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train_full.py
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import argparse
import time
from torch.autograd import Variable
from torch.utils.data import DataLoader
from net import Net
from dataset import *
import matplotlib.pyplot as plt
from metrics import *
import numpy as np
import os
from tqdm import tqdm
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
parser = argparse.ArgumentParser(description="PyTorch LESPS train")
parser.add_argument("--model_names", default=['DNANet'], nargs='+',
help="model_name: 'ACM', 'ALCNet', 'DNANet'")
parser.add_argument("--dataset_names", default=['SIRST3'], nargs='+',
help="dataset_name: 'NUAA-SIRST', 'NUDT-SIRST', 'IRSTD-1K', 'NUDT-SIRST-Sea', 'SIRST3'")
parser.add_argument("--label_type", default='full', type=str, help="label type: centroid, coarse")
parser.add_argument("--img_norm_cfg", default=None, type=dict,
help="specific a img_norm_cfg, default=None (using img_norm_cfg values of each dataset)")
parser.add_argument("--dataset_dir", default='./datasets/', type=str, help="train_dataset_dir, default: './datasets/")
parser.add_argument("--batchSize", type=int, default=16, help="Training batch sizse, default: 16")
parser.add_argument("--patchSize", type=int, default=256, help="Training patch size, default: 256")
parser.add_argument("--save", default='./log', type=str, help="Save path, default: './log")
parser.add_argument("--resume", default=None, type=str, help="Resume path, default: None")
parser.add_argument("--nEpochs", type=int, default=400, help="Number of epochs, default: 400")
parser.add_argument("--lr", type=float, default=5e-4, help="Learning Rate, default: 5e-4")
parser.add_argument('--gamma', type=float, default=0.1, help='Gamma, default: 0.1')
parser.add_argument("--step", type=int, default=[200, 300], help="Sets the learning rate decayed by step, default: [200, 300]")
parser.add_argument("--threads", type=int, default=1, help="Number of threads for data loader to use, default: 1")
parser.add_argument("--threshold", type=float, default=0.5, help="Threshold for test, default: 0.5")
parser.add_argument("--cache", default=False, type=str, help="True: cache intermediate mask results, False: save intermediate mask results")
global opt
opt = parser.parse_args()
def train():
train_set = TrainSetLoader_full(dataset_dir=opt.dataset_dir, dataset_name=opt.dataset_name, patch_size=opt.patchSize, img_norm_cfg=opt.img_norm_cfg)
train_loader = DataLoader(dataset=train_set, num_workers=1, batch_size=opt.batchSize, shuffle=True)
epoch_state = 0
total_loss_list = []
total_loss_epoch = []
net = Net(model_name=opt.model_name, mode='train').cuda()
if opt.resume:
ckpt = torch.load(opt.resume)
net.load_state_dict(ckpt['state_dict'])
epoch_state = ckpt['epoch']
total_loss_list = ckpt['total_loss']
for i in range(len(opt.step)):
opt.step[i] = opt.step[i] - epoch_state
optimizer = torch.optim.Adam(net.parameters(), lr=opt.lr)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=opt.step, gamma=opt.gamma)
for idx_epoch in range(epoch_state, opt.nEpochs):
for idx_iter, (img, gt_mask) in enumerate(train_loader):
net.train()
img, gt_mask = Variable(img).cuda(), Variable(gt_mask).cuda()
pred = net.forward(img)
loss = net.loss(pred, gt_mask)
total_loss_epoch.append(loss.detach().cpu())
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
if (idx_epoch + 1) % 1 == 0:
total_loss_list.append(float(np.array(total_loss_epoch).mean()))
print(time.ctime()[4:-5] + ' Epoch---%d, total_loss---%f,' % (idx_epoch + 1, total_loss_list[-1]))
opt.f.write(time.ctime()[4:-5] + ' Epoch---%d, total_loss---%f,\n' % (idx_epoch + 1, total_loss_list[-1]))
total_loss_epoch = []
save_pth = opt.save + '/' + opt.dataset_name + '/' + opt.save_perdix + '_' + str(idx_epoch + 1) + '.pth.tar'
if (idx_epoch + 1) == opt.nEpochs:
save_pth = opt.save + '/' + opt.dataset_name + '/' + opt.save_perdix + '_' + str(idx_epoch + 1) + '.pth.tar'
save_checkpoint({
'epoch': idx_epoch + 1,
'state_dict': net.state_dict(),
'total_loss': total_loss_list,
'train_iou_list': opt.train_iou_list,
'test_iou_list': opt.test_iou_list,
}, save_pth)
test(save_pth)
def test(save_pth):
test_set = TestSetLoader(opt.dataset_dir, opt.dataset_name, opt.dataset_name, opt.img_norm_cfg)
test_loader = DataLoader(dataset=test_set, num_workers=1, batch_size=1, shuffle=False)
net = Net(model_name=opt.model_name, mode='test').cuda()
ckpt = torch.load(save_pth)
net.load_state_dict(ckpt['state_dict'])
net.eval()
eval_mIoU = mIoU()
eval_PD_FA = PD_FA()
for idx_iter, (img, gt_mask, size, _) in enumerate(test_loader):
img = Variable(img).cuda()
pred = net.forward(img)
pred = pred[:,:,:size[0],:size[1]]
gt_mask = gt_mask[:,:,:size[0],:size[1]]
eval_mIoU.update((pred>opt.threshold).cpu(), gt_mask)
eval_PD_FA.update((pred[0,0,:,:]>opt.threshold).cpu(), gt_mask[0,0,:,:], size)
results1 = eval_mIoU.get()
results2 = eval_PD_FA.get()
print("Inference mask pixAcc, mIoU:\t" + str(results1))
print("Inference mask PD, FA:\t" + str(results2))
opt.f.write("pixAcc, mIoU:\t" + str(results1) + '\n')
opt.f.write("PD, FA:\t" + str(results2) + '\n')
def save_checkpoint(state, save_path):
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
torch.save(state, save_path)
if __name__ == '__main__':
for dataset_name in opt.dataset_names:
opt.dataset_name = dataset_name
for model_name in opt.model_names:
opt.model_name = model_name
opt.save_perdix = opt.model_name + opt.label_type
### save intermediate loss vaules
if not os.path.exists(opt.save):
os.makedirs(opt.save)
opt.f = open(opt.save + '/' + opt.dataset_name + '_' + opt.model_name + opt.label_type + '_' + (time.ctime()).replace(' ', '_').replace(':', '_') + '.txt', 'w')
opt.train_iou_list = []
opt.test_iou_list = []
print(opt.dataset_name + '\t' + opt.model_name)
train()
print('\n')
opt.f.close()