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train.py
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train.py
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import sys
sys.path.insert(0, '.')
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.nn.parallel import gather
import torch.optim.lr_scheduler
import datasets.dataset as myDataLoader
import datasets.Transforms as myTransforms
from utils.metric_tool import ConfuseMatrixMeter
import os, time
import numpy as np
from argparse import ArgumentParser
from models.model import get_model
def label_edge_prediction(label):
ero = 1 - F.max_pool2d(1 - label, kernel_size=5, stride=1, padding=2) # erosion
dil = F.max_pool2d(label, kernel_size=5, stride=1, padding=2) # dilation
edge = dil - ero
return edge
def BCEDiceLoss(pres, gts):
bce = F.binary_cross_entropy(pres, gts)
inter = (pres * gts).sum()
eps = 1e-5
dice = (2 * inter + eps) / (pres.sum() + gts.sum() + eps)
return bce + 1 - dice
@torch.no_grad()
def val(args, val_loader, model):
model.eval()
salEvalVal = ConfuseMatrixMeter(n_class=2)
epoch_loss = []
total_batches = len(val_loader)
print(len(val_loader))
for iter, batched_inputs in enumerate(val_loader):
img, target = batched_inputs
start_time = time.time()
if args.onGPU == True:
img = img.cuda()
target = target.cuda()
img_var = torch.autograd.Variable(img).float()
target_var = torch.autograd.Variable(target).float()
# run the mdoel
change_mask, mask_d2, mask_d3, mask_d4, mask_d5, boundary_mask = model(img_var)
output = change_mask
#
loss = BCEDiceLoss(change_mask, target_var)
pred = torch.where(output > 0.5, torch.ones_like(output), torch.zeros_like(output)).long()
# torch.cuda.synchronize()
time_taken = time.time() - start_time
epoch_loss.append(loss.data.item())
# compute the confusion matrix
if args.onGPU and torch.cuda.device_count() > 1:
output = gather(pred, 0, dim=0)
# salEvalVal.addBatch(pred, target_var)
f1 = salEvalVal.update_cm(pr=pred.cpu().numpy(), gt=target_var.cpu().numpy())
if iter % 5 == 0:
print('\r[%d/%d] F1: %3f loss: %.3f time: %.3f' % (iter, total_batches, f1, loss.data.item(), time_taken),
end='')
average_epoch_loss_val = sum(epoch_loss) / len(epoch_loss)
scores = salEvalVal.get_scores()
return average_epoch_loss_val, scores
def train(args, train_loader, model, optimizer, epoch, max_batches, cur_iter=0, lr_factor=1.):
# switch to train mode
model.train()
salEvalVal = ConfuseMatrixMeter(n_class=2)
epoch_loss = []
for iter, batched_inputs in enumerate(train_loader):
img, target = batched_inputs
target_boundary = label_edge_prediction(target.float())
#
start_time = time.time()
if args.onGPU == True:
img = img.cuda()
target = target.cuda()
target_boundary = target_boundary.cuda()
img_var = torch.autograd.Variable(img).float()
target_var = torch.autograd.Variable(target).float()
target_boundary_var = torch.autograd.Variable(target_boundary).float()
# adjust the learning rate
lr = adjust_learning_rate(args, optimizer, epoch, iter + cur_iter, max_batches, lr_factor=lr_factor)
# run the mdoel
change_mask, mask_d2, mask_d3, mask_d4, mask_d5, boundary_mask = model(img_var)
output = change_mask
#
loss1 = BCEDiceLoss(change_mask, target_var)
loss2 = BCEDiceLoss(mask_d2, target_var)
loss3 = BCEDiceLoss(mask_d3, target_var)
loss4 = BCEDiceLoss(mask_d4, target_var)
loss5 = BCEDiceLoss(mask_d5, target_var)
#
change_pre = change_mask.detach()
uncertainty_gt = torch.mul(target_var, (1 - change_pre)) + torch.mul(change_pre, (1 - target_var))
uncertainty_loss = F.binary_cross_entropy(boundary_mask, uncertainty_gt)
# import utils.torchutils as vis
# vis.visulize_features(uncertainty_gt)
loss = loss1 + loss2 + loss3 + loss4 + loss5 + uncertainty_loss
pred = torch.where(output > 0.5, torch.ones_like(output), torch.zeros_like(output)).long()
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss.append(loss.data.item())
time_taken = time.time() - start_time
