def test_contrastive_loss_value(self):
count = 0
for i in range(0, 100):
x0_val = Variable(self.x0)
x1_val = Variable(self.x1)
t_val = Variable(self.t)
tml = ContrastiveLoss(margin=self.margin)
loss = tml.forward(x0_val, x1_val, t_val)
self.assertEqual(loss.data.numpy().shape, (1, ))
self.assertEqual(loss.data.numpy().dtype, np.float32)
loss_value = float(loss.data.numpy())
# Compute expected value
loss_expect = 0
for i in range(self.x0.size()[0]):
x0d, x1d, td = self.x0[i], self.x1[i], self.t[i]
d = torch.sum(torch.pow(x0d - x1d, 2))
if td == 1: # similar pair
loss_expect += d
elif td == 0: # dissimilar pair
loss_expect += max(1 - np.sqrt(d), 0)**2
loss_expect /= 2.0 * self.t.size()[0]
#print("expected %s got %s" % (loss_expect, loss_value))
if (round(loss_expect, 6) == round(loss_value, 6)):
count += 1
#print (self.assertAlmostEqual(loss_expect, loss_value, places=5))
print(count * 1. / 100)
def test_contrastive_loss(self):
input1 = Variable(torch.randn(4, 4), requires_grad=True)
input2 = Variable(torch.randn(4, 4), requires_grad=True)
target = Variable(torch.randn(4), requires_grad=True)
tml = ContrastiveLoss(margin=self.margin)
self.assertTrue(
gradcheck(lambda x1, x2, t: tml.forward(x1, x2, t),
(input1, input2, target)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', required=True, help='model path')
parser.add_argument('--batch_size',
type=int,
default=1000,
help='batch size (default: 1000)')
opt = parser.parse_args()
opt.use_gpu = torch.cuda.is_available()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
test_dataset = datasets.MNIST(root='./data',
train=False,
download=True,
transform=transform)
test_loader = DataLoader(test_dataset,
batch_size=opt.batch_size,
shuffle=False)
siamese_net = SiameseNetwork()
siamese_net.load_state_dict(torch.load(opt.model))
if opt.use_gpu:
siamese_net = siamese_net.cuda()
criterion = ContrastiveLoss()
running_loss = 0
num_itrs = len(test_loader)
for inputs, labels in test_loader:
x1, x2, t = create_pairs(inputs, labels)
x1, x2, t = Variable(x1), Variable(x2), Variable(t)
if opt.use_gpu:
x1, x2, t = x1.cuda(), x2.cuda(), t.cuda()
y1, y2 = siamese_net(x1, x2)
loss = criterion(y1, y2, t)
running_loss += loss.item()
print('loss: {:.4f}'.format(running_loss / num_itrs))
def test_fun():
#TODO;加载模型参数,测试模型
test_model_para = torch.load("model-epoch-5.pth")
test_model = SiameseNetwork()
test_model.load_state_dict(test_model_para)
testdata = dsets.MNIST(
root='../data/',
train=False,
# XXX ToTensor scale to 0-1
transform=transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,))
)
tsdata = testdata.test_data.reshape(-1, 1, 28, 28).type(torch.FloatTensor)
labels = testdata.test_labels.reshape(-1,1).type(torch.FloatTensor)
loss = ContrastiveLoss()
out1, out2 = test_model(tsdata[99].unsqueeze(0)/255., tsdata[16].unsqueeze(0)/255.)
a = out1.detach()
b = out2.detach()
print(a, b)
print(labels[99],labels[16]) # 同为数字9,可以看到,损失比较低,输出点也靠的比较近
c = loss(a, b, torch.tensor([1.]))
