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main_synflow.py
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main_synflow.py
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'''
main process for a Lottery Tickets experiments
'''
import os
import pdb
import time
import pickle
import random
import shutil
import argparse
import numpy as np
from copy import deepcopy
import matplotlib.pyplot as plt
import torch
import torch.optim
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
import torchvision.models as models
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data.sampler import SubsetRandomSampler
from utils import NormalizeByChannelMeanStd
from trainer import train, validate
from utils import *
from pruner import *
import arg_parser
best_sa = 0
def main():
global args, best_sa
args = arg_parser.parse_args()
print(args)
torch.cuda.set_device(int(args.gpu))
os.makedirs(args.save_dir, exist_ok=True)
if args.seed:
setup_seed(args.seed)
# prepare dataset
if args.dataset == "cifar10" or args.dataset == "cifar100" :
model, train_loader, val_loader, test_loader,_ = setup_model_dataset(args)
else:
model, train_loader, val_loader, test_loader = setup_model_dataset(args)
model.cuda()
criterion = nn.CrossEntropyLoss()
decreasing_lr = list(map(int, args.decreasing_lr.split(',')))
if args.prune_type == 'lt':
print('lottery tickets setting (rewind to the same random init)')
initalization = deepcopy(model.state_dict())
elif args.prune_type == 'pt':
print('lottery tickets from best dense weight')
initalization = None
elif args.prune_type == 'rewind_lt':
print('lottery tickets with early weight rewinding')
initalization = None
else:
raise ValueError('unknown prune_type')
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=decreasing_lr, gamma=0.1) # 0.1 is fixed
if args.resume:
print('resume from checkpoint {}'.format(args.checkpoint))
checkpoint = torch.load(
args.checkpoint, map_location=torch.device('cuda:'+str(args.gpu)))
best_sa = checkpoint['best_sa']
start_epoch = checkpoint['epoch']
all_result = checkpoint['result']
start_state = checkpoint['state']
if start_state > 0:
current_mask = extract_mask(checkpoint['state_dict'])
prune_model_custom(model, current_mask)
check_sparsity(model)
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=decreasing_lr, gamma=0.1)
model.load_state_dict(checkpoint['state_dict'], strict=False)
# adding an extra forward process to enable the masks
x_rand = torch.rand(1, 3, args.input_size, args.input_size).cuda()
model.eval()
with torch.no_grad():
model(x_rand)
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
initalization = checkpoint['init_weight']
print('loading state:', start_state)
print('loading from epoch: ', start_epoch, 'best_sa=', best_sa)
check_sparsity(model)
else:
all_result = {}
all_result['train_ta'] = []
all_result['test_ta'] = []
all_result['val_ta'] = []
start_epoch = 0
start_state = 0
print('######################################## Start Standard Synflow Pruning ########################################')
if args.rate != 0:
global_prune_model(model, args.rate, "synflow", train_loader)
check_sparsity(model)
check_sparsity(model)
state = 0
for epoch in range(start_epoch, args.epochs):
start_time = time.time()
print(optimizer.state_dict()['param_groups'][0]['lr'])
acc = train(train_loader, model, criterion, optimizer, epoch, args)
# evaluate on validation set
tacc = validate(val_loader, model, criterion, args)
# evaluate on test set
test_tacc = validate(test_loader, model, criterion, args)
scheduler.step()
all_result['train_ta'].append(acc)
all_result['val_ta'].append(tacc)
all_result['test_ta'].append(test_tacc)
# remember best prec@1 and save checkpoint
is_best_sa = tacc > best_sa
best_sa = max(tacc, best_sa)
save_checkpoint({
'state': state,
'result': all_result,
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_sa': best_sa,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'init_weight': initalization
}, is_SA_best=is_best_sa, pruning=state, save_path=args.save_dir)
# plot training curve
plt.plot(all_result['train_ta'], label='train_acc')
plt.plot(all_result['val_ta'], label='val_acc')
plt.plot(all_result['test_ta'], label='test_acc')
plt.legend()
plt.savefig(os.path.join(args.save_dir,
str(state)+'net_train.png'))
plt.close()
print("one epoch duration:{}".format(time.time()-start_time))
# report result
check_sparsity(model)
print("Performance on the test data set")
test_tacc = validate(test_loader, model, criterion, args)
if len(all_result['val_ta']) != 0:
val_pick_best_epoch = np.argmax(np.array(all_result['val_ta']))
print('* best SA = {}, Epoch = {}'.format(
all_result['test_ta'][val_pick_best_epoch], val_pick_best_epoch+1))
all_result = {}
all_result['train_ta'] = []
all_result['test_ta'] = []
all_result['val_ta'] = []
best_sa = 0
start_epoch = 0
if __name__ == '__main__':
main()