def train():
if os.name == 'nt':
data_dir = 'C:/Users/marky/Documents/Courses/saliency/datasets/DUTS/'
else:
data_dir = os.getenv(
"HOME") + '/Documents/Courses/EE298-CV/finalproj/datasets/DUTS/'
tra_image_dir = 'DUTS-TR/DUTS-TR-Image/'
tra_label_dir = 'DUTS-TR/DUTS-TR-Mask/'
test_image_dir = 'DUTS-TE/DUTS-TE-Image/'
test_label_dir = 'DUTS-TE/DUTS-TE-Mask/'
image_ext = '.jpg'
label_ext = '.png'
model_dir = "./saved_models/basnet_bsi_aug/"
resume_train = False
resume_model_path = model_dir + "basnet_bsi_epoch_81_itr_106839_train_1.511335_tar_0.098392.pth"
last_epoch = 1
epoch_num = 100000
batch_size_train = 8
batch_size_val = 1
train_num = 0
val_num = 0
enableInpaintAug = False
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# ------- 5. training process --------
print("---start training...")
test_increments = 6250
ite_num = 0
running_loss = 0.0
running_tar_loss = 0.0
ite_num4val = 1
next_test = ite_num + 0
visdom_tab_title = "StructArchWithoutStructImgs(WithHFlip)"
############
############
############
############
tra_img_name_list = glob.glob(data_dir + tra_image_dir + '*' + image_ext)
print("data_dir + tra_image_dir + '*' + image_ext: ",
data_dir + tra_image_dir + '*' + image_ext)
test_img_name_list = glob.glob(data_dir + test_image_dir + '*' + image_ext)
print("data_dir + test_image_dir + '*' + image_ext: ",
data_dir + test_image_dir + '*' + image_ext)
tra_lbl_name_list = []
for img_path in tra_img_name_list:
img_name = img_path.split("/")[-1]
aaa = img_name.split(".")
bbb = aaa[0:-1]
imidx = bbb[0]
for i in range(1, len(bbb)):
imidx = imidx + "." + bbb[i]
tra_lbl_name_list.append(data_dir + tra_label_dir + imidx + label_ext)
test_lbl_name_list = []
for img_path in test_img_name_list:
img_name = img_path.split("/")[-1]
aaa = img_name.split(".")
bbb = aaa[0:-1]
imidx = bbb[0]
for i in range(1, len(bbb)):
imidx = imidx + "." + bbb[i]
test_lbl_name_list.append(data_dir + test_label_dir + imidx +
label_ext)
print("---")
print("train images: ", len(tra_img_name_list))
print("train labels: ", len(tra_lbl_name_list))
print("---")
print("---")
print("test images: ", len(test_img_name_list))
print("test labels: ", len(test_lbl_name_list))
print("---")
train_num = len(tra_img_name_list)
test_num = len(test_img_name_list)
salobj_dataset = SalObjDataset(img_name_list=tra_img_name_list,
lbl_name_list=tra_lbl_name_list,
transform=transforms.Compose([
RescaleT(256),
RandomCrop(224),
ToTensorLab(flag=0)
]),
category="train",
enableInpaintAug=enableInpaintAug)
salobj_dataset_test = SalObjDataset(img_name_list=test_img_name_list,
lbl_name_list=test_lbl_name_list,
transform=transforms.Compose([
RescaleT(256),
RandomCrop(224),
ToTensorLab(flag=0)
]),
category="test",
enableInpaintAug=enableInpaintAug)
salobj_dataloader = DataLoader(salobj_dataset,
batch_size=batch_size_train,
shuffle=True,
num_workers=1)
salobj_dataloader_test = DataLoader(salobj_dataset_test,
batch_size=batch_size_val,
shuffle=True,
num_workers=1)
# ------- 3. define model --------
# define the net
net = BASNet(3, 1)
if resume_train:
# print("resume_model_path:", resume_model_path)
checkpoint = torch.load(resume_model_path)
net.load_state_dict(checkpoint)
if torch.cuda.is_available():
net.to(device)
# ------- 4. define optimizer --------
print("---define optimizer...")
