_argument(
    "--imageSize",
    type=int,
    default=64,
    help="the height / width of the input image to network",
)
parser.add_argument("--nz", type=int, default=100, help="size of the latent z vector")
parser.add_argument("--ngf", type=int, default=128)
parser.add_argument("--ndf", type=int, default=128)
parser.add_argument(
    "--epochs", type=int, default=25, help="number of epochs to train for"
)
parser.add_argument(
    "--lr", type=float, default=0.0002, help="learning rate, default=0.0002"
)
parser.add_argument(
    "--beta1", type=float, default=0.5, help="beta1 for adam. default=0.5"
)
parser.add_argument("--ngpu", type=int, default=1, help="number of GPUs to use")
parser.add_argument("--netG", default="", help="path to netG (to continue training)")
parser.add_argument("--netD", default="", help="path to netD (to continue training)")
parser.add_argument(
    "--outf", default=".", help="folder to output images and model checkpoints"
)
parser.add_argument("--manualSeed", type=int, help="manual seed")
parser.add_argument(
    "--target-digit",
    type=int,
    default=8,
    help="the target digit(0~9) for MNIST training",
)
parser.add_argument(
    "--device",
    type=str,
    default="cuda",
    help="GPU ID for this process (default: 'cuda')",
)
parser.add_argument(
    "--disable-dp",
    action="store_true",
    default=False,
    help="Disable privacy training and just train with vanilla SGD",
)
parser.add_argument(
    "--secure-rng",
    action="store_true",
    default=False,
    help="Enable Secure RNG to have trustworthy privacy guarantees. Comes at a performance cost",
)
parser.add_argument(
    "-r",
    "--n-runs",
    type=int,
    default=1,
    metavar="R",
    help="number of runs to average on (default: 1)",
)
parser.add_argument(
    "--sigma",
    type=float,
    default=1.0,
    metavar="S",
    help="Noise multiplier (default 1.0)",
)
parser.add_argument(
    "-c",
    "--max-per-sample-grad_norm",
    type=float,
    default=1.0,
    metavar="C",
    help="Clip per-sample gradients to this norm (default 1.0)",
)
parser.add_argument(
    "--delta",
    type=float,
    default=1e-5,
    metavar="D",
    help="Target delta (default: 1e-5)",
)

opt = parser.parse_args()

try:
    os.makedirs(opt.outf)
except OSError:
    pass

if opt.manualSeed is None:
    opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)

cudnn.benchmark = True


try:
    dataset = dset.MNIST(
        root=opt.data_root,
        download=True,
        transform=transforms.Compose(
            [
                transforms.Resize(opt.imageSize),
                transforms.ToTensor(),
                transforms.Normalize((0.5,), (0.5,)),
            ]
        ),
    )
    idx = dataset.targets == opt.target_digit
    dataset.targets = dataset.targets[idx]
    dataset.data = dataset.data[idx]
    nc = 1
except ValueError:
    print("Cannot load dataset")

dataloader = torch.utils.data.DataLoader(
    dataset,
    num_workers=int(opt.workers),
    batch_size=opt.batch_size,
)

device = torch.device(opt.device)
ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)


# custom weights initialization called on netG and netD
def weights_init(m):
    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 Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
            nn.GroupNorm(min(32, ndf * 8), ndf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.GroupNorm(min(32, ndf * 4), ndf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.GroupNorm(min(32, ndf * 2), ndf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.GroupNorm(min(32, ndf), ndf),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )

    def forward(self, input):
        if input.is_cuda and self.ngpu > 1:
            output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
        else:
            output = self.main(input)
        return output


netG = Generator(ngpu)
netG = netG.to(device)
netG.apply(weights_init)
if opt.netG != "":
    netG.load_state_dict(torch.load(opt.netG))


class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is (nc) x 64 x 64
            nn.Conv2d(nc, 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.GroupNorm(min(32, ndf * 2), 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.GroupNorm(min(32, ndf * 4), 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.GroupNorm(min(32, ndf * 8), 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, input):
        if input.is_cuda and self.ngpu > 1:
            output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))
        else:
            output = self.main(input)

        return output.view(-1, 1).squeeze(1)


netD = Discriminator(ngpu)
netD = netD.to(device)
netD.apply(weights_init)
if opt.netD != "":
    netD.load_state_dict(torch.load(opt.netD))

criterion = nn.BCELoss()

FIXED_NOISE = torch.randn(opt.batch_size, nz, 1, 1, device=device)
REAL_LABEL = 1.0
FAKE_LABEL = 0.0

# setup optimizer
optimizerD = optim.Adam(netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))

if not opt.disable_dp:
    privacy_engine = PrivacyEngine(secure_mode=opt.secure_rng)

    netD, optimizerD, dataloader = privacy_engine.make_private(
        module=netD,
        optimizer=optimizerD,
        data_loader=dataloader,
        noise_multiplier=opt.sigma,
        max_grad_norm=opt.max_per_sample_grad_norm,
    )

optimizerG = optim.Adam(netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))

for epoch in range(opt.epochs):
    data_bar = tqdm(dataloader)
    for i, data in enumerate(data_bar, 0):
        ############################
        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
        ###########################

        optimizerD.zero_grad()

        real_data = data[0].to(device)
        batch_size = real_data.size(0)
        # train with fake
        noise = torch.randn(batch_size, nz, 1, 1, device=device)
        fake = netG(noise)
        label_fake = torch.full((batch_size,), FAKE_LABEL, device=device)
        output = netD(fake.detach())
        errD_fake = criterion(output, label_fake)
        errD_fake.backward()
        optimizerD.step()
        optimizerD.zero_grad()

        # train with real
        label_true = torch.full((batch_size,), REAL_LABEL, device=device)
        output = netD(real_data)
        errD_real = criterion(output, label_true)
        errD_real.backward()
        optimizerD.step()
        D_x = output.mean().item()

        D_G_z1 = output.mean().item()
        errD = errD_real + errD_fake

        ############################
        # (2) Update G network: maximize log(D(G(z)))
        ###########################
        optimizerG.zero_grad()
        optimizerD.zero_grad()

        label_g = torch.full((batch_size,), REAL_LABEL, device=device)
        output_g = netD(fake)
        errG = criterion(output_g, label_g)
        errG.backward()
        D_G_z2 = output.mean().item()
        optimizerG.step()
        data_bar.set_description(
            f"epoch: {epoch}, Loss_D: {errD.item()} "
            f"Loss_G: {errG.item()} D(x): {D_x} "
            f"D(G(z)): {D_G_z1}/{D_G_z2}"
        )

        if not opt.disable_dp:
            epsilon, best_alpha = privacy_engine.accountant.get_privacy_spent(
                delta=opt.delta
            )
            print(
                "(ε = %.2f, δ = %.2f) for α = %.2f" % (epsilon, opt.delta, best_alpha)
            )

        if i % 100 == 0:
            vutils.save_image(
                real_data, "%s/real_samples.png" % opt.outf, normalize=True
            )
            fake = netG(FIXED_NOISE)
            vutils.save_image(
                fake.detach(),
                "%s/fake_samples_epoch_%03d.png" % (opt.outf, epoch),
                normalize=True,
            )

    # do checkpointing
    torch.save(netG.state_dict(), "%s/netG_epoch_%d.pth" % (opt.outf, epoch))
    torch.save(netD.state_dict(), "%s/netD_epoch_%d.pth" % (opt.outf, epoch))