def train_bin(config, compression_ctrl, model, criterion, is_inception, optimizer_scheduler, model_name, optimizer,
train_loader, train_sampler, val_loader, kd_loss_calculator, batch_multiplier, best_acc1=0):
for epoch in range(config.start_epoch, config.epochs):
config.cur_epoch = epoch
if config.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train_epoch_bin(train_loader, batch_multiplier, model, criterion, optimizer, optimizer_scheduler,
kd_loss_calculator, compression_ctrl, epoch, config, is_inception)
# compute compression algo statistics
stats = compression_ctrl.statistics()
acc1 = best_acc1
if epoch % config.test_every_n_epochs == 0:
# evaluate on validation set
acc1, _ = validate(val_loader, model, criterion, config)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
# update compression scheduler state at the end of the epoch
compression_ctrl.scheduler.epoch_step()
optimizer_scheduler.epoch_step()
if is_main_process():
print_statistics(stats)
checkpoint_path = osp.join(config.checkpoint_save_dir, get_name(config) + '_last.pth')
checkpoint = {
'epoch': epoch + 1,
'arch': model_name,
'state_dict': model.state_dict(),
'original_model_state_dict': kd_loss_calculator.original_model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
'compression_scheduler': compression_ctrl.scheduler.state_dict(),
'optimizer_scheduler': optimizer_scheduler.state_dict()
}
torch.save(checkpoint, checkpoint_path)
make_additional_checkpoints(checkpoint_path, is_best, epoch + 1, config)
for key, value in stats.items():
if isinstance(value, (int, float)):
config.tb.add_scalar("compression/statistics/{0}".format(key), value, len(train_loader) * epoch)
def main_worker_binarization(current_gpu, config):
config.current_gpu = current_gpu
config.distributed = config.execution_mode in (ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED)
if config.distributed:
configure_distributed(config)
config.device = get_device(config)
if is_main_process():
configure_logging(logger, config)
print_args(config)
if config.seed is not None:
manual_seed(config.seed)
cudnn.deterministic = True
cudnn.benchmark = False
# create model
model_name = config['model']
weights = config.get('weights')
model = load_model(model_name,
pretrained=config.get('pretrained', True) if weights is None else False,
num_classes=config.get('num_classes', 1000),
model_params=config.get('model_params'))
original_model = copy.deepcopy(model)
compression_ctrl, model = create_compressed_model(model, config)
if not isinstance(compression_ctrl, BinarizationController):
raise RuntimeError("The binarization sample worker may only be run with the binarization algorithm!")
if weights:
load_state(model, torch.load(weights, map_location='cpu'))
model, _ = prepare_model_for_execution(model, config)
original_model.to(config.device)
if config.distributed:
compression_ctrl.distributed()
is_inception = 'inception' in model_name
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss()
criterion = criterion.to(config.device)
params_to_optimize = model.parameters()
compression_config = config['compression']
binarization_config = compression_config if isinstance(compression_config, dict) else compression_config[0]
optimizer = get_binarization_optimizer(params_to_optimize, binarization_config)
optimizer_scheduler = BinarizationOptimizerScheduler(optimizer, binarization_config)
kd_loss_calculator = KDLossCalculator(original_model)
resuming_checkpoint = config.resuming_checkpoint
best_acc1 = 0
# optionally resume from a checkpoint
if resuming_checkpoint is not None:
model, config, optimizer, optimizer_scheduler, kd_loss_calculator, compression_ctrl, best_acc1 = \
resume_from_checkpoint(resuming_checkpoint, model,
config, optimizer, optimizer_scheduler, kd_loss_calculator, compression_ctrl)
if config.to_onnx is not None:
compression_ctrl.export_model(config.to_onnx)
logger.info("Saved to {}".format(config.to_onnx))
return
if config.execution_mode != ExecutionMode.CPU_ONLY:
cudnn.benchmark = True
# Data loading code
train_dataset, val_dataset = create_datasets(config)
train_loader, train_sampler, val_loader = create_data_loaders(config, train_dataset, val_dataset)
if config.mode.lower() == 'test':
print_statistics(compression_ctrl.statistics())
validate(val_loader, model, criterion, config)
if config.mode.lower() == 'train':
if not resuming_checkpoint:
compression_ctrl.initialize(data_loader=train_loader, criterion=criterion)
batch_multiplier = (binarization_config.get("params", {})).get("batch_multiplier", 1)
train_bin(config, compression_ctrl, model, criterion, is_inception, optimizer_scheduler, model_name, optimizer,
train_loader, train_sampler, val_loader, kd_loss_calculator, batch_multiplier, best_acc1)
def staged_quantization_main_worker(current_gpu, config):
config.current_gpu = current_gpu
config.distributed = config.execution_mode in (
ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED)
if config.distributed:
configure_distributed(config)
config.device = get_device(config)
if is_main_process():
configure_logging(logger, config)
print_args(config)
if config.seed is not None:
manual_seed(config.seed)
cudnn.deterministic = True
cudnn.benchmark = False
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss()
criterion = criterion.to(config.device)
train_loader = train_sampler = val_loader = None
resuming_checkpoint_path = config.resuming_checkpoint_path
nncf_config = config.nncf_config
pretrained = is_pretrained_model_requested(config)
if config.to_onnx is not None:
assert pretrained or (resuming_checkpoint_path is not None)
else:
# Data loading code
train_dataset, val_dataset = create_datasets(config)
train_loader, train_sampler, val_loader = create_data_loaders(
config, train_dataset, val_dataset)
nncf_config = register_default_init_args(nncf_config, criterion,
train_loader)
# create model
model_name = config['model']
model = load_model(model_name,
pretrained=pretrained,
num_classes=config.get('num_classes', 1000),
model_params=config.get('model_params'),
weights_path=config.get('weights'))
original_model = copy.deepcopy(model)
model.to(config.device)
resuming_model_sd = None
resuming_checkpoint = None
if resuming_checkpoint_path is not None:
resuming_checkpoint = load_resuming_checkpoint(
resuming_checkpoint_path)
resuming_model_sd = resuming_checkpoint['state_dict']
compression_ctrl, model = create_compressed_model(model, nncf_config,
resuming_model_sd)
if not isinstance(compression_ctrl,
(BinarizationController, QuantizationController)):
raise RuntimeError(
"The stage quantization sample worker may only be run with the binarization and quantization algorithms!"
