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RubbingGAN.py
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RubbingGAN.py
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from __future__ import print_function
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
from misc import *
import RubbingNet as net
from tensorboardX import SummaryWriter
from torch.autograd import Variable
import torchvision.utils as vutils
import torch.optim as optim
import argparse
from distutils.file_util import write_file
import os
import sys
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
cudnn.fastest = True
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', required=False,
default='rubbing', help='')
parser.add_argument('--dataroot', required=False,
default='', help='path to train dataset')
parser.add_argument('--valDataroot', required=False,
default='', help='path to val dataset')
parser.add_argument('--mode', type=str, default='A2B',
help='B2A: facade, A2B: edges2shoes')
parser.add_argument('--batchSize', type=int,
default=1, help='input batch size')
parser.add_argument('--valBatchSize', type=int,
default=64, help='input batch size')
parser.add_argument('--originalSize', type=int,
default=256, help='the height / width of the original input image')
parser.add_argument('--imageSize', type=int,
default=256, help='the height / width of the cropped input image to network')
parser.add_argument('--inputChannelSize', type=int,
default=3, help='size of the input channels')
parser.add_argument('--outputChannelSize', type=int,
default=3, help='size of the output channels')
parser.add_argument('--ngf', type=int, default=64)
parser.add_argument('--ndf', type=int, default=64)
parser.add_argument('--niter', type=int, default=50,
help='number of epochs to train for')
parser.add_argument('--lrD', type=float, default=0.0002,
help='learning rate, default=0.0002')
parser.add_argument('--lrG', type=float, default=0.0002,
help='learning rate, default=0.0002')
parser.add_argument('--annealStart', type=int, default=0,
help='annealing learning rate start to')
parser.add_argument('--annealEvery', type=int, default=400,
help='epoch to reaching at learning rate of 0')
parser.add_argument('--lambdaGAN', type=float, default=1, help='lambdaGAN')
parser.add_argument('--lambdaIMG', type=float, default=0.1, help='lambdaIMG')
parser.add_argument('--poolSize', type=int, default=50,
help='Buffer size for storing previously generated samples from G')
parser.add_argument('--lambda_k', type=float,
default=0.001, help='learning rate of k')
parser.add_argument('--gamma', type=float, default=0.7,
help='balance bewteen D and G')
parser.add_argument('--wd', type=float, default=0.0000,
help='weight decay in D')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam')
parser.add_argument('--netG', default='', help="")
parser.add_argument('--netD', default='',
help="path to netD (to continue training)")
parser.add_argument('--workers', type=int,
help='number of data loading workers', default=0)
parser.add_argument('--exp', default='',
help='folder to output images and model checkpoints')
parser.add_argument('--display', type=int, default=200,
help='interval for displaying train-logs')
parser.add_argument('--evalIter', type=int, default=500,
help='interval for evauating(generating) images from valDataroot')
parser.add_argument('--hidden_size', type=int, default=64,
help='bottleneck dimension of Discriminator')
parser.add_argument('--log', default='', help="path to the log")
opt = parser.parse_args()
print(opt)
create_exp_dir(opt.exp)
opt.manualSeed = 101
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
torch.cuda.manual_seed_all(opt.manualSeed)
print("Random Seed: ", opt.manualSeed)
writeFile = opt.log
print("Log File:", writeFile)
writer1 = SummaryWriter(writeFile)
# get dataloader
dataloader = getLoader(opt.dataset,
opt.dataroot,
opt.originalSize,
opt.imageSize,
opt.batchSize,
