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auto_pruners_torch.py
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auto_pruners_torch.py
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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
'''
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.
'''
import argparse
import os
import sys
import json
import torch
from torch.optim.lr_scheduler import StepLR, MultiStepLR
from torchvision import datasets, transforms
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 import count_flops_params
from pathlib import Path
sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
from mnist.lenet import LeNet
from cifar10.vgg import VGG
from cifar10.resnet import ResNet18, ResNet50
def get_data(dataset, data_dir, batch_size, test_batch_size):
'''
get data
'''
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)
val_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)
val_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, val_loader, criterion
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()))
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
def get_trained_model_optimizer(args, device, train_loader, val_loader, criterion):
if args.model == 'LeNet':
model = LeNet().to(device)
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)
elif args.model == 'vgg16':
model = VGG(depth=16).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=0.01, 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)
elif args.model == 'resnet18':
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
for epoch in range(args.pretrain_epochs):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
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'))
print('Model trained saved to %s' % args.experiment_data_dir)
return model, optimizer
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_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
def main(args):
# prepare dataset
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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)
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):
return train(args, model, device, train_loader, criterion, optimizer, epoch=epoch)
def evaluator(model):
return test(model, device, criterion, val_loader)
# used to save the performance of the original & pruned & finetuned models
result = {'flops': {}, 'params': {}, 'performance':{}}
flops, params, _ = count_flops_params(model, get_input_size(args.dataset))
result['flops']['original'] = flops
result['params']['original'] = params
evaluation_result = evaluator(model)
print('Evaluation result (original model): %s' % evaluation_result)
result['performance']['original'] = evaluation_result
# module types to prune, only "Conv2d" supported for channel pruning
if args.base_algo in ['l1', 'l2', 'fpgm']:
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)
elif args.pruner == 'L2FilterPruner':
pruner = L2FilterPruner(model, config_list)
elif args.pruner == 'FPGMPruner':
pruner = FPGMPruner(model, config_list)
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':
if args.base_algo in ['l1', 'l2', 'fpgm']:
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, epochs_per_iteration=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_epochs_per_iteration=5,
experiment_data_dir=args.experiment_data_dir)
else:
raise ValueError(
"Pruner not supported.")
# Pruner.compress() returns the masked model
# but for AutoCompressPruner, Pruner.compress() returns directly the pruned model
model = pruner.compress()
evaluation_result = evaluator(model)
print('Evaluation result (masked model): %s' % evaluation_result)
result['performance']['pruned'] = evaluation_result
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'))
print('Masked model saved to %s' % args.experiment_data_dir)
# model speedup
if args.speedup:
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 (speedup model): %s' % evaluation_result)
result['performance']['speedup'] = evaluation_result
torch.save(model.state_dict(), os.path.join(args.experiment_data_dir, 'model_speedup.pth'))
print('Speedup model saved to %s' % args.experiment_data_dir)
flops, params, _ = count_flops_params(model, get_input_size(args.dataset))
result['flops']['speedup'] = flops
result['params']['speedup'] = params
if args.fine_tune:
if args.dataset == 'mnist':
optimizer = torch.optim.Adadelta(model.parameters(), lr=1)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
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'))
print('Evaluation result (fine tuned): %s' % best_acc)
print('Fined tuned model saved to %s' % args.experiment_data_dir)
result['performance']['finetuned'] = best_acc
with open(os.path.join(args.experiment_data_dir, 'result.json'), 'w+') as f:
json.dump(result, f)
if __name__ == '__main__':
def str2bool(s):
if isinstance(s, bool):
return s
if s.lower() in ('yes', 'true', 't', 'y', '1'):
return True
if s.lower() in ('no', 'false', 'f', 'n', '0'):
return False
raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser(description='PyTorch Example for SimulatedAnnealingPruner')
# 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
parser.add_argument('--pruner', type=str, default='SimulatedAnnealingPruner',
help='pruner to use')
parser.add_argument('--base-algo', type=str, default='l1',
help='base pruning algorithm. level, l1, l2, or fpgm')
parser.add_argument('--sparsity', type=float, default=0.1,
help='target overall target sparsity')
# 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')
# speedup
parser.add_argument('--speedup', type=str2bool, default=False,
help='Whether to speedup the pruned model')
# others
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')
args = parser.parse_args()
if not os.path.exists(args.experiment_data_dir):
os.makedirs(args.experiment_data_dir)
main(args)