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train_efsatem.py
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train_efsatem.py
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import os
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as utils
from torch.autograd import Variable
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
# from DerainDataset import *
from utils import *
from torch.optim.lr_scheduler import MultiStepLR, ReduceLROnPlateau
from SSIM import seq_SSIM
from mymodel import *
torch.cuda.empty_cache()
import random
random.seed(0)
torch.manual_seed(0)
np.random.seed(0)
parser = argparse.ArgumentParser(description="EFSATeM_train")
parser.add_argument("--preprocess", action="store_true", default=False, help='run prepare_data or not')
parser.add_argument("--batch_size", type=int, default=8, help="Training batch size")
parser.add_argument("--epochs", type=int, default=50, help="Number of training epochs")
parser.add_argument("--milestone", type=int, default=[30,50,80], help="When to decay learning rate")
parser.add_argument("--lr", type=float, default=1e-3, help="initial learning rate")
parser.add_argument("--save_path", type=str, default="logs/s7_i6_100_ev_sa2", help='path to save models and log files')
parser.add_argument("--save_freq",type=int,default=1,help='save intermediate model')
parser.add_argument("--data_path",type=str, default="/home1/ssq/proj1/evnet/data/",help='path to training data')
parser.add_argument("--use_gpu", type=bool, default=True, help='use GPU or not')
parser.add_argument("--gpu_id", type=str, default="0", help='GPU id')
parser.add_argument("--sa_loss_epoch", type=int, default=2, help='when (in which epoch) to use self-alignment loss')
parser.add_argument("--sb", type=int, default=0, help='small batch for quick test or not')
parser.add_argument("--data_name", type=str, default="NTURain", help='data name')
parser.add_argument("--dim_feature", type=int, default=16, help='dimension of hidden features')
opt = parser.parse_args()
print(opt)
if opt.use_gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
device_ids = [int(i) for i in opt.gpu_id.strip().split(',')]
def main():
print('Loading dataset ...\n')
dataset_train = dataLoader_evnet.NTURainData(data_path=opt.data_path, if_event=True, data_name=opt.data_name)
loader_train = DataLoader(dataset=dataset_train, num_workers=4, batch_size=opt.batch_size, shuffle=True)
print("# of training samples: %d\n" % int(len(dataset_train)))
# Build model
model = Model(dim=opt.dim_feature)
model = torch.nn.DataParallel(model)
model = model.to(device_ids[0])
print_network(model)
# loss function
# criterion = nn.MSELoss(size_average=False)
criterion = seq_SSIM()
# Move to GPU
if opt.use_gpu:
model = model.cuda()
criterion.cuda()
# Optimizer
optimizer = optim.Adam(model.parameters(), lr=opt.lr)
# scheduler = MultiStepLR(optimizer, milestones=opt.milestone, gamma=0.2) # learning rates
scheduler = ReduceLROnPlateau(optimizer, mode="max", factor=0.1, patience=1, threshold=1e-6) # learning rates
# record training
writer = SummaryWriter(opt.save_path)
# load the lastest model
initial_epoch = findLastCheckpoint(save_dir=opt.save_path)
if initial_epoch > 0:
print('resuming by loading epoch %d' % initial_epoch)
state_dict = torch.load(os.path.join(opt.save_path, 'net_epoch%d.pth' % initial_epoch))
model.load_state_dict(state_dict, strict=False)
optimizer.load_state_dict(torch.load(os.path.join(opt.save_path, 'param_epoch%d.pth' % initial_epoch)), )
# start training
step = initial_epoch * dataset_train.__len__() / opt.batch_size
prev_psnr = 0
if_sa_loss = True
for epoch in range(initial_epoch, opt.epochs):
for param_group in optimizer.param_groups:
print('learning rate %f' % param_group["lr"])
epoch_loss = 0
epoch_psnr = 0
## epoch training start
for i, (input_train, target_train, event_train) in enumerate(loader_train, 0):
if opt.sb == 1 and i > 10:
break
model.train()
model.zero_grad()
optimizer.zero_grad()
# input_train, target_train = Variable(input_train), Variable(target_train)
if opt.use_gpu:
input_train, target_train, event_train = input_train.cuda(), target_train.cuda(), event_train.cuda()
input_train.requires_grad_()
event_train.requires_grad_()
#print(input_train.requires_grad, event_train.requires_grad, target_train.requires_grad)
out_train = model(input_train, event_train)
#print(out_train.requires_grad)
if (epoch >= opt.sa_loss_epoch and prev_psnr >= 34):
if_sa_loss = True
prev_psnr = 0
pixel_metric = criterion(target_train, out_train, if_sa_loss)
loss = -pixel_metric
loss.backward()
# nn.utils.clip_grad_norm_(model.parameters(), max_norm=2., norm_type=2)
optimizer.step()
torch.cuda.empty_cache()
# training curve
model.eval()
with torch.no_grad():
out_train = out_train.detach()#model(input_train, event_train)
out_train = torch.clamp(out_train, 0., 1.)