optimizer = optim.Adam(net.parameters(),
lr=0.001,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0)
plotter = VisdomLinePlotter(env_name=visdom_tab_title)
best_ave_mae = 100000
best_max_fmeasure = 0
best_relaxed_fmeasure = 0
best_ave_maxf = 0
best_own_RelaxedFmeasure = 0
for epoch in range(last_epoch - 1, epoch_num):
### Train network
train_loss0 = AverageMeter()
train_loss1 = AverageMeter()
train_loss2 = AverageMeter()
train_loss3 = AverageMeter()
train_loss4 = AverageMeter()
train_loss5 = AverageMeter()
train_loss6 = AverageMeter()
train_loss7 = AverageMeter()
train_struct_loss1 = AverageMeter()
train_struct_loss2 = AverageMeter()
train_struct_loss3 = AverageMeter()
train_struct_loss4 = AverageMeter()
train_struct_loss5 = AverageMeter()
train_struct_loss6 = AverageMeter()
train_struct_loss7 = AverageMeter()
test_loss0 = AverageMeter()
test_loss1 = AverageMeter()
test_loss2 = AverageMeter()
test_loss3 = AverageMeter()
test_loss4 = AverageMeter()
test_loss5 = AverageMeter()
test_loss6 = AverageMeter()
test_loss7 = AverageMeter()
test_struct_loss1 = AverageMeter()
test_struct_loss2 = AverageMeter()
test_struct_loss3 = AverageMeter()
test_struct_loss4 = AverageMeter()
test_struct_loss5 = AverageMeter()
test_struct_loss6 = AverageMeter()
test_struct_loss7 = AverageMeter()
average_mae = AverageMeter()
average_maxf = AverageMeter()
average_relaxedf = AverageMeter()
average_own_RelaxedFMeasure = AverageMeter()
net.train()
for i, data in enumerate(salobj_dataloader):
ite_num = ite_num + 1
ite_num4val = ite_num4val + 1
inputs, labels, labels_struct = data['image'], data['label'], data[
'label2']
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
labels_struct = labels_struct.type(torch.FloatTensor)
# wrap them in Variable
if torch.cuda.is_available():
inputs_v, labels_v, labels_struct_v = Variable(
inputs.to(device), requires_grad=False), Variable(
labels.to(device), requires_grad=False), Variable(
labels_struct.to(device), requires_grad=False)
else:
inputs_v, labels_v, labels_struct_v = Variable(
inputs, requires_grad=False), Variable(
labels,
requires_grad=False), Variable(labels_struct,
requires_grad=False)
# y zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct = net(
inputs_v)
loss2, loss = muti_bce_loss_fusion(
d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct,
d3_struct, d4_struct, d5_struct, d6_struct, d7_struct,
labels_v, train_loss0, train_loss1, train_loss2, train_loss3,
train_loss4, train_loss5, train_loss6, train_loss7,
train_struct_loss1, train_struct_loss2, train_struct_loss3,
train_struct_loss4, train_struct_loss5, train_struct_loss6,
train_struct_loss7)
loss.backward()
optimizer.step()
# # print statistics
running_loss += loss.data
running_tar_loss += loss2.data
# del temporary outputs and loss
del d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct, loss2, loss
print(
"[train epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f "
% (epoch + 1, epoch_num,
(i + 1) * batch_size_train, train_num, ite_num,
running_loss / ite_num4val, running_tar_loss / ite_num4val))
plotter.plot('loss0', 'train', 'Main Loss 0', epoch + 1,
float(train_loss0.avg))
plotter.plot('loss1', 'train', 'Main Loss 1', epoch + 1,
float(train_loss1.avg))
plotter.plot('loss2', 'train', 'Main Loss 2', epoch + 1,
float(train_loss2.avg))
plotter.plot('loss3', 'train', 'Main Loss 3', epoch + 1,
float(train_loss3.avg))
plotter.plot('loss4', 'train', 'Main Loss 4', epoch + 1,
float(train_loss4.avg))
plotter.plot('loss5', 'train', 'Main Loss 5', epoch + 1,
float(train_loss5.avg))
plotter.plot('loss6', 'train', 'Main Loss 6', epoch + 1,
float(train_loss6.avg))
plotter.plot('loss7', 'train', 'Main Loss 7', epoch + 1,
float(train_loss7.avg))
plotter.plot('structloss1', 'train', 'Struct Loss 1', epoch + 1,
float(train_struct_loss1.avg))
plotter.plot('structloss2', 'train', 'Struct Loss 2', epoch + 1,
float(train_struct_loss2.avg))
plotter.plot('structloss3', 'train', 'Struct Loss 3', epoch + 1,
float(train_struct_loss3.avg))
plotter.plot('structloss4', 'train', 'Struct Loss 4', epoch + 1,
float(train_struct_loss4.avg))