res_time = (max_batches * args.max_epochs - iter - cur_iter) * time_taken / 3600
if args.onGPU and torch.cuda.device_count() > 1:
output = gather(pred, 0, dim=0)
# Computing F-measure and IoU on GPU
with torch.no_grad():
f1 = salEvalVal.update_cm(pr=pred.cpu().numpy(), gt=target_var.cpu().numpy())
if iter % 5 == 0:
print('\riteration: [%d/%d] f1: %.3f lr: %.7f loss: %.3f time:%.3f h' % (
iter + cur_iter, max_batches * args.max_epochs, f1, lr, loss.data.item(),
res_time), end='')
average_epoch_loss_train = sum(epoch_loss) / len(epoch_loss)
scores = salEvalVal.get_scores()
return average_epoch_loss_train, scores, lr
def adjust_learning_rate(args, optimizer, epoch, iter, max_batches, lr_factor=1):
if args.lr_mode == 'poly':
cur_iter = iter
max_iter = max_batches * args.max_epochs
lr = args.lr * (1 - cur_iter * 1.0 / max_iter) ** 0.9
else:
raise ValueError('Unknown lr mode {}'.format(args.lr_mode))
if epoch == 0 and iter < 200:
lr = args.lr * 0.9 * (iter + 1) / 200 + 0.1 * args.lr # warm_up
lr *= lr_factor
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
def train_val_change_detection(args):
torch.backends.cudnn.benchmark = True
SEED = 2333
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
model = get_model()
args.save_dir = args.save_dir + '_iter_' + str(args.max_steps) + '_lr_' + str(args.lr) + '/'
args.train_data_root = '/mnt/2800c818-54bc-4e2a-83d3-f418982b79e6/Change Detection/Datasets_BCD/LEVIR+TR_VAL_TE'
args.test_data_root_1 = '/mnt/2800c818-54bc-4e2a-83d3-f418982b79e6/Change Detection/Datasets_BCD/LEVIR+TR_VAL_TE'
args.test_data_root_2 = '/mnt/2800c818-54bc-4e2a-83d3-f418982b79e6/Change Detection/Datasets_BCD/BCDD-512_TE'
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
if args.onGPU:
model = model.cuda()
total_params = sum([np.prod(p.size()) for p in model.parameters()])
print('Total network parameters (excluding idr): ' + str(total_params))
mean = [0.406, 0.456, 0.485, 0.406, 0.456, 0.485]
std = [0.225, 0.224, 0.229, 0.225, 0.224, 0.229]
# compose the data with transforms
trainDataset_main = myTransforms.Compose([
myTransforms.Normalize(mean=mean, std=std),
myTransforms.Scale(args.inWidth, args.inHeight),
myTransforms.RandomCropResize(),
myTransforms.RandomFlip(),
myTransforms.RandomExchange(),
myTransforms.ToTensor()
])
valDataset = myTransforms.Compose([
myTransforms.Normalize(mean=mean, std=std),
myTransforms.Scale(args.inWidth, args.inHeight),
myTransforms.ToTensor()
])
train_data = myDataLoader.Dataset("train", file_root=args.train_data_root, transform=trainDataset_main)
trainLoader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=False, drop_last=True
)
val_data = myDataLoader.Dataset("val", file_root=args.train_data_root, transform=valDataset)
valLoader = torch.utils.data.DataLoader(
val_data, shuffle=False,
batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=False)
test_data_1 = myDataLoader.Dataset("test", file_root=args.test_data_root_1, transform=valDataset)
testLoader_1 = torch.utils.data.DataLoader(
test_data_1, shuffle=False,
batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=False)
test_data_2 = myDataLoader.Dataset("test", file_root=args.test_data_root_2, transform=valDataset)
testLoader_2 = torch.utils.data.DataLoader(
test_data_2, shuffle=False,
batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=False)
max_batches = len(trainLoader)
print('For each epoch, we have {} batches'.format(max_batches))
if args.onGPU:
cudnn.benchmark = True
args.max_epochs = int(np.ceil(args.max_steps / max_batches))
start_epoch = 0
cur_iter = 0
max_F1_val = 0
logFileLoc = args.save_dir + args.logFile
if os.path.isfile(logFileLoc):
logger = open(logFileLoc, 'a')
else:
logger = open(logFileLoc, 'w')
logger.write("Parameters: %s" % (str(total_params)))
logger.write(
"\n%s\t%s\t%s\t%s\t%s\t%s" % ('Epoch', 'Kappa (val)', 'IoU (val)', 'F1 (val)', 'R (val)', 'P (val)'))
logger.flush()
