def train_siamese_network(nclasses, fp16, transform, batch_size, num_epochs):
since = time.time()
net = SiameseNetwork().cuda()
# net.classifier.classifier = nn.Sequential()
print(net)
print("Start time: ", since)
criterion = ContrastiveLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0005)
if fp16:
# model = network_to_half(model)
# optimizer_ft = FP16_Optimizer(optimizer_ft, static_loss_scale = 128.0)
print("Memory saving is on using fp16")
net, optimizer = amp.initialize(net, optimizer, opt_level="O1")
counter = []
loss_history = []
iteration_number = 0
train_dataloader = get_dataloader(transform, batch_size)
print("Started training siamese network")
for epoch in range(0, num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
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 fp16: # we use optimier to backward loss
with amp.scale_loss(loss_contrastive,
optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss_contrastive.backward()
optimizer.step()
if i % 10 == 0:
iteration_number += 10
counter.append(iteration_number)
loss_history.append(loss_contrastive.item())
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print("Epoch number {} finished, Current loss {}\n".format(
epoch, loss_contrastive.item()))
if epoch % 10 == 9:
save_model(epoch, net, loss_contrastive, optimizer)
show_plot(counter, loss_history)
def siam_train(vectors, seq2seq_model, batch_size, layers, directory):
first, sec, answ = readBcb(vectors + '/train')
first_enc = seq2seq_model.get_encoder_status(first)
sec_enc = seq2seq_model.get_encoder_status(sec)
siam_model = SiameseNetwork(first_enc[0].shape[1], batch_size, layers)
siam_model.train(first_enc, sec_enc, answ, directory)
return siam_model
def __init__(self, input_shape=(240, 180, 1), name=None):
self.input_shape = input_shape
self.image_height, self.image_width, _ = input_shape
self.data_loader = CASPEALR1DataLoader(image_height=self.image_height, \
image_width=self.image_width)
self.siamese_network = SiameseNetwork(input_shape)
self.siamese_network.compile(loss='binary_crossentropy', \
optimizer=Adam(lr=0.001), metrics=['accuracy'])
self.name = name
pass
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 ***************************************
'''
def __init__(self):
data_src = './data_repository/geological_similarity/'
self.model_path = './trained_model/model_triplet/'
self.dataset = PreProcessing(data_src)
self.model = SiameseNetwork()
# Define Tensor
self.img_placeholder = tf.placeholder(tf.float32, [None, 28, 28, 3],
name='img')
self.net = self.model.conv_net(self.img_placeholder, reuse=False)
self.normalized_training_vectors = self.generate_db_normed_vectors()
print('Prediction object loaded successfully.')
def get_siamese_model(nclasses):
device = torch.device("cuda")
model = SiameseNetwork()
# model.classifier.classifier = nn.Sequential()
checkpoint = torch.load('./model/siamese/net_59.pth')
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
return model
def mlnet_method(X, Y, splits=5):
accuracy, cnt = 0, 0
mskf = my_SKF(X, Y, splits)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
for X_train, Y_train, X_test, Y_test in mskf:
new_X = transform(X_train, Y_train)
dataset = ImageDataset(new_X)
dataloader = DataLoader(dataset,
batch_size=128,
shuffle=True,
num_workers=4)
net = SiameseNetwork().to(device)
net.fit(dataloader)
net.eval()
hit = 0
for i, x in enumerate(X_test):
y_pred = knn_pred(net, X_train, Y_train, x, batch_num=1)
y_real = Y_test[i]
if y_pred == y_real:
hit += 1
acc = hit / X_test.shape[0]
accuracy += acc
cnt += 1
print('Split:{}, Acc:{:.4f}'.format(cnt, acc))
return accuracy / splits
def main():
global plotter
plotter = VisdomLinePlotter(env_name=config.visdom_name)
# instantiate model and initialize weights
model = SiameseNetwork()
if config.cuda:
model.cuda()
optimizer = create_optimizer(model, config.lr)
# optionally resume from a checkpoint
if config.resume:
if os.path.isfile(config.resume):
print('=> loading checkpoint {}'.format(config.resume))
checkpoint = torch.load(config.resume)
config.start_epoch = checkpoint['epoch']
checkpoint = torch.load(config.resume)
model.load_state_dict(checkpoint['state_dict'])
else:
print('=> no checkpoint found at {}'.format(config.resume))
start = config.start_epoch
end = start + config.epochs
for epoch in range(start, end):
train(train_loader, model, optimizer, epoch)
def checkOverfit(num_samples, input_dims, hidden_dims, labels, num_tries=3):
for _ in range(num_tries):
random_dataset = RandomDataset(num_samples=num_samples, input_dims=input_dims, useLabels=True, labels=labels)
model = SiameseNetwork(input_dims=input_dims, hidden_dims=hidden_dims, doConv=False)
trainer = Trainer(random_dataset, model=model, model_parameters=model.parameters,
batch_size=8, lr=1, shuffle=True, doValidation=False)
trainer.train(num_epochs=30) # This should print status
if trainer.training_error_plot[-1] == 0:
return True
return False
class Predict:
def __init__(self):
data_src = './data_repository/geological_similarity/'
self.model_path = './trained_model/model_triplet/'
self.dataset = PreProcessing(data_src)
self.model = SiameseNetwork()
# Define Tensor
self.img_placeholder = tf.placeholder(tf.float32, [None, 28, 28, 3],
name='img')
self.net = self.model.conv_net(self.img_placeholder, reuse=False)
self.normalized_training_vectors = self.generate_db_normed_vectors()
print('Prediction object loaded successfully.')
