def pruning_process(model):
print("------------------------- Before pruning --------------------------------")
criterion = nn.CrossEntropyLoss().cuda()
accuracy = util.validate(args, test_loader, model, criterion)
print("------------------------- pruning --------------------------------------")
if args.model_mode == 'c' or args.model_mode =='a':
model.prune_by_percentile( ['conv1'], q=100-58.0)
model.prune_by_percentile( ['conv2'], q=100-22.0)
model.prune_by_percentile( ['conv3'], q=100-34.0)
model.prune_by_percentile( ['conv4'], q=100-36.0)
model.prune_by_percentile( ['conv5'], q=100-53.0)
model.prune_by_percentile( ['conv6'], q=100-24.0)
model.prune_by_percentile( ['conv7'], q=100-42.0)
model.prune_by_percentile( ['conv8'], q=100-32.0)
model.prune_by_percentile( ['conv9'], q=100-27.0)
model.prune_by_percentile( ['conv10'], q=100-34.0)
model.prune_by_percentile( ['conv11'], q=100-35.0)
model.prune_by_percentile( ['conv12'], q=100-29.0)
model.prune_by_percentile( ['conv13'], q=100-36.0)
if args.model_mode == 'd' or args.model_mode == 'a':
model.prune_by_percentile( ['fc1'], q=100-10.0)
model.prune_by_percentile( ['fc2'], q=100-10.0)
model.prune_by_percentile( ['fc3'], q=100-10.0)
print("------------------------- After pruning --------------------------------")
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
accuracy = util.validate(args, test_loader, model, criterion)
torch.save(model, f"{args.save_dir}/model_pruned.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"weight\t{args.save_dir}/{args.out_pruned_folder}")
util.log(f"{args.save_dir}/{args.log}", f"model\t{args.save_dir}/model_pruned.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"accuracy after pruning\t{accuracy}")
util.layer2torch(model, f"{args.save_dir}/{args.out_pruned_folder}")
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_pruned_folder}", weight_list)
# Retrain
print("------------------------- Prune and Retrain ----------------------------")
tok="prune_re"
util.initial_train(model, args, train_loader, test_loader, tok, use_cuda=True)
print("------------------------- After Retraining -----------------------------")
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
accuracy = util.validate(args, test_loader, model, criterion)
torch.save(model, f"{args.save_dir}/model_prune_retrain_{args.reepochs}.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"weight\t{args.save_dir}/{args.out_pruned_re_folder}")
util.log(f"{args.save_dir}/{args.log}", f"model\t{args.save_dir}/mmodel_prune_retrain_{args.reepochs}.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"accuracy after prune retrain\t{accuracy}")
util.layer2torch(model, f"{args.save_dir}/{args.out_pruned_re_folder}")
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_pruned_re_folder}", weight_list)
return model
def initial_process(model):
print(model)
util.print_model_parameters(model)
print(
"------------------------- Initial training -------------------------------"
)
tok = "initial"
criterion = nn.CrossEntropyLoss().cuda()
util.initial_train(model,
args,
train_loader,
test_loader,
tok,
use_cuda=True)
accuracy = util.validate(args, test_loader, model, criterion)
torch.save(model, f"{args.save_dir}/model_initial_end.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"weight:{args.save_dir}/{args.out_oldweight_folder}")
util.log(f"{args.save_dir}/{args.log}",
f"model:{args.save_dir}/model_initial_end.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"initial_accuracy {accuracy}")
util.layer2torch(model, f"{args.save_dir}/{args.out_oldweight_folder}")
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_oldweight_folder}",
weight_list)
return model
def pruning_process(model):
print(
"------------------------- Before pruning --------------------------------"
)
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
accuracy = util.validate(val_loader, model, args)
print(
"------------------------- pruning CNN--------------------------------------"
)
model.prune_by_percentile(['conv1'], q=100 - 66.0)
model.prune_by_percentile(['conv2'], q=100 - 12.0)
print(
"------------------------------- After prune CNN ----------------------------"
)
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
prec1 = util.validate(val_loader, model, args)
torch.save(model, f"{args.save_dir}/model_pruned.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"weight\t{args.save_dir}/{args.out_pruned_folder}")
