def main():
device = torch.device('cuda:9' if torch.cuda.is_available else 'cpu')
train_dataset_dir = tdatasets.ImageFolder('images/all')
train_dataset = SiameseNetworkDataset(imageFolderDataset = train_dataset_dir, transform = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()]))
vis_dataloader = DataLoader(train_dataset,
shuffle=False,
num_workers=0,
batch_size=10)
# dataiter = iter(vis_dataloader)
# example_batch = next(dataiter)
# concatenated = torch.cat((example_batch[0],example_batch[1]),0)
# imshow(torchvision.utils.make_grid(concatenated))
# print(example_batch[2].numpy())
#
# example_batch = next(dataiter)
# concatenated = torch.cat((example_batch[0], example_batch[1]), 0)
# imshow(torchvision.utils.make_grid(concatenated))
# print(example_batch[2].numpy())
net = SiameseNetwork()
criterion = ContrastiveLoss()
optimizer = optim.Adam(net.parameters(),lr = 0.0005 )
loss_vals = []
'''
Training Starts
'''
print('Training started')
for epoch in range(10):
loss_epoch = 0
for i, data in enumerate(vis_dataloader,0):
img_0, img_1, label = data
print(i, label)
# img_0, img_1, label = img_0.to(device), img_1.to(device), label.to(device)
optimizer.zero_grad()
out_0, out_1 = net(img_0, img_1)
loss = criterion(out_0, out_1, label)
loss_epoch += loss.item()
loss.backward()
optimizer.step()
loss_vals.append(loss_epoch)
print('Epoch',str(epoch+1), str(loss_epoch))
print('Epoch done')
torch.save(net.state_dict(), 'siamese.pt')
print('Training completed')
plt.plot(loss_vals)
plt.savefig('loss_siamese.png')
# ****************************** Training ends ***************************************
'''
),
should_invert=False
)
train_dataloader = DataLoader(siamese_dataset,
shuffle=True,
num_workers=8,
batch_size=Config.train_batch_size)
net = SiameseNetwork().cuda()
print(net)
criterion = ContrastiveLoss()
optimizer = optim.Adam(net.parameters(),lr = 0.0005 )
counter = []
loss_history = []
iteration_number= 0
for epoch in range(0,Config.train_number_epochs):
for i, data in enumerate(train_dataloader,0):
img0, img1 , label = data
img0, img1 , label = img0.cuda(), img1.cuda() , label.cuda()
optimizer.zero_grad()
output1,output2 = net(img0,img1)
loss_contrastive = criterion(output1,output2,label)
loss_contrastive.backward()
optimizer.step()
if i %10 == 0 :
print("Epoch number {}\n Current loss {}\n".format(epoch,loss_contrastive.item()))
iteration_number +=10
counter.append(iteration_number)
loss_history.append(loss_contrastive.item())
torch.save(net.state_dict(), "./model.pth")
show_plot(counter,loss_history)
def main():
device = torch.device('cuda:9' if torch.cuda.is_available else 'cpu')
train_dataset_dir = tdatasets.ImageFolder('images/train')
train_dataset = SiameseNetworkDataset(imageFolderDataset = train_dataset_dir, transform = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()]))
vis_dataloader = DataLoader(train_dataset,
shuffle=False,
num_workers=0,
batch_size=1)
# dataiter = iter(vis_dataloader)
# example_batch = next(dataiter)
# concatenated = torch.cat((example_batch[0],example_batch[1]),0)
# imshow(torchvision.utils.make_grid(concatenated))
# print(example_batch[2].numpy())
#
# example_batch = next(dataiter)
# concatenated = torch.cat((example_batch[0], example_batch[1]), 0)
# imshow(torchvision.utils.make_grid(concatenated))
# print(example_batch[2].numpy())
net = SiameseNetwork()
criterion = ContrastiveLoss()
optimizer = optim.Adam(net.parameters(),lr = 0.0005 )
loss_vals = []
net.to(device)
'''
Training Starts
'''
print('Training started')
for epoch in range(1000):
loss_epoch = 0
for i, data in enumerate(vis_dataloader,0):
img_0, img_1, label = data
img_0, img_1, label = img_0.to(device), img_1.to(device), label.to(device)
optimizer.zero_grad()
out_0, out_1 = net(img_0, img_1)
loss = criterion(out_0, out_1, label)
loss_epoch += loss.item()
loss.backward()
optimizer.step()
loss_vals.append(loss_epoch)
print('Epoch',str(epoch+1), str(loss_epoch))
print('Training completed')
plt.plot(loss_vals)
plt.savefig('loss_siamese.png')
# ****************************** Training ends ***************************************
'''
Testing starts
'''
test_dataset_dir = tdatasets.ImageFolder('images/test')
net.load_state_dict(torch.load('siamese.pt'))
test_dataset = SiameseNetworkDataset(imageFolderDataset = test_dataset_dir, transform = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()]))
test_dataloader = DataLoader(test_dataset,
shuffle=True,
num_workers=2,
batch_size=1)
print('Testing starts')
correct = 0
total = 0
test_img_sub = None
for i, data in enumerate(test_dataloader, 0):
img_0, img_1, label = data
if test_img_sub is None:
test_img_sub = img_0
#concat = torch.cat((test_img_sub, img_1), 0)
concat = torch.cat((img_0, img_1), 0)
test_img_sub, img_1, label = test_img_sub.to(device), img_1.to(device), label.to(device)
img_0, img_1, label = img_0.to(device), img_1.to(device), label.to(device)
out_0, out_1 = net(img_0, img_1)
dist = F.pairwise_distance(out_0, out_1)
if dist <= 0.5 and label == 0:
correct = correct + 1
elif label == 1:
correct = correct + 1
else:
pass
total = total + 1
imshow(torchvision.utils.make_grid(concat),i,'Dissimilarity: {:.2f}'.format(dist.item()), True)
test_img_sub = test_img_sub.cpu()
# dist = dist.cpu().detach()
# print(dist.numpy())
# dist = torch.sigmoid(dist)
# print(dist.numpy())
print(correct/total)
print('Testing complete')
torch.save(net.state_dict(), 'siamese_blog.pt')
time_ = datetime.datetime.now()
for iter_, data in enumerate(validate_dataloader, 0):
iter1_, img0, iter2_, img1, label = data
img0, img1, label = img0.cuda(), img1.cuda(), label.cuda()
with torch.no_grad():
output1, output2 = model(img0, img1)
loss_contrastive = criterion(output1, output2, label)
eval_loss += loss_contrastive.item()
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
elapsed = datetime.datetime.now() - time_
print('evaluation done^^')
print('[epoch {}] elapsed: {}'.format(epoch + 1, elapsed))
if best_loss > eval_loss:
best_loss = eval_loss
# save the best model!!!!
print('achieve best loss!')
best_dir = os.path.join('./model_save/', 'best_model/')
os.makedirs(best_dir, exist_ok=True)
tmp_state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(tmp_state,
os.path.join(best_dir,
str(epoch + 1) + '.pth'))