# Compute Vector representation for each training images and normalize those
def generate_db_normed_vectors(self):
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(self.model_path)
saver.restore(sess, self.model_path + "model.ckpt")
train_vectors = sess.run(
self.net,
feed_dict={self.img_placeholder: self.dataset.images_train})
del self.dataset.images_train
gc.collect()
normalized_train_vectors = train_vectors / np.linalg.norm(
train_vectors, axis=1).reshape(-1, 1)
return normalized_train_vectors
# Find k nearest neighbour using cosine similarity
def find_k_nn(self, normalized_train_vectors, vec, k):
dist_arr = np.matmul(normalized_train_vectors, vec.T)
return np.argsort(-dist_arr.flatten())[:k]
def predict(self, im, k=10):
# run the test image through the network to get the test features
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(self.model_path)
saver.restore(sess, self.model_path + "model.ckpt")
search_vector = sess.run(self.net,
feed_dict={self.img_placeholder: [im]})
normalized_search_vec = search_vector / np.linalg.norm(search_vector)
candidate_index = self.find_k_nn(self.normalized_training_vectors,
normalized_search_vec, k)
return list(candidate_index)
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')
class FaceRecognition(object):
def __init__(self, input_shape=(240, 180, 1), name=None):
self.input_shape = input_shape
self.image_height, self.image_width, _ = input_shape
self.data_loader = CASPEALR1DataLoader(image_height=self.image_height, \
image_width=self.image_width)
self.siamese_network = SiameseNetwork(input_shape)
self.siamese_network.compile(loss='binary_crossentropy', \
optimizer=Adam(lr=0.001), metrics=['accuracy'])
self.name = name
pass
def prepare(self):
self.data_loader.write_infos()
pass
def train(self, epochs=1000, batch_size=3, load_pretrained=False, seed=0):
if load_pretrained:
self.siamese_network.load_weights(
'./weights/siamese_network_weights.h5')
print('Info: weights loaded.')
pass
for epoch in range(epochs):
seed += 1
for batch_i, (anchor_images, positive_images, negative_images) \
in enumerate(self.data_loader.load_batches(batch_size, seed=seed)):
images_A = np.concatenate((anchor_images, anchor_images),
axis=0)
images_B = np.concatenate((positive_images, negative_images),
axis=0)
y_true = np.concatenate((np.ones(
(batch_size, 1)), np.zeros((batch_size, 1))),
axis=0)
loss, accuracy = self.siamese_network.train_on_batch(
[images_A, images_B], y_true)
print('[epoch: {0:}/{1:}][batch: {2:}/{3:}][loss: {4:}][accuracy: {5:}]'.format(epoch+1, \
epochs, batch_i+1, self.data_loader.n_batches, loss, accuracy))
if (batch_i + 1) % 250 == 0:
self.siamese_network.save_weights(
'./weights/siamese_network_weights.h5')
print('Info: weights saved.')
pass
pass
if (epoch + 1) % 10 == 0:
self.siamese_network.save_weights(
'./weights/siamese_network_weights.h5')
print('Info: weights saved.')