opt.workers,
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=opt.manualSeed)
valDataloader = getLoader(opt.dataset,
opt.valDataroot,
opt.imageSize, # opt.originalSize,
opt.imageSize,
opt.valBatchSize,
opt.workers,
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
split='val',
shuffle=False,
seed=opt.manualSeed)
# get logger
trainLogger = open('%s/train.log' % opt.exp, 'w')
ngf = opt.ngf
ndf = opt.ndf
inputChannelSize = opt.inputChannelSize
outputChannelSize = opt.outputChannelSize
# get models
netG = net.G(inputChannelSize, outputChannelSize, ngf)
netG.apply(weights_init)
if opt.netG != '':
netG.load_state_dict(torch.load(opt.netG))
print(netG)
netD = net.D(inputChannelSize + outputChannelSize, ndf)
netD.apply(weights_init)
if opt.netD != '':
netD.load_state_dict(torch.load(opt.netD))
print(netD)
netD1 = net.D1(inputChannelSize, ndf, opt.hidden_size)
netD1.apply(weights_init)
print(netD1)
netG.train()
netD.train()
netD1.train()
criterionBCE = nn.BCELoss()
criterionCAE = nn.L1Loss()
target = torch.FloatTensor(
opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize)
input = torch.FloatTensor(
opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
val_target = torch.FloatTensor(
opt.valBatchSize, outputChannelSize, opt.imageSize, opt.imageSize)
val_input = torch.FloatTensor(
opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize)
label_d = torch.FloatTensor(opt.batchSize)
# NOTE: size of 2D output maps in the discriminator
sizePatchGAN = 30
real_label = 1
fake_label = 0
# image pool storing previously generated samples from G
imagePool = ImagePool(opt.poolSize)
# NOTE weight for L_cGAN and L_L1 (i.e. Eq.(4) in the paper)
lambdaGAN = opt.lambdaGAN
lambdaIMG = opt.lambdaIMG
netD.cuda()
netG.cuda()
netD1.cuda()
criterionBCE.cuda()
criterionCAE.cuda()
target, input, label_d = target.cuda(), input.cuda(), label_d.cuda()
val_target, val_input = val_target.cuda(), val_input.cuda()
target = Variable(target)
input = Variable(input)
label_d = Variable(label_d)
# get randomly sampled validation images and save it
val_iter = iter(valDataloader)
data_val = val_iter.next()
if opt.mode == 'B2A':
val_target_cpu, val_input_cpu = data_val
elif opt.mode == 'A2B':
val_input_cpu, val_target_cpu = data_val
val_target_cpu, val_input_cpu = val_target_cpu.cuda(), val_input_cpu.cuda()
val_target.resize_as_(val_target_cpu).copy_(val_target_cpu)
val_input.resize_as_(val_input_cpu).copy_(val_input_cpu)
vutils.save_image(val_target, '%s/real_target.png' % opt.exp, normalize=True)
vutils.save_image(val_input, '%s/real_input.png' % opt.exp, normalize=True)
# get optimizer
optimizerD = optim.Adam(netD.parameters(), lr=opt.lrD,
betas=(opt.beta1, 0.999), weight_decay=opt.wd)
optimizerG = optim.Adam(netG.parameters(), lr=opt.lrG,
betas=(opt.beta1, 0.999), weight_decay=0.0)
optimizerD1 = optim.Adam(netD1.parameters(), lr=opt.lrD, betas=(
opt.beta1, 0.999), weight_decay=opt.wd)
# NOTE training loop
ganIterations = 0
k = 0 # control how much emphasis is put on L(G(z_D)) during gradient descent.
M_global = AverageMeter()
for epoch in range(opt.niter):
if epoch > opt.annealStart:
adjust_learning_rate(optimizerD, opt.lrD, epoch, None, opt.annealEvery)
adjust_learning_rate(optimizerG, opt.lrG, epoch, None, opt.annealEvery)
adjust_learning_rate(optimizerD1, opt.lrD, epoch,
None, opt.annealEvery)
for i, data in enumerate(dataloader, 0):
if opt.mode == 'B2A':
target_cpu, input_cpu = data
elif opt.mode == 'A2B':
input_cpu, target_cpu = data
batch_size = target_cpu.size(0)
target_cpu, input_cpu = target_cpu.cuda(), input_cpu.cuda()
# NOTE paired samples
target.resize_as_(target_cpu).copy_(target_cpu)
input.resize_as_(input_cpu).copy_(input_cpu)
# max_D first
for p in netD.parameters():
p.requires_grad = True
netD.zero_grad()
# NOTE: compute L_cGAN in eq.(2)
label_d.resize_((batch_size, 1, sizePatchGAN,
sizePatchGAN)).fill_(real_label)
output = netD(torch.cat([target, input], 1)) # conditional