plotter.plot('structloss5', 'train', 'Struct Loss 5', epoch + 1,
float(train_struct_loss5.avg))
plotter.plot('structloss6', 'train', 'Struct Loss 6', epoch + 1,
float(train_struct_loss6.avg))
plotter.plot('structloss7', 'train', 'Struct Loss 7', epoch + 1,
float(train_struct_loss7.avg))
### Validate model
print("---Evaluate model---")
if ite_num >= next_test: # test and save model 10000 iterations, due to very large DUTS-TE dataset
next_test = ite_num + test_increments
net.eval()
max_epoch_fmeasure = 0
for i, data in enumerate(salobj_dataloader_test):
inputs, labels = data['image'], data['label']
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_v, labels_v = Variable(
inputs.to(device),
requires_grad=False), Variable(labels.to(device),
requires_grad=False)
else:
inputs_v, labels_v = Variable(
inputs,
requires_grad=False), Variable(labels,
requires_grad=False)
d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct = net(
inputs_v)
pred = d0[:, 0, :, :]
pred = normPRED(pred)
pred = pred.squeeze()
predict_np = pred.cpu().data.numpy()
im = Image.fromarray(predict_np * 255).convert('RGB')
img_name = test_img_name_list[i]
image = cv2.imread(img_name)
imo = im.resize((image.shape[1], image.shape[0]),
resample=Image.BILINEAR)
imo = imo.convert("L") ### Convert to grayscale 1-channel
resizedImg_np = np.array(
imo) ### Result is 2D numpy array predicted salient map
img__lbl_name = test_lbl_name_list[i]
gt_img = np.array(Image.open(img__lbl_name).convert(
"L")) ### Ground truth salient map
### Compute metrics
result_mae = getMAE(gt_img, resizedImg_np)
average_mae.update(result_mae, 1)
precision, recall = getPRCurve(gt_img, resizedImg_np)
result_maxfmeasure = getMaxFMeasure(precision, recall)
result_maxfmeasure = result_maxfmeasure.mean()
average_maxf.update(result_maxfmeasure, 1)
if (result_maxfmeasure > max_epoch_fmeasure):
max_epoch_fmeasure = result_maxfmeasure
result_relaxedfmeasure = getRelaxedFMeasure(
gt_img, resizedImg_np)
result_ownrelaxedfmeasure = own_RelaxedFMeasure(
gt_img, resizedImg_np)
average_relaxedf.update(result_relaxedfmeasure, 1)
average_own_RelaxedFMeasure.update(result_ownrelaxedfmeasure,
1)
loss2, loss = muti_bce_loss_fusion(
d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct,
d3_struct, d4_struct, d5_struct, d6_struct, d7_struct,
labels_v, test_loss0, test_loss1, test_loss2, test_loss3,
test_loss4, test_loss5, test_loss6, test_loss7,
test_struct_loss1, test_struct_loss2, test_struct_loss3,
test_struct_loss4, test_struct_loss5, test_struct_loss6,
test_struct_loss7)
del d0, d1, d2, d3, d4, d5, d6, d7, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct, loss2, loss
print(
"[test epoch: %3d/%3d, batch: %5d/%5d, ite: %d] test loss: %3f, tar: %3f "
% (epoch + 1, epoch_num, (i + 1) * batch_size_val,
test_num, ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val))
model_name = model_dir + "basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f.pth" % (
epoch + 1, ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val)
torch.save(net.state_dict(), model_name)
running_loss = 0.0
running_tar_loss = 0.0
net.train() # resume train
ite_num4val = 1
if (average_mae.avg < best_ave_mae):
best_ave_mae = average_mae.avg
newname = model_dir + "bestMAE/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_mae_%3f.pth" % (
epoch + 1, ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val, best_ave_mae)
fold_dir = newname.rsplit("/", 1)
if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0])
copyfile(model_name, newname)
if (max_epoch_fmeasure > best_max_fmeasure):
best_max_fmeasure = max_epoch_fmeasure
newname = model_dir + "bestEpochMaxF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_maxfmeas_%3f.pth" % (
epoch + 1, ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val, best_max_fmeasure)
fold_dir = newname.rsplit("/", 1)
if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0])
copyfile(model_name, newname)
if (average_maxf.avg > best_ave_maxf):
best_ave_maxf = average_maxf.avg
newname = model_dir + "bestAveMaxF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_avemfmeas_%3f.pth" % (
