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dmnist_train.py
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dmnist_train.py
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import argparse
import datetime
import subprocess
import time
import warnings
import os
import torch
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid
from data.dmnist import DialogMNISTDataset
from model.rcnn_discriminator_dmnist import CombineDiscriminator128
from model.resnet_generator_dmnist import ResnetGenerator128
from model.sync_batchnorm import DataParallelWithCallback
from utils.logger import setup_logger
warnings.filterwarnings("ignore", category=UserWarning, module="torch.nn.functional")
def get_dataset():
global data
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
data = DialogMNISTDataset(csv_file=args.dataset_path + 'MNIST_Dialog/train_annotations.csv',
root_dir=args.dataset_path + 'MNIST_Dialog/train_images/',
transform=transform)
return data
def get_recent_commit():
return str(subprocess.check_output(["git", "describe", "--always"]).strip().decode())
def main(args):
# parameters
z_dim = 128
lamb_obj = args.lamb_obj
lamb_img = args.lamb_img
lamb_dc = args.lamb_dc
lamb_bgc = args.lamb_bgc
lamb_ds = args.lamb_ds
num_classes = 11 # 10 total digits + 1 null class
num_obj = 8
num_dcolor = 5
num_bgcolor = 4
num_dstyle = 3
# data loader
train_data = get_dataset()
dataloader = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
drop_last=True, shuffle=True, num_workers=8)
# Load model
netG = ResnetGenerator128(num_classes=num_classes, output_dim=3,
num_dcolor=num_dcolor, num_bgcolor=num_bgcolor, num_dstyle=num_dstyle).cuda()
netD = CombineDiscriminator128(num_classes=num_classes,
num_dcolor=num_dcolor, num_bgcolor=num_bgcolor, num_dstyle=num_dstyle).cuda()
parallel = True
if parallel:
netG = DataParallelWithCallback(netG)
netD = torch.nn.DataParallel(netD)
g_lr, d_lr = args.g_lr, args.d_lr
gen_parameters = []
for key, value in dict(netG.named_parameters()).items():
if value.requires_grad:
if 'mapping' in key:
gen_parameters += [{'params': [value], 'lr': g_lr * 0.1}]
else:
gen_parameters += [{'params': [value], 'lr': g_lr}]
g_optimizer = torch.optim.Adam(gen_parameters, betas=(0, 0.999))
dis_parameters = []
for key, value in dict(netD.named_parameters()).items():
if value.requires_grad:
dis_parameters += [{'params': [value], 'lr': d_lr}]
d_optimizer = torch.optim.Adam(dis_parameters, betas=(0, 0.999))
# get most recent commit
commit_obj = get_recent_commit()
current_time = time.strftime("%H_%M_%dd_%mm", time.localtime())
# make dirs
if not os.path.exists(args.out_path):
os.makedirs(args.out_path)
if not os.path.exists(os.path.join(args.out_path, 'model/')):
os.makedirs(os.path.join(args.out_path, 'model/'))
writer = SummaryWriter(os.path.join(args.out_path, 'log'))
logger = setup_logger("AttrLostGAN", args.out_path, 0,
filename=current_time + '_log.txt')
logger.info('Commit Tag: ' + commit_obj)
logger.info(args.message)
logger.info(args)
logger.info(netG)
logger.info(netD)
total_steps = len(dataloader)
start_time = time.time()
for epoch in range(args.total_epoch):
netG.train()
netD.train()
print('epoch ', epoch)
for idx, data in enumerate(dataloader):
real_images, label, bbox, dcolor, bgcolor, dstyle = data
real_images, label, bbox = real_images.cuda(), label.float(), bbox.float()
dcolor, bgcolor, dstyle = dcolor.float(), bgcolor.float(), dstyle.float()
# update D network
netD.zero_grad()
real_images, label = real_images.float().cuda(), label.long().cuda()
dcolor, bgcolor, dstyle = dcolor.long().cuda(), bgcolor.long().cuda(), dstyle.long().cuda()
d_out_real, d_out_robj, d_out_rdcolor, d_out_rbgcolor, d_out_rdstyle = netD(real_images, bbox, label,
dcolor, bgcolor, dstyle)
d_loss_real = torch.nn.ReLU()(1.0 - d_out_real).mean()
d_loss_robj = torch.nn.ReLU()(1.0 - d_out_robj).mean()
d_loss_rdcolor = torch.nn.ReLU()(1.0 - d_out_rdcolor).mean()
d_loss_rbgcolor = torch.nn.ReLU()(1.0 - d_out_rbgcolor).mean()
d_loss_rdstyle = torch.nn.ReLU()(1.0 - d_out_rdstyle).mean()
z = torch.randn(real_images.size(0), num_obj, z_dim).cuda()
fake_images = netG(z, bbox, y=label.squeeze(dim=-1), dc=dcolor, bgc=bgcolor, ds=dstyle)
d_out_fake, d_out_fobj, d_out_fdcolor, d_out_fbgcolor, d_out_fdstyle = netD(fake_images.detach(),
bbox, label,
dcolor, bgcolor, dstyle)
d_loss_fake = torch.nn.ReLU()(1.0 + d_out_fake).mean()
d_loss_fobj = torch.nn.ReLU()(1.0 + d_out_fobj).mean()
d_loss_fdcolor = torch.nn.ReLU()(1.0 + d_out_fdcolor).mean()
d_loss_fbgcolor = torch.nn.ReLU()(1.0 + d_out_fbgcolor).mean()
