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main_ann_ae.py
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main_ann_ae.py
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import os
import os.path
import numpy as np
import logging
import argparse
import pycuda.driver as cuda
import torch
import torchvision
from torch.nn.utils import clip_grad_norm_
from torch.nn.utils import clip_grad_value_
from torch.utils.tensorboard import SummaryWriter
from utils import AverageMeter
from utils import aboutCudaDevices
from datasets import load_dataset_ann
import models.ann_ae as ann_ae
max_accuracy = 0
min_loss = 1000
def train(network, trainloader, opti, epoch):
loss_meter = AverageMeter()
network = network.train()
for batch_idx, (real_img, label) in enumerate(trainloader):
opti.zero_grad()
real_img = real_img.to(device)
recons, latent = network(real_img)
loss = network.loss_function(recons, real_img)
loss.backward()
opti.step()
loss_meter.update(loss.detach().cpu().item())
print(f'Train[{epoch}/{max_epoch}] [{batch_idx}/{len(trainloader)}] Loss: {loss_meter.avg}')
if batch_idx == len(trainloader)-1:
os.makedirs(f'checkpoint/{args.name}/imgs/train/', exist_ok=True)
torchvision.utils.save_image((real_img+1)/2, f'checkpoint/{args.name}/imgs/train/epoch{epoch}_input.png')
torchvision.utils.save_image((recons+1)/2, f'checkpoint/{args.name}/imgs/train/epoch{epoch}_recons.png')
writer.add_images('Train/input_img', (real_img+1)/2, epoch)
writer.add_images('Train/recons_img', (recons+1)/2, epoch)
logging.info(f"Train [{epoch}] Loss: {loss_meter.avg}")
writer.add_scalar('Train/loss', loss_meter.avg, epoch)
return loss_meter.avg
def test(network, trainloader, epoch):
loss_meter = AverageMeter()
network = network.eval()
with torch.no_grad():
for batch_idx, (real_img, label) in enumerate(trainloader):
real_img = real_img.to(device)
#normalized_img = normalized_img.to(device)
recons, latent = network(real_img)
loss = network.loss_function(recons, real_img)
loss_meter.update(loss.detach().cpu().item())
print(f'Test[{epoch}/{max_epoch}] [{batch_idx}/{len(trainloader)}] Loss: {loss_meter.avg}')
if batch_idx == len(trainloader)-1:
os.makedirs(f'checkpoint/{args.name}/imgs/test/', exist_ok=True)
torchvision.utils.save_image((real_img+1)/2, f'checkpoint/{args.name}/imgs/test/epoch{epoch}_input.png')
torchvision.utils.save_image((recons+1)/2, f'checkpoint/{args.name}/imgs/test/epoch{epoch}_recons.png')
writer.add_images('Test/input_img', (real_img+1)/2, epoch)
writer.add_images('Test/recons_img', (recons+1)/2, epoch)
logging.info(f"Test [{epoch}] Loss: {loss_meter.avg}")
writer.add_scalar('Test/loss', loss_meter.avg, epoch)
return loss_meter.avg
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('name', type=str)
parser.add_argument('-dataset', type=str, required=True)
parser.add_argument('-batch_size', type=int, default=250)
parser.add_argument('-latent_dim', type=int, default=128)
parser.add_argument('-checkpoint', action='store', dest='checkpoint', help='The path of checkpoint, if use checkpoint')
parser.add_argument('-device', type=int, default=0)
try:
args = parser.parse_args()
except:
parser.print_help()
exit(0)
if args.device is None:
device = torch.device("cuda:0")
else:
device = torch.device(f"cuda:{args.device}")
if args.dataset.lower() == 'mnist':
train_loader, test_loader = load_dataset_ann.load_mnist(args.batch_size)
in_channels = 1
net = ann_ae.AE(in_channels, args.latent_dim)
elif args.dataset.lower() == 'fashion':
train_loader, test_loader = load_dataset_ann.load_fashionmnist(args.batch_size)
in_channels = 1
net = ann_ae.AE(in_channels, args.latent_dim)
elif args.dataset.lower() == 'celeba':
train_loader, test_loader = load_dataset_ann.load_celeba(args.batch_size)
in_channels = 3
net = ann_ae.AELarge(in_channels, args.latent_dim)
elif args.dataset.lower() == 'cifar10':
train_loader, test_loader = load_dataset_ann.load_cifar10(args.batch_size)
in_channels = 3
net = ann_ae.AE(in_channels, args.latent_dim)
else:
raise ValueError("invalid dataset")
net = net.to(device)
os.makedirs(f'checkpoint/{args.name}', exist_ok=True)
writer = SummaryWriter(log_dir=f'checkpoint/{args.name}/tb')
logging.basicConfig(filename=f'checkpoint/{args.name}/{args.name}.log', level=logging.INFO)
logging.info(args)
if torch.cuda.is_available():
cuda.init()
c_device = aboutCudaDevices()
print(c_device.info())
print("selected device: ", args.device)
else:
raise Exception("only support gpu")
if args.checkpoint is not None:
checkpoint_path = args.checkpoint
checkpoint = torch.load(checkpoint_path)
net.load_state_dict(checkpoint)
optimizer = torch.optim.AdamW(net.parameters(), lr=0.001, betas=(0.9, 0.999))
best_loss = 1e8
max_epoch = 150
for e in range(max_epoch):
train_loss = train(net, train_loader, optimizer, e)
test_loss = test(net, test_loader, e)
torch.save(net.state_dict(), f'checkpoint/{args.name}/checkpoint.pth')
if test_loss < best_loss:
best_loss = test_loss
torch.save(net.state_dict(), f'checkpoint/{args.name}/best.pth')
writer.close()