pass
pass
pass
def accuracy(self, batch_size=3, seed=0):
self.siamese_network.load_weights(
'./weights/siamese_network_weights.h5')
print('Info: weights loaded.')
num_true = 0
for batch_i, (anchor_images, positive_images, negative_images) \
in enumerate(self.data_loader.load_batches(batch_size, seed=seed)):
images_A = np.concatenate((anchor_images, anchor_images), axis=0)
images_B = np.concatenate((positive_images, negative_images),
axis=0)
y_true = np.concatenate((np.ones(
(batch_size, 1)), np.zeros((batch_size, 1))),
axis=0)
loss, accuracy = self.siamese_network.evaluate(
[images_A, images_B], y_true)
num_true += int(accuracy * batch_size * 2 + 0.5)
num_sum = (batch_i + 1) * batch_size * 2
accuracy = num_true / num_sum
print('[after batch: {0:}][accuracy: {1:}]'.format(
batch_i + 1, accuracy))
pass
num_sum = self.data_loader.n_batches * batch_size * 2
total_accuracy = num_true / num_sum
print('total accuracy: {0:}'.format(total_accuracy))
pass
def predict(self, image_A_path, image_B_path, have_loaded_weights=False):
if have_loaded_weights == False:
self.siamese_network.load_weights(
'./weights/siamese_network_weights.h5')
print('Info: weights loaded.')
pass
images_A, images_B = [], []
image_A = self.data_loader.imread(image_A_path)
images_A.append(image_A)
images_A = np.array(images_A) / 127.5 - 1.0
image_B = self.data_loader.imread(image_B_path)
images_B.append(image_B)
images_B = np.array(images_B) / 127.5 - 1.0
predictions = self.siamese_network.predict([images_A, images_B])
prediction = np.squeeze(predictions)
if prediction >= 0.5:
return 1
else:
return 0
pass
pass
"-------------------------------------------------------------------------------------"
+ "\n")
for hp in sorted(hyper_parameters.keys()):
if hp in ["V_c", "V_w"]:
open(file_results, "a").write("num " + hp + ": " +
str(len(hyper_parameters[hp])) + "\n")
else:
open(file_results,
"a").write(hp + ": " + str(hyper_parameters[hp]) + "\n")
###############################
print("build tensorflow graph")
###############################
model = SiameseNetwork(
hyper_parameters=hyper_parameters,
E_w=E_w,
)
#######################################
print("start siamese network training")
#######################################
train_set = (docs_L_tr, docs_R_tr, labels_tr)
test_set = (docs_L_te, docs_R_te, labels_te)
model_type = hyper_parameters["model_type"]
func = {
"HRSN": model.train_model_hrsn,
"AdHominem": model.train_model_adhominem,
}[model_type]
func(train_set, test_set, file_results)
hidden_size = 100
num_layers = 2
num_classes = 2
batch_size = 32
num_epochs = 50
learning_rate = 0.003
print_freq = 500
model_time = strftime('%H:%M:%S', gmtime())
#num-perspective = 20?
#dropout=0.1
# Dataset
print('loading Quora data...')
data = Quora(batch_size, input_size)
word_vocab_size = len(data.TEXT.vocab)
siamese = SiameseNetwork(input_size, word_vocab_size, hidden_size, num_layers,
data)
parameters = filter(lambda p: p.requires_grad, siamese.parameters())
# Loss and Optimizer
optimizer = torch.optim.Adam(parameters, lr=learning_rate)
criterion = nn.CrossEntropyLoss()
writer = SummaryWriter(log_dir='runs/' + model_time)
siamese.train()
loss, last_epoch = 0, -1
max_dev_acc, max_test_acc = 0, 0
best_model = copy.deepcopy(siamese)
import torchvision
from torch import optim
from torch.utils.data import DataLoader
from torchvision import transforms
import config
from model import SiameseNetwork, ContrastiveLoss, train
from utils import SiameseNetworkDataset
opt = config.args()
folder_dataset = torchvision.datasets.ImageFolder(root=opt.training_dir)
# 定义图像dataset
transform = transforms.Compose([transforms.Resize((100, 100)), # 有坑，传入int和tuple有区别
transforms.ToTensor()])
siamese_dataset = SiameseNetworkDataset(imageFolderDataset=folder_dataset,
transform=transform,
should_invert=False)
# 定义图像dataloader
train_dataloader = DataLoader(siamese_dataset,
shuffle=True,
