def main():
global args
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
fn = os.path.join(args.datadir, 'polyvore_outfits',
'polyvore_item_metadata.json')
meta_data = json.load(open(fn, 'r'))
text_feature_dim = 6000
kwargs = {'num_workers': 8, 'pin_memory': True} if args.cuda else {}
test_loader = torch.utils.data.DataLoader(TripletImageLoader(
args,
'test',
meta_data,
transform=transforms.Compose([
transforms.Scale(112),
transforms.CenterCrop(112),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
**kwargs)
model = Resnet_18.resnet18(pretrained=True, embedding_size=args.dim_embed)
csn_model = TypeSpecificNet(args, model,
len(test_loader.dataset.typespaces))
criterion = torch.nn.MarginRankingLoss(margin=args.margin)
tnet = Tripletnet(args, csn_model, text_feature_dim, criterion)
if args.cuda:
tnet.cuda()
train_loader = torch.utils.data.DataLoader(TripletImageLoader(
args,
'train',
meta_data,
text_dim=text_feature_dim,
transform=transforms.Compose([
transforms.Scale(112),
transforms.CenterCrop(112),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(TripletImageLoader(
args,
'valid',
meta_data,
transform=transforms.Compose([
transforms.Scale(112),
transforms.CenterCrop(112),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
**kwargs)
best_acc = 0
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, encoding='latin1')
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
if args.test:
test_acc = test(test_loader, tnet)
sys.exit()
parameters = filter(lambda p: p.requires_grad, tnet.parameters())
optimizer = optim.Adam(parameters, lr=args.lr)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
for epoch in range(args.start_epoch, args.epochs + 1):
# update learning rate
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, tnet, criterion, optimizer, epoch)
# evaluate on validation set
acc = test(val_loader, tnet)
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
checkpoint = torch.load('runs/%s/' % (args.name) + 'model_best.pth.tar')
tnet.load_state_dict(checkpoint['state_dict'])
test_acc = test(test_loader, tnet)
def main():
global args, best_acc, writer
args = parser.parse_args()
writer = SummaryWriter(comment='_' + args.name + '_triplet_network')
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# global plotter
# plotter = VisdomLinePlotter(env_name=args.name)
kwargs = {
'num_workers': 1 if args.name == 'stl10' else 4,
'pin_memory': True
} if args.cuda else {} # change num_workers from 1 to 4
train_triplet_loader, test_triplet_loader, train_loader, test_loader = \
get_TripletDataset(args.name, args.batch_size, **kwargs)
cmd = "model=%s()" % args.net
local_dict = locals()
exec(cmd, globals(), local_dict)
model = local_dict['model']
print(args.use_fc)
if not args.use_fc:
tnet = Tripletnet(model)
else:
tnet = Tripletnet(Classifier(model))
if args.cuda:
tnet.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
criterion = torch.nn.MarginRankingLoss(margin=args.margin)
optimizer = optim.SGD(tnet.parameters(),
lr=args.lr,
momentum=args.momentum)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
time_string = time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))
log_directory = "runs/%s/" % (time_string + '_' + args.name)
with Context(os.path.join(log_directory, args.log), parallel=True):
for epoch in range(1, args.epochs + 1):
# train for one epoch
train(train_triplet_loader, tnet, criterion, optimizer, epoch)
# evaluate on validation set
acc = test(test_triplet_loader, tnet, criterion, epoch)
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
checkpoint_file = 'runs/%s/' % (args.name) + 'model_best.pth.tar'
assert os.path.isfile(checkpoint_file), 'Nothing to load...'
