def compare_train():
if args.resume == 'none':
save_path = os.path.join(tmp_dir, args.arch+'_compare.pth')
model_state_pre_best = get_model(args.ref_model, device=device)
best_acc = 0 # best test accuracy
cfg = model_state_pre_best['cfg']
net_pruned = eval(args.arch)(args.num_class, cfg=cfg)
net_pruned.to(device)
optimizer = optim.SGD(net_pruned.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
else:
model_state_pre_best = get_model(args.resume, device=device)
best_acc = model_state_pre_best['lasted_best_prec1']
save_path = os.path.join(tmp_dir, args.resume)
cfg = model_state_pre_best['cfg']
net_pruned = eval(args.arch)(args.num_class, cfg=cfg)
net_pruned.load_state_dict(model_state_pre_best['state_dict'])
net_pruned.to(device)
optimizer = optim.SGD(net_pruned.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
optimizer.load_state_dict(model_state_pre_best['optimizer'])
end_epoch = 100
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
criterion = nn.CrossEntropyLoss()
for epoch in range(start_epoch + 1, end_epoch):
net_pruned.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
with torch.cuda.device(device):
inputs = inputs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = net_pruned(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
# print(batch_idx,len(trainloader),
# ' Loss: %.3f | Acc: %.3f%% (%d/%d)'
# % (train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
net_pruned.eval()
num_iterations = len(testloader)
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net_pruned(inputs)
prec1, prec5 = utils.accuracy(outputs, targets, topk=(1, 5))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
print(
'Epoch[{0}]({1}/{2}): '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f}'.format(
epoch, batch_idx, num_iterations, top1=top1, top5=top5))
if (top1.avg.item() > best_acc):
print('当前测试精度%f大于当前层剪枝周期的最号精度%f,保存模型%s)' % (
top1.avg.item(), best_acc, save_path))
best_acc = top1.avg.item()
model_state = {
'state_dict': net_pruned.state_dict(),
'lasted_best_prec1': round(best_acc, 2),
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'cfg': cfg,
}
torch.save(model_state, save_path)
def prune_train():
print('checkpoint %s exists,train from checkpoint' % args.resume)
save_path = os.path.join(tmp_dir, args.resume)
model_state = get_model(args.resume, device=device)
conv_list = get_conv_name_list(model_state)
conv_list_asc = conv_list.copy()
conv_list.reverse()
try:
start_epoch = model_state['epoch']
except KeyError:
start_epoch = 0
end_epoch = start_epoch + 40
cudnn.benchmark = True
print('\nEpoch: %d' % start_epoch)
criterion = nn.CrossEntropyLoss()
for conv_layername in conv_list:
model_state_pre_best = get_model(args.resume, device=device)
try:
pruned_layers = model_state['pruned_layer']
except KeyError:
pruned_layers = dict()
try:
baseline_best_prec1 = model_state_pre_best['baseline_best_prec1']
except KeyError:
baseline_best_prec1 = model_state_pre_best['best_prec1']
model_state_pre_best['baseline_best_prec1'] = baseline_best_prec1
try:
previous_lay_qualified_top1_mean = model_state_pre_best[
'previous_lay_qualified_top1_mean']
except KeyError:
model_state_pre_best[
'previous_lay_qualified_top1_mean'] = baseline_best_prec1
if not 'lasted_best_prec1' in model_state_pre_best.keys():
model_state_pre_best['lasted_best_prec1'] = model_state_pre_best[
'baseline_best_prec1']
# 剪枝某一层时初始化当前层最好精度为0
lasted_best_prec1 = 0
try:
cfg = model_state_pre_best['cfg']
except KeyError:
cfg = None
net_pre_best = eval(args.arch)(args.num_class, cfg=cfg)
net_pre_best.load_state_dict(model_state_pre_best['state_dict'])
optimizer = optim.SGD(net_pre_best.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
optimizer.load_state_dict(model_state_pre_best['optimizer'])
print("获取上轮剪枝最佳模型,剪枝清单为%s,测试后准确率为%f" %
(pruned_layers, lasted_best_prec1))
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
#如果已经剪枝则跳过下面的逻辑进行下一层的剪枝
if conv_layername in pruned_layers.keys():
continue
#获取当前卷积层在所有卷积层总顺序派的索引号
conv_prune_dict_cleaned = {name: -1 for name in conv_list_asc}
conv_idx = get_conv_idx_by_name(conv_layername, conv_list_asc)
print('%s在所有卷积层总顺序派的索引号为%d' % (conv_layername, conv_idx))
print("处理%s,剪枝测试中" % conv_layername)
# conv_prune_dict_cleaned[conv_layername] = {i if i%10==0 else 0 for i in range(512)}
conv_prune_dict_cleaned[
