def count_images(blog, directory_seq, basedir, seconddir="logs"):
targetpath = '%s/%s' % (basedir, seconddir)
checkdir(targetpath)
filename = '%s/count_images_%02d.txt' % (targetpath, directory_seq)
f = open(filename, 'a')
f.write(str(len(blog["images"])) + '\n')
f.close()
def count_images(blog, directory_seq, basedir, seconddir = "logs"):
targetpath = '%s/%s' % (basedir, seconddir)
checkdir(targetpath)
filename = '%s/count_images_%02d.txt' % (targetpath, directory_seq)
f = open(filename, 'a')
f.write(str(len(blog["images"]))+'\n')
f.close()
def error_log_url(blog_id, log_no, date, directory_seq, basedir, seconddir = "logs"):
targetpath = '%s/%s' % (basedir, seconddir)
checkdir(targetpath)
filename = '%s/error_url_comment_%s-%02d-%02d.txt' % (targetpath, int(date[0:4]), int(date[5:7]), int(date[8:10]))
f = open(filename, 'a')
url = '%s, http://m.blog.naver.com/%s/%s, access denied\n' % (directory_seq, blog_id, log_no)
f.write(url)
f.close()
def make_json(blog, blog_id, log_no, date, directory_seq, basedir, seconddir = "comments"):
PATH = '%s/%02d/%02d' % (int(date[0:4]), int(date[5:7]), int(date[8:10]))
targetpath = '%s/%s/%02d/%s' % (basedir, seconddir, directory_seq, PATH)
checkdir(targetpath)
filename = '%s/%s.json' % (targetpath, log_no)
f = open(filename, 'w')
jsonstr = json.dumps(blog, sort_keys=True, indent=4, encoding='utf-8')
f.write(jsonstr)
f.close()
def Paser_data(self, prune_classes, fine_tune_classes, prune_rate,
file_path):
prune_label = np.array([])
prune_data = np.array([])
prune_file_name = np.array([])
fine_tune_label = np.array([])
fine_tune_data = np.array([])
fine_tuen_file_name = np.array([])
prune_iter = int(1 / prune_rate)
with open(file_path, 'rb') as f:
data = pickle.load(f, encoding='latin1')
size = len(data['labels'])
iter_ = 0
for i in range(size):
if data['labels'][i] in prune_classes and iter_ % prune_iter == 0:
iter_ += 1
prune_label = np.append(prune_label, data['labels'][i])
prune_data = np.append(prune_data, data['data'][i])
prune_file_name = np.append(prune_file_name,
data['filenames'][i])
if data['labels'][i] in fine_tune_classes and data['labels'][i] in prune_classes\
and i % prune_iter != 0:
iter_ += 1
fine_tune_label = np.append(fine_tune_label, data['labels'][i])
fine_tune_data = np.append(fine_tune_data, data['data'][i])
fine_tuen_file_name = np.append(fine_tuen_file_name,
data['filenames'][i])
if data['labels'][i] in fine_tune_classes and data['labels'][
i] not in prune_classes:
fine_tune_label = np.append(fine_tune_label, data['labels'][i])
fine_tune_data = np.append(fine_tune_data, data['data'][i])
fine_tuen_file_name = np.append(fine_tuen_file_name,
data['filenames'][i])
new_dataset = {}
new_dataset['labels'] = prune_label
new_dataset['data'] = prune_data.reshape((