)
model, _ = prepare_model_for_execution(model, config)
original_model.to(config.device)
if config.distributed:
compression_ctrl.distributed()
is_inception = 'inception' in model_name
params_to_optimize = model.parameters()
compression_config = config['compression']
quantization_config = compression_config if isinstance(
compression_config, dict) else compression_config[0]
optimizer = get_quantization_optimizer(params_to_optimize,
quantization_config)
optimizer_scheduler = PolyLRDropScheduler(optimizer, quantization_config)
kd_loss_calculator = KDLossCalculator(original_model)
best_acc1 = 0
# optionally resume from a checkpoint
if resuming_checkpoint is not None and config.to_onnx is None:
config.start_epoch = resuming_checkpoint['epoch']
best_acc1 = resuming_checkpoint['best_acc1']
kd_loss_calculator.original_model.load_state_dict(
resuming_checkpoint['original_model_state_dict'])
compression_ctrl.scheduler.load_state_dict(
resuming_checkpoint['compression_scheduler'])
optimizer.load_state_dict(resuming_checkpoint['optimizer'])
optimizer_scheduler.load_state_dict(
resuming_checkpoint['optimizer_scheduler'])
if config.mode.lower() == 'train':
logger.info(
"=> loaded checkpoint '{}' (epoch: {}, best_acc1: {:.3f})".
format(resuming_checkpoint_path, resuming_checkpoint['epoch'],
best_acc1))
else:
logger.info(
"=> loaded checkpoint '{}'".format(resuming_checkpoint_path))
if config.to_onnx:
compression_ctrl.export_model(config.to_onnx)
logger.info("Saved to {}".format(config.to_onnx))
return
if config.execution_mode != ExecutionMode.CPU_ONLY:
cudnn.benchmark = True
if config.mode.lower() == 'test':
print_statistics(compression_ctrl.statistics())
validate(val_loader, model, criterion, config)
if config.mode.lower() == 'train':
batch_multiplier = (quantization_config.get("params", {})).get(
"batch_multiplier", 1)
train_staged(config, compression_ctrl, model, criterion, is_inception,
optimizer_scheduler, model_name, optimizer, train_loader,
train_sampler, val_loader, kd_loss_calculator,
batch_multiplier, best_acc1)
def train_staged(config,
compression_ctrl,
model,
criterion,
is_inception,
optimizer_scheduler,
model_name,
optimizer,
train_loader,
train_sampler,
val_loader,
kd_loss_calculator,
batch_multiplier,
best_acc1=0):
best_compression_level = CompressionLevel.NONE
for epoch in range(config.start_epoch, config.epochs):
config.cur_epoch = epoch
if config.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train_epoch_staged(train_loader, batch_multiplier, model, criterion,
optimizer, optimizer_scheduler, kd_loss_calculator,
compression_ctrl, epoch, config, is_inception)
# compute compression algo statistics
stats = compression_ctrl.statistics()
acc1 = best_acc1
if epoch % config.test_every_n_epochs == 0:
# evaluate on validation set
acc1, _ = validate(val_loader, model, criterion, config)
compression_level = compression_ctrl.compression_level()
# remember best acc@1, considering compression level. If current acc@1 less then the best acc@1, checkpoint
# still can be best if current compression level is bigger then best one. Compression levels in ascending
# order: NONE, PARTIAL, FULL.
is_best_by_accuracy = acc1 > best_acc1 and compression_level == best_compression_level
is_best = is_best_by_accuracy or compression_level > best_compression_level
best_acc1 = max(acc1, best_acc1)
best_compression_level = max(compression_level, best_compression_level)
# statistics (e.g. portion of the enabled quantizers) is related to the finished epoch,
# hence printing should happen before epoch_step, which may inform about state of the next epoch (e.g. next
# portion of enabled quantizers)
if is_main_process():
print_statistics(stats)
# update compression scheduler state at the end of the epoch
compression_ctrl.scheduler.epoch_step()
optimizer_scheduler.epoch_step()
if is_main_process():
checkpoint_path = osp.join(config.checkpoint_save_dir,
get_name(config) + '_last.pth')
checkpoint = {
'epoch':
epoch + 1,
'arch':
model_name,
'state_dict':
model.state_dict(),
'original_model_state_dict':
kd_loss_calculator.original_model.state_dict(),
'best_acc1':
best_acc1,
'compression_level':
compression_level,
'optimizer':
optimizer.state_dict(),
'compression_scheduler':
compression_ctrl.scheduler.state_dict(),
'optimizer_scheduler':
optimizer_scheduler.state_dict()
}
torch.save(checkpoint, checkpoint_path)
make_additional_checkpoints(checkpoint_path, is_best, epoch + 1,
config)
for key, value in stats.items():
if isinstance(value, (int, float)):
config.tb.add_scalar(
"compression/statistics/{0}".format(key), value,
len(train_loader) * epoch)
def autoq_eval_fn(model, eval_loader):
_, top5 = validate(eval_loader, model, criterion, config)
return top5
