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train.py
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import torch
import os, argparse
os.environ["CUDA_VISIBLE_DEVICES"] ='0'
from datetime import datetime
from model.MVANet import MVANet
from utils.dataset_strategy_fpn import get_loader
from utils.misc import adjust_lr, AvgMeter
import torch.nn.functional as F
from torch.autograd import Variable
from torch.backends import cudnn
from torchvision import transforms
import torch.nn as nn
from torch.cuda import amp
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
cudnn.benchmark = True
parser = argparse.ArgumentParser()
parser.add_argument('--epoch', type=int, default=80, help='epoch number')
parser.add_argument('--lr_gen', type=float, default=1e-5, help='learning rate')
parser.add_argument('--batchsize', type=int, default=1, help='training batch size')
parser.add_argument('--trainsize', type=int, default=1024, help='training dataset size')
parser.add_argument('--decay_rate', type=float, default=0.9, help='decay rate of learning rate')
parser.add_argument('--decay_epoch', type=int, default=80, help='every n epochs decay learning rate')
opt = parser.parse_args()
print('Generator Learning Rate: {}'.format(opt.lr_gen))
# build models
if hasattr(torch.cuda, 'empty_cache'):
torch.cuda.empty_cache()
generator = MVANet()
generator.cuda()
generator_params = generator.parameters()
generator_optimizer = torch.optim.Adam(generator_params, opt.lr_gen)
image_root = './data/DIS5K/DIS-TR/images/'
gt_root = './data/DIS5K/DIS-TR/masks/'
train_loader = get_loader(image_root, gt_root, batchsize=opt.batchsize, trainsize=opt.trainsize)
total_step = len(train_loader)
to_pil = transforms.ToPILImage()
## define loss
CE = torch.nn.BCELoss()
mse_loss = torch.nn.MSELoss(size_average=True, reduce=True)
size_rates = [1]
criterion = nn.BCEWithLogitsLoss().cuda()
criterion_mae = nn.L1Loss().cuda()
criterion_mse = nn.MSELoss().cuda()
use_fp16 = True
scaler = amp.GradScaler(enabled=use_fp16)
def structure_loss(pred, mask):
weit = 1+5*torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15)-mask)
wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none')
wbce = (weit*wbce).sum(dim=(2,3))/weit.sum(dim=(2,3))
pred = torch.sigmoid(pred)
inter = ((pred * mask) * weit).sum(dim=(2, 3))
union = ((pred + mask) * weit).sum(dim=(2, 3))
wiou = 1-(inter+1)/(union-inter+1)
return (wbce+wiou).mean()
for epoch in range(1, opt.epoch+1):
torch.cuda.empty_cache()
generator.train()
loss_record = AvgMeter()
print('Generator Learning Rate: {}'.format(generator_optimizer.param_groups[0]['lr']))
for i, pack in enumerate(train_loader, start=1):
torch.cuda.empty_cache()
for rate in size_rates:
torch.cuda.empty_cache()
generator_optimizer.zero_grad()
images, gts = pack
images = Variable(images)
gts = Variable(gts)
images = images.cuda()
gts = gts.cuda()
trainsize = int(round(opt.trainsize * rate / 32) * 32)
if rate != 1:
images = F.upsample(images, size=(trainsize, trainsize), mode='bilinear',
align_corners=True)
gts = F.upsample(gts, size=(trainsize, trainsize), mode='bilinear', align_corners=True)
b, c, h, w = gts.size()
target_1 = F.upsample(gts, size=h // 4, mode='nearest')
target_2 = F.upsample(gts, size=h // 8, mode='nearest').cuda()
target_3 = F.upsample(gts, size=h // 16, mode='nearest').cuda()
target_4 = F.upsample(gts, size=h // 32, mode='nearest').cuda()
target_5 = F.upsample(gts, size=h // 64, mode='nearest').cuda()
with amp.autocast(enabled=use_fp16):
sideout5, sideout4, sideout3, sideout2, sideout1, final, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3,tokenattmap2,tokenattmap1= generator.forward(images)
loss1 = structure_loss(sideout5, target_4)
loss2 = structure_loss(sideout4, target_3)
loss3 = structure_loss(sideout3, target_2)
loss4 = structure_loss(sideout2, target_1)
loss5 = structure_loss(sideout1, target_1)
loss6 = structure_loss(final, gts)
loss7 = structure_loss(glb5, target_5)
loss8 = structure_loss(glb4, target_4)
loss9 = structure_loss(glb3, target_3)
loss10 = structure_loss(glb2, target_2)
loss11 = structure_loss(glb1, target_2)
loss12 = structure_loss(tokenattmap4, target_3)
loss13 = structure_loss(tokenattmap3, target_2)
loss14 = structure_loss(tokenattmap2, target_1)
loss15 = structure_loss(tokenattmap1, target_1)
loss = loss1 + loss2 + loss3 + loss4 + loss5 + loss6 + 0.3*(loss7 + loss8 + loss9 + loss10 + loss11)+ 0.3*(loss12 + loss13 + loss14 + loss15)
Loss_loc = loss1 + loss2 + loss3 + loss4 + loss5 + loss6
Loss_glb = loss7 + loss8 + loss9 + loss10 + loss11
Loss_map = loss12 + loss13 + loss14 + loss15
writer.add_scalar('loss', loss.item(), epoch * len(train_loader) + i)
generator_optimizer.zero_grad()
scaler.scale(loss).backward()
scaler.step(generator_optimizer)
scaler.update()
if rate == 1:
loss_record.update(loss.data, opt.batchsize)
if i % 10 == 0 or i == total_step:
print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], gen Loss: {:.4f}'.
format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record.show()))
adjust_lr(generator_optimizer, opt.lr_gen, epoch, opt.decay_rate, opt.decay_epoch)
# save checkpoints every 20 epochs
if epoch % 20== 0 :
save_path = './saved_model/MVANet/'
if not os.path.exists(save_path):
os.mkdir(save_path)
torch.save(generator.state_dict(), save_path + 'Model' + '_%d' % epoch + '.pth')