auto_pruners_torch.py 18.6 KB
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
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'''
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Example for supported automatic pruning algorithms.
In this example, we present the usage of automatic pruners (NetAdapt, AutoCompressPruner). L1, L2, FPGM pruners are also executed for comparison purpose.
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'''

import argparse
import os
import json
import torch
from torch.optim.lr_scheduler import StepLR, MultiStepLR
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from torchvision import datasets, transforms
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from models.mnist.lenet import LeNet
from models.cifar10.vgg import VGG
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from models.cifar10.resnet import ResNet18, ResNet50
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from nni.algorithms.compression.pytorch.pruning import L1FilterPruner, L2FilterPruner, FPGMPruner
from nni.algorithms.compression.pytorch.pruning import SimulatedAnnealingPruner, ADMMPruner, NetAdaptPruner, AutoCompressPruner
from nni.compression.pytorch import ModelSpeedup
from nni.compression.pytorch.utils.counter import count_flops_params
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def get_data(dataset, data_dir, batch_size, test_batch_size):
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    '''
    get data
    '''
    kwargs = {'num_workers': 1, 'pin_memory': True} if torch.cuda.is_available() else {
    }

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    if dataset == 'mnist':
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        train_loader = torch.utils.data.DataLoader(
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            datasets.MNIST(data_dir, train=True, download=True,
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                           transform=transforms.Compose([
                               transforms.ToTensor(),
                               transforms.Normalize((0.1307,), (0.3081,))
                           ])),
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            batch_size=batch_size, shuffle=True, **kwargs)
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        val_loader = torch.utils.data.DataLoader(
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            datasets.MNIST(data_dir, train=False,
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                           transform=transforms.Compose([
                               transforms.ToTensor(),
                               transforms.Normalize((0.1307,), (0.3081,))
                           ])),
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            batch_size=test_batch_size, shuffle=True, **kwargs)
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        criterion = torch.nn.NLLLoss()
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    elif dataset == 'cifar10':
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        normalize = transforms.Normalize(
            (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
        train_loader = torch.utils.data.DataLoader(
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            datasets.CIFAR10(data_dir, train=True, transform=transforms.Compose([
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                transforms.RandomHorizontalFlip(),
                transforms.RandomCrop(32, 4),
                transforms.ToTensor(),
                normalize,
            ]), download=True),
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            batch_size=batch_size, shuffle=True, **kwargs)
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        val_loader = torch.utils.data.DataLoader(
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            datasets.CIFAR10(data_dir, train=False, transform=transforms.Compose([
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                transforms.ToTensor(),
                normalize,
            ])),
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            batch_size=batch_size, shuffle=False, **kwargs)
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        criterion = torch.nn.CrossEntropyLoss()
    return train_loader, val_loader, criterion


def train(args, model, device, train_loader, criterion, optimizer, epoch, callback=None):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        # callback should be inserted between loss.backward() and optimizer.step()
        if callback:
            callback()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


def test(model, device, criterion, val_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in val_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            # sum up batch loss
            test_loss += criterion(output, target).item()
            # get the index of the max log-probability
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(val_loader.dataset)
    accuracy = correct / len(val_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
        test_loss, correct, len(val_loader.dataset), 100. * accuracy))

    return accuracy


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def get_trained_model_optimizer(args, device, train_loader, val_loader, criterion):
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    if args.model == 'LeNet':
        model = LeNet().to(device)
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        if args.load_pretrained_model:
            model.load_state_dict(torch.load(args.pretrained_model_dir))
            optimizer = torch.optim.Adadelta(model.parameters(), lr=1e-4)
        else:
            optimizer = torch.optim.Adadelta(model.parameters(), lr=1)
            scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
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    elif args.model == 'vgg16':
        model = VGG(depth=16).to(device)
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        if args.load_pretrained_model:
            model.load_state_dict(torch.load(args.pretrained_model_dir))
            optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-4)
        else:
            optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
            scheduler = MultiStepLR(
                optimizer, milestones=[int(args.pretrain_epochs*0.5), int(args.pretrain_epochs*0.75)], gamma=0.1)
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    elif args.model == 'resnet18':
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        model = ResNet18().to(device)
        if args.load_pretrained_model:
            model.load_state_dict(torch.load(args.pretrained_model_dir))
            optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-4)
        else:
            optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
            scheduler = MultiStepLR(
                optimizer, milestones=[int(args.pretrain_epochs*0.5), int(args.pretrain_epochs*0.75)], gamma=0.1)
    elif args.model == 'resnet50':
        model = ResNet50().to(device)
        if args.load_pretrained_model:
            model.load_state_dict(torch.load(args.pretrained_model_dir))
            optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-4)
        else:
            optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
            scheduler = MultiStepLR(
                optimizer, milestones=[int(args.pretrain_epochs*0.5), int(args.pretrain_epochs*0.75)], gamma=0.1)
    else:
        raise ValueError("model not recognized")

    if not args.load_pretrained_model:
        best_acc = 0
        best_epoch = 0
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        for epoch in range(args.pretrain_epochs):
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            train(args, model, device, train_loader, criterion, optimizer, epoch)
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            scheduler.step()
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            acc = test(model, device, criterion, val_loader)
            if acc > best_acc:
                best_acc = acc
                best_epoch = epoch
                state_dict = model.state_dict()
        model.load_state_dict(state_dict)
        print('Best acc:', best_acc)
        print('Best epoch:', best_epoch)

        if args.save_model:
            torch.save(state_dict, os.path.join(args.experiment_data_dir, 'model_trained.pth'))
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            print('Model trained saved to %s' % args.experiment_data_dir)
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    return model, optimizer


def get_dummy_input(args, device):
    if args.dataset == 'mnist':
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        dummy_input = torch.randn([args.test_batch_size, 1, 28, 28]).to(device)
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    elif args.dataset in ['cifar10', 'imagenet']:
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        dummy_input = torch.randn([args.test_batch_size, 3, 32, 32]).to(device)
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    return dummy_input


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def get_input_size(dataset):
    if dataset == 'mnist':
        input_size = (1, 1, 28, 28)
    elif dataset == 'cifar10':
        input_size = (1, 3, 32, 32)
    elif dataset == 'imagenet':
        input_size = (1, 3, 256, 256)
    return input_size


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def main(args):
    # prepare dataset
    torch.manual_seed(0)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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    train_loader, val_loader, criterion = get_data(args.dataset, args.data_dir, args.batch_size, args.test_batch_size)
    model, optimizer = get_trained_model_optimizer(args, device, train_loader, val_loader, criterion)
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    def short_term_fine_tuner(model, epochs=1):
        for epoch in range(epochs):
            train(args, model, device, train_loader, criterion, optimizer, epoch)

    def trainer(model, optimizer, criterion, epoch, callback):
        return train(args, model, device, train_loader, criterion, optimizer, epoch=epoch, callback=callback)

    def evaluator(model):
        return test(model, device, criterion, val_loader)

    # used to save the performance of the original & pruned & finetuned models
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    result = {'flops': {}, 'params': {}, 'performance':{}}

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    flops, params, _ = count_flops_params(model, get_input_size(args.dataset))
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    result['flops']['original'] = flops
    result['params']['original'] = params
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    evaluation_result = evaluator(model)
    print('Evaluation result (original model): %s' % evaluation_result)
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    result['performance']['original'] = evaluation_result
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    # module types to prune, only "Conv2d" supported for channel pruning
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    if args.base_algo in ['l1', 'l2', 'fpgm']:
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        op_types = ['Conv2d']
    elif args.base_algo == 'level':
        op_types = ['default']

