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train_context_app_v2.py
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train_context_app_v2.py
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import argparse
import os
import pickle
import time
import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
from torchvision.utils import make_grid
from collections import OrderedDict
from utils.util import *
from data.cocostuff_loader import *
from data.vg import *
from model.resnet_generator_app_v2 import *
from model.rcnn_discriminator_app import *
from model.sync_batchnorm import DataParallelWithCallback
from utils.logger import setup_logger
from tqdm import tqdm
def get_dataset(dataset, img_size):
if dataset == "coco":
data = CocoSceneGraphDataset(image_dir='./datasets/coco/images/train2017/',
instances_json='./datasets/coco/annotations/instances_train2017.json',
stuff_json='./datasets/coco/annotations/stuff_train2017.json',
stuff_only=True, image_size=(img_size, img_size), left_right_flip=True)
elif dataset == 'vg':
data = VgSceneGraphDataset(vocab_json='./data/tmp/vocab.json', h5_path='./data/tmp/preprocess_vg/train.h5',
image_dir='./datasets/vg/',
image_size=(img_size, img_size), max_objects=30, left_right_flip=True)
return data
def main(args):
# parameters
img_size = 128
z_dim = 128
lamb_obj = 1.0
lamb_app = 1.0
lamb_img = 0.1
num_classes = 184 if args.dataset == 'coco' else 179
num_obj = 8 if args.dataset == 'coco' else 31
args.out_path = os.path.join(args.out_path, args.dataset, str(img_size))
num_gpus = torch.cuda.device_count()
num_workers = 2
if num_gpus > 1:
parallel = True
args.batch_size = args.batch_size * num_gpus
num_workers = num_workers * num_gpus
else:
parallel = False
# data loader
train_data = get_dataset(args.dataset, img_size)
dataloader = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
drop_last=True, shuffle=True, num_workers=num_workers)
# Load model
device = torch.device('cuda')
netG = ResnetGenerator128_context(num_classes=num_classes, output_dim=3).to(device)
netD = CombineDiscriminator128_app(num_classes=num_classes).to(device)
if args.checkpoint_epoch is not None:
load_G = args.out_path + '/model/G_{}.pth'.format(args.checkpoint_epoch)
load_D = args.out_path + '/model/D_{}.pth'.format(args.checkpoint_epoch)
if not os.path.isfile(load_G) or not os.path.isfile(load_D):
return
# load generator
state_dict = torch.load(load_G)
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`nvidia
new_state_dict[name] = v
model_dict = netG.state_dict()
pretrained_dict = {k: v for k, v in new_state_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
netG.load_state_dict(model_dict)
netG.cuda()
# load discriminator
state_dict = torch.load(load_D)
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`nvidia
new_state_dict[name] = v
model_dict = netD.state_dict()
pretrained_dict = {k: v for k, v in new_state_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
netD.load_state_dict(model_dict)
netD.cuda()
else:
args.checkpoint_epoch = 0
# parallel = False
if parallel:
netG = DataParallelWithCallback(netG)
netD = 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))
# 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("lostGAN", args.out_path, 0)
logger.info(netG)
logger.info(netD)
start_time = time.time()
vgg_loss = VGGLoss()
vgg_loss = nn.DataParallel(vgg_loss)
l1_loss = nn.DataParallel(nn.L1Loss())
for epoch in range(args.checkpoint_epoch, args.total_epoch):
netG.train()
netD.train()
print("Epoch {}/{}".format(epoch, args.total_epoch))
for idx, data in enumerate(tqdm(dataloader)):
real_images, label, bbox = data
# print(real_images.shape)
# print(label.shape)
# print(bbox.shape)
real_images, label, bbox = real_images.to(device), label.long().to(device).unsqueeze(-1), bbox.float()
# update D network
netD.zero_grad()
real_images, label = real_images.to(device), label.long().to(device)
d_out_real, d_out_robj, d_out_robj_app = netD(real_images, bbox, label)
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_robj_app = torch.nn.ReLU()(1.0 - d_out_robj_app).mean()
# print(d_loss_robj)
# print(d_loss_robj_app)
z = torch.randn(real_images.size(0), num_obj, z_dim).to(device)
fake_images = netG(z, bbox, y=label.squeeze(dim=-1))
d_out_fake, d_out_fobj, d_out_fobj_app = netD(fake_images.detach(), bbox, label)
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_fobj_app = torch.nn.ReLU()(1.0 + d_out_fobj_app).mean()
d_loss = lamb_obj * (d_loss_robj + d_loss_fobj) + lamb_img * (d_loss_real + d_loss_fake) + lamb_app * (d_loss_robj_app + d_loss_fobj_app)
d_loss.backward()
d_optimizer.step()
# update G network
if (idx % 1) == 0:
netG.zero_grad()
g_out_fake, g_out_obj, g_out_obj_app = netD(fake_images, bbox, label)
g_loss_fake = - g_out_fake.mean()
g_loss_obj = - g_out_obj.mean()
g_loss_obj_app = - g_out_obj_app.mean()
pixel_loss = l1_loss(fake_images, real_images).mean()
feat_loss = vgg_loss(fake_images, real_images).mean()
g_loss = g_loss_obj * lamb_obj + g_loss_fake * lamb_img + pixel_loss + feat_loss + lamb_app * g_loss_obj_app
g_loss.backward()
g_optimizer.step()
if (idx + 1) % 500 == 0:
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))
logger.info("Time Elapsed: [{}]".format(elapsed))
logger.info("Step[{}/{}], d_out_real: {:.4f}, d_out_fake: {:.4f}, g_out_fake: {:.4f} ".format(epoch + 1,
idx + 1,
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(" d_obj_real_app: {:.4f}, d_obj_fake_app: {:.4f}, g_obj_fake_app: {:.4f} ".format(
d_loss_robj_app.item(),
d_loss_fobj_app.item(),
g_loss_obj_app.item()))
logger.info(" pixel_loss: {:.4f}, feat_loss: {:.4f}".format(pixel_loss.item(), feat_loss.item()))
writer.add_image("real images", make_grid(real_images.cpu().data * 0.5 + 0.5, nrow=4), epoch * len(dataloader) + idx + 1)
writer.add_image("fake images", make_grid(fake_images.cpu().data * 0.5 + 0.5, nrow=4), epoch * len(dataloader) + 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)))
torch.save(netD.state_dict(), os.path.join(args.out_path, 'model/', 'D_%d.pth' % (epoch + 1)))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='coco',
help='training dataset')
parser.add_argument('--batch_size', type=int, default=16,
help='mini-batch size of training data. Default: 32')
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('--out_path', type=str, default='./outputs/tmp/app2',
help='path to output files')
parser.add_argument('--checkpoint_epoch', type=int, default=55,
help='checkpoint epoch')
args = parser.parse_args()
main(args)