-1,
data['data'][0].shape[0],
))
new_dataset['filenames'] = prune_file_name
utils.checkdir(os.path.join(self.root, 'parsed_data', 'prune_data'))
with open(os.path.join(self.root, 'parsed_data', 'prune_data'),
'wb') as f:
pickle.dump(new_dataset, f, 0)
new_dataset = {}
new_dataset['labels'] = fine_tune_label
new_dataset['data'] = fine_tune_data.reshape((
-1,
data['data'][0].shape[0],
))
new_dataset['filenames'] = fine_tuen_file_name
utils.checkdir(os.path.join(self.root, 'parsed_data',
'fine_tune_data'))
with open(os.path.join(self.root, 'parsed_data', 'prune_data'),
'wb') as f:
pickle.dump(new_dataset, f, 0)
def write_json(static_blog, date, seconddir='statistics'): basedir ='/home/web/public_html/data/naver-blog' PATH = '%s-%02d-%02d' % (int(date[0:4]), int(date[5:7]), int(date[8:10])) targetpath = '%s/%s' % (basedir, seconddir) checkdir(targetpath) filename = '%s/%s.json' % (targetpath, PATH) f = open(filename, 'w') jsonstr = json.dumps(static_blog, sort_keys=True, indent=4, encoding='utf-8') f.write(jsonstr) f.close()
def crawl_blog_posts_for_query_per_date(query, date, db_pool=None):
def get_keys_from_page(query, date, pagenum):
root = html.parse(listurl % (query, date, date, pagenum))
items = root.xpath('//ul[@class="list_type_1 search_list"]')[0]
blog_ids = items.xpath('./input[@name="blogId"]/@value')
log_nos = items.xpath('./input[@name="logNo"]/@value')
times = [utils.format_datetime(utils.parse_datetime(time))\
for time in items.xpath('./li/div[@class="list_data"]/span[@class="date"]/text()')]
return {(b, l): t for b, l, t in zip(blog_ids, log_nos, times)}
if db_pool is None:
# make directories
subdir = '/'.join([DATADIR, query, date.split('-')[0]])
utils.checkdir(subdir)
if REMOTE:
rsubdir = '/'.join([REMOTE['dir'], query, date.split('-')[0]])
utils.rcheckdir(sftp, rsubdir)
# check number of items
try:
nitems = get_nitems_for_query(query, date, date)
except IndexError:
print query, date, 'None'
return
# crawl items
for pagenum in range(int(nitems / 10.)):
keys = get_keys_from_page(query, date, pagenum + 1)
tags = get_tags_for_items(keys)
for (blog_id, log_no), written_time in keys.items():
try:
info = crawl_blog_post(blog_id,
log_no,
tags,
written_time,
verbose=False)
if db_pool is None:
localpath = '%s/%s.json' % (subdir, log_no)
utils.write_json(info, localpath)
if REMOTE:
remotepath = '%s/%s.json' % (rsubdir, log_no)
sftp.put(localpath, remotepath)
else:
db_pool.insert_blog_to_db(info)
except IndexError:
print Exception(\
'Crawl failed for http://blog.naver.com/%s/%s' % (blog_id, log_no))
time.sleep(SLEEP)
overwrite_queries(query, date)
print query, date, nitems
def save_checkpoint(self, val_loss, model, cpath, spath):
'''Saves model when validation loss decrease.'''