    config_list = [{
        'sparsity': args.sparsity,
        'op_types': op_types
    }]
    dummy_input = get_dummy_input(args, device)
    if args.pruner == 'L1FilterPruner':
        pruner = L1FilterPruner(model, config_list)
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    elif args.pruner == 'L2FilterPruner':
        pruner = L2FilterPruner(model, config_list)
    elif args.pruner == 'FPGMPruner':
        pruner = FPGMPruner(model, config_list)
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    elif args.pruner == 'NetAdaptPruner':
        pruner = NetAdaptPruner(model, config_list, short_term_fine_tuner=short_term_fine_tuner, evaluator=evaluator,
                                base_algo=args.base_algo, experiment_data_dir=args.experiment_data_dir)
    elif args.pruner == 'ADMMPruner':
        # users are free to change the config here
        if args.model == 'LeNet':
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            if args.base_algo in ['l1', 'l2', 'fpgm']:
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                config_list = [{
                    'sparsity': 0.8,
                    'op_types': ['Conv2d'],
                    'op_names': ['conv1']
                }, {
                    'sparsity': 0.92,
                    'op_types': ['Conv2d'],
                    'op_names': ['conv2']
                }]
            elif args.base_algo == 'level':
                config_list = [{
                    'sparsity': 0.8,
                    'op_names': ['conv1']
                }, {
                    'sparsity': 0.92,
                    'op_names': ['conv2']
                }, {
                    'sparsity': 0.991,
                    'op_names': ['fc1']
                }, {
                    'sparsity': 0.93,
                    'op_names': ['fc2']
                }]
        else:
            raise ValueError('Example only implemented for LeNet.')
        pruner = ADMMPruner(model, config_list, trainer=trainer, num_iterations=2, training_epochs=2)
    elif args.pruner == 'SimulatedAnnealingPruner':
        pruner = SimulatedAnnealingPruner(
            model, config_list, evaluator=evaluator, base_algo=args.base_algo,
            cool_down_rate=args.cool_down_rate, experiment_data_dir=args.experiment_data_dir)
    elif args.pruner == 'AutoCompressPruner':
        pruner = AutoCompressPruner(
            model, config_list, trainer=trainer, evaluator=evaluator, dummy_input=dummy_input,
            num_iterations=3, optimize_mode='maximize', base_algo=args.base_algo,
            cool_down_rate=args.cool_down_rate, admm_num_iterations=30, admm_training_epochs=5,
            experiment_data_dir=args.experiment_data_dir)
    else:
        raise ValueError(
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            "Pruner not supported.")
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    # Pruner.compress() returns the masked model
    # but for AutoCompressPruner, Pruner.compress() returns directly the pruned model
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    model = pruner.compress()
    evaluation_result = evaluator(model)
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    print('Evaluation result (masked model): %s' % evaluation_result)
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    result['performance']['pruned'] = evaluation_result
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    if args.save_model:
        pruner.export_model(
            os.path.join(args.experiment_data_dir, 'model_masked.pth'), os.path.join(args.experiment_data_dir, 'mask.pth'))
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        print('Masked model saved to %s' % args.experiment_data_dir)
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    # model speed up
    if args.speed_up:
        if args.pruner != 'AutoCompressPruner':
            if args.model == 'LeNet':
                model = LeNet().to(device)
            elif args.model == 'vgg16':
                model = VGG(depth=16).to(device)
            elif args.model == 'resnet18':
                model = ResNet18().to(device)
            elif args.model == 'resnet50':
                model = ResNet50().to(device)

            model.load_state_dict(torch.load(os.path.join(args.experiment_data_dir, 'model_masked.pth')))
            masks_file = os.path.join(args.experiment_data_dir, 'mask.pth')

            m_speedup = ModelSpeedup(model, dummy_input, masks_file, device)
            m_speedup.speedup_model()
            evaluation_result = evaluator(model)
            print('Evaluation result (speed up model): %s' % evaluation_result)
            result['performance']['speedup'] = evaluation_result

            torch.save(model.state_dict(), os.path.join(args.experiment_data_dir, 'model_speed_up.pth'))
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            print('Speed up model saved to %s' % args.experiment_data_dir)
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        flops, params, _ = count_flops_params(model, get_input_size(args.dataset))
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        result['flops']['speedup'] = flops
        result['params']['speedup'] = params