epoch + 1, ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val, best_ave_maxf)
fold_dir = newname.rsplit("/", 1)
if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0])
copyfile(model_name, newname)
if (average_relaxedf.avg > best_relaxed_fmeasure):
best_relaxed_fmeasure = average_relaxedf.avg
newname = model_dir + "bestAveRelaxF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_averelaxfmeas_%3f.pth" % (
epoch + 1, ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val, best_relaxed_fmeasure)
fold_dir = newname.rsplit("/", 1)
if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0])
copyfile(model_name, newname)
if (average_own_RelaxedFMeasure.avg > best_own_RelaxedFmeasure):
best_own_RelaxedFmeasure = average_own_RelaxedFMeasure.avg
newname = model_dir + "bestOwnRelaxedF/basnet_bsi_epoch_%d_itr_%d_train_%3f_tar_%3f_averelaxfmeas_%3f.pth" % (
epoch + 1, ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val, best_own_RelaxedFmeasure)
fold_dir = newname.rsplit("/", 1)
if not os.path.isdir(fold_dir[0]): os.mkdir(fold_dir[0])
copyfile(model_name, newname)
plotter.plot('loss0', 'test', 'Main Loss 0', epoch + 1,
float(test_loss0.avg))
plotter.plot('loss1', 'test', 'Main Loss 1', epoch + 1,
float(test_loss1.avg))
plotter.plot('loss2', 'test', 'Main Loss 2', epoch + 1,
float(test_loss2.avg))
plotter.plot('loss3', 'test', 'Main Loss 3', epoch + 1,
float(test_loss3.avg))
plotter.plot('loss4', 'test', 'Main Loss 4', epoch + 1,
float(test_loss4.avg))
plotter.plot('loss5', 'test', 'Main Loss 5', epoch + 1,
float(test_loss5.avg))
plotter.plot('loss6', 'test', 'Main Loss 6', epoch + 1,
float(test_loss6.avg))
plotter.plot('loss7', 'test', 'Main Loss 7', epoch + 1,
float(test_loss7.avg))
plotter.plot('structloss1', 'test', 'Struct Loss 1', epoch + 1,
float(test_struct_loss1.avg))
plotter.plot('structloss2', 'test', 'Struct Loss 2', epoch + 1,
float(test_struct_loss2.avg))
plotter.plot('structloss3', 'test', 'Struct Loss 3', epoch + 1,
float(test_struct_loss3.avg))
plotter.plot('structloss4', 'test', 'Struct Loss 4', epoch + 1,
float(test_struct_loss4.avg))
plotter.plot('structloss5', 'test', 'Struct Loss 5', epoch + 1,
float(test_struct_loss5.avg))
plotter.plot('structloss6', 'test', 'Struct Loss 6', epoch + 1,
float(test_struct_loss6.avg))
plotter.plot('structloss7', 'test', 'Struct Loss 7', epoch + 1,
float(test_struct_loss7.avg))
plotter.plot('mae', 'test', 'Average Epoch MAE', epoch + 1,
float(average_mae.avg))
plotter.plot('max_maxf', 'test', 'Max Max Epoch F-Measure',
epoch + 1, float(max_epoch_fmeasure))
plotter.plot('ave_maxf', 'test', 'Average Max F-Measure',
epoch + 1, float(average_maxf.avg))
plotter.plot('ave_relaxedf', 'test', 'Average Relaxed F-Measure',
epoch + 1, float(average_relaxedf.avg))
plotter.plot('own_RelaxedFMeasure', 'test',
'Own Average Relaxed F-Measure', epoch + 1,
float(average_own_RelaxedFMeasure.avg))
print('-------------Congratulations! Training Done!!!-------------')
loss.backward()
optimizer.step()
# # print statistics
running_loss += loss.data[0]
running_tar_loss += loss2.data[0]
# del temporary outputs and loss
del d0, d1, d2, d3, d4, d5, d6, d7, loss2, loss
print(
"[epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f "
% (epoch + 1, epoch_num,
(i + 1) * batch_size_train, train_num, ite_num,
running_loss / ite_num4val, running_tar_loss / ite_num4val))
if ite_num % 2000 == 0: # save model every 2000 iterations
torch.save(
net.state_dict(),
model_dir + "basnet_bsi_itr_%d_train_%3f_tar_%3f.pth" %
(ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val))
running_loss = 0.0
running_tar_loss = 0.0
net.train() # resume train
ite_num4val = 0
print('-------------Congratulations! Training Done!!!-------------')
def train():
data_dir = './train_data/'
tra_image_dir = 'DUTS-TR/DUTS-TR-Image/'
tra_label_dir = 'DUTS-TR/DUTS-TR-Mask/'
image_ext = '.jpg'
label_ext = '.png'
model_dir = "./saved_models/basnet_bsi/"
epoch_num = 100
batch_size_train = 1
batch_size_val = 1
train_num = 0
val_num = 0
tra_img_name_list = glob.glob(data_dir + tra_image_dir + '*' + image_ext)
tra_lbl_name_list = glob.glob(data_dir + tra_label_dir + '*' + label_ext)