conv_layername], current_lay_qualified_top1_mean = search_by_conv_idx(
model_state_pre_best, net_pre_best.origin_cfg, conv_layername,
conv_idx, len(conv_list), testloader, args)
print("剪枝字典为%s" % str(conv_prune_dict_cleaned))
net_current_pruned, pruned_cfg = get_net_by_prune_dict(
net_pre_best, args, conv_prune_dict_cleaned)
net_current_pruned = net_current_pruned.to(device)
pruned_layers[conv_layername] = conv_prune_dict_cleaned[conv_layername]
total_drop_list = [
list(v) if isinstance(v, list) else -1
for k, v in conv_prune_dict_cleaned.items()
]
total_drop_list_resnet34 = []
try:
for k, v in model_state['pruned_layer'].items():
total_drop_list_resnet34.append(v)
except KeyError:
print('pruned_layer为空')
for i in range(len(conv_list) - len(total_drop_list_resnet34)):
total_drop_list_resnet34.append(-1)
total_drop_list_resnet34.reverse()
for epoch in range(start_epoch + 1, end_epoch):
net_current_pruned.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
with torch.cuda.device(device):
inputs = inputs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = net_current_pruned(inputs,
total_drop_list_resnet34)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
# print(batch_idx,len(trainloader),
# ' Loss: %.3f | Acc: %.3f%% (%d/%d)'
# % (train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
net_current_pruned.eval()
num_iterations = len(testloader)
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net_current_pruned(inputs,
total_drop_list_resnet34)
prec1, prec5 = utils.accuracy(outputs,
targets,
topk=(1, 5))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
print('Epoch[{0}]({1}/{2}): '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f}'.format(
epoch,
batch_idx,
num_iterations,
top1=top1,
top5=top5))
if (top1.avg.item() > lasted_best_prec1):
print(
'当前测试精度%f大于当前层剪枝周期的最号精度%f,baseline最好精度为%f,当前层剪枝精度平均%f,保存模型%s)'
% (top1.avg.item(), lasted_best_prec1, baseline_best_prec1,
current_lay_qualified_top1_mean, save_path))
lasted_best_prec1 = top1.avg.item()
model_state = {
'state_dict': net_current_pruned.state_dict(),
'baseline_best_prec1': baseline_best_prec1,
'lasted_best_prec1': round(lasted_best_prec1, 2),
'epoch': epoch,
'previous_lay_qualified_top1_mean':
current_lay_qualified_top1_mean,
'optimizer': optimizer.state_dict(),
'pruned_layer': pruned_layers,
'cfg': pruned_cfg,
}
torch.save(model_state, save_path)
best_model_path = os.path.join(
tmp_dir,
str(lasted_best_prec1) + '_' + conv_layername + '.pth')
torch.save(model_state, best_model_path)
m.layer_mask(cov_id + 1,
resume=args.resume_mask,
param_per_cov=param_per_cov_dic[args.arch],
arch=args.arch)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=lr_decay_step,
gamma=0.1)
if cov_id == 0:
pruned_checkpoint = get_model(args.resume, device=device)
from collections import OrderedDict
new_state_dict = OrderedDict()
if args.arch == 'resnet_50':
tmp_ckpt = pruned_checkpoint
else:
tmp_ckpt = pruned_checkpoint['state_dict']
if len(args.gpu) > 1:
for k, v in tmp_ckpt.items():
new_state_dict['module.' + k.replace('module.', '')] = v
else:
for k, v in tmp_ckpt.items():
new_state_dict[k.replace('module.', '')] = v
def train_baseline():
if args.resume != "none":
print('checkpoint %s exists,train from checkpoint' % args.resume)
save_path = os.path.join(baseline_dir, args.resume)
# model_state = torch.load(args.resume, map_location=device)
model_state = get_model(args.resume, device=device)
cfg = model_state['cfg']
net = eval(args.arch)(args.num_class)
current_model_best_acc = model_state['best_prec1']
net.load_state_dict(model_state['state_dict'])
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
optimizer.load_state_dict(model_state['optimizer'])
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
try:
start_epoch = model_state['epoch']
except KeyError:
start_epoch = 0
end_epoch = start_epoch + 280
else:
save_path = os.path.join(baseline_dir,
args.arch + '_' + args.dataset + '.pth')
current_model_best_acc = 0
net = eval(args.arch)(args.num_class)
cfg = net.cfg
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
start_epoch = 0
end_epoch = 100
net = net.to(device)
cudnn.benchmark = True
print('\nEpoch: %d' % start_epoch)
fake_drop_out_list = [-1 for i in range(31)]