psnr_train = batch_PSNR(out_train, target_train, 1.)
epoch_psnr += psnr_train
epoch_loss += pixel_metric.item()
print("[epoch %d][%d/%d] loss: %.4f, pixel_metric: %.4f, PSNR: %.4f" %
(epoch+1, i+1, len(loader_train), loss.item(), pixel_metric.item(), psnr_train))
if step % 10 == 0:
# Log the scalar values
writer.add_scalar('loss', loss.item(), step)
writer.add_scalar('PSNR on training data', psnr_train, step)
writer.add_scalar('Learning rate', optimizer.param_groups[0]["lr"], epoch)
step += 1
#if i == 10:
# break
## epoch training end
print("The average loss for epoch {} is {}".format(epoch, epoch_loss / len(loader_train)))
print("The average psnr for epoch {} is {}".format(epoch, epoch_psnr / len(loader_train)))
scheduler.step(epoch_loss / len(loader_train))
prev_psnr = epoch_psnr / len(loader_train)
# log the images
model.eval()
with torch.no_grad():
out_train = out_train.detach()#model(input_train, event_train)
out_train = torch.clamp(out_train, 0., 1.)
print(target_train.data.shape)
_, dim_c, dim_s, dim_w, dim_h = target_train.data.shape
im_target = utils.make_grid(target_train.data.transpose(2,1).reshape(-1, dim_c, dim_w, dim_h), nrow=dim_s, normalize=True, scale_each=True)
im_input = utils.make_grid(input_train.data.transpose(2,1).reshape(-1, dim_c, dim_w, dim_h), nrow=dim_s, normalize=True, scale_each=True)
im_event = utils.make_grid(event_train.data.transpose(2,1).reshape(-1, 2, dim_w, dim_h), nrow=dim_s+1, normalize=True, scale_each=True)
im_derain = utils.make_grid(out_train.data.transpose(2,1).reshape(-1, dim_c, dim_w, dim_h), nrow=dim_s, normalize=True, scale_each=True)
writer.add_image('clean image', im_target, epoch+1)
writer.add_image('rainy image', im_input, epoch+1)
writer.add_image('event image', im_event, epoch+1)
writer.add_image('deraining image', im_derain, epoch+1)
# save model
torch.save(model.state_dict(), os.path.join(opt.save_path, 'net_latest.pth'))
torch.save(optimizer.state_dict(), os.path.join(opt.save_path, 'param_latest.pth'))
if epoch % opt.save_freq == 0:
torch.save(model.state_dict(), os.path.join(opt.save_path, 'net_epoch%d.pth' % (epoch+1)))
torch.save(optimizer.state_dict(), os.path.join(opt.save_path, 'param_epoch%d.pth' % (epoch+1)))
writer.add_scalar('Epoch loss', epoch_loss / len(loader_train), epoch)
writer.add_scalar('Epoch psnr', epoch_psnr / len(loader_train), epoch)
if __name__ == "__main__":
if opt.preprocess:
if opt.data_path.find('RainTrainH') != -1:
print(opt.data_path.find('RainTrainH'))
prepare_data_RainTrainH(data_path=opt.data_path, patch_size=100, stride=80)
elif opt.data_path.find('RainTrainL') != -1:
prepare_data_RainTrainL(data_path=opt.data_path, patch_size=100, stride=80)
elif opt.data_path.find('Rain12600') != -1:
prepare_data_Rain12600(data_path=opt.data_path, patch_size=100, stride=100)
elif opt.data_path.find('NTURain') != -1:
dataLoader_evnet.event_preprocess(patch_size=128, stride=128, seq_crop_len=7, seq_crop_stride=7)
elif opt.data_path.find('RainVIDSS') != -1:
dataLoader_evnet.event_preprocess_RainVIDSS(patch_size=128, stride=128, seq_crop_len=7, seq_crop_stride=7)
else:
print('unkown datasets: please define prepare data function in DerainDataset.py')
main()