util.log(f"{args.save_dir}/{args.log}",
f"model\t{args.save_dir}/model_pruned.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"prune acc\t{prec1}")
util.layer2torch(f"{args.save_dir}/{args.out_pruned_folder}", model)
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_pruned_folder}",
weight_list)
print(
"------------------------- start retrain after prune CNN----------------------------"
)
util.initial_train(model, args, train_loader, val_loader, 'prune_re')
print(
"------------------------- After Retraining -----------------------------"
)
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
accuracy = util.validate(val_loader, model, args)
torch.save(model,
f"{args.save_dir}/model_prune_retrain_{args.reepochs}.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"weight\t{args.save_dir}/{args.out_pruned_re_folder}")
util.log(
f"{args.save_dir}/{args.log}",
f"model\t{args.save_dir}/model_prune_retrain_{args.reepochs}.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"prune and retrain acc\t{accuracy}")
util.layer2torch(f"{args.save_dir}/{args.out_pruned_re_folder}", model)
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_pruned_re_folder}",
weight_list)
return model
def pruning_process(model):
print("------------------------- Before pruning --------------------------------")
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
accuracy = util.validate(val_loader, model, args)
print("------------------------- pruning CNN--------------------------------------")
model.prune_by_percentile( ['conv1'], q=100-58.0)
model.prune_by_percentile( ['conv2'], q=100-22.0)
model.prune_by_percentile( ['conv3'], q=100-34.0)
model.prune_by_percentile( ['conv4'], q=100-36.0)
model.prune_by_percentile( ['conv5'], q=100-53.0)
model.prune_by_percentile( ['conv6'], q=100-24.0)
model.prune_by_percentile( ['conv7'], q=100-42.0)
model.prune_by_percentile( ['conv8'], q=100-32.0)
model.prune_by_percentile( ['conv9'], q=100-27.0)
model.prune_by_percentile( ['conv10'], q=100-34.0)
model.prune_by_percentile( ['conv11'], q=100-35.0)
model.prune_by_percentile( ['conv12'], q=100-29.0)
model.prune_by_percentile( ['conv13'], q=100-36.0)
print("------------------------------- After prune CNN ----------------------------")
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
prec1 = util.validate(val_loader, model, args)
util.save_checkpoint({
'state_dict': model.state_dict(),
'best_prec1': prec1,
}, True, filename=os.path.join(args.save_dir, 'checkpoint_{}_alpha_{}.tar'.format('pruned',args.alpha)))
util.log(f"{args.save_dir}/{args.log}", f"weight\t{args.save_dir}/{args.out_pruned_folder}")
util.log(f"{args.save_dir}/{args.log}", f"model\t{args.save_dir}/model_pruned.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"prune acc\t{prec1}")
util.layer2torch(f"{args.save_dir}/{args.out_pruned_folder}" , model)
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_pruned_folder}", weight_list)
print("------------------------- start retrain after prune CNN----------------------------")
util.initial_train(model, args, train_loader, val_loader, 'prune_re')
print("------------------------- After Retraining -----------------------------")
util.print_nonzeros(model, f"{args.save_dir}/{args.log}")
accuracy = util.validate(val_loader, model, args)
util.log(f"{args.save_dir}/{args.log}", f"weight\t{args.save_dir}/{args.out_pruned_re_folder}")
util.log(f"{args.save_dir}/{args.log}", f"model\t{args.save_dir}/model_prune_retrain_{args.reepochs}.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"prune and retrain acc\t{accuracy}")
util.layer2torch(f"{args.save_dir}/{args.out_pruned_re_folder}" , model)
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_pruned_re_folder}", weight_list)
return model
def initial_process(model):
print(model)
util.print_model_parameters(model)
print("------------------------- Initial training -------------------------------")
util.initial_train(model, args, train_loader, val_loader, 'initial')
accuracy = util.validate(val_loader, model, args)
util.log(f"{args.save_dir}/{args.log}", f"weight\t{args.save_dir}/{args.out_oldweight_folder}")
util.log(f"{args.save_dir}/{args.log}", f"model\t{args.save_dir}/model_initial_end.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"initial_accuracy\t{accuracy}")
util.layer2torch(f"{args.save_dir}/{args.out_oldweight_folder}",model)
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_oldweight_folder}", weight_list)
return model