msg = ""
if self.verbose:
msg = f'saved (VLoss {self.val_loss_min:.4f}->{val_loss:.4f})'
# print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
# torch.save(model.state_dict(), 'checkpoint.pt')
utils.checkdir(cpath)
torch.save(model, spath)
self.val_loss_min = val_loss
return msg
def crawl_blog_posts_for_query_per_date(query, date, db_pool=None):
def get_keys_from_page(query, date, pagenum):
root = html.parse(listurl % (query, date, date, pagenum))
items = root.xpath('//ul[@class="list_type_1 search_list"]')[0]
blog_ids = items.xpath('./input[@name="blogId"]/@value')
log_nos = items.xpath('./input[@name="logNo"]/@value')
times = [utils.format_datetime(utils.parse_datetime(time))\
for time in items.xpath('./li/div[@class="list_data"]/span[@class="date"]/text()')]
return {(b, l): t for b, l, t in zip(blog_ids, log_nos, times)}
if db_pool is None:
# make directories
subdir = '/'.join([DATADIR, query, date.split('-')[0]])
utils.checkdir(subdir)
if REMOTE:
rsubdir = '/'.join([REMOTE['dir'], query, date.split('-')[0]])
utils.rcheckdir(sftp, rsubdir)
# check number of items
try:
nitems = get_nitems_for_query(query, date, date)
except IndexError:
print query, date, 'None'
return
# crawl items
for pagenum in range(int(nitems/10.)):
keys = get_keys_from_page(query, date, pagenum + 1)
tags = get_tags_for_items(keys)
for (blog_id, log_no), written_time in keys.items():
try:
info = crawl_blog_post(blog_id, log_no, tags, written_time, verbose=False)
if db_pool is None:
localpath = '%s/%s.json' % (subdir, log_no)
utils.write_json(info, localpath)
if REMOTE:
remotepath = '%s/%s.json' % (rsubdir, log_no)
sftp.put(localpath, remotepath)
else:
db_pool.insert_blog_to_db(info)
except IndexError:
print Exception(\
'Crawl failed for http://blog.naver.com/%s/%s' % (blog_id, log_no))
time.sleep(SLEEP)
overwrite_queries(query, date)
print query, date, nitems
def make_json(blog,
blog_id,
log_no,
date,
directory_seq,
basedir,
seconddir="comments"):
PATH = '%s/%02d/%02d' % (int(date[0:4]), int(date[5:7]), int(date[8:10]))
targetpath = '%s/%s/%02d/%s' % (basedir, seconddir, directory_seq, PATH)
checkdir(targetpath)
filename = '%s/%s.json' % (targetpath, log_no)
f = open(filename, 'w')
jsonstr = json.dumps(blog, sort_keys=True, indent=4, encoding='utf-8')
f.write(jsonstr)
f.close()
def error_log_url(blog_id,
log_no,
date,
directory_seq,
basedir,
seconddir="logs"):
targetpath = '%s/%s' % (basedir, seconddir)
checkdir(targetpath)
filename = '%s/error_url_comment_%s-%02d-%02d.txt' % (
targetpath, int(date[0:4]), int(date[5:7]), int(date[8:10]))
f = open(filename, 'a')
url = '%s, http://m.blog.naver.com/%s/%s, access denied\n' % (
directory_seq, blog_id, log_no)
f.write(url)
f.close()
def train(model, pad_index,train_iter,valid_iter,args, num_epochs=10, lr=0.0003, print_every=100):
"""Train a model on IWSLT"""
if USE_CUDA:
model.cuda()
# optionally add label smoothing; see the Annotated Transformer
criterion = nn.NLLLoss(reduction="sum", ignore_index=pad_index)
optim = torch.optim.Adam(model.parameters(), lr=lr)
comp1 = utils.print_nonzeros(model)
dev_perplexities = []
tr_perplexities = []
for epoch in range(num_epochs):
print("Epoch", epoch)
model.train()
train_perplexity = run_epoch((rebatch(pad_index, b) for b in train_iter),
model,
SimpleLossCompute(model.generator, criterion, optim),
print_every=print_every)
tr_perplexities.append(train_perplexity)
model.eval()
with torch.no_grad():
print_examples((rebatch(pad_index, x) for x in valid_iter),
model, n=3, src_vocab=SRC.vocab, trg_vocab=TRG.vocab)
dev_perplexity = run_epoch((rebatch(pad_index, b) for b in valid_iter),
model,
SimpleLossCompute(model.generator, criterion, None))
print("Validation perplexity: %f" % dev_perplexity)
dev_perplexities.append(dev_perplexity)
