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train_RINDNet.py
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train_RINDNet.py
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import os
from tqdm import tqdm
from utils.lr_scheduler import LR_Scheduler
from dataloaders.datasets.bsds_hd5 import Mydataset
from torch.utils.data import DataLoader
from my_options.RINDNet_options import myNet_Options
from modeling.rindnet import MyNet
from modeling.sync_batchnorm.replicate import patch_replication_callback
from utils.edge_loss2 import *
from utils.loss import SegmentationLosses
from utils.saver import Saver
from utils.summaries import TensorboardSummary
import scipy.io as sio
class Trainer(object):
def __init__(self, args):
self.args = args
# Define Saver
self.saver = Saver(args)
self.saver.save_experiment_config()
# Define Tensorboard Summary
self.summary = TensorboardSummary(self.saver.experiment_dir)
self.writer = self.summary.create_summary()
print(self.saver.experiment_dir)
self.output_dir = os.path.join(self.saver.experiment_dir)
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
# Define Dataloader
self.train_dataset = Mydataset(root_path=self.args.data_path, split='trainval', crop_size=self.args.crop_size)
self.test_dataset = Mydataset(root_path=self.args.data_path, split='test', crop_size=self.args.crop_size)
self.train_loader = DataLoader(self.train_dataset, batch_size=self.args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True, drop_last=True)
self.test_loader = DataLoader(self.test_dataset, batch_size=1, shuffle=False,
num_workers=args.workers)
# Define network
self.model = MyNet()
if self.args.resnet:
self.model.load_resnet(args.resnet)
# Using cuda
if self.args.cuda:
self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids)
patch_replication_callback(self.model)
self.model = self.model.cuda()
# Define Criterion
self.criterion = AttentionLoss2()
self.criterion2 = SegmentationLosses(weight=None, cuda=args.cuda).build_loss(mode='focal')
# Define Optimizer
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.args.lr, momentum=self.args.momentum,
weight_decay=self.args.weight_decay)
# Define lr scheduler
self.scheduler = LR_Scheduler(self.args.lr_scheduler, self.args.lr,
args.epochs, len(self.train_loader))
# Resuming checkpoint
self.best_pred = 0.0
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.module.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
def training(self, epoch):
train_loss = 0.0
self.model.train()
tbar = tqdm(self.train_loader)
num_img_tr = len(self.train_loader)
for i, (image, target) in enumerate(tbar):
if self.args.cuda:
image, target = image.cuda(), target.cuda() #(b,3,w,h) (b,5,w,h)
unet1, out_depth,out_normal,out_reflectance,out_illumination = self.model(image)
focal_loss = self.criterion2(unet1, target[:, 0, :, :])
loss = self.criterion([out_depth,out_normal,out_reflectance,out_illumination], target[:, 1:5, :, :])
total_loss = 0.9 * focal_loss + 0.1 * loss
self.scheduler(self.optimizer, i, epoch, self.best_pred)
self.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
train_loss += total_loss.item()
tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1)))
self.writer.add_scalar('train/total_loss_iter', total_loss.item(), i + num_img_tr * epoch)
self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch)
print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
print('Loss: %.3f' % train_loss)
if self.args.no_val:
# save checkpoint every epoch
if (epoch + 1) % 10 == 0:
is_best = False
self.saver.save_checkpoint({
'epoch': epoch + 1,
'state_dict': self.model.module.state_dict(),
'optimizer': self.optimizer.state_dict(),
'best_pred': self.best_pred,
}, is_best)
def test(self, epoch):
print('Test epoch: %d' % epoch)
self.output_dir = os.path.join(self.saver.experiment_dir, str(epoch+1))
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
self.depth_output_dir = os.path.join(self.saver.experiment_dir, str(epoch+1), 'depth/met')
if not os.path.exists(self.depth_output_dir):
os.makedirs(self.depth_output_dir)
self.normal_output_dir = os.path.join(self.saver.experiment_dir, str(epoch+1), 'normal/met')
if not os.path.exists(self.normal_output_dir):
os.makedirs(self.normal_output_dir)
self.reflectance_output_dir = os.path.join(self.saver.experiment_dir, str(epoch+1), 'reflectance/met')
if not os.path.exists(self.reflectance_output_dir):
os.makedirs(self.reflectance_output_dir)
self.illumination_output_dir = os.path.join(self.saver.experiment_dir, str(epoch+1), 'illumination/met')
if not os.path.exists(self.illumination_output_dir):
os.makedirs(self.illumination_output_dir)
self.model.eval()
tbar = tqdm(self.test_loader, desc='\r')
for i, image in enumerate(tbar):
name = self.test_loader.dataset.images_name[i]
if self.args.cuda:
image = image.cuda()
with torch.no_grad():
[_, out_depth, out_normal, out_reflectance, out_illumination] = self.model(image)
depth_pred = out_depth.data.cpu().numpy()
depth_pred = depth_pred.squeeze()
sio.savemat(os.path.join(self.depth_output_dir, '{}.mat'.format(name)), {'result': depth_pred})
normal_pred = out_normal.data.cpu().numpy()
normal_pred = normal_pred.squeeze()
sio.savemat(os.path.join(self.normal_output_dir, '{}.mat'.format(name)), {'result': normal_pred})
reflectance_pred = out_reflectance.data.cpu().numpy()
reflectance_pred = reflectance_pred.squeeze()
sio.savemat(os.path.join(self.reflectance_output_dir, '{}.mat'.format(name)), {'result': reflectance_pred})
illumination_pred = out_illumination.data.cpu().numpy()
illumination_pred = illumination_pred.squeeze()
sio.savemat(os.path.join(self.illumination_output_dir, '{}.mat'.format(name)),
{'result': illumination_pred})
def main():
options = myNet_Options()
args = options.parse()
#args.cuda = True
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
if args.sync_bn is None:
if args.cuda and len(args.gpu_ids) > 1:
args.sync_bn = True
else:
args.sync_bn = False
args.lr = 1e-5
print(args)
torch.manual_seed(args.seed)
trainer = Trainer(args)
print('Starting Epoch:', trainer.args.start_epoch)
print('Total Epoches:', trainer.args.epochs)
for epoch in range(trainer.args.start_epoch, trainer.args.epochs):
#trainer.test(epoch)
trainer.training(epoch)
if (epoch+1)%10==0:
trainer.test(epoch)
if __name__ == "__main__":
main()