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u2net_train.py
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u2net_train.py
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import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.optim as optim
import torchvision.transforms as standard_transforms
import numpy as np
import glob
from data_loader import Rescale
from data_loader import RescaleT
from data_loader import RandomCrop
from data_loader import ToTensor
from data_loader import ToTensorLab
from data_loader import SalObjDataset
from model import U2NET
from model import U2NETP
# ------- 1. define loss function --------
bce_loss = nn.BCELoss(size_average=True)
def muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, labels_v):
loss0 = bce_loss(d0,labels_v)
loss1 = bce_loss(d1,labels_v)
loss2 = bce_loss(d2,labels_v)
loss3 = bce_loss(d3,labels_v)
loss4 = bce_loss(d4,labels_v)
loss5 = bce_loss(d5,labels_v)
loss6 = bce_loss(d6,labels_v)
loss = loss0 + loss1 + loss2 + loss3 + loss4 + loss5 + loss6
print("l0: %3f, l1: %3f, l2: %3f, l3: %3f, l4: %3f, l5: %3f, l6: %3f\n"%(loss0.data[0],loss1.data[0],loss2.data[0],loss3.data[0],loss4.data[0],loss5.data[0],loss6.data[0]))
return loss0, loss
# ------- 2. set the directory of training dataset --------
model_name = 'u2net' #'u2netp'
data_dir = os.path.join(os.getcwd(), 'train_data' + os.sep)
tra_image_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'im_aug' + os.sep)
tra_label_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'gt_aug' + os.sep)
image_ext = '.jpg'
label_ext = '.png'
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name + os.sep)
epoch_num = 100000
batch_size_train = 12
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 = []
for img_path in tra_img_name_list:
img_name = img_path.split(os.sep)[-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)
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(320),
RandomCrop(288),
ToTensorLab(flag=0)]))
salobj_dataloader = DataLoader(salobj_dataset, batch_size=batch_size_train, shuffle=True, num_workers=1)
# ------- 3. define model --------
# define the net
if(model_name=='u2net'):
net = U2NET(3, 1)
elif(model_name=='u2netp'):
net = U2NETP(3,1)
if 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
save_frq = 2000 # save the model every 2000 iterations
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 = net(inputs_v)
loss2, loss = muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, 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, 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 % save_frq == 0:
torch.save(net.state_dict(), model_dir + model_name+"_bce_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