errD_real = criterionBCE(output, label_d)
errD_real.backward()
D_x = output.data.mean()
x_hat = netG(input)
fake = x_hat.detach()
fake = Variable(imagePool.query(fake.data))
label_d.data.fill_(fake_label)
output = netD(torch.cat([fake, input], 1)) # conditional
errD_fake = criterionBCE(output, label_d)
errD_fake.backward()
errD = errD_real + errD_fake
optimizerD.step() # update Discriminator parameters
# prevent computing gradients of weights in Discriminator
for p in netD.parameters():
p.requires_grad = False
netG.zero_grad() # start to update G
# compute L_L1 (eq.(4) in the paper
L_img_ = criterionCAE(x_hat, target)
L_img = lambdaIMG * L_img_
if lambdaIMG != 0:
# in case of current version of pytorch
L_img.backward(retain_graph=True)
# compute L_cGAN (eq.(2) in the paper
label_d.data.fill_(real_label)
output = netD(torch.cat([x_hat, input], 1))
errG_ = criterionBCE(output, label_d)
errG = lambdaGAN * errG_
if lambdaGAN != 0:
errG.backward() # update Generator parameters
optimizerG.step()
####
# max_D1 first
for p in netD1.parameters():
p.requires_grad = True
netD1.zero_grad()
# NOTE: compute L_D
recon_real1 = netD1(target)
x_hat1 = netG(input)
fake1 = x_hat1.detach()
# sample from image buffer
fake1 = Variable(imagePool.query(fake1.data))
recon_fake1 = netD1(fake1)
# compute L(x,D) = |x-D(x)|
errD_real1 = torch.mean(torch.abs(recon_real1 - target))
# compute L(G(z_D),D1) = |G(z)-D(G(z))|
errD_fake1 = torch.mean(torch.abs(recon_fake1 - fake1))
# compute L_D1 = L(x,D1)- L(G(z_D),D1)
errD1 = errD_real1 - k * errD_fake1
errD1.backward()
optimizerD1.step()
# prevent computing gradients of weights in Discriminator
for p in netD1.parameters():
p.requires_grad = False
netG.zero_grad() # start to update G
recon_fake1 = netD1(x_hat1) # reuse previously computed x_hat
# compute L_G = |G(z) - D(G(z))|
errG_1 = torch.mean(torch.abs(recon_fake1 - x_hat1))
errG1 = lambdaGAN * errG_1
if lambdaGAN != 0:
errG1.backward()
# update praams
optimizerG.step()
# NOTE compute k_t and M_global
balance = (opt.gamma * errD_real1 - errD_fake1).item()
k = min(max(k + opt.lambda_k * balance, 0), 1)
measure = errD_real1.item() + np.abs(balance)
M_global.update(measure, target.size(0))
ganIterations += 1
if ganIterations % opt.display == 0:
print('[%d/%d][%d/%d] L_D: %f L_img: %f L_G: %f D(x): %f L_D1: %f L_G1: %f'
% (epoch, opt.niter, i, len(dataloader),
errD.item(), L_img.item(), errG.item(), D_x, errD1.item(), errG1.item()))
sys.stdout.flush()
writer1.add_scalar('Loss_D1', errD.item(), global_step=epoch)
writer1.add_scalar('L_img', L_img.item(), global_step=epoch)
writer1.add_scalar('Loss_G', errG.item(), global_step=epoch)
writer1.add_scalar('Loss_D2', errD1.item(), global_step=epoch)
writer1.add_scalar('D(x)', D_x, global_step=epoch)
writer1.add_scalar('L_D1', errD1.item(), global_step=epoch)
writer1.add_scalar('balance', balance, global_step=epoch)
writer1.add_scalar('k', k, global_step=epoch)
writer1.add_scalar('measure', measure, global_step=epoch)
writer1.add_scalar('M_global', M_global.avg, global_step=epoch)
writer1.add_scalar('L_G1', errG1.item(), global_step=epoch)
trainLogger.write('%d\t%f\t%f\t%f\t%f\t%f\t%f\t%f\t%f\t%f\n' %
(epoch, errD.item(), errG.item(), L_img.item(), D_x, errD1.item(), k, M_global.avg, balance, errG1.item()))
trainLogger.flush()
if ganIterations % opt.evalIter == 0:
val_batch_output = torch.FloatTensor(val_input.size()).fill_(0)
for idx in range(val_input.size(0)):
single_img = val_input[idx, :, :, :].unsqueeze(0)
with torch.no_grad():
val_inputv = Variable(single_img)
x_hat_val = netG(val_inputv)
val_batch_output[idx, :, :, :].copy_(x_hat_val.data.squeeze(0))
vutils.save_image(val_batch_output, '%s/generated_epoch_%08d_iter%08d.png' %
(opt.exp, epoch, ganIterations), normalize=True)
# do checkpointing
torch.save(netG.state_dict(), '%s/netG_epoch_%08d_iter%08d.pth' %
(opt.exp, epoch, ganIterations))
trainLogger.close()