num_workers=0,
collate_fn=trainset_valid.collate_fn)
net = RetinaNet()
net = torch.nn.DataParallel(net, device_ids=[0])
net.cuda()
id_net = Idnet()
id_net = torch.nn.DataParallel(id_net, device_ids=[0])
id_net.cuda()
#MCP = arcface_loss2.Arcface(1024, 3000).cuda()
criterion = torch.nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(
[{
'params': id_net.parameters()
}], #, {'params':MCP.parameters()}],
lr=1e-3,
momentum=0.9,
weight_decay=1e-4)
net.load_state_dict(torch.load("./trained model/originalFAN_model.pth"))
net.eval()
coder = DataEncoder()
def save_model(model, filename):
state = model.state_dict()
for key in state:
state[key] = state[key].clone().cpu()
torch.save(state, filename)
collate_fn=testset.collate_fn)
#net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
#net = torch.nn.DataParallel(net, device_ids=[0])
net = RetinaNet()
net = torch.nn.DataParallel(net, device_ids=[0])
net.cuda()
id_net = Idnet()
id_net = torch.nn.DataParallel(id_net, device_ids=[0])
id_net.cuda()
MCP = MarginCosineProduct(1024, 3000).cuda()
criterion = torch.nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD([{'params': id_net.parameters()}, {'params':MCP.parameters()}],
lr=1e-3,
momentum=0.9,
weight_decay=1e-4)
net.load_state_dict(torch.load("./trained model/originalFAN_model.pth"))
net.eval()
coder = DataEncoder()
def save_model(model, filename):
state = model.state_dict()
for key in state: state[key] = state[key].clone().cpu()
torch.save(state, filename)
def train(epoch, file_obj, acc):
num_workers=1,
collate_fn=testset.collate_fn)
#net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
#net = torch.nn.DataParallel(net, device_ids=[0])
net = RetinaNet()
net = torch.nn.DataParallel(net, device_ids=[0])
net.cuda()
id_net = Idnet()
id_net = torch.nn.DataParallel(id_net, device_ids=[0])
id_net.cuda()
criterion = torch.nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(id_net.parameters(),
lr=1e-3,
momentum=0.9,
weight_decay=1e-4)
net.load_state_dict(torch.load("./trained model/originalFAN_model.pth"))
net.eval()
coder = DataEncoder()
def save_model(model, filename):
state = model.state_dict()
for key in state:
state[key] = state[key].clone().cpu()
torch.save(state, filename)