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)
