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mynet_train.py
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mynet_train.py
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import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.optim as optim
import glob
from data_loader import Rescale
from data_loader import ToTensor
from data_loader import SalObjDataset
from model import FSMINet
import pytorch_ssim
import pytorch_iou
# ------- processes the training dataset -------
image_train_dir = ""
label_train_dir = ""
image_ext = '.jpg'
label_ext = '.png'
model_dir = ""
epoch_num = 52
batch_size_train = 6
image_train_name_list = glob.glob(image_train_dir + '*' + image_ext)
label_train_name_list = []
for image_path in image_train_name_list:
image_name = image_path.split("/")[-1]
t = image_name.split(".")
t = t[0:-1]
imidx = t[0]
for i in range(1,len(t)):
imidx = imidx + "." + t[i]
label_train_name_list.append(label_train_dir + imidx + label_ext)
print("---")
print("train images: ", len(image_train_name_list))
print("train labels: ", len(label_train_name_list))
print("---")
train_num = len(image_train_name_list)
salobj_dataset = SalObjDataset(
img_name_list=image_train_name_list,
lbl_name_list=label_train_name_list,
transform=transforms.Compose([
Rescale(384),
ToTensor(flag=0)]))
salobj_dataloader = DataLoader(salobj_dataset, batch_size=batch_size_train, shuffle=True, num_workers=1)
# ------- define loss function -------
bce_loss = nn.BCELoss(reduction='mean')
ssim_loss = pytorch_ssim.SSIM(window_size=11,size_average=True)
iou_loss = pytorch_iou.IOU(size_average=True)
def hybrid_loss(pred,target):
bce_out = bce_loss(pred,target)
iou_out = iou_loss(pred,target)
ssim_out = 1 - ssim_loss(pred,target)
loss = bce_out + iou_out + ssim_out
return loss
def muti_loss_fusion(d0, d1, d2, d3, d4, d5, labels_v):
loss0 = hybrid_loss(d0,labels_v)
loss1 = hybrid_loss(d1,labels_v)
loss2 = hybrid_loss(d2,labels_v)
loss3 = hybrid_loss(d3,labels_v)
loss4 = hybrid_loss(d4,labels_v)
loss5 = hybrid_loss(d5,labels_v)
loss = loss0 + loss1 + loss2 + loss3 + loss4 + loss5
print("l0: %.3f, l1: %.3f, l2: %.3f, l3: %.3f, l4: %.3f, l5: %.3f"%(loss0.item(), loss1.item(), loss2.item(), loss3.item(), loss4.item(), loss5.item()))
return loss
# ------- define model --------
# define the net
net = FSMINet()
if torch.cuda.is_available():
net.cuda()
# ------- define optimizer --------
print("---define optimizer...")
optimizer = optim.Adam(net.parameters(), lr=1e-4, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
# ------- training process --------
print("---start training...")
ite_num = 0
for epoch in range(0, epoch_num):
net.train()
for i, data in enumerate(salobj_dataloader):
ite_num = ite_num + 1
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.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)
optimizer.zero_grad()
d0, d1, d2, d3, d4, d5 = net(inputs_v)
loss = muti_loss_fusion(d0, d1, d2, d3, d4, d5, labels_v)
loss.backward()
optimizer.step()
if ite_num % 2000 == 0: # save model every 2000 iterations
torch.save(net.state_dict(), model_dir + "MYNet_epoch_%d_items_%d.pth" % (epoch, ite_num))
net.train()
print("[epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %.3f" %
(epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, loss.item()))
del d0, d1, d2, d3, d4, d5, loss
print('-------------Congratulations! Training Done!-------------')