print(c.item())
param.requires_grad = False
for param in netS.conv53.parameters():
param.requires_grad = False
for param in netS.convfc.parameters():
param.requires_grad = False
#-----------------params freeze-----------------
if(opt.cuda):
netS.cuda()
#-------------------Loss & Optimization
optimizerS = torch.optim.Adam(filter(lambda p: p.requires_grad, netS.parameters()),lr=opt.lr, betas=(opt.beta1, 0.999))
poss_contrastive_loss = ContrastiveLoss() # load from the begining
light_contrastive_loss = ContrastiveLoss()
identity_contrastive_loss = ContrastiveLoss()
reconstructe_loss = nn.MSELoss()
pose_class_loss = nn.CrossEntropyLoss()
light_class_loss = nn.CrossEntropyLoss()
#------------------ Global Variables------------------
input_pose_1 = torch.LongTensor(opt.batchSize)
input_light_1 = torch.LongTensor(opt.batchSize)
# input_pose_2 = torch.LongTensor(opt.batchSize)
# input_light_2 = torch.LongTensor(opt.batchSize)
inputImg_1 = torch.FloatTensor(opt.batchSize, 3, opt.fineSize, opt.fineSize)
inputImg_2 = torch.FloatTensor(opt.batchSize, 3, opt.fineSize, opt.fineSize)
GT = torch.FloatTensor(opt.batchSize, 3,opt.fineSize, opt.fineSize)
output1 = self.forward_once(input1)
# forward pass of input 2
output2 = self.forward_once(input2)
return output1, output2
# Load the dataset as pytorch tensors using dataloader
train_dataloader = DataLoader(siamese_dataset,
shuffle=True,
num_workers=8,
batch_size=config.batch_size)
# Declare Siamese Network
net = SiameseNetwork().cuda()
# Decalre Loss Function
criterion = ContrastiveLoss()
# Declare Optimizer
optimizer = torch.optim.Adam(net.parameters(), lr=1e-3, weight_decay=0.0005)
# train the model
def train():
loss = []
counter = []
iteration_number = 0
for epoch in range(1, config.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()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--epoch',
'-e',
type=int,
default=5,
help='Number of sweeps over the dataset to train')
parser.add_argument('--batchsize',
'-b',
type=int,
default=128,
help='Number of images in each mini-batch')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--model',
'-m',
default='',
help='Give a model to test')
parser.add_argument('--train-plot',
action='store_true',
default=True,
help='Plot train loss')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
print("Args: %s" % args)
# create pair dataset iterator
train = dsets.MNIST(root='../data/', train=True, download=True)
test = dsets.MNIST(root='../data/',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
]))
train_iter = create_iterator(train.train_data.numpy(),
train.train_labels.numpy(), args.batchsize)
# model
model = SiameseNetwork()
if args.cuda:
model.cuda()
learning_rate = 0.01
momentum = 0.9
# Loss and Optimizer
criterion = ContrastiveLoss()
optimizer = torch.optim.SGD(model.parameters(),
lr=learning_rate,
momentum=momentum)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(train_iter,
batch_size=args.batchsize,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(test,
batch_size=args.batchsize,
shuffle=True,
**kwargs)
def train_model(epochs):
train_loss = []
for epoch in range(epochs):
print('Train Epoch:' + str(epoch) + "------------------")
for batch_idx, (x0, x1, labels) in enumerate(train_loader):
labels = labels.float()
if args.cuda:
x0, x1, labels = x0.cuda(), x1.cuda(), labels.cuda()
x0, x1, labels = Variable(x0), Variable(x1), Variable(labels)
output1, output2 = model(x0, x1)
loss = criterion(output1, output2, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss.append(loss.item())
if batch_idx % args.batchsize == 0:
print(
'Batch id: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
batch_idx, batch_idx * len(labels),
len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
torch.save(model.state_dict(),
'./weights/model-epoch-%s.pth' % epoch)
print("finished_training")
return train_loss
def test_model(model):
model.eval()
all = []
all_labels = []
with torch.nograd():
for batch_idx, (x, labels) in enumerate(test_loader):
if args.cuda:
x, labels = x.cuda(), labels.cuda()
x, labels = Variable(x, volatile=True), Variable(labels)
output = model.forward_once(x)
all.extend(output.data.cpu().numpy().tolist())
all_labels.extend(labels.data.cpu().numpy().tolist())
numpy_all = np.array(all)
numpy_labels = np.array(all_labels)
# numpy_all is obtained output and numpy_labels is real output
return numpy_all, numpy_labels,
