def main():
# Views the training images and displays the distance on anchor-negative and anchor-positive
test_display_triplet_distance = True
# print the experiment configuration
print('\nparsed options:\n{}\n'.format(vars(args)))
print('\nNumber of Classes:\n{}\n'.format(len(train_dir.classes)))
# instantiate model and initialize weights
model = FaceModel(embedding_size=args.embedding_size,
num_classes=len(train_dir.classes),
pretrained=False)
model.to(device)
triplet_loss = TripletMarginLoss(args.margin)
optimizer = create_optimizer(model, args.lr)
# 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']
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
else:
print('=> no checkpoint found at {}'.format(args.resume))
start = args.start_epoch
end = start + args.epochs
for epoch in range(start, end):
print(80 * '=')
print('Epoch [{}/{}]'.format(epoch, end - 1))
time0 = time.time()
own_train(train_loader, model, triplet_loss, optimizer, epoch,
data_size)
print(f' Execution time = {time.time() - time0}')
print(80 * '=')
if test_display_triplet_distance:
display_triplet_distance(model, train_loader,
LOG_DIR + "/train_{}".format(epoch))
print(80 * '=')
time0 = time.time()
own_test(test_loader, model, epoch)
print(f' Execution time = {time.time() - time0}')
print(80 * '=')
if test_display_triplet_distance:
display_triplet_distance_test(model, test_loader,
LOG_DIR + "/test_{}".format(epoch))
def main():
# Views the training images and displays the distance on anchor-negative and anchor-positive
test_display_triplet_distance = True
# print the experiment configuration
print('\nparsed options:\n{}\n'.format(vars(args)))
print('\nNumber of Classes:\n{}\n'.format(len(train_dir.classes)))
# instantiate model and initialize weights
model = FaceModel(inceptionresnet_v1,
embedding_size=args.embedding_size,
num_classes=len(train_dir.classes),
pretrained=False)
if args.cuda:
model.cuda()
optimizer = create_optimizer(model, args.lr)
# 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']
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
else:
print('=> no checkpoint found at {}'.format(args.resume))
start = args.start_epoch
end = start + args.epochs
para_model = torch.nn.parallel.data_parallel(model)
for epoch in range(start, end):
train(train_loader, para_model, optimizer, epoch)
# test(test_loader, model, epoch)
# do checkpointing
torch.save({
'epoch': epoch + 1,
'state_dict': model.state_dict()
}, '{}/checkpoint_{}.pth'.format(LOG_DIR, epoch))
if test_display_triplet_distance:
display_triplet_distance(model, train_loader,
LOG_DIR + "/train_{}".format(epoch))
def main():
# Views the training images and displays the distance on anchor-negative and anchor-positive
test_display_triplet_distance = False
# print the experiment configuration
print('\nparsed options:\n{}\n'.format(vars(args)))
print('\nNumber of Classes:\n{}\n'.format(len(train_dir.classes)))
# instantiate model and initialize weights
model = DeepSpeakerModel(embedding_size=args.embedding_size,
num_classes=len(train_dir.classes))
if args.cuda:
model.cuda()
optimizer = create_optimizer(model, args.lr)
# 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']
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
else:
print('=> no checkpoint found at {}'.format(args.resume))
start = args.start_epoch
#start = 0
end = start + args.epochs
train_loader = torch.utils.data.DataLoader(train_dir,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
for epoch in range(start, end):
train(train_loader, model, optimizer, epoch)
#test(test_loader, model, epoch)
#break;
if test_display_triplet_distance:
display_triplet_distance(model, train_loader,
LOG_DIR + "/train_{}".format(epoch))
def main():
#test_display_triplet_distance= True
'''
why test_display_triplet_distance= True in center loss.py?????
'''
test_display_triplet_distance = True
# print the experiment configuration
print('\nparsed options:\n{}\n'.format(vars(args)))
print('\nNumber of Classes:\n{}\n'.format(str(6400)))
num_classes = 6400
# 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']
else:
checkpoint = None
print('=> no checkpoint found at {}'.format(args.resume))
#print(checkpoint)
# instantiate model and initialize weights
#model = FaceModelSoftmax(embedding_size=args.embedding_size,num_classes=len(train_dir.classes),checkpoint=checkpoint)
model = FaceModelSoftmax(embedding_size=args.embedding_size,
num_classes=num_classes,
checkpoint=checkpoint)
if args.cuda:
#print("you are using gpu")
model.cuda()
optimizer = create_optimizer(model, args.lr)
start = args.start_epoch
end = start + args.epochs
for epoch in range(start, end):
train(train_loader, model, optimizer, epoch)
test(test_loader, model, epoch)
testaccuracy(testaccuracy_loader, model, epoch)
testRecall(testaccuracy_loader, model, epoch)
if test_display_triplet_distance:
display_triplet_distance_test(model, test_loader,
LOG_DIR + "/test_{}".format(epoch))
display_triplet_distance(model, train_loader,
LOG_DIR + "/train_{}".format(epoch))