def update_model(model):
""" """
# if not resume:
# return model
if dataset.startswith('cifar'):
# apply mask right now
utils.apply_mask(model, excludes=excludes_for_applying_mask)
return model
def update_model(model):
""" """
if not resume:
return model
if apply_mask:
# apply mask right now
utils.apply_mask(model)
return model
def main():
parser = create_cli_parser()
args = parser.parse_args()
set_random_seed(42)
cfg = GumiConfig(**vars(args))
cfg = patch_dataset(cfg)
logging.info("==> Initializing ModelPruner")
model_pruner = ModelPruner(cfg)
model = model_pruner.load_model(update_state_dict_fn=update_state_dict)
model_pruner.validate(model, record_top5=False)
logging.info("==> Replacing Conv2d in model by MaskConv2d ...")
utils.apply_mask(model)
model_pruner.validate(model, record_top5=False)
logging.info("==> Pruning model ...")
get_num_groups = group_utils.create_get_num_groups_fn(
G=cfg.num_groups, MCPG=cfg.mcpg, group_cfg=cfg.group_cfg)
model_pruner.prune(
model,
get_num_groups_fn=get_num_groups,
perm=cfg.perm,
no_weight=cfg.no_weight,
num_iters=cfg.num_sort_iters,
keep_mask=False,
)
model_pruner.validate(model, record_top5=False)
logging.info("==> Fine-tuning model ...")
model_pruner.fine_tune(model, record_top5=False)
model_pruner.validate(model, record_top5=False)
logging.info("==> Exporting the model ...")
model = GroupExporter.export(model)
logging.debug("Total params: {:.2f}M FLOPS: {:.2f}M".format(
model_utils.get_model_num_params(model),
utils.get_model_num_ops(model, args.dataset),
))
logging.info("==> Saving exported model ...")
torch.save(model, os.path.join(cfg.checkpoint, "pruned.pth.tar"))
def main():
""" Main """
logging.info("==> Initializing ModelPruner ...")
model_pruner = ModelPruner(args)
# load model
logging.info("==> Loading model ...")
update_model_fn = create_update_model_fn(
args.arch,
args.dataset,
args.pretrained,
args.resume,
apply_mask=args.apply_mask,
condensenet=args.condensenet,
)
model = model_pruner.load_model(
update_model_fn=update_model_fn,
update_state_dict_fn=create_update_state_dict_fn(
no_mask=not args.resume, condensenet=args.condensenet),
fine_tune=args.fine_tune,
)
# evaluate the performance of the model in the beginning
if not args.skip_validation:
logging.info("==> Validating the loaded model ...")
loss1, acc1 = model_pruner.validate(model)
#################################################
# Pruning #
# #
#################################################
if not args.apply_mask:
# NOTE: we have not applied mask yet
# # major pruning function
logging.info("==> Replacing Conv2d in model by MaskConv2d ...")
# TODO - duplicated with update_model_fn?
# not quite, if not resume the model won't be updated
utils.apply_mask(model)
if not args.skip_validation:
logging.info("==> Validating the masked model ...")
loss2, acc2 = model_pruner.validate(model)
assert torch.allclose(acc1, acc2)
# run pruning (update the content of mask)
logging.info("==> Pruning model ...")
if not args.skip_prune:
get_num_groups = create_get_num_groups_fn(G=args.num_groups,
MCPG=args.mcpg,
group_cfg=args.group_cfg)
logging.debug("Pruning configuration:")
logging.debug("PERM: {}".format(args.perm))
logging.debug("NS: {}".format(args.num_sort_iters))
logging.debug("No weight: {}".format(args.no_weight))
logging.debug("Keep mask: {}".format(args.keep_mask))
logging.debug("")
model_pruner.prune(
model,
get_num_groups_fn=get_num_groups,
perm=args.perm,
no_weight=args.no_weight,
num_iters=args.num_sort_iters,
keep_mask=args.keep_mask,
)
if not args.skip_validation:
logging.info("==> Validating the pruned model ...")
loss3, acc3 = model_pruner.validate(model)
else:
logging.info("Pruning has been skipped, you have the original model.")
#################################################
# Fine-tuning #
# #
#################################################
logging.info("==> Fine-tuning the pruned model ...")
if args.train_from_scratch:
logging.info("==> Training the pruned topology from scratch ...")
# reset weight parameters
# TODO: refactorize
for name, mod in model.named_modules():
if hasattr(
mod,
"weight") and len(mod.weight.shape) >= 2: # re-initialize
torch.nn.init.kaiming_normal_(mod.weight, nonlinearity="relu")
# if hasattr(mod, 'G'):
# mod.weight.data.mul_(mod.G)
if hasattr(mod, "bias") and mod.bias is not None:
mod.bias.data.fill_(0.0)
if not args.skip_fine_tune:
model_pruner.fine_tune(model)
if not args.skip_validation:
logging.info("==> Validating the fine-tuned pruned model ...")
loss4, acc4 = model_pruner.validate(model)
logging.info(
"==> Final validation accuracy of the pruned model: {:.2f}%".
format(acc4))
else:
logging.info("Fine-tuning has been skipped.")
#################################################
# Export #
# #
#################################################
logging.info("==> Exporting the model ...")
model = GroupExporter.export(model)
if use_cuda:
model.cuda()
logging.debug("Total params: {:.2f}M FLOPS: {:.2f}M".format(
model_utils.get_model_num_params(model),
utils.get_model_num_ops(model, args.dataset),
))
if not args.skip_validation:
logging.info("==> Validating the exported pruned model ...")
loss5, acc5 = model_pruner.validate(model)
logging.info(
"==> Final validation accuracy of the exported model: {:.2f}%".
format(acc5))
def update_model(model):
""" """
utils.apply_mask(model, use_cuda=False)
return model
