def quantize_process(model):
#util.print_model_parameters(model)
print(
'---------------------- Before weight sharing ---------------------------'
)
criterion = nn.CrossEntropyLoss().cuda()
acc = util.validate(args, test_loader, model, criterion)
util.log(f"{args.save_dir}/{args.log}",
f"accuracy before weight sharing\t{acc}")
# Weight sharing
old_weight_list, new_weight_list, quantized_index_list, quantized_center_list = apply_weight_sharing(
model, args.model_mode, args.bits)
print(
'----------------------- After weight sharing ---------------------------'
)
acc = util.validate(args, test_loader, model, criterion)
torch.save(model, f"{args.save_dir}/model_quantized.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"weight\t{args.save_dir}/{args.out_quantized_folder}")
util.log(f"{args.save_dir}/{args.log}",
f"model\t{args.save_dir}/model_quantized.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"accuracy after weight sharing {args.bits}bits\t{acc}")
util.layer2torch(model, f"{args.save_dir}/{args.out_quantized_folder}")
util.save_parameters(f"{args.save_dir}/{args.out_quantized_folder}",
new_weight_list)
print(
'----------------------- quantize retrain -------------------------------'
)
util.quantized_retrain(model, args, quantized_index_list,
quantized_center_list, train_loader, use_cuda)
#acc = util.test(model, test_loader, use_cuda=True)
acc = util.validate(args, test_loader, model, criterion)
torch.save(
model,
f"{args.save_dir}/model_quantized_retrain{args.reepochs}.ptmodel")
util.layer2torch(model, f"{args.save_dir}/{args.out_quantized_re_folder}")
util.log(f"{args.save_dir}/{args.log}",
f"weight\t{args.save_dir}/{args.out_quantized_re_folder}")
util.log(
f"{args.save_dir}/{args.log}",
f"model\t{args.save_dir}/model_quantized_bit{args.bits}_retrain{args.reepochs}.ptmodel"
)
util.log(f"{args.save_dir}/{args.log}",
f"accuracy retrain after weight sharing\t{acc}")
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_quantized_re_folder}",
weight_list)
return model
def quantize_process(model):
print('------------------------------- accuracy before weight sharing ----------------------------------')
acc = util.validate(val_loader, model, args)
util.log(f"{args.save_dir}/{args.log}", f"accuracy before weight sharing\t{acc}")
print('------------------------------- accuacy after weight sharing -------------------------------')
tempfc1=torch.index_select(model.fc1.weight, 0, model.invrow1.cuda())
model.fc1.weight=torch.nn.Parameter(torch.index_select(tempfc1, 1, model.invcol1.cuda()))
tempfc2=torch.index_select(model.fc2.weight, 0, model.invrow2.cuda())
model.fc2.weight=torch.nn.Parameter(torch.index_select(tempfc2, 1, model.invcol2.cuda()))
tempfc3=torch.index_select(model.fc3.weight, 0, model.invrow3.cuda())
model.fc3.weight=torch.nn.Parameter(torch.index_select(tempfc3, 1, model.invcol3.cuda()))
old_weight_list, new_weight_list, quantized_index_list, quantized_center_list = apply_weight_sharing(model, args.model_mode, args.bits)
temp1=torch.index_select(model.fc1.weight, 0, model.rowp1.cuda())
model.fc1.weight=torch.nn.Parameter(torch.index_select(temp1, 1, model.colp1.cuda()))
temp2=torch.index_select(model.fc2.weight, 0, model.rowp2.cuda())
model.fc2.weight=torch.nn.Parameter(torch.index_select(temp2, 1, model.colp2.cuda()))
temp3=torch.index_select(model.fc3.weight, 0, model.rowp3.cuda())
model.fc3.weight=torch.nn.Parameter(torch.index_select(temp3, 1, model.colp3.cuda()))
acc = util.validate(val_loader, model, args)
util.save_checkpoint({
'state_dict': model.state_dict(),
'best_prec1': acc,
}, True, filename=os.path.join(args.save_dir, 'checkpoint_{}_alpha_{}.tar'.format('quantized',args.alpha)))
util.log(f"{args.save_dir}/{args.log}", f"weight\t{args.save_dir}/{args.out_quantized_folder}")
util.log(f"{args.save_dir}/{args.log}", f"model\t{args.save_dir}/model_quantized.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"accuracy after weight sharing {args.bits}bits\t{acc}")
util.layer2torch(f"{args.save_dir}/{args.out_quantized_folder}" , model)
util.save_parameters(f"{args.save_dir}/{args.out_quantized_folder}", new_weight_list)
print('------------------------------- retraining -------------------------------------------')
util.quantized_retrain(model, args, quantized_index_list, quantized_center_list, train_loader, val_loader)
acc = util.validate(val_loader, model, args)
util.save_checkpoint({
'state_dict': model.state_dict(),
'best_prec1': acc,
}, True, filename=os.path.join(args.save_dir, 'checkpoint_{}_alpha_{}.tar'.format('quantized_re',args.alpha)))
util.layer2torch(f"{args.save_dir}/{args.out_quantized_re_folder}" , model)
util.log(f"{args.save_dir}/{args.log}", f"weight:{args.save_dir}/{args.out_quantized_re_folder}")
util.log(f"{args.save_dir}/{args.log}", f"model:{args.save_dir}/model_quantized_bit{args.bits}_retrain{args.reepochs}.ptmodel")
util.log(f"{args.save_dir}/{args.log}", f"acc after qauntize and retrain\t{acc}")
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_quantized_re_folder}", weight_list)
return model
def quantize_process(model):
print(
'------------------------------- accuracy before weight sharing ----------------------------------'