plt.plot(np.arange(0,len(tr_perplexities)), tr_perplexities, c="blue", label="train perplexity")
plt.plot(np.arange(0,len(tr_perplexities)), dev_perplexities, c="red", label="validation perplexity")
plt.title(f"Perplexity through epochs (IWSLT, transformer)")
plt.xlabel("Iterations")
plt.ylabel("Perplexity")
plt.legend()
plt.grid(color="gray")
utils.checkdir(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_Perplexity_{comp1}.png", dpi=1200)
plt.close()
return sum(dev_perplexities)/len(dev_perplexities)
def run(self):
try:
utils.checkdir(self.SaveDir)
# create a temporary folder
self.tmp_folder = tempfile.TemporaryDirectory(suffix = ".TMP",
prefix="_BrukerGUI_",
dir = self.SaveDir)
if self.trigger == "all":
self._SaveAllChecked()
elif self.trigger == "single":
self._SaveSingle()
except CANCELThread:
self.quit()
def extractJsFromHtmlDir(inputdir, outputdir): if not utils.checkdir(inputdir, outputdir): return infiles = utils.lsresult(inputdir) for infile in infiles: outfile = outputdir + '/' + infile infile = inputdir + infile if utils.checkfile(infile, outfile): extractJsFromHtml(infile, outfile)
def main():
expdir = os.path.abspath(os.path.expanduser('exp'))
checkdir(expdir)
config = Configures(configFile)
os.environ["CUDA_VISIBLE_DEVICES"] = config('val', 'WORKER')
max_steps = config('data', 'SIZE')
net = ConvNet(config, task='val')
net, starting_epoch = init_net(net, config)
voc_loader = VOCloader.Loader(configure=config, task='val')
train_loader = voc_loader()
train_iterator = tqdm(train_loader, total=max_steps)
count = 0
net.eval()
for x, y, y_cls in train_iterator:
x, y, y_cls = Variable(x).cuda(), Variable(y).cuda(), Variable(
y_cls).cuda()
out_cls, out = net(x, func='all')
count += 1
outdir = os.path.join(expdir, str(count).zfill(6))
checkdir(outdir)
name_x = os.path.join(outdir, 'X.npy')
name_y = os.path.join(outdir, 'y.npy')
name_out = os.path.join(outdir, 'out.npy')
xs = x.data[0].cpu().transpose(0, 2).transpose(0, 1).numpy()
np.save(name_x, xs)
ys = y.data[0].cpu().numpy()
np.save(name_y, ys)
outs = out.data[0].cpu().numpy()
np.save(name_out, outs)
def ploter(obs_domain, qc_domain, fill_domain, stat_id):
'''
绘制时间范围内三类数据的时间序列图.
'''
start = T_DOMAIN.get('start', None)
end = T_DOMAIN.get('end', None)
out_dir = PATHS.get('save_dir')
savedir = checkdir(os.path.join(out_dir, str(stat_id)))
for _, x in enumerate(SNA+OCEC):
plt.figure(figsize=(22, 18))
for i, (name, color) in enumerate(zip(['obs','qc','fill'], ['y', 'r', 'b'])):
ax = plt.subplot(3, 1, i+1)
if i == 0:
data = obs_domain
elif i == 1:
data = qc_domain
elif i == 2:
data = fill_domain
ax.plot(data['time'], data[x], color=color, label='{}_{}'.format(name, x))
ax.set_xlabel('Time (h)', fontsize=13)
ax.set_ylabel('Conc (ug/m3)', fontsize=13)
ax1 = ax.twinx()
ax1.plot(data['time'], data['PM25'], color='g', label='PM25')
ax1.set_ylabel('Conc (ug/m3)', fontsize=13)
ax.set_title('{}_{}'.format(x, name),
fontsize=14)
# legennd
ax.legend(fontsize=12, loc='upper left')
ax1.legend(fontsize=12, loc='upper right')
plt.savefig(os.path.join(
savedir, '{}_{}_{}_{}'.format(stat_id,
x,
start.strftime("%Y%m%d%H"),
end.strftime("%Y%m%d%H"))))
def train(model, train_loader, optimizer, criterion, mask, score):
best_accuracy = 0
compress = []
bestacc = []
loss = []
acc = []
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.train()
pbar = tqdm(train_loader)
for i, (imgs, targets) in enumerate(pbar):
imgs, targets = imgs.to(device), targets.to(device)
lr = optimizer.param_groups[0]['lr']
optimizer.zero_grad()
if args.binarize:
W = []
cnt = 0
for name, p in model.named_parameters():
if 'weight' in name:
M = torch.from_numpy(mask[cnt]).to(device)
alpha = (torch.sum((p.data * M)**2) / torch.sum(M**2))**0.5
W.append(p.clone().detach())
p.data.sign_().mul_(M).mul_(alpha)