batch_size=opt.batch_size)
net = SiameseNetwork().cuda() #定义模型且移至GPU
print(net)
criterion = ContrastiveLoss(margin=2.0) #定义损失函数
optimizer = optim.Adam(net.parameters(), lr=opt.lr) #定义优化器
train(net, optimizer, criterion, train_dataloader, opt)
tf.flags.DEFINE_float('momentum', '0.99', 'Momentum')
tf.flags.DEFINE_string('model', 'siamese_net', 'siamese model to run')
tf.flags.DEFINE_string('data_src', './data_repository/questions.csv',
'source of training dataset')
flags = tf.app.flags
FLAGS = flags.FLAGS
if __name__ == "__main__":
# Setup Dataset
dataset = PreProcessing(FLAGS.data_src)
model = SiameseNetwork(sequence_length=dataset.X.shape[1],
vocab_size=len(dataset.vocab_processor.vocabulary_),
embedding_size=FLAGS.embedding_dim,
filter_sizes=list(
map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
output_embedding_size=FLAGS.output_embedding_size,
dropout_keep_prob=FLAGS.dropout_keep_prob,
embeddings_lookup=dataset.embeddings_lookup,
l2_reg_lambda=FLAGS.l2_reg_lambda)
placeholder_shape = [None] + [dataset.X.shape[1]]
print("placeholder_shape", placeholder_shape)
# Setup Network
next_batch = dataset.get_siamese_batch
left_input = tf.placeholder(tf.int32, placeholder_shape, name='left_input')
right_input = tf.placeholder(tf.int32,
placeholder_shape,
name='right_input')
margin = 2.5
def train(args):
# basic arguments.
ngpu = args.ngpu
margin = args.margin
num_epochs = args.num_epochs
train_batch_size = args.train_batch_size
test_batch_size = args.test_batch_size
gamma = args.gamma # for learning rate decay
root_dir = args.root_dir
image_txt = args.image_txt
train_test_split_txt = args.train_test_split_txt
label_txt = args.label_txt
ckpt_dir = args.ckpt_dir
eval_step = args.eval_step
pretrained = args.pretrained
aux_logits = args.aux_logits
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
kargs = {'ngpu': ngpu, 'pretrained': pretrained, 'aux_logits':aux_logits}
# network and loss
siamese_network = SiameseNetwork(**kargs)
gpu_number = torch.cuda.device_count()
if device.type == 'cuda' and gpu_number > 1:
siamese_network = nn.DataParallel(siamese_network, list(range(torch.cuda.device_count())))
siamese_network.to(device)
contrastive_loss = ContrastiveLoss(margin=margin)
# params = siamese_network.parameters()
# optimizer = optim.Adam(params, lr=0.0005)
# optimizer = optim.SGD(params, lr=0.01, momentum=0.9)
# using different lr
optimizer = optim.SGD([
{'params': siamese_network.module.inception_v3.parameters() if gpu_number > 1 else siamese_network.inception_v3.parameters()},
{'params': siamese_network.module.main.parameters() if gpu_number > 1 else siamese_network.main.parameters(), 'lr': 1e-2}
], lr=0.00001, momentum=0.9)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma, last_epoch=-1)
transform = transforms.Compose([transforms.Resize((299, 299)),
transforms.CenterCrop(299),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
)
cub_dataset = CubDataset(root_dir, image_txt, train_test_split_txt, label_txt, transform=transform, is_train=True, offset=1)
dataloader = DataLoader(dataset=cub_dataset, batch_size=train_batch_size, shuffle=True, num_workers=4)
cub_dataset_eval = CubDataset(root_dir, image_txt, train_test_split_txt, label_txt, transform=transform, is_train=False, offset=1)
dataloader_eval = DataLoader(dataset=cub_dataset_eval, batch_size=test_batch_size, shuffle=False, num_workers=4)
for epoch in range(num_epochs):
if epoch == 0:
feature_set, label_set = get_feature_and_label(siamese_network, dataloader_eval, device)
evaluation(feature_set, label_set)
siamese_network.train()
for i, data in enumerate(dataloader, 0):
img_1, img_2, sim_label = data['img_1'].to(device), data['img_2'].to(device), data['sim_label'].type(torch.FloatTensor).to(device)
optimizer.zero_grad()
output_1, output_2 = siamese_network(img_1, img_2)
loss = contrastive_loss(output_1, output_2, sim_label)
loss.backward()
optimizer.step()
if i % 20 == 0 and i > 0:
print("{}, Epoch [{:3d}/{:3d}], Iter [{:3d}/{:3d}], Current loss: {}".format(
datetime.datetime.now(), epoch, num_epochs, i, len(dataloader), loss.item()))
if epoch % eval_step == 0:
print("Start evalution")
feature_set, label_set = get_feature_and_label(siamese_network, dataloader_eval, device)
evaluation(feature_set, label_set)
torch.save(siamese_network.module.state_dict(), os.path.join(ckpt_dir, 'model_' + str(epoch) +'_.pth'))
from torch import optim
import torch.nn.functional as F
from config import Config
from utils import imshow,show_plot
from data_reader import SiameseNetworkDataset
from model import SiameseNetwork
from loss import ContrastiveLoss
folder_dataset_test = dset.ImageFolder(root=Config.testing_dir)
siamese_dataset = SiameseNetworkDataset(imageFolderDataset=folder_dataset_test,
transform=transforms.Compose([transforms.Resize((100,100)),
transforms.ToTensor()
])
,should_invert=False)
test_dataloader = DataLoader(siamese_dataset,num_workers=6,batch_size=1,shuffle=True)
dataiter = iter(test_dataloader)
x0,_,_ = next(dataiter)
model = SiameseNetwork().cuda()
model.load_state_dict(torch.load("./model.pth"))
model.eval()
for i in range(10):
_,x1,label2 = next(dataiter)
concatenated = torch.cat((x0,x1),0)
output1,output2 = model(Variable(x0).cuda(),Variable(x1).cuda())
euclidean_distance = F.pairwise_distance(output1, output2)
imshow(torchvision.utils.make_grid(concatenated),"output/"+str(i),'Dissimilarity: {:.2f}'.format(euclidean_distance.item()))
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=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 = []
'''
Training Starts
'''
print('Training started')
# for epoch in range(100):
# 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')
#
# torch.save(net.state_dict(), 'siamese.pt')
# ****************************** Training ends ***************************************
'''
Testing starts
'''
net.load_state_dict(torch.load('siamese.pt'))
test_dataset = SiameseTestDataset(train_dataset_dir, \
transform=transforms.Compose([transforms.Resize((100, 100)), transforms.ToTensor()]))
test_vis_dataloader = DataLoader(test_dataset,
shuffle=False,
num_workers=0,
batch_size=1)
train_dataset_dir = tdatasets.ImageFolder('images/all')
train_dataset = FacesDataset(train_dataset_dir, \
transform=transforms.Compose([transforms.Resize((100, 100)), transforms.ToTensor()]))
_, test = split_train_val(train_dataset)
test_dataloader = DataLoader(test,
shuffle=False,
num_workers=0,
batch_size=1)
correct = 0
total = 0
for i, data in enumerate(test_dataloader, 0):
total += 1
img_1, labels = data
min_dist = float("inf")
pred = -1
print('Testing begins', i)
for j, data_test_vis in enumerate(test_vis_dataloader, 0):
img_0 = data_test_vis
out_0, out_1 = net(img_0, img_1)
dist = F.pairwise_distance(out_0, out_1)
if min_dist > dist:
min_dist = dist
pred = j
if pred == labels.item():
correct += 1
print('Testing ends', i, pred)
print('Accuracy: ', str(correct / total))
from model import SiameseNetwork
from utils import SiameseNetworkDataset, imshow
opt = config.args()
folder_dataset_test = torchvision.datasets.ImageFolder(root=opt.testing_dir)
# 定义图像dataset
transform_test = transforms.Compose([transforms.Resize((100, 100)),
transforms.ToTensor()])
siamese_dataset_test = SiameseNetworkDataset(imageFolderDataset=folder_dataset_test,
transform=transform_test,
should_invert=False)
# 定义图像dataloader
test_dataloader = DataLoader(siamese_dataset_test,
shuffle=True,
batch_size=1)
net = SiameseNetwork().cuda()
net.load_state_dict(torch.load('checkpoint/siameseNet49.pth'))
# 生成对比图像
dataiter = iter(test_dataloader)
x0, _, _ = next(dataiter)
for i in range(10):
_, x1, label2 = next(dataiter)
concatenated = torch.cat((x0, x1), 0)
output1, output2 = net(x0.cuda(), x1.cuda())
euclidean_distance = F.pairwise_distance(output1, output2)
imshow(torchvision.utils.make_grid(concatenated), 'Dissimilarity: {:.2f}'.format(euclidean_distance.item()))
config = args.parse_args()
num_classes = config.num_classes
base_lr = config.lr
cuda = config.cuda
num_epochs = config.num_epochs
print_iter = config.print_iter
model_name = config.model_name
prediction_file = config.prediction_file
best_prediction_file = config.best_prediction_file #DBY
batch = config.batch
mode = config.mode
# create model
model = SiameseNetwork()
#model = SiameseEfficientNet()
model = Vgg19()
if mode == 'test':
load_model(model_name, model)
if cuda:
model = model.cuda()
# Define 'best loss' - DBY
best_loss = 0.1
last_loss = 0
if mode == 'train':
# define loss function
# loss_fn = nn.CrossEntropyLoss()
from flask import Flask, request, Response, jsonify
from http import HTTPStatus
from model import SiameseNetwork
import json
import torch
from utils import cosine_similarity
import numpy as np
import pandas as pd
device = "cpu"
app = Flask(__name__)
model = SiameseNetwork().to(device)
model.load_state_dict(
torch.load("./model2.pt", map_location=torch.device(device)))
model.eval()
A = 1
@app.route("/a")
def hello():
return "<h1 style='color:blue'>Hello There!</h1>"
@app.route('/b', methods=['POST'])
def map_recomend():
request_data = json.loads(request.data)["user"]
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.manual_seed(0)
random.seed(0)
np.random.seed(0)
base_lr = config.lr
cuda = config.cuda
num_epochs = config.num_epochs
print_iter = config.print_iter
model_name = config.model_name
prediction_file = config.prediction_file
batch = config.batch
# create model
model = SiameseNetwork()
if cuda:
model = model.cuda()
# define loss function
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
"""
def __init__(self, margin=2.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
def train(args):
# basic arguments.
ngpu = args.ngpu
margin = args.margin
manual_seed = args.manual_seed
torch.manual_seed(manual_seed)
mean_value = args.mean_value
std_value = args.std_value
print("margin = {:5.2f}".format(margin))
print("manual_seed = {:5.2f}".format(manual_seed))
print("mean_value = {:5.2f}".format(mean_value))
print("std_value = {:5.2f}".format(std_value))
num_epochs = args.num_epochs
train_batch_size = args.train_batch_size
test_batch_size = args.test_batch_size
gamma = args.gamma # for learning rate decay
learning_rate = args.learning_rate
learning_rate2 = args.learning_rate2
loss_type = args.loss_type
dataset_name = args.dataset_name
pair_type = args.pair_type
mode = args.mode
weight_file = args.weight_file
print("pair_type = {}".format(pair_type))
print("loss_type = {}".format(loss_type))
print("mode = {}".format(mode))
print("weight_file = {}".format(weight_file))
root_dir = args.root_dir
image_txt = args.image_txt
train_test_split_txt = args.train_test_split_txt
label_txt = args.label_txt
ckpt_dir = args.ckpt_dir
eval_step = args.eval_step
display_step = args.display_step
embedding_size = args.embedding_size
pretrained = args.pretrained
aux_logits = args.aux_logits
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
kargs = {'ngpu': ngpu, 'pretrained': pretrained, 'aux_logits':aux_logits, 'embedding_size': embedding_size}
# create directory
model_dir = os.path.join(ckpt_dir, dataset_name, loss_type, str(int(embedding_size)))
print("model_dir = {}".format(model_dir))
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
# network and loss
siamese_network = SiameseNetwork(**kargs)
first_group, second_group = siamese_network.separate_parameter_group()
param_lr_dict = [
{'params': first_group, 'lr': learning_rate2},
{'params': second_group, 'lr': learning_rate}
]
gpu_number = torch.cuda.device_count()
if device.type == 'cuda' and gpu_number > 1:
siamese_network = nn.DataParallel(siamese_network, list(range(torch.cuda.device_count())))
siamese_network.to(device)
