def automated_deep_compression(model, criterion, optimizer, loggers, args): train_loader, val_loader, test_loader, _ = apputils.load_data( args.dataset, os.path.expanduser(args.data), args.batch_size, args.workers, args.validation_split, args.deterministic, args.effective_train_size, args.effective_valid_size, args.effective_test_size) args.display_confusion = True validate_fn = partial(test, test_loader=test_loader, criterion=criterion, loggers=loggers, args=args, activations_collectors=None) train_fn = partial(train, train_loader=train_loader, criterion=criterion, loggers=loggers, args=args) save_checkpoint_fn = partial(apputils.save_checkpoint, arch=args.arch, dir=msglogger.logdir) optimizer_data = { 'lr': args.lr, 'momentum': args.momentum, 'weight_decay': args.weight_decay } adc.do_adc(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
def automated_deep_compression(model, criterion, optimizer, loggers, args): train_loader, val_loader, test_loader, _ = load_data(args) args.display_confusion = True validate_fn = partial(test, test_loader=test_loader, criterion=criterion, loggers=loggers, args=args, activations_collectors=None) train_fn = partial(train, train_loader=train_loader, criterion=criterion, loggers=loggers, args=args) save_checkpoint_fn = partial(apputils.save_checkpoint, arch=args.arch, dir=msglogger.logdir) optimizer_data = { 'lr': args.lr, 'momentum': args.momentum, 'weight_decay': args.weight_decay } adc.do_adc(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
def automated_deep_compression(model, criterion, optimizer, loggers, args): # 自动化的深层压缩 train_loader, val_loader, test_loader, _ = get_data_loaders( datasets_fn, r'/home/tian/Desktop/image_yasuo', args.batch_size, args.workers, args.validation_split, args.deterministic, args.effective_train_size, args.effective_valid_size, args.effective_test_size) args.display_confusion = True validate_fn = partial(test, test_loader=test_loader, criterion=criterion, loggers=loggers, args=args, activations_collectors=None) train_fn = partial(train, train_loader=train_loader, criterion=criterion, loggers=loggers, args=args) save_checkpoint_fn = partial(apputils.save_checkpoint, arch=args.arch, dir=msglogger.logdir) optimizer_data = { 'lr': args.lr, 'momentum': args.momentum, 'weight_decay': args.weight_decay } adc.do_adc(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)