cnt += 1
output = model(imgs)
cnt = 0
for name, p in model.named_parameters():
if 'weight' in name:
p.data.zero_().add_(W[cnt])
cnt += 1
else:
output = model(imgs)
train_loss = criterion(output, targets)
train_loss.backward()
cnt = 0
for name, p in model.named_parameters():
if 'weight' in name:
tensor = p.data.cpu().numpy()
grad_tensor = p.grad.data.cpu().numpy()
grad_tensor = np.where(tensor < 1e-6, 0, grad_tensor)
p.grad.data = torch.from_numpy(grad_tensor).to(device)
score[cnt] -= lr * p.grad.data
cnt += 1
optimizer.step()
if args.mini_batch:
cnt = 0
for name, p in model.named_parameters():
if 'weight' in name:
if args.score:
if score[cnt].dim() > 3:
sorted, indices = torch.sort(torch.abs(score[cnt]),
dim=0)
tensor = indices.cpu().numpy()
percentile_value = np.percentile(
tensor, args.prune_percent)
mask[cnt] = np.where(tensor < percentile_value, 0,
mask[cnt])
#print(np.count_nonzero(mask[cnt])/np.prod(mask[cnt].shape))
else:
tensor = p.data.cpu().numpy()
alive = tensor[np.nonzero(tensor)]
percentile_value = np.percentile(
abs(alive), args.prune_percent)
mask[cnt] = np.where(
abs(tensor) < percentile_value, 0, mask[cnt])
p.data = torch.from_numpy(
mask[cnt] * p.data.cpu().numpy()).to(p.device)
cnt += 1
#print(utils.print_nonzeros(model))
comp1 = utils.print_nonzeros(model)
if i % (len(train_loader) // args.test_freq) == 0:
loss.append(train_loss.item())
accuracy = test(model, mask, test_loader, criterion)
acc.append(accuracy)
# Save Weights
if accuracy > best_accuracy:
best_accuracy = accuracy
utils.checkdir(
f'{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/')
torch.save(
model,
f'{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{i}_model.pth.tar'
)
#print(f'Train Epoch: {i}/{train_ite} Loss: {train_loss.item():.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%')
#pbar.set_description(f'Train Epoch: {i}/{train_ite} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%')
if args.mini_batch:
compress.append(comp1)
bestacc.append(best_accuracy)
utils.checkdir(f'{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/')
with open(
f'{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/mask_{comp1}.pkl',
'wb') as fp:
pickle.dump(mask, fp)
if args.mini_batch:
return loss, acc, compress, bestacc
else:
return loss, acc, best_accuracy
def _SaveAllChecked(self):
completed = 0
data = self.parent.tree.ImageData
checkedItemList = []
self.parent.tree.findCheckedItems(self.parent.tree.invisibleRootItem(), checkedItemList)
allDim = 0
self.progressText.emit(self.tr("Data size counting"))
for expNumItem in checkedItemList:
allDim += int(utils.num_pattern.findall(expNumItem.text(0))[1])
for expNumItem in checkedItemList:
exp_name = self.parent.tree.getExpNameItem(expNumItem).text(0)
exp_num = utils.num_pattern.findall(expNumItem.text(0))[0]
saveDir = os.path.join(self.tmp_folder.name, exp_name)
utils.checkdir(saveDir)
if self.saveType == "Image":
saveDir = os.path.join(saveDir, exp_num)
utils.checkdir(saveDir)
if self.saveType != "Image":
fname = '{0}{1}Experiment_{2}.{3}'.format(saveDir,
os.sep,
exp_num,
self.form)
img_data = data[exp_name][exp_num]["data"]
for i in range(img_data.Dimension[0]):
if self.cancelThread:
raise CANCELThread()
if self.saveType == "Image":
fname = '{0}{1}Image_{2}.{3}'.format(saveDir,
os.sep,
i+1,
self.form)
self.progressText.emit(
self.tr("Writting Image_{0}.{1} to the folder /{2}/{3}").format(
i+1,
self.form,
exp_name,
exp_num))
toimage(img_data.IntenseData[i,:,:],
cmin=img_data.min_val, cmax=img_data.max_val).save(fname)
else:
self.progressText.emit(
self.tr("Writting Image {0}\{1} to the Experiment_{2}.{3}").format(
i+1,
img_data.Dimension[0],
exp_num,
self.form))
eval("bruker.SingleWriteTo{}File".format(self.saveType))(fname,
img_data,
i,
i==0)
