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basic_pruners_torch.py
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basic_pruners_torch.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
'''
NNI example for supported basic pruning algorithms.
In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
You can also try auto_pruners_torch.py to see the usage of some automatic pruning algorithms.
'''
import logging
import argparse
import os
import sys
import torch
from torch.optim.lr_scheduler import StepLR, MultiStepLR
from torchvision import datasets, transforms
sys.path.append('../models')
from mnist.lenet import LeNet
from cifar10.vgg import VGG
from nni.compression.pytorch.utils.counter import count_flops_params
import nni
from nni.compression.pytorch import ModelSpeedup
from nni.algorithms.compression.pytorch.pruning import (
LevelPruner,
SlimPruner,
FPGMPruner,
TaylorFOWeightFilterPruner,
L1FilterPruner,
L2FilterPruner,
AGPPruner,
ActivationMeanRankFilterPruner,
ActivationAPoZRankFilterPruner
)
_logger = logging.getLogger('mnist_example')
_logger.setLevel(logging.INFO)
str2pruner = {
'level': LevelPruner,
'l1filter': L1FilterPruner,
'l2filter': L2FilterPruner,
'slim': SlimPruner,
'agp': AGPPruner,
'fpgm': FPGMPruner,
'mean_activation': ActivationMeanRankFilterPruner,
'apoz': ActivationAPoZRankFilterPruner,
'taylorfo': TaylorFOWeightFilterPruner
}
def get_dummy_input(args, device):
if args.dataset == 'mnist':
dummy_input = torch.randn([args.test_batch_size, 1, 28, 28]).to(device)
elif args.dataset in ['cifar10', 'imagenet']:
dummy_input = torch.randn([args.test_batch_size, 3, 32, 32]).to(device)
return dummy_input
def get_data(dataset, data_dir, batch_size, test_batch_size):
kwargs = {'num_workers': 1, 'pin_memory': True} if torch.cuda.is_available() else {
}
if dataset == 'mnist':
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(data_dir, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(data_dir, train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=test_batch_size, shuffle=True, **kwargs)
criterion = torch.nn.NLLLoss()
elif dataset == 'cifar10':
normalize = transforms.Normalize(
(0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(data_dir, train=True, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize,
]), download=True),
batch_size=batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(data_dir, train=False, transform=transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=batch_size, shuffle=False, **kwargs)
criterion = torch.nn.CrossEntropyLoss()
return train_loader, test_loader, criterion
def get_model_optimizer_scheduler(args, device, train_loader, test_loader, criterion):
if args.model == 'lenet':
model = LeNet().to(device)
if args.pretrained_model_dir is None:
optimizer = torch.optim.Adadelta(model.parameters(), lr=1)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
elif args.model == 'vgg16':
model = VGG(depth=16).to(device)
if args.pretrained_model_dir is None:
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 == 'vgg19':
model = VGG(depth=19).to(device)
if args.pretrained_model_dir is None:
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 args.pretrained_model_dir is None:
print('start pre-training...')
best_acc = 0
for epoch in range(args.pretrain_epochs):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
acc = test(args, model, device, criterion, test_loader)
if acc > best_acc:
best_acc = acc
state_dict = model.state_dict()
model.load_state_dict(state_dict)
acc = best_acc
torch.save(state_dict, os.path.join(args.experiment_data_dir, f'pretrain_{args.dataset}_{args.model}.pth'))
print('Model trained saved to %s' % args.experiment_data_dir)
else:
model.load_state_dict(torch.load(args.pretrained_model_dir))
best_acc = test(args, model, device, criterion, test_loader)
# setup new opotimizer for fine-tuning
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)
print('Pretrained model acc:', best_acc)
return model, optimizer, scheduler
def train(args, model, device, train_loader, criterion, optimizer, epoch):
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()
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()))
if args.dry_run:
break
def test(args, model, device, criterion, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += criterion(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
acc = 100 * correct / len(test_loader.dataset)
print('Test Loss: {} Accuracy: {}%\n'.format(
test_loss, acc))
return acc
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.experiment_data_dir, exist_ok=True)
# prepare model and data
train_loader, test_loader, criterion = get_data(args.dataset, args.data_dir, args.batch_size, args.test_batch_size)
model, optimizer, scheduler = get_model_optimizer_scheduler(args, device, train_loader, test_loader, criterion)
dummy_input = get_dummy_input(args, device)
flops, params, results = count_flops_params(model, dummy_input)
print(f"FLOPs: {flops}, params: {params}")
print(f'start {args.pruner} pruning...')