checkpoint_cl = torch.load(checkpoint_file)
cmd = "model_cl=%s()" % args.net
exec(cmd, globals(), local_dict)
model_cl = local_dict['model_cl']
if not args.use_fc:
tnet = Tripletnet(model_cl)
else:
tnet = Tripletnet(Classifier(model_cl))
tnet.load_state_dict(checkpoint_cl['state_dict'])
classifier(tnet.embeddingnet
if not args.use_fc else tnet.embeddingnet.embedding,
train_loader,
test_loader,
writer,
logdir=log_directory)
writer.close()
def main():
global args, best_acc
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
#global plotter
#plotter = VisdomLinePlotter(env_name=args.name)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(TripletImageLoader(
'.',
'./filenames_filename.txt',
'./triplets_train_name.txt',
transform=transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
valid_loader = torch.utils.data.DataLoader(TripletImageLoader(
'.',
'./filenames_filename.txt',
'./triplets_valid_name.txt',
transform=transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv1_drop = nn.Dropout2d()
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(16820, 128)
self.fc2 = nn.Linear(128, 20)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 16820)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
return self.fc2(x)
model = Net()
tnet = Tripletnet(model)
if args.cuda:
tnet.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
criterion = torch.nn.MarginRankingLoss(margin=args.margin)
#optimizer = optim.SGD(tnet.parameters(), lr=args.lr, momentum=args.momentum)
def make_optimizer(model, opt, lr, weight_decay, momentum, nesterov=True):
if opt == 'SGD':
optimizer = getattr(torch.optim, opt)(model.parameters(),
lr=lr,
weight_decay=weight_decay,
momentum=momentum,
nesterov=nesterov)
elif opt == 'AMSGRAD':
optimizer = getattr(torch.optim, 'Adam')(model.parameters(),
lr=lr,
weight_decay=weight_decay,
amsgrad=True)
elif opt == 'Ranger':
optimizer = Ranger(params=filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr)
elif opt == 'RMS':
optimizer = torch.optim.RMSprop(model.parameters(),
lr=lr,
alpha=0.99,
eps=1e-08,
weight_decay=weight_decay,
momentum=momentum,
centered=False)
return optimizer
optimizer = make_optimizer(tnet,
opt=args.opt,
lr=args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum,
nesterov=True)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
for epoch in range(1, args.epochs + 1):
# train for one epoch
train(train_loader, tnet, criterion, optimizer, epoch)
# evaluate on validation set
acc = test(valid_loader, tnet, criterion, epoch)
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
def main():
global args, best_acc
args = parser.parse_args()
args.cuda = args.cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.visdom:
global plotter
plotter = VisdomLinePlotter(env_name=args.name)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(MNIST_t(
'./data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(MNIST_t(
'./data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
return self.fc2(x)
model = Net()
tnet = Tripletnet(model)
if args.cuda:
tnet.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
criterion = torch.nn.MarginRankingLoss(margin=args.margin)
optimizer = optim.SGD(tnet.parameters(),
lr=args.lr,
momentum=args.momentum)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
for epoch in range(1, args.epochs + 1):
# train for one epoch
train(train_loader, tnet, criterion, optimizer, epoch)
# evaluate on validation set
acc = test(test_loader, tnet, criterion, epoch)
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
def main():
global args, best_acc
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
global plotter
plotter = VisdomLinePlotter(env_name=args.name)
# Normalize on RGB Value
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if args.arch.startswith('inception'):
size = (299, 299)
else:
size = (224, 256)
kwargs = {'num_workers': 0, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(TripletImageLoader(
'../video_segmentation/multi_mask',
train=True,
transform=transforms.Compose([
transforms.Resize((size[0], size[0])),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(TripletImageLoader(
'../video_segmentation/multi_mask',
train=False,
transform=transforms.Compose([
transforms.Resize((size[0], size[0])),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
print("=> creating model '{}'".format(args.arch))
model = models.setup(args)
tnet = Tripletnet(model, args)
if args.cuda:
tnet.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
criterion = torch.nn.MarginRankingLoss(margin=args.margin)
optimizer = optim.SGD(tnet.parameters(),
lr=args.lr,
momentum=args.momentum)
shaduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, args.lr_decay)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