completed += 100/allDim
self.progress.emit(completed)
def main(args, ITE=0):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
reinit = True if args.prune_type == "reinit" else False
if args.save_dir:
utils.checkdir(
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/"
)
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/"
)
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/"
)
else:
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
utils.checkdir(f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/")
# Data Loader
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
if args.dataset == "mnist":
traindataset = datasets.MNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.MNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar10":
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
traindataset = datasets.CIFAR10('../data',
train=True,
download=True,
transform=transform_train)
testdataset = datasets.CIFAR10('../data',
train=False,
transform=transform_test)
from archs.cifar10 import AlexNet, LeNet5, fc1, vgg, resnet, densenet
elif args.dataset == "fashionmnist":
traindataset = datasets.FashionMNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.FashionMNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar100":
traindataset = datasets.CIFAR100('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR100('../data',
train=False,
transform=transform)
from archs.cifar100 import AlexNet, fc1, LeNet5, vgg, resnet
# If you want to add extra datasets paste here
else:
print("\nWrong Dataset choice \n")
exit()
if args.dataset == "cifar10":
#trainsampler = torch.utils.data.RandomSampler(traindataset, replacement=True, num_samples=45000) # 45K train dataset
#train_loader = torch.utils.data.DataLoader(traindataset, batch_size=args.batch_size, shuffle=False, num_workers=0, drop_last=False, sampler=trainsampler)
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
else:
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
#train_loader = cycle(train_loader)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
# Importing Network Architecture
#Initalize hessian dataloader, default batch_num 1
for inputs, labels in train_loader:
hessian_dataloader = (inputs, labels)
break
global model
if args.arch_type == "fc1":
model = fc1.fc1().to(device)
elif args.arch_type == "lenet5":
model = LeNet5.LeNet5().to(device)
elif args.arch_type == "alexnet":
model = AlexNet.AlexNet().to(device)
elif args.arch_type == "vgg16":
model = vgg.vgg16().to(device)
elif args.arch_type == "resnet18":
model = resnet.resnet18().to(device)
elif args.arch_type == "densenet121":
model = densenet.densenet121().to(device)
# If you want to add extra model paste here
else:
print("\nWrong Model choice\n")
exit()
model = nn.DataParallel(model)
# Weight Initialization
model.apply(weight_init)
# Copying and Saving Initial State
initial_state_dict = copy.deepcopy(model.state_dict())
if args.save_dir:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/initial_state_dict_{args.prune_type}.pth"
)
else:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth"
)
# global total_params
total_params = 0
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
total_params += param.numel()
# Making Initial Mask
make_mask(model, total_params)
# Optimizer and Loss
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=1e-4)
# warm up schedule; scheduler_warmup is chained with schduler_steplr
scheduler_steplr = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[0, 15],
gamma=0.1,
last_epoch=-1)
if args.warmup:
scheduler_warmup = GradualWarmupScheduler(
optimizer,
multiplier=1,
total_epoch=50,
after_scheduler=scheduler_steplr) # 20K=(idx)56, 35K=70
criterion = nn.CrossEntropyLoss(
) # Default was F.nll_loss; why test, train different?