)
acc = util.validate(val_loader, model, args)
util.log(f"{args.save_dir}/{args.log}",
f"accuracy before weight sharing\t{acc}")
print(
'------------------------------- accuacy after weight sharing -------------------------------'
)
old_weight_list, new_weight_list, quantized_index_list, quantized_center_list = apply_weight_sharing(
model, args.model_mode, args.bits)
acc = util.validate(val_loader, model, args)
util.log(f"{args.save_dir}/{args.log}",
f"weight\t{args.save_dir}/{args.out_quantized_folder}")
util.log(f"{args.save_dir}/{args.log}",
f"model\t{args.save_dir}/model_quantized.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"accuracy after weight sharing {args.bits}bits\t{acc}")
util.layer2torch(f"{args.save_dir}/{args.out_quantized_folder}", model)
util.save_parameters(f"{args.save_dir}/{args.out_quantized_folder}",
new_weight_list)
print(
'------------------------------- retraining -------------------------------------------'
)
util.quantized_retrain(model, args, quantized_index_list,
quantized_center_list, train_loader, val_loader)
acc = util.validate(val_loader, model, args)
util.layer2torch(f"{args.save_dir}/{args.out_quantized_re_folder}", model)
util.log(f"{args.save_dir}/{args.log}",
f"weight:{args.save_dir}/{args.out_quantized_re_folder}")
util.log(
f"{args.save_dir}/{args.log}",
f"model:{args.save_dir}/model_quantized_retrain{args.reepochs}.ptmodel"
)
util.log(f"{args.save_dir}/{args.log}",
f"acc after qauntize and retrain\t{acc}")
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_quantized_re_folder}",
weight_list)
return model
def quantize_process():
util.topic_log("Accuracy before weight sharing")
top1_acc, top5_acc = util.val_epoch(val_loader, model, args, topk=(1, 5))
util.log(args.log_file_path,
f"accuracy before weight sharing\t{top1_acc} ({top5_acc})")
util.topic_log("Accuracy after weight sharing")
layer_mame_to_quan_indices = apply_weight_sharing(model, args)
top1_acc, top5_acc = util.val_epoch(val_loader, model, args, topk=(1, 5))
util.save_masked_checkpoint(model, "quantized", top1_acc, "initial", args)
util.log(
args.log_file_path,
f"accuracy after weight sharing {args.bits}bits\t{top1_acc} ({top5_acc})"
)
util.topic_log("Quantize retraining")
util.quantized_retrain(model, args, layer_mame_to_quan_indices,
train_loader, val_loader)
top1_acc, top5_acc = util.val_epoch(val_loader, model, args, topk=(1, 5))
util.save_masked_checkpoint(model, "quantized", top1_acc, "end", args)
util.log(args.log_file_path,
f"accuracy after qauntize and retrain\t{top1_acc} ({top5_acc})")
def quantize_process(model):
print(
'------------------------------- accuracy before weight sharing ----------------------------------'
)
acc = util.validate(val_loader, model, args)
util.log(f"{args.save_dir}/{args.log}",
f"accuracy before weight sharing\t{acc}")
print(
'------------------------------- accuacy after weight sharing -------------------------------'
)
tempfc1 = torch.index_select(model.fc1.weight, 0, model.invrow1.cuda())
model.fc1.weight = torch.nn.Parameter(
torch.index_select(tempfc1, 1, model.invcol1.cuda()))
tempfc2 = torch.index_select(model.fc2.weight, 0, model.invrow2.cuda())
model.fc2.weight = torch.nn.Parameter(
torch.index_select(tempfc2, 1, model.invcol2.cuda()))
old_weight_list, new_weight_list, quantized_index_list, quantized_center_list = apply_weight_sharing(
model, args.model_mode, args.bits)
temp1 = torch.index_select(model.fc1.weight, 0, model.rowp1.cuda())
model.fc1.weight = torch.nn.Parameter(
torch.index_select(temp1, 1, model.colp1.cuda()))
temp2 = torch.index_select(model.fc2.weight, 0, model.rowp2.cuda())
model.fc2.weight = torch.nn.Parameter(
torch.index_select(temp2, 1, model.colp2.cuda()))
acc = util.validate(val_loader, model, args)
torch.save(model, f"{args.save_dir}/model_quantized.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"weight\t{args.save_dir}/{args.out_quantized_folder}")
util.log(f"{args.save_dir}/{args.log}",
f"model\t{args.save_dir}/model_quantized.ptmodel")
util.log(f"{args.save_dir}/{args.log}",
f"accuracy after weight sharing {args.bits}bits\t{acc}")
util.layer2torch(f"{args.save_dir}/{args.out_quantized_folder}", model)
util.save_parameters(f"{args.save_dir}/{args.out_quantized_folder}",
new_weight_list)
print(
'------------------------------- retraining -------------------------------------------'
)
util.quantized_retrain(model, args, quantized_index_list,
quantized_center_list, train_loader, val_loader)
acc = util.validate(val_loader, model, args)
torch.save(
model,
f"{args.save_dir}/model_quantized_retrain{args.reepochs}.ptmodel")
util.layer2torch(f"{args.save_dir}/{args.out_quantized_re_folder}", model)
util.log(f"{args.save_dir}/{args.log}",
f"weight:{args.save_dir}/{args.out_quantized_re_folder}")
util.log(
f"{args.save_dir}/{args.log}",
f"model:{args.save_dir}/model_quantized_retrain{args.reepochs}.ptmodel"
)
util.log(f"{args.save_dir}/{args.log}",
f"acc after qauntize and retrain\t{acc}")
weight_list = util.parameters2list(model.children())
util.save_parameters(f"{args.save_dir}/{args.out_quantized_re_folder}",
weight_list)
return model