i = 0
checkpoint_epoch = 13
print('load checkpoint ' + str(checkpoint_epoch))
tnet.load_state_dict(
torch.load('./out/TripletNet/checkpoint.pth.tar')['state_dict'])
best_acc = torch.load('./out/model_best.pth.tar')['best_prec1']
for epoch in range(1, args.epochs + 1):
if (i) % args.decay_epoch == 0:
shaduler.step()
if epoch <= checkpoint_epoch:
continue
# train for one epoch
train(train_loader, tnet, criterion, optimizer, epoch)
# evaluate on validation set
acc = test(test_loader, tnet, criterion, epoch)
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
i += 1
def main():
global args, best_acc
args = parser.parse_args()
data_path = args.data
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
global plotter
plotter = VisdomLinePlotter(env_name=args.name)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
num_classes = 5
num_triplets = num_classes*64
train_data_set = CUB_t(data_path, n_train_triplets=num_triplets, train=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]),
num_classes=num_classes)
train_loader = torch.utils.data.DataLoader(
train_data_set,
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
CUB_t(data_path, n_test_triplets=num_classes*16, train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]),
num_classes=num_classes),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
kNN_loader = CUB_t_kNN(data_path, train=False,
n_test = args.kNN_test_size, n_train = args.kNN_train_size,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
# image length
im_len = 64
# size of first fully connected layer
h1_len = (im_len-4)/2
h2_len = (h1_len-4)/2
fc1_len = h2_len*h2_len*20
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(fc1_len, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, fc1_len)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
return self.fc2(x)
model = Net()
tnet = Tripletnet(model)
if args.cuda:
tnet.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
criterion = torch.nn.MarginRankingLoss(margin = args.margin)
optimizer = optim.SGD(tnet.parameters(), lr=args.lr, momentum=args.momentum)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
sampler = OurSampler(num_classes, num_triplets/args.batch_size)
for epoch in range(1, args.epochs + 1):
# train for one epoch
train(train_loader, tnet, criterion, optimizer, epoch, sampler)
# evaluate on validation set
acc = test(test_loader, tnet, criterion, epoch)
acc_kNN = test_kNN(kNN_loader, tnet, epoch, args.kNN_k)
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
# reset sampler and regenerate triplets every few epochs
if epoch % args.triplet_freq == 0:
# TODO: regenerate triplets
train_data_set.regenerate_triplet_list(num_triplets, sampler, num_triplets*hard_frac)
# then reset sampler
sampler.Reset()
dataset_test = Data_loader(matches_test, descript_test, transform=transform)
dataloader_test = torch.utils.data.DataLoader(dataset_test,
num_workers=workers,
batch_size=batch_size,
shuffle=False)
print('Len dataloader test:' + str(len(dataloader_test)))
# Load model
model = Get_model()
model = model.to(device)
tnet = Tripletnet(model)
# Define criterion and optimizer
criterion1 = torch.nn.MarginRankingLoss(margin=0.2)
criterion2 = torch.nn.MSELoss()
optimizer = optim.SGD(tnet.parameters(), lr=0.001, momentum=0.5)
# accuracy calculations
def accuracy(dista, distb):
dista = dista.cpu().detach().numpy().reshape(dista.shape[0], -1)
distb = distb.cpu().detach().numpy().reshape(dista.shape[0], -1)
y = np.zeros(dista.shape)
y[dista < distb] = 1
return sum(y) / dista.shape[0], y
# Train function
def train(tnet, dataloader, criterion1, criterion2, optimizer, epoch):
tnet.train()
def main():
print('pid:', os.getpid())
global args, best_acc
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if (not args.cuda):
print('no cuda!')
else:
print('we\'ve got cuda!')
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
global plotter
plotter = VisdomLinePlotter(env_name=args.name)
if args.pred:
np.random.seed(args.seed) # Numpy module.
random.seed(args.seed) # Python random module.
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
######################
base_path = args.base_path
embed_size = args.emb_size
######################
kwargs = {'num_workers': 0, 'pin_memory': True} if args.cuda else {}
m = 'train'
#m = 'test'
#trainortest = 'test'
trainortest = 'small_train'
if args.binary_classify:
coll = bin_collate_wrapper
else:
coll = collate_wrapper
if not args.pred:
print('loading training data...')
train_loader = torch.utils.data.DataLoader(TripletEmbedLoader(
args, base_path, m + '_embed_index.csv', trainortest + '.json',
'train', m + '_embeddings.pt'),
batch_size=args.batch_size,
shuffle=True,
collate_fn=coll,
**kwargs)
print('loading testing data...')