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float)
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
for _ite in range(args.start_iter, ITERATION):
if not _ite == 0:
prune_by_percentile(args.prune_percent,
resample=resample,
reinit=reinit,
total_params=total_params,
hessian_aware=args.hessian,
criterion=criterion,
dataloader=hessian_dataloader,
cuda=torch.cuda.is_available())
if reinit:
model.apply(weight_init)
#if args.arch_type == "fc1":
# model = fc1.fc1().to(device)
#elif args.arch_type == "lenet5":
# model = LeNet5.LeNet5().to(device)
#elif args.arch_type == "alexnet":
# model = AlexNet.AlexNet().to(device)
#elif args.arch_type == "vgg16":
# model = vgg.vgg16().to(device)
#elif args.arch_type == "resnet18":
# model = resnet.resnet18().to(device)
#elif args.arch_type == "densenet121":
# model = densenet.densenet121().to(device)
#else:
# print("\nWrong Model choice\n")
# exit()
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
param_frac = param.numel() / total_params
if param_frac > 0.01:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict, total_params)
# optimizer = torch.optim.SGD([{'params': model.parameters(), 'initial_lr': 0.03}], lr=args.lr, momentum=0.9, weight_decay=1e-4)
# scheduler_steplr = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[0, 14], gamma=0.1, last_epoch=-1)
# scheduler_warmup = GradualWarmupScheduler(optimizer, multiplier=1, total_epoch=56, after_scheduler=scheduler_steplr) # 20K=(idx)56, 35K=70
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Optimizer and Loss
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=1e-4)
# warm up schedule; scheduler_warmup is chained with schduler_steplr
scheduler_steplr = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[0, 15], gamma=0.1, last_epoch=-1)
if args.warmup:
scheduler_warmup = GradualWarmupScheduler(
optimizer,
multiplier=1,
total_epoch=50,
after_scheduler=scheduler_steplr) # 20K=(idx)56, 35K=70
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
pbar = tqdm(range(args.end_iter)) # process bar
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, test_loader, criterion)
# Save Weights for each _ite
if accuracy > best_accuracy:
best_accuracy = accuracy
if args.save_dir:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/{_ite}_model_{args.prune_type}.pth"
)
else:
# torch.save(model,f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth")
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth"
)
# Training
loss = train(model, train_loader, optimizer, criterion,
total_params)
all_loss[iter_] = loss
all_accuracy[iter_] = accuracy
# warm up
if args.warmup:
scheduler_warmup.step()
_lr = optimizer.param_groups[0]['lr']
# Save the model during training
if args.save_freq > 0 and iter_ % args.save_freq == 0:
if args.save_dir:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/{_ite}_model_{args.prune_type}_epoch{iter_}.pth"
)
else:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}_epoch{iter_}.pth"
)
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}% Learning Rate: {_lr:.6f}%'
)
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_loss - np.min(all_loss)) /
np.ptp(all_loss).astype(float),
c="blue",
label="Loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
all_accuracy,
c="red",
label="Accuracy")
plt.title(
f"Loss Vs Accuracy Vs Iterations ({args.dataset},{args.arch_type})"
)
plt.xlabel("Iterations")
plt.ylabel("Loss and Accuracy")
plt.legend()
plt.grid(color="gray")
if args.save_dir:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=1200)
else:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=1200)
plt.close()
# Dump Plot values
if args.save_dir:
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
else:
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
# Dumping mask
if args.save_dir:
