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)
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()
# if cuda:
# loss_fn = loss_fn.cuda()
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):