if args.pred:
shuff = False
else:
shuff = True
test_loader = torch.utils.data.DataLoader(TripletEmbedLoader(
args, base_path, 'test_embed_index.csv', 'test.json', 'train',
'test_embeddings.pt'),
batch_size=args.batch_size,
shuffle=shuff,
collate_fn=coll,
**kwargs)
class Net(nn.Module):
def __init__(self, embed_size):
super(Net, self).__init__()
if args.binary_classify:
self.nfc1 = nn.Linear(embed_size * 3, 480)
else:
self.nfc1 = nn.Linear(embed_size * 2, 480)
self.nfc2 = nn.Linear(480, 320)
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 1)
if args.binary_classify:
self.out = nn.Sigmoid()
def forward(self, x):
x = F.relu(self.nfc1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = F.relu(self.nfc2(x))
x = F.dropout(x, p=0.75, training=self.training)
x = F.relu(self.fc1(x))
x = F.dropout(x, p=0.75, training=self.training)
#if args.binary_classify:
#x = self.fc2(x)
#return self.out(x)
return self.fc2(x)
if (args.cnn):
model = CNNNet()
else:
model = Net(embed_size)
#model = CNNNet()
if (args.cuda):
model.cuda()
if (args.binary_classify):
tnet = model
else:
tnet = Tripletnet(model, args)
print('net built.')
if args.cuda:
tnet.cuda()
print('tnet.cuda()')
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if not args.pred:
cudnn.benchmark = True
if args.binary_classify:
#criterion = torch.nn.BCELoss()
criterion = torch.nn.BCEWithLogitsLoss()
else:
criterion = torch.nn.MarginRankingLoss(margin=args.margin)
#optimizer = optim.SGD(tnet.parameters(), lr=args.lr, momentum=args.momentum)
optimizer = optim.Adam(tnet.parameters(), lr=args.lr)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
if args.pred:
print('testing...')
acc = test(test_loader, tnet, criterion, 0)
#exit(1)
print('predicting...')
predict(test_loader, tnet)
exit(1)
print('start training!')
for epoch in range(1, args.epochs + 1):
# train for one epoch
#start_time = time.time()
if args.binary_classify:
bin_train(train_loader, tnet, criterion, optimizer, epoch)
acc = bin_test(test_loader, tnet, criterion, epoch)
else:
train(train_loader, tnet, criterion, optimizer, epoch)
acc = test(test_loader, tnet, criterion, epoch)
#print("------- train: %s seconds ---" % (time.time()-start_time))
# evaluate on validation set
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
def main():
global args, best_acc
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# global plotter
# plotter = VisdomLinePlotter(env_name=args.name)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
root_dir = "../caltech-data/"
triplet_data_dir = os.path.join(root_dir, "triplet_data")
train_triplet_path_file = os.path.join(triplet_data_dir,
"triplet_paths_train.txt")
train_triplet_idx_file = os.path.join(triplet_data_dir,
"triplet_index_train.txt")
val_triplet_path_file = os.path.join(triplet_data_dir,
"triplet_paths_val.txt")
val_triplet_idx_file = os.path.join(triplet_data_dir,
"triplet_index_val.txt")
train_loader = torch.utils.data.DataLoader(TripletImageLoader(
filenames_filename=train_triplet_path_file,
triplets_file_name=train_triplet_idx_file,
transform=transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(TripletImageLoader(
filenames_filename=val_triplet_path_file,
triplets_file_name=val_triplet_idx_file,
transform=transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
embedingnet = Vgg_Net()
tnet = Tripletnet(embedingnet)
print(tnet)
if args.cuda:
tnet.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
tnet.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
criterion = torch.nn.MarginRankingLoss(margin=args.margin)
optimizer = optim.SGD(tnet.parameters(),
lr=args.lr,
momentum=args.momentum)
n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
print(' + Number of params: {}'.format(n_parameters))
for epoch in range(1, args.epochs + 1):
# train for one epoch
train(train_loader, tnet, criterion, optimizer, epoch)
# evaluate on validation set
acc = test(test_loader, tnet, criterion, epoch)
# remember best acc and save checkpoint
is_best = acc > best_acc
best_acc = max(acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': tnet.state_dict(),
'best_prec1': best_acc,
}, is_best)
