def load_model(model_dir):
net = BASNet(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
return net
def main():
image_dir = '/media/markytools/New Volume/Courses/EE298CompVis/finalproject/datasets/DUTS/DUTS-TE/DUTS-TE-Image/'
prediction_dir = './predictionout/'
model_dir = './saved_models/basnet_bsi_dataaugandarchi/basnet_bsi_epoch_207_itr_272000_train_9.345837_tar_1.082428.pth'
img_name_list = glob.glob(image_dir + '*.jpg')
# --------- 2. dataloader ---------
#1. dataload
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(256),
ToTensorLab(flag=0)]),
category="test")
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
print("...load BASNet...")
net = BASNet(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split("/")[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
# d1,d2,d3,d4,d5,d6,d7,d8 = net(inputs_test)
d1, d2, d3, d4, d5, d6, d7, d8, d1_struct, d2_struct, d3_struct, d4_struct, d5_struct, d6_struct, d7_struct = net(
inputs_test)
# normalization
pred = d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7, d8
def load_BASNet():
global net, BASNet_loaded
if BASNet_loaded:
print('BASNet is already loaded')
else:
print("Loading BASNet...")
net = BASNet(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
def run_prediction(files):
img_name_list = files
# --------- 2. dataloader ---------
#1. dataload
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(256),
ToTensorLab(flag=0)]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
print("...load BASNet...")
net = BASNet(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split("/")[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7, d8 = net(inputs_test)
# normalization
pred = d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7, d8
img_name_list = glob.glob(image_dir + '*.jpg')
# --------- 2. dataloader ---------
# 1. dataload
test_salobj_dataset = SalObjDataset(img_name_list=img_name_list, lbl_name_list=[
], transform=transforms.Compose([RescaleT(256), ToTensorLab(flag=0)]))
test_salobj_dataloader = DataLoader(
test_salobj_dataset, batch_size=1, shuffle=False, num_workers=1)
# --------- 3. model define ---------
print("...load BASNet...")
net = BASNet(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split("/")[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
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!!!-------------')