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    if args.fine_tune:
        if args.dataset == 'mnist':
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            optimizer = torch.optim.Adadelta(model.parameters(), lr=1)
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            scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
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        elif args.dataset == 'cifar10' and args.model == 'vgg16':
            optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
            scheduler = MultiStepLR(
                optimizer, milestones=[int(args.fine_tune_epochs*0.5), int(args.fine_tune_epochs*0.75)], gamma=0.1)
        elif args.dataset == 'cifar10' and args.model == 'resnet18':
            optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
            scheduler = MultiStepLR(
                optimizer, milestones=[int(args.fine_tune_epochs*0.5), int(args.fine_tune_epochs*0.75)], gamma=0.1)
        elif args.dataset == 'cifar10' and args.model == 'resnet50':
            optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
            scheduler = MultiStepLR(
                optimizer, milestones=[int(args.fine_tune_epochs*0.5), int(args.fine_tune_epochs*0.75)], gamma=0.1)
        best_acc = 0
        for epoch in range(args.fine_tune_epochs):
            train(args, model, device, train_loader, criterion, optimizer, epoch)
            scheduler.step()
            acc = evaluator(model)
            if acc > best_acc:
                best_acc = acc
                torch.save(model.state_dict(), os.path.join(args.experiment_data_dir, 'model_fine_tuned.pth'))
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    print('Evaluation result (fine tuned): %s' % best_acc)
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    print('Fined tuned model saved to %s' % args.experiment_data_dir)
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    result['performance']['finetuned'] = best_acc
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    with open(os.path.join(args.experiment_data_dir, 'result.json'), 'w+') as f:
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        json.dump(result, f)


if __name__ == '__main__':
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    def str2bool(s):
        if isinstance(s, bool):
            return s
        if s.lower() in ('yes', 'true', 't', 'y', '1'):
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            return True
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        if s.lower() in ('no', 'false', 'f', 'n', '0'):
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            return False
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        raise argparse.ArgumentTypeError('Boolean value expected.')
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    parser = argparse.ArgumentParser(description='PyTorch Example for SimulatedAnnealingPruner')

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    # dataset and model
    parser.add_argument('--dataset', type=str, default='cifar10',
                        help='dataset to use, mnist, cifar10 or imagenet')
    parser.add_argument('--data-dir', type=str, default='./data/',
                        help='dataset directory')
    parser.add_argument('--model', type=str, default='vgg16',
                        help='model to use, LeNet, vgg16, resnet18 or resnet50')
    parser.add_argument('--load-pretrained-model', type=str2bool, default=False,
                        help='whether to load pretrained model')
    parser.add_argument('--pretrained-model-dir', type=str, default='./',
                        help='path to pretrained model')
    parser.add_argument('--pretrain-epochs', type=int, default=100,
                        help='number of epochs to pretrain the model')
    parser.add_argument('--batch-size', type=int, default=64,
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=64,
                        help='input batch size for testing (default: 64)')
    parser.add_argument('--fine-tune', type=str2bool, default=True,
                        help='whether to fine-tune the pruned model')
    parser.add_argument('--fine-tune-epochs', type=int, default=5,
                        help='epochs to fine tune')
    parser.add_argument('--experiment-data-dir', type=str, default='./experiment_data',
                        help='For saving experiment data')

    # pruner
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    parser.add_argument('--pruner', type=str, default='SimulatedAnnealingPruner',
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                        help='pruner to use')
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    parser.add_argument('--base-algo', type=str, default='l1',
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                        help='base pruning algorithm. level, l1, l2, or fpgm')
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    parser.add_argument('--sparsity', type=float, default=0.1,
                        help='target overall target sparsity')
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    # param for SimulatedAnnealingPruner
    parser.add_argument('--cool-down-rate', type=float, default=0.9,
                        help='cool down rate')
    # param for NetAdaptPruner
    parser.add_argument('--sparsity-per-iteration', type=float, default=0.05,
                        help='sparsity_per_iteration of NetAdaptPruner')

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    # speed-up
    parser.add_argument('--speed-up', type=str2bool, default=False,
                        help='Whether to speed-up the pruned model')
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    # others
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    parser.add_argument('--log-interval', type=int, default=200,
                        help='how many batches to wait before logging training status')
    parser.add_argument('--save-model', type=str2bool, default=True,
                        help='For Saving the current Model')
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    args = parser.parse_args()

    if not os.path.exists(args.experiment_data_dir):
        os.makedirs(args.experiment_data_dir)

    main(args)