criterion = nn.CrossEntropyLoss()
for epoch in range(start_epoch + 1, end_epoch):
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
with torch.cuda.device(device):
inputs = inputs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
print(
batch_idx, len(trainloader),
' Loss: %.3f | Acc: %.3f%% (%d/%d)' %
(train_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
net.eval()
num_iterations = len(testloader)
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
prec1, prec5 = utils.accuracy(outputs, targets, topk=(1, 5))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
print('Epoch[{0}]({1}/{2}): '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f}'.format(
epoch, batch_idx, num_iterations, top1=top1, top5=top5))
if (top1.avg.item() > current_model_best_acc):
current_model_best_acc = top1.avg.item()
model_state = {
'state_dict': net.state_dict(),
'best_prec1': current_model_best_acc,
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'cfg': cfg,
}
torch.save(model_state, save_path)
print("=>Best accuracy {:.3f}".format(model_state['best_prec1']))
def main():
# Init logger
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
log = open(
os.path.join(args.save_path, 'log_seed_{}.txt'.format(args.seed)), 'w')
print_log('save path : {}'.format(args.save_path), log)
state = {k: v for k, v in args._get_kwargs()}
print_log(state, log)
print_log("Random Seed: {}".format(args.seed), log)
print_log("python version : {}".format(sys.version.replace('\n', ' ')),
log)
print_log("torch version : {}".format(torch.__version__), log)
print_log("cudnn version : {}".format(torch.backends.cudnn.version()),
log)
print_log("Norm Pruning Rate: {}".format(args.rate_norm), log)
print_log("Distance Pruning Rate: {}".format(args.rate_dist), log)
print_log("Layer Begin: {}".format(args.layer_begin), log)
print_log("Layer End: {}".format(args.layer_end), log)
print_log("Layer Inter: {}".format(args.layer_inter), log)
print_log("Epoch prune: {}".format(args.epoch_prune), log)
print_log("use pretrain: {}".format(args.use_pretrain), log)
print_log("Pretrain path: {}".format(args.pretrain_path), log)
print_log("Dist type: {}".format(args.dist_type), log)
print_log("Pre cfg: {}".format(args.use_precfg), log)
if args.dataset == 'cifar10':
train_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
args.data_path,
train=True,
download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(args.data_path,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
else:
train_loader = torch.utils.data.DataLoader(datasets.CIFAR100(
args.data_path,
train=True,
download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100(args.data_path,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
print_log("=> creating model '{}'".format(args.arch), log)
model = eval(args.arch)(args.num_class)
print_log("=> network :\n {}".format(model), log)
if args.cuda:
model.cuda()
if args.use_pretrain:
pretrain = get_model(args.resume, device='cuda')
if args.use_state_dict:
model.load_state_dict(pretrain['state_dict'])
else:
model = pretrain['state_dict']
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
checkpoint = get_model(args.resume, device='cuda')
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model = eval(args.arch)(args.num_class, cfg=checkpoint['cfg'])
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {}) Prec1: {:f}".format(
args.resume, checkpoint['epoch'], best_prec1))
if args.cuda:
model = model.cuda()
if args.evaluate:
time1 = time.time()
test(test_loader, model, log)
time2 = time.time()
print('function took %0.3f ms' % ((time2 - time1) * 1000.0))
return
m = Mask(model)
m.init_length()
print("-" * 10 + "one epoch begin" + "-" * 10)
print("remaining ratio of pruning : Norm is %f" % args.rate_norm)
print("reducing ratio of pruning : Distance is %f" % args.rate_dist)
print("total remaining ratio is %f" % (args.rate_norm - args.rate_dist))
val_acc_1 = test(test_loader, model, log)
print(" accu before is: %.3f %%" % val_acc_1)
m.model = model
m.init_mask(args.rate_norm, args.rate_dist, args.dist_type)
# m.if_zero()
m.do_mask()
m.do_similar_mask()
model = m.model
# m.if_zero()
if args.cuda:
model = model.cuda()
val_acc_2 = test(test_loader, model, log)
print(" accu after is: %s %%" % val_acc_2)
best_prec1 = 0.
for epoch in range(args.start_epoch, args.epochs):
if epoch in [args.epochs * 0.5, args.epochs * 0.75]:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.1
train(train_loader, model, optimizer, epoch, log)
prec1 = test(test_loader, model, log)
if epoch % args.epoch_prune == 0 or epoch == args.epochs - 1:
m.model = model
m.if_zero()
m.init_mask(args.rate_norm, args.rate_dist, args.dist_type)
m.do_mask()
m.do_similar_mask()
# small_filter_index.append(m.filter_small_index)
# large_filter_index.append(m.filter_large_index)
# save_obj(small_filter_index, 'small_filter_index_2')
# save_obj(large_filter_index, 'large_filter_index_2')
m.if_zero()
model = m.model
if args.cuda:
model = model.cuda()
val_acc_2 = test(test_loader, model, log)
is_best = val_acc_2 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
'cfg': model.cfg
},
is_best,
filepath=args.save_path)
help='how many batches to wait before logging training status')
parser.add_argument('--num_class', type=int, default='100')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader, test_loader = get_loaders(args.dataset, args.data_dir,
args.train_batch_size,
args.eval_batch_size, args.arch)
if args.refine:
checkpoint = get_model(args.refine, device='cuda')
model = vgg_16_bn(cfg=checkpoint['cfg'], num_class=args.num_class)
model.cuda()
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
try:
best_prec1 = checkpoint['best_prec1']
except KeyError:
best_prec1 = checkpoint['lasted_best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=5e-4)
# optimizer.load_state_dict(checkpoint['optimizer'])
else:
def train_baseline():
if args.resume != "none":
print('checkpoint %s exists,train from checkpoint' % args.resume)
save_path = os.path.join(baseline_dir, args.resume)
# model_state = torch.load(args.resume, map_location=device)
model_state = get_model(args.resume, device=device)
net = model_state['net']
current_model_best_acc = model_state['best_prec1']
# optimizer.load_state_dict(model_state['optimizer'])
# for state in optimizer.state.values():
# for k, v in state.items():
# if torch.is_tensor(v):
# state[k] = v.cuda()
try:
start_epoch = model_state['epoch']
except KeyError:
start_epoch = 0
end_epoch = start_epoch + 130
else:
save_path = os.path.join(baseline_dir,
args.arch + '_' + args.dataset + '.pth')
current_model_best_acc = 0
net = eval(args.arch)(args.num_class)
# net = VGG(args.num_class)
# optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
start_epoch = 0
end_epoch = 2
net = net.to(device)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
cudnn.benchmark = True
print('\nEpoch: %d' % start_epoch)
count_dict = {}
criterion = nn.CrossEntropyLoss()
filter_count = Counter()
for epoch in range(start_epoch + 1, end_epoch):
net.train()
train_loss = 0
correct = 0
total = 0
temp_tensor = torch.zeros(512, 512)
for batch_idx, (inputs, targets) in enumerate(trainloader):
with torch.cuda.device(device):
inputs = inputs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
# np.linalg.norm(net.layer0_conv.weight.grad.view(64, -1).cpu().numpy(),axis=0)
loss = criterion(outputs, targets)
loss.backward()
count_dict = get_count_dict(net, args.compress_rate,
count_dict)
# print(count_dict)
if (batch_idx in [100]):
model_state1 = {
'net': net,
'best_prec1': current_model_best_acc,
'epoch': epoch,
'counter_dict': count_dict
}
# net.load_state_dict(model_state)
# net = model_state['net']
#
# total = sum([param.nelement() for param in model_state['net'].parameters()])
# print(' + Number of params: %.2fM' % (total / 1e6)) # 每一百万为一个单位
# input = torch.randn(100, 3, 32, 32)
# flops, params = profile(net, inputs=(input.cuda(),))
# print('flops %f', flops/1e6)
torch.save(model_state1, save_path)
print('saved at batch' + str(batch_idx))
# grad_dist_matrix = get_dist_matrix(net, 4)
# likely_ndarray = get_likely_point(grad_dist_matrix,(1-args.compress_rate)*100)
# filter_count += get_filter_index(likely_ndarray)
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
# print(batch_idx,len(trainloader),
# ' Loss: %.3f | Acc: %.3f%% (%d/%d)'
# % (train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
net.eval()
num_iterations = len(testloader)
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
prec1, prec5 = utils.accuracy(outputs, targets, topk=(1, 5))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
print('Epoch[{0}]({1}/{2}): '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f}'.format(
epoch, batch_idx, num_iterations, top1=top1, top5=top5))
if (top1.avg.item() > current_model_best_acc):
current_model_best_acc = top1.avg.item()
model_state = {
'net': net,
'best_prec1': current_model_best_acc,
'epoch': epoch,
'counter_dict': count_dict
}
torch.save(model_state, save_path)
print("=>Best accuracy {:.3f}".format(model_state['best_prec1']))
torch.save(model_state, save_path)
# Model
print('==> Building model..')
