def __init__(self, args, model, loss, loader, ckpt):
self.args = args
self.train_loader = loader.train_loader
self.train_batch_num = len(loader.trainset) // (args.batchid * args.batchimage)
self.test_loader = loader.test_loader
self.query_loader = loader.query_loader
self.testset = loader.testset
self.queryset = loader.queryset
self.ckpt = ckpt
self.model = model
self.loss = loss
self.lr = 0.
self.optimizer = utility.make_optimizer(args, self.model)
self.scheduler = utility.make_scheduler(args, self.optimizer)
if args.nGPU > 1:
self.mirrored_strategy = tf.distribute.MirroredStrategy()
self.distributed_train_loader = self.mirrored_strategy.experimental_distribute_dataset(self.train_loader)
if args.load != '':
self.optimizer.load_state_dict(
# torch.load(os.path.join(ckpt.dir, 'optimizer.pt'))
keras.models.load_model(os.path.join(ckpt.dir, 'optimizer.h5'))
)
for _ in range(len(ckpt.log)*args.test_every): self.scheduler.step()
def __init__(self, args, model, loss, loader, ckpt):
self.args = args
self.train_loader = loader.train_loader
self.test_loader = loader.test_loader
self.trainset = loader.trainset
self.testset = loader.testset
self.evaluateset = loader.evaluateset
self.evaluate_loader = loader.evaluate_loader
self.class_index = self.evaluateset.activity_index
self.index_class = dict(zip(self.class_index.values(), self.class_index.keys()))
self.ckpt = ckpt
self.model = model
self.loss = loss
self.lr = 0.
self.optimizer = utility.make_optimizer(args, self.model)
self.scheduler = utility.make_scheduler(args, self.optimizer)
self.device = torch.device('cpu' if args.cpu else 'cuda')
if args.load != '':
self.optimizer.load_state_dict(
torch.load(os.path.join(ckpt.dir, 'optimizer.pt'))
)
for _ in range(len(ckpt.log)*args.test_every): self.scheduler.step()
def __init__(self, args, model, loss, loader, loader2, ckpt):
self.args = args
self.train_loader = loader.train_loader
self.test_loader = loader.test_loader
self.query_loader = loader.query_loader
self.testset = loader.testset
self.queryset = loader.queryset
self.train_loader2 = loader2.train_loader
self.test_loader2 = loader2.test_loader
self.query_loader2 = loader2.query_loader
self.testset2 = loader2.testset
self.queryset2 = loader2.queryset
self.losses = []
self.ckpt = ckpt
self.model = model
self.loss = loss
self.lr = 0.
self.optimizer = utility.make_optimizer(args, self.model)
self.scheduler = utility.make_scheduler(args, self.optimizer)
self.device = torch.device('cpu' if args.cpu else 'cuda')
if args.load != '':
self.optimizer.load_state_dict(
torch.load(os.path.join(ckpt.dir, 'optimizer.pt')))
for _ in range(len(ckpt.log) * args.test_every):
self.scheduler.step()
def __init__(self, args, model, loss, loader, ckpt):
self.args = args
self.train_loader = loader.train_loader
self.test_loader = loader.test_loader
self.query_loader = loader.query_loader
self.testset = loader.testset
self.queryset = loader.queryset
self.ckpt = ckpt
self.model = model
self.loss = loss
self.lr = 0.
self.optimizer = utility.make_optimizer(args, self.model)
self.scheduler = utility.make_scheduler(args, self.optimizer)
self.apex = args.apex;
if self.apex == "yes":
self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level = "O1")
self.device = torch.device('cpu' if args.cpu else 'cuda')
self.feature_center = torch.zeros(args.num_classes, args.num_attentions * args.num_features).to(self.device)
self.beta = args.L2_beta
if args.load != '':
self.optimizer.load_state_dict(
torch.load(os.path.join(ckpt.dir, 'optimizer.pt'))
)
self.feature_center = torch.load(os.path.join(ckpt.dir, 'feature_center.pt')).to(self.device)
for _ in range(len(ckpt.log)*args.test_every): self.scheduler.step()
def __init__(self, args, model, loss, loader, ckpt):
self.args = args
# if args.data_train == 'GTA':
# transform_train_list = [
# # transforms.RandomResizedCrop(size=128, scale=(0.75,1.0), ratio=(0.75,1.3333), interpolation=3), #Image.BICUBIC)
# transforms.Resize((384, 128), interpolation=3),
# transforms.Pad(10),
# transforms.RandomCrop((384, 128)),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# ]
# # train_dataset = datasets.ImageFolder(os.path.join(args.datadir, 'pytorch', 'train_all'),
# # transforms.Compose(transform_train_list))
# train_dataset = datasets.ImageFolder(os.path.join(args.datadir, 'train'),
# transforms.Compose(transform_train_list))
# self.train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchid * args.batchimage, sampler=a_RandomIdentitySampler(
# train_dataset, args.batchid * args.batchimage, args.batchimage), num_workers=8, pin_memory=True) # 8 workers may work faster
# print('GTA has {} classes'.format(train_dataset.classes))
# else:
self.train_loader = loader.train_loader
self.test_loader = loader.test_loader
self.query_loader = loader.query_loader
self.testset = loader.galleryset
self.queryset = loader.queryset
self.ckpt = ckpt
self.model = model
self.loss = loss
self.lr = 0.
self.optimizer = utility.make_optimizer(args, self.model)
self.device = torch.device('cpu' if args.cpu else 'cuda')
last_epoch = -1
if torch.cuda.is_available():
self.ckpt.write_log(torch.cuda.get_device_name(0))
if args.load != '':
self.optimizer.load_state_dict(
torch.load(os.path.join(ckpt.dir, 'optimizer.pt'))
)
last_epoch = int(ckpt.log[-1, 0]) - 1
# for _ in range(last_epoch):
# self.scheduler.step()
if args.pre_train != '' and args.resume:
resume_epoch = args.pre_train.split(
'/')[-1].split('.')[0].split('_')[-1]
self.optimizer.load_state_dict(
torch.load(args.pre_train.replace('model', 'optimizer'))
)
# for _ in range(len(ckpt.log) * args.test_every):
# self.scheduler.step()
last_epoch = resume_epoch - 1
self.scheduler = utility.make_scheduler(
args, self.optimizer, last_epoch)
self.ckpt.write_log(
'Continue from epoch {}'.format(self.scheduler.last_epoch))
print(ckpt.log)
print(self.scheduler._last_lr)