def trainer(model, optimizer, criterion, epoch):
return train(args, model, device, train_loader, criterion, optimizer, epoch=epoch)
pruner_cls = str2pruner[args.pruner]
kw_args = {}
config_list = [{
'sparsity': args.sparsity,
'op_types': ['Conv2d']
}]
if args.pruner == 'level':
config_list = [{
'sparsity': args.sparsity,
'op_types': ['default']
}]
else:
if args.dependency_aware:
dummy_input = get_dummy_input(args, device)
print('Enable the dependency_aware mode')
# note that, not all pruners support the dependency_aware mode
kw_args['dependency_aware'] = True
kw_args['dummy_input'] = dummy_input
if args.pruner not in ('l1filter', 'l2filter', 'fpgm'):
# set only work for training aware pruners
kw_args['trainer'] = trainer
kw_args['optimizer'] = optimizer
kw_args['criterion'] = criterion
if args.pruner in ('mean_activation', 'apoz', 'taylorfo'):
kw_args['sparsifying_training_batches'] = 1
if args.pruner == 'slim':
kw_args['sparsifying_training_epochs'] = 1
if args.pruner == 'agp':
kw_args['pruning_algorithm'] = 'l1'
kw_args['num_iterations'] = 2
kw_args['epochs_per_iteration'] = 1
# Reproduced result in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS',
# Conv_1, Conv_8, Conv_9, Conv_10, Conv_11, Conv_12 are pruned with 50% sparsity, as 'VGG-16-pruned-A'
if args.pruner == 'slim':
config_list = [{
'sparsity': args.sparsity,
'op_types': ['BatchNorm2d'],
}]
else:
config_list = [{
'sparsity': args.sparsity,
'op_types': ['Conv2d'],
'op_names': ['feature.0', 'feature.24', 'feature.27', 'feature.30', 'feature.34', 'feature.37']
}]
pruner = pruner_cls(model, config_list, **kw_args)
# Pruner.compress() returns the masked model
model = pruner.compress()
pruner.get_pruned_weights()
# export the pruned model masks for model speedup
model_path = os.path.join(args.experiment_data_dir, 'pruned_{}_{}_{}.pth'.format(
args.model, args.dataset, args.pruner))
mask_path = os.path.join(args.experiment_data_dir, 'mask_{}_{}_{}.pth'.format(
args.model, args.dataset, args.pruner))
pruner.export_model(model_path=model_path, mask_path=mask_path)
if args.test_only:
test(args, model, device, criterion, test_loader)
if args.speed_up:
# Unwrap all modules to normal state
pruner._unwrap_model()
m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
m_speedup.speedup_model()
print('start finetuning...')
best_top1 = 0
save_path = os.path.join(args.experiment_data_dir, f'finetuned.pth')
for epoch in range(args.fine_tune_epochs):
print('# Epoch {} #'.format(epoch))
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
top1 = test(args, model, device, criterion, test_loader)
if top1 > best_top1:
best_top1 = top1
torch.save(model.state_dict(), save_path)
flops, params, results = count_flops_params(model, dummy_input)
print(f'Finetuned model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {best_top1: .2f}')
if args.nni:
nni.report_final_result(best_top1)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch Example for model comporession')
# 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',
choices=['lenet', 'vgg16', 'vgg19', 'resnet18'],
help='model to use')
parser.add_argument('--pretrained-model-dir', type=str, default=None,
help='path to pretrained model')
parser.add_argument('--pretrain-epochs', type=int, default=160,
help='number of epochs to pretrain the model')
parser.add_argument('--batch-size', type=int, default=128,
help='input batch size for training')
parser.add_argument('--test-batch-size', type=int, default=200,
help='input batch size for testing')
parser.add_argument('--experiment-data-dir', type=str, default='./experiment_data',
help='For saving output checkpoints')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--multi-gpu', action='store_true', default=False,
help='run on mulitple gpus')
parser.add_argument('--test-only', action='store_true', default=False,
help='run test only')
# pruner
parser.add_argument('--sparsity', type=float, default=0.5,
help='target overall target sparsity')
parser.add_argument('--dependency-aware', action='store_true', default=False,
help='toggle dependency aware mode')
parser.add_argument('--pruner', type=str, default='l1filter',
choices=['level', 'l1filter', 'l2filter', 'slim', 'agp',
'fpgm', 'mean_activation', 'apoz', 'taylorfo'],
help='pruner to use')
# speed-up
parser.add_argument('--speed-up', action='store_true', default=False,
help='Whether to speed-up the pruned model')
# fine-tuning
parser.add_argument('--fine-tune-epochs', type=int, default=160,
help='epochs to fine tune')
parser.add_argument('--nni', action='store_true', default=False,
help="whether to tune the pruners using NNi tuners")
args = parser.parse_args()
if args.nni:
params = nni.get_next_parameter()
print(params)
args.sparsity = params['sparsity']
args.pruner = params['pruner']
args.model = params['model']
main(args)