d_loss_fdstyle = torch.nn.ReLU()(1.0 + d_out_fdstyle).mean()
d_loss = lamb_obj * (d_loss_robj + d_loss_fobj) + lamb_img * (d_loss_real + d_loss_fake) + \
lamb_dc * (d_loss_rdcolor + d_loss_fdcolor) + lamb_bgc * (d_loss_rbgcolor + d_loss_fbgcolor) + \
lamb_ds * (d_loss_rdstyle + d_loss_fdstyle)
d_loss.backward()
d_optimizer.step()
# update G network
if (idx % 1) == 0:
netG.zero_grad()
g_out_fake, g_out_obj, g_out_fdcolor, g_out_fbgcolor, g_out_fdstyle = netD(fake_images, bbox, label,
dcolor, bgcolor,
dstyle)
g_loss_fake = - g_out_fake.mean()
g_loss_obj = - g_out_obj.mean()
g_loss_fdcolor = - g_out_fdcolor.mean()
g_loss_fbgcolor = - g_out_fbgcolor.mean()
g_loss_fdstyle = - g_out_fdstyle.mean()
g_loss = g_loss_obj * lamb_obj + g_loss_fake * lamb_img + \
g_loss_fdcolor * lamb_dc + \
g_loss_fbgcolor * lamb_bgc + \
g_loss_fdstyle * lamb_ds
g_loss.backward()
g_optimizer.step()
if (idx + 1) % 100 == 0:
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))
logger.info("Time Elapsed: [{}]".format(elapsed))
logger.info("Epoch[{}/{}], Step[{}/{}], "
"d_out_real: {:.4f}, d_out_fake: {:.4f}, g_out_fake: {:.4f} ".format(epoch + 1,
args.total_epoch,
idx + 1,
total_steps,
d_loss_real.item(),
d_loss_fake.item(),
g_loss_fake.item()))
logger.info("d_obj_real: {:.4f}, d_obj_fake: {:.4f}, g_obj_fake: {:.4f} ".format(
d_loss_robj.item(),
d_loss_fobj.item(),
g_loss_obj.item()))
logger.info(args)
writer.add_image("real images", make_grid(real_images.cpu().data * 0.5 + 0.5, nrow=4),
epoch * total_steps + idx + 1)
writer.add_image("fake images", make_grid(fake_images.cpu().data * 0.5 + 0.5, nrow=4),
epoch * total_steps + idx + 1)
writer.add_scalar('DLoss', d_loss, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/real_images', d_loss_real, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/fake_images', d_loss_fake, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/real_objects', d_loss_robj, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/real_dcolor', d_loss_rdcolor, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/real_bgcolor', d_loss_rbgcolor, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/real_dstyle', d_loss_rdstyle, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/fake_objects', d_loss_fobj, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/fake_dcolor', d_loss_fdcolor, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/fake_bgcolor', d_loss_fbgcolor, epoch * total_steps + idx + 1)
writer.add_scalar('DLoss/fake_dstyle', d_loss_fdstyle, epoch * total_steps + idx + 1)
writer.add_scalar('GLoss', g_loss.item(), epoch * total_steps + idx + 1)
writer.add_scalar('GLoss/fake_images', g_loss_fake.item(), epoch * total_steps + idx + 1)
writer.add_scalar('GLoss/fake_objects', g_loss_obj.item(), epoch * total_steps + idx + 1)
writer.add_scalar('GLoss/fake_dcolor', g_loss_fdcolor.item(), epoch * total_steps + idx + 1)
writer.add_scalar('GLoss/fake_bgcolor', g_loss_fbgcolor.item(), epoch * total_steps + idx + 1)
writer.add_scalar('GLoss/fake_dstyle', g_loss_fdstyle.item(), epoch * total_steps + idx + 1)
# save model
if (epoch + 1) % 5 == 0:
torch.save(netG.state_dict(),
os.path.join(args.out_path, 'model/', 'G_%d.pth' % (epoch + 1)))
if __name__ == "__main__":
commit_obj = get_recent_commit()
current_time = time.strftime("_%H_%M_%dd_%mm", time.localtime())
path = commit_obj + current_time
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', type=str,
default='./datasets/',
help='path to mnist dialog dataset')
parser.add_argument('--out_path', type=str,
default='/outputs/' + path,
help='path to output files')
parser.add_argument('--batch_size', type=int, default=128,
help='mini-batch size of training data. Default: 128')
parser.add_argument('--total_epoch', type=int, default=200,
help='number of total training epoch')
parser.add_argument('--d_lr', type=float, default=0.0001,
help='learning rate for discriminator')
parser.add_argument('--g_lr', type=float, default=0.0001,
help='learning rate for generator')
parser.add_argument('--lamb_obj', type=float, default=1.0,
help='Loss weight for objects')
parser.add_argument('--lamb_img', type=float, default=0.1,
help='Loss weight for objects')
parser.add_argument('--lamb_dc', type=float, default=1.0,
help='Loss weight for dcolor attribute')
parser.add_argument('--lamb_bgc', type=float, default=1.0,
help='Loss weight for bgcolor attribute')
parser.add_argument('--lamb_ds', type=float, default=1.0,
help='Loss weight for dstyle attribute')
parser.add_argument('--message', type=str, default='MNIST Dialog',
help='Print a message in log')
args = parser.parse_args()
main(args)