# optimizer = torch.optim.Adam(model.parameters(), args.lr, (0.9, 0.99), eps=1e-08, weight_decay=1e-4)
optimizer = torch.optim.AdamW(model.parameters(), args.lr, (0.9, 0.999), weight_decay=1e-2)
for epoch in range(start_epoch, args.max_epochs):
lossTr, score_tr, lr = \
train(args, trainLoader, model, optimizer, epoch, max_batches, cur_iter)
cur_iter += len(trainLoader)
torch.cuda.empty_cache()
# evaluate on validation set
if epoch == 0:
continue
lossVal, score_val = val(args, valLoader, model)
torch.cuda.empty_cache()
logger.write("\n%d\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f" % (epoch, score_val['Kappa'], score_val['IoU'],
score_val['F1'], score_val['recall'],
score_val['precision']))
logger.flush()
torch.save({
'epoch': epoch + 1,
'arch': str(model),
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lossTr': lossTr,
'lossVal': lossVal,
'F_Tr': score_tr['F1'],
'F_val': score_val['F1'],
'lr': lr
}, args.save_dir + 'checkpoint.pth.tar')
# save the model
model_file_name = args.save_dir + 'best_model.pth'
if epoch % 1 == 0 and max_F1_val <= score_val['F1']:
max_F1_val = score_val['F1']
torch.save(model.state_dict(), model_file_name)
print("Epoch " + str(epoch) + ': Details')
print("\nEpoch No. %d:\tTrain Loss = %.4f\tVal Loss = %.4f\t F1(tr) = %.4f\t F1(val) = %.4f" \
% (epoch, lossTr, lossVal, score_tr['F1'], score_val['F1'])
)
#
state_dict = torch.load(model_file_name)
model.load_state_dict(state_dict)
#
loss_test_LEVIR, score_test_LEVIR = val(args, testLoader_1, model)
torch.cuda.empty_cache()
print("\nLEVIR_Test :\t Kappa (te) = %.4f\t IoU (te) = %.4f\t F1 (te) = %.4f\t R (te) = %.4f\t P (te) = %.4f" \
% (score_test_LEVIR['Kappa'], score_test_LEVIR['IoU'], score_test_LEVIR['F1'], score_test_LEVIR['recall'],
score_test_LEVIR['precision']))
logger.write("\n%s\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f" % ('LEVIR_Test',
score_test_LEVIR['Kappa'],
score_test_LEVIR['IoU'],
score_test_LEVIR['F1'],
score_test_LEVIR['recall'],
score_test_LEVIR['precision']))
logger.flush()
#
loss_test_BCDD, score_test_BCDD = val(args, testLoader_2, model)
torch.cuda.empty_cache()
print("\nBCDD_Test :\t Kappa (te) = %.4f\t IoU (te) = %.4f\t F1 (te) = %.4f\t R (te) = %.4f\t P (te) = %.4f" \
% (score_test_BCDD['Kappa'], score_test_BCDD['IoU'], score_test_BCDD['F1'], score_test_BCDD['recall'],
score_test_BCDD['precision']))
logger.write("\n%s\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f" % ('BCDD_Test',
score_test_BCDD['Kappa'],
score_test_BCDD['IoU'],
score_test_BCDD['F1'],
score_test_BCDD['recall'],
score_test_BCDD['precision']))
logger.flush()
logger.close()
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--file_root', default="LEVIR", help='Data directory')
parser.add_argument('--inWidth', type=int, default=512, help='Width of RGB image')
parser.add_argument('--inHeight', type=int, default=512, help='Height of RGB image')
parser.add_argument('--max_steps', type=int, default=20000, help='Max. number of iterations')
parser.add_argument('--num_workers', type=int, default=4, help='No. of parallel threads')
parser.add_argument('--batch_size', type=int, default=8, help='Batch size')
parser.add_argument('--lr', type=float, default=5e-4, help='Initial learning rate')
parser.add_argument('--lr_mode', default='poly', help='Learning rate policy')
parser.add_argument('--save_dir', default='./weights/model', help='Directory to save the results')
parser.add_argument('--logFile', default='trainValLog.txt',
help='File that stores the training and validation logs')
parser.add_argument('--onGPU', default=True, type=lambda x: (str(x).lower() == 'true'),
help='Run on CPU or GPU. If TRUE, then GPU.')
parser.add_argument('--weight', default='', type=str, help='pretrained weight, can be a non-strict copy')
parser.add_argument('--ms', type=int, default=0, help='apply multi-scale training, default False')
args = parser.parse_args()
print('Called with args:')
print(args)
train_val_change_detection(args)