print(compress_rate)
net = eval(args.arch)(compress_rate=compress_rate, num_class=args.num_class)
net = net.to(device)
if len(args.gpu) > 1 and torch.cuda.is_available():
device_id = []
for i in range((len(args.gpu) + 1) // 2):
device_id.append(i)
net = torch.nn.DataParallel(net, device_ids=device_id)
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
checkpoint = get_model('vgg_16_bn_cifar10_hr_pruning.pth', device=device)
from collections import OrderedDict
new_state_dict = OrderedDict()
if args.adjust_ckpt:
for k, v in checkpoint.items():
new_state_dict[k.replace('module.', '')] = v
else:
for k, v in checkpoint['state_dict'].items():
new_state_dict[k.replace('module.', '')] = v
net.load_state_dict(new_state_dict)
criterion = nn.CrossEntropyLoss()
feature_result = torch.tensor(0.)
total = torch.tensor(0.)
def get_filter_idx(count_dict, compress_rate):
result_dict ={}
for k,v in count_dict.items():
filter_total_number = len(v)
keys = list(map(lambda x :x[0], v.most_common(int((1- compress_rate)*filter_total_number))))
result_dict[k] = keys
return result_dict
def get_last_convidx(net):
idx = -1
for m in net.modules():
if isinstance(m,nn.Conv2d):
idx+=1
return idx
# def store_pruned_model(model_name,cop):
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# model = get_model(model_name, device=device)
# pruned_model_100 = prune_model(model, 0.5)
# torch.save(pruned_model_100, "D:\\workspace\\prune_paper\\main\\startover\\baseline\\VGG_cifar100_pruned.pth");
if __name__ == '__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = get_model("resnet_34_cifar100.pth",device=device)
pruned_model_100 = prune_model(model,0.5)
torch.save(pruned_model_100,"D:\\workspace\\prune_paper\\main\\startover\\baseline\\VGG_cifar100_pruned.pth");
(1 - compress_rate) * 100)
filter_cpunt = get_filter_index(likely_ndarray)
try:
count_dict[layer_name] += filter_cpunt
except KeyError:
count_dict[layer_name] = filter_cpunt
# grad_dist_matrix = get_dist_matrix(net, 4)
#
# likely_ndarray = get_likely_point(grad_dist_matrix, (1 - args.compress_rate) * 100)
# filter_count += get_filter_index(likely_ndarray)
return count_dict
if __name__ == '__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_state_dict = get_model('VGG_cifar10.pth', device=device)
f_idx, count = get_count_statics(model_state_dict['counter_dict'], 20)
count = list(map(lambda x: int(x / 30), count))
n_bins = f_idx[:20] # 总的卷积核的个数按照顺序排
x = count[:20] # 每个卷积核被相似的频率数
# ['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright',
# 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted',
# 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks',
# 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']
plt.style.use(['bmh'])
#梯度前20名柱状图，10min
plt.title('Gradient similarity frequency after 10 mini-batches')
n_bins = [
16, 28, 39, 159, 178, 222, 239, 254, 283, 293, 296, 301, 306, 316, 321,
405, 408, 451, 452, 508
]