def testing_plots(name_file, model):
filehandler = open(name_file, "wb")
dict_pickle = {}
numpy_all, numpy_labels = test_model(model)
dict_pickle["numpy_all"] = numpy_all
dict_pickle["numpy_labels"] = numpy_labels
pickle.dump(dict_pickle, filehandler)
plot_mnist(numpy_all, numpy_labels)
filehandler.close()
if len(args.model) == 0:
train_loss = train_model(args.epoch)
if args.train_plot:
plot_loss(train_loss)
else:
saved_model = torch.load(args.model)
model = SiameseNetwork()
model.load_state_dict(saved_model)
if args.cuda:
model.cuda()
testing_plots("./../Results/embeddings.pickle", model)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--epoch', '-e', type=int, default=5,
help='Number of sweeps over the dataset to train')
parser.add_argument('--batchsize', '-b', type=int, default=128,
help='Number of images in each mini-batch')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--model', '-m', default='',
help='Give a model to test')
parser.add_argument('--train-plot', action='store_true', default=False,
help='Plot train loss')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
print("Args: %s" % args)
# create pair dataset iterator
train = dsets.MNIST(
root='../data/',
train=True,
# transform=transforms.Compose([
# transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,))
# ]),
download=True
)
test = dsets.MNIST(
root='../data/',
train=False,
# XXX ToTensor scale to 0-1
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,))
])
)
train_iter = create_iterator(
train.train_data.numpy(),
train.train_labels.numpy(),
args.batchsize)
# model
model = SiameseNetwork()
if args.cuda:
model.cuda()
learning_rate = 0.01
momentum = 0.9
# Loss and Optimizer
criterion = ContrastiveLoss()
# optimizer = torch.optim.Adam(
# [p for p in model.parameters() if p.requires_grad],
# lr=learning_rate
# )
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate,
momentum=momentum)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(
train_iter,
batch_size=args.batchsize, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
test,
batch_size=args.batchsize, shuffle=True, **kwargs)
def train(epoch):
train_loss = []
model.train()
start = time.time()
start_epoch = time.time()
for batch_idx, (x0, x1, labels) in enumerate(train_loader):
labels = labels.float()
if args.cuda:
x0, x1, labels = x0.cuda(), x1.cuda(), labels.cuda()
x0, x1, labels = Variable(x0), Variable(x1), Variable(labels)
output1, output2 = model(x0, x1)
loss = criterion(output1, output2, labels)
train_loss.append(loss.data[0])
optimizer.zero_grad()
loss.backward()
optimizer.step()
accuracy = []
for idx, logit in enumerate([output1, output2]):
corrects = (torch.max(logit, 1)[1].data == labels.long().data).sum()
accu = float(corrects) / float(labels.size()[0])
accuracy.append(accu)
if batch_idx % args.batchsize == 0:
end = time.time()
took = end - start
for idx, accu in enumerate(accuracy):
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss:{:.6f}\tTook: {:.2f}\tOut: {}\tAccu: {:.2f}'.format(
epoch, batch_idx * len(labels), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data[0],
took, idx, accu * 100.))
start = time.time()
torch.save(model.state_dict(), './model-epoch-%s.pth' % epoch)
end = time.time()
took = end - start_epoch
print('Train epoch: {} \tTook:{:.2f}'.format(epoch, took))
return train_loss
def test(model):
model.eval()
all = []
all_labels = []
for batch_idx, (x, labels) in enumerate(test_loader):
if args.cuda:
x, labels = x.cuda(), labels.cuda()
x, labels = Variable(x, volatile=True), Variable(labels)
output = model.forward_once(x)
all.extend(output.data.cpu().numpy().tolist())
all_labels.extend(labels.data.cpu().numpy().tolist())
numpy_all = np.array(all)
numpy_labels = np.array(all_labels)
return numpy_all, numpy_labels
def plot_mnist(numpy_all, numpy_labels):
c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',
'#ff00ff', '#990000', '#999900', '#009900', '#009999']
for i in range(10):
f = numpy_all[np.where(numpy_labels == i)]
plt.plot(f[:, 0], f[:, 1], '.', c=c[i])
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
plt.savefig('result.png')
if len(args.model) == 0:
train_loss = []
for epoch in range(1, args.epoch + 1):
train_loss.extend(train(epoch))
if args.train_plot:
plt.gca().cla()
plt.plot(train_loss, label="train loss")
plt.legend()
plt.draw()
plt.savefig('train_loss.png')
plt.gca().clear()
else:
saved_model = torch.load(args.model)