# contrastive_loss = ContrastiveLoss(margin=margin)
# params = siamese_network.parameters()
print("args.optimizer = {:10s}".format(args.optimizer))
print("learning_rate = {:5.5f}".format(learning_rate))
print("learning_rate2 = {:5.5f}".format(learning_rate2))
optimizer = configure_optimizer(param_lr_dict, optimizer=args.optimizer)
# using different lr
# scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma, last_epoch=-1)
transform = transforms.Compose([transforms.Resize((299, 299)),
transforms.CenterCrop(299),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
)
if dataset_name == 'cub200':
"""
print("dataset_name = {:10s}".format(dataset_name))
print(root_dir)
print(image_txt)
print(train_test_split_txt)
print(label_txt)
"""
dataset_train = CubDataset(root_dir, image_txt, train_test_split_txt, label_txt, transform=transform, is_train=True, offset=1)
dataset_eval = CubDataset(root_dir, image_txt, train_test_split_txt, label_txt, transform=transform, is_train=False, offset=1)
elif dataset_name == 'online_product':
"""
print("dataset_name = {:10s}".format(dataset_name))
"""
dataset_train = OnlineProductDataset(root_dir, train_txt=image_txt, test_txt=train_test_split_txt, transform=transform, is_train=True, offset=1)
dataset_eval = OnlineProductDataset(root_dir, train_txt=image_txt, test_txt=train_test_split_txt, transform=transform, is_train=False, offset=1)
elif dataset_name == "car196":
print("dataset_name = {}".format(dataset_name))
dataset_train = CarDataset(root_dir, image_info_mat=image_txt, transform=transform, is_train=True, offset=1)
dataset_eval = CarDataset(root_dir, image_info_mat=image_txt, transform=transform, is_train=False, offset=1)
dataloader = DataLoader(dataset=dataset_train, batch_size=train_batch_size, shuffle=False, num_workers=4)
dataloader_eval = DataLoader(dataset=dataset_eval, batch_size=test_batch_size, shuffle=False, num_workers=4)
log_for_loss = []
if mode == 'evaluation':
print("Do one time evluation and exit")
print("Load pretrained model")
siamese_network.module.load_state_dict(torch.load(weight_file))
print("Finish loading")
print("Calculting features")
feature_set, label_set, path_set = get_feature_and_label(siamese_network, dataloader_eval, device)
rec_pre = evaluation(feature_set, label_set)
# np.save("car196_rec_pre_ftl.npy", rec_pre)
# for visualization
sum_dict = {'feature': feature_set, 'label': label_set, 'path': path_set}
np.save('car196_fea_label_path.npy', sum_dict)
sys.exit()
print("Finish eval")
for epoch in range(num_epochs):
if epoch == 0:
feature_set, label_set, _ = get_feature_and_label(siamese_network, dataloader_eval, device)
# distance_type: Euclidean or cosine
rec_pre = evaluation(feature_set, label_set, distance_type='cosine')
siamese_network.train()
for i, data in enumerate(dataloader, 0):
# img_1, img_2, sim_label = data['img_1'].to(device), data['img_2'].to(device), data['sim_label'].type(torch.FloatTensor).to(device)
img_1, img_2, label_1, label_2 = data['img_1'].to(device), data['img_2'].to(device), data['label_1'].to(device), data['label_2'].to(device)
optimizer.zero_grad()
output_1, output_2 = siamese_network(img_1, img_2)
pair_dist, pair_sim_label = calculate_distance_and_similariy_label(output_1, output_2, label_1, label_2, sqrt=True, pair_type=pair_type)
if loss_type == "contrastive_loss":
loss, positive_loss, negative_loss = contrastive_loss(pair_dist, pair_sim_label, margin)
elif loss_type == "focal_contrastive_loss":
loss, positive_loss, negative_loss = focal_contrastive_loss(pair_dist, pair_sim_label, margin, mean_value, std_value)
elif loss_type == "triplet_loss":
loss, positive_loss, negative_loss = triplet_loss(pair_dist, pair_sim_label, margin)
elif loss_type == "focal_triplet_loss":
loss, positive_loss, negative_loss = focal_triplet_loss(pair_dist, pair_sim_label, margin, mean_value, std_value)
elif loss_type == "angular_loss":
center_output = (output_1 + output_2)/2.