# tra_lbl_name_list = []
# for img_path in tra_img_name_list:
# img_name = img_path.split("/")[-1]
# aaa = img_name.split(".")
# print(aaa)
# bbb = aaa[0:-1]
# imidx = bbb[0]
# for i in range(1,len(bbb)):
# imidx = imidx + "." + bbb[i]
# print(imidx)
# tra_lbl_name_list.append(data_dir + tra_label_dir + '*' + label_ext)
print("---")
print("train images: ", len(tra_img_name_list))
print("train labels: ", len(tra_lbl_name_list))
print("---")
train_num = len(tra_img_name_list)
salobj_dataset = SalObjDataset(img_name_list=tra_img_name_list,
lbl_name_list=tra_lbl_name_list,
transform=transforms.Compose([
RescaleT(256),
RandomCrop(224),
ToTensorLab(flag=0)
]))
salobj_dataloader = DataLoader(salobj_dataset,
batch_size=batch_size_train,
shuffle=True,
num_workers=1)
# ------- 3. define model --------
# define the net
net = BASNet(3, 1)
#if torch.cuda.is_available():
# print(torch.cuda.is_available())
net.cuda()
# ------- 4. define optimizer --------
print("---define optimizer...")
optimizer = optim.Adam(net.parameters(),
lr=0.001,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0)
# ------- 5. training process --------
print("---start training...")
ite_num = 0
running_loss = 0.0
running_tar_loss = 0.0
ite_num4val = 0
for epoch in range(0, epoch_num):
net.train()
for i, data in enumerate(salobj_dataloader):
ite_num = ite_num + 1
ite_num4val = ite_num4val + 1
inputs, labels = data['image'], data['label']
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
# wrap them in Variable
# if torch.cuda.is_available():
inputs_v, labels_v = Variable(inputs.cuda(),
requires_grad=False), Variable(
labels.cuda(),
requires_grad=False)
# else:
# inputs_v, labels_v = Variable(inputs, requires_grad=False), Variable(labels, requires_grad=False)
# y zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
d0, d1, d2, d3, d4, d5, d6, d7 = net(inputs_v)
loss2, loss = muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, d7,
labels_v)
loss.backward()
optimizer.step()
# # print statistics
running_loss += loss.data
running_tar_loss += loss2.data
# del temporary outputs and loss
del d0, d1, d2, d3, d4, d5, d6, d7, loss2, loss
print(
"[epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f "
% (epoch + 1, epoch_num,
(i + 1) * batch_size_train, train_num, ite_num,
running_loss / ite_num4val, running_tar_loss / ite_num4val))
if ite_num % 2000 == 0: # save model every 2000 iterations
torch.save(
net.state_dict(),
model_dir + "basnet_bsi_itr_%d_train_%3f_tar_%3f.pth" %
(ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val))
running_loss = 0.0
running_tar_loss = 0.0
net.train() # resume train
ite_num4val = 0
print('-------------Congratulations! Training Done!!!-------------')
# y zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
d0, d1, d2, d3, d4, d5, d6, d7 = net(inputs_v)
loss2, loss = muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, d7, labels_v)
loss.backward()
optimizer.step()
# # print statistics
running_loss += loss.data[0]
running_tar_loss += loss2.data[0]
# del temporary outputs and loss
del d0, d1, d2, d3, d4, d5, d6, d7, loss2, loss
print("[epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f " % (
epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val))
if ite_num % 2000 == 0: # save model every 2000 iterations
torch.save(net.state_dict(), model_dir + "basnet_bsi_itr_%d_train_%3f_tar_%3f.pth" % (ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val))
running_loss = 0.0
running_tar_loss = 0.0
net.train() # resume train
ite_num4val = 0
print('-------------Congratulations! Training Done!!!-------------')