model = SiameseNetwork()
model.load_state_dict(saved_model)
if args.cuda:
model.cuda()
numpy_all, numpy_labels = test(model)
plot_mnist(numpy_all, numpy_labels)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--margin',
type=float,
default=1.0,
help='margin for contrastive loss')
parser.add_argument('--lr', type=float, default=0.01, help='learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--num_epochs',
type=int,
default=100,
help='number of epochs')
parser.add_argument('--batch_size',
type=int,
default=1000,
help='batch size')
parser.add_argument('--log_dir', required=True, help='log directory')
parser.add_argument('--num_workers',
type=int,
default=4,
help='number of workers for data loading')
opt = parser.parse_args()
opt.use_gpu = torch.cuda.is_available()
if not os.path.exists(opt.log_dir):
os.makedirs(opt.log_dir)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
train_dataset = datasets.MNIST(root='./data',
train=True,
download=True,
transform=transform)
train_loader = DataLoader(train_dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers)
siamese_net = SiameseNetwork()
if opt.use_gpu:
siamese_net = siamese_net.cuda()
criterion = ContrastiveLoss()
optimizer = torch.optim.SGD(siamese_net.parameters(),
lr=opt.lr,
momentum=opt.momentum)
history = {}
history['loss'] = []
for epoch in range(opt.num_epochs):
num_itrs = len(train_loader)
running_loss = 0
for itr, (inputs, labels) in enumerate(train_loader):
optimizer.zero_grad()
x1, x2, t = create_pairs(inputs, labels)
x1, x2, t = Variable(x1), Variable(x2), Variable(t)
if opt.use_gpu:
x1, x2, t = x1.cuda(), x2.cuda(), t.cuda()
y1, y2 = siamese_net(x1, x2)
loss = criterion(y1, y2, t)
loss.backward()
optimizer.step()
running_loss += loss.item()
sys.stdout.write('\r\033[Kitr [{}/{}], loss: {:.4f}'.format(
itr, num_itrs, loss.item()))
sys.stdout.flush()
history['loss'].append(running_loss / num_itrs)
sys.stdout.write('\r\033[Kepoch [{}/{}], loss: {:.4f}'.format(
epoch + 1, opt.num_epochs, running_loss / num_itrs))
sys.stdout.write('\n')
torch.save(siamese_net.state_dict(), os.path.join(opt.log_dir,
'model.pth'))
with open(os.path.join(opt.log_dir, 'history.pkl'), 'wb') as f:
pickle.dump(history, f)
plt.plot(history['loss'])
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.grid()
plt.savefig(os.path.join(opt.log_dir, 'loss.png'))
def main(options):
# Path configuration
TRAINING_PATH = '/home/ye/Works/googleCloud-SDK/train.txt'
TESTING_PATH = '/home/ye/Works/googleCloud-SDK/test.txt'
IMG_PATH = '/home/ye/Works/googleCloud-SDK/lfw'
transformations = transforms.Compose([transforms.Scale((128,128)),
transforms.ToTensor()
])
dset_train = DatasetProcessing(IMG_PATH, TRAINING_PATH, transformations)
dset_test = DatasetProcessing(IMG_PATH, TESTING_PATH, transformations)
train_loader = DataLoader(dset_train,
batch_size = options.batch_size,
shuffle = True,
num_workers = 4
)
test_loader = DataLoader(dset_test,
batch_size = options.batch_size,
shuffle = False,
num_workers = 4
)
use_cuda = (len(options.gpuid) >= 1)
if options.gpuid:
cuda.set_device(options.gpuid[0])
# Initial the model
cnn_model = SIAMESE2()
if use_cuda > 0:
cnn_model.cuda()
else:
cnn_model.cpu()
# Binary cross-entropy loss
criterion = ContrastiveLoss()
optimizer = eval("torch.optim." + options.optimizer)(cnn_model.parameters())
# main training loop
last_dev_avg_loss = float("inf")
for epoch_i in range(options.epochs):
logging.info("At {0}-th epoch.".format(epoch_i))
train_loss = 0.0
correct_prediction = 0.0
for it, train_data in enumerate(train_loader):
img0, img1, labels = train_data
if use_cuda:
img0, img1 , labels = Variable(img0).cuda(), Variable(img1).cuda() , Variable(labels).cuda()
else:
img0, img1 , labels = Variable(img0), Variable(img1), Variable(labels)
print '##########', img0.size()
output1, output2 = cnn_model(img0,img1)
loss = criterion(output1, output2, labels)
train_loss += loss
# predict = torch.round(train_output)
# correct_prediction += (predict.view(-1) == labels.view(-1)).sum().float()
logging.debug("loss at batch {0}: {1}".format(it, loss.data[0]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_avg_loss = train_loss / (len(dset_train) / options.batch_size)
# training_accuracy = (correct_prediction / len(dset_train)).data.cpu().numpy()[0]
logging.info("Average training loss value per instance is {0} at the end of epoch {1}".format(train_avg_loss.data[0], epoch_i))