pair_dist_2, _ = calculate_distance_and_similariy_label(center_output, output_2, label_1, label_2, sqrt=True, pair_type=pair_type)
# angle margin is 45^o
loss, positive_loss, negative_loss = angular_loss(pair_dist, pair_dist_2, pair_sim_label, 45)
else:
print("Unknown loss function")
sys.exit()
# try my own customized loss function
# loss = contrastive_loss(output_1, output_2, pair_sim_label)
loss.backward()
optimizer.step()
log_for_loss.append(loss.detach().item())
if i % display_step == 0 and i > 0:
print("{}, Epoch [{:3d}/{:3d}], Iter [{:3d}/{:3d}], Loss: {:6.5f}, Positive loss: {:6.5f}, Negative loss: {:6.5f}".format(
datetime.datetime.now(), epoch, num_epochs, i, len(dataloader), loss.item(), positive_loss.item(), negative_loss.item()))
if epoch % eval_step == 0:
print("Start evalution")
# np.save(loss_type +'.npy', log_for_loss)
feature_set, label_set, _ = get_feature_and_label(siamese_network, dataloader_eval, device)
# distance_type: Euclidean or cosine
rec_pre = evaluation(feature_set, label_set, distance_type='cosine')
torch.save(siamese_network.module.state_dict(), os.path.join(model_dir, 'model_' + str(epoch) +'_.pth'))
folder_dataset = dset.ImageFolder(root=Config.training_dir)
siamese_dataset = SiameseNetworkDataset(
imageFolderDataset=folder_dataset,
transform=transforms.Compose(
[
transforms.Resize((100,100)),
transforms.ToTensor()
]
),
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()
from torch.utils.data import DataLoader
from evaluation import get_feature_and_label, evaluation
os.environ['CUDA_VISIBLE_DEVICES'] = '1,3'
ngpu = 2
margin = 1.
num_epochs = 1000
train_batch_size = 64
test_batch_size = 32
gamma = 0.98 # for learning rate decay
pretrained = False
aux_logits = False
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
kargs = {'ngpu': ngpu, 'pretrained': pretrained, 'aux_logits': aux_logits}
siamese_network = SiameseNetwork(**kargs)
if device.type == 'cuda' and torch.cuda.device_count() > 1:
siamese_network = nn.DataParallel(siamese_network,
list(range(torch.cuda.device_count())))
siamese_network.to(device)
contrastive_loss = ContrastiveLoss(margin=margin)
# params = siamese_network.parameters()
# optimizer = optim.Adam(params, lr=0.0005)
# optimizer = optim.SGD(params, lr=0.01, momentum=0.9)
# using different lr
optimizer = optim.SGD(
[{
'params': siamese_network.module.inception_v3.parameters()
images = [dataset_path + each_sub_directory + '/' + x for x in images]
fake.append(images[:30])
genuine.append(images[30:])
genuine_training_data, genuine_validation_data, genuine_test_data = genuine[:80], genuine[80:90], genuine[90:]
fake_training_data, fake_validation_data, fake_test_data = fake[:80], fake[80:90], fake[90:]
del genuine, fake
image_height, image_width = configuration.configure_parameter["image_height"],configuration.configure_parameter["image_width"]
batch_size = configuration.configure_parameter["batch_size"]
num_train_samples = 57600
num_val_samples = num_test_samples = 11520
input_shape= image_height, image_width,1
get_network = SiameseNetwork(input_shape)
input_1 = Input(shape=input_shape)
input_2 = Input(shape=input_shape)
feature_a = get_network(input_1)
feature_b = get_network(input_2)
feature_distance=Lambda(euclidean_distance)([feature_a, feature_b])
model = Model([input_1, input_2], feature_distance)
adam=Adam(0.001, decay=2.5e-4)
model.compile(loss=contrastive_loss, optimizer=adam, metrics=[accuracy])
callbacks = [
EarlyStopping(patience=12, verbose=1),
ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.000001, verbose=1),
ModelCheckpoint('/home/rafiqul/SiameseNetwork/siamese-{epoch:05d}.h5', verbose=1, save_weights_only=True)
]
siamese_dataset = SiameseNetworkDataset(
imageFolderDataset=images_folder_dataset,
transform=transforms.Compose([transforms.ToTensor()]),
should_invert=False)
""" Train Dataset """
train_dataloader = DataLoader(siamese_dataset,
shuffle=True,
num_workers=0,
batch_size=config.train_batch_size)
if os.path.exists(args.savefile):
print("Loading Existing Model")
net = torch.load(args.savefile)
else:
print("Creating New Model")
net = SiameseNetwork().cuda()
criterion = ContrastiveLoss()
optimizer = optim.Adam(net.parameters(), lr=args.learnrate)
counter = []
loss_history = []
iteration_number = 0
total_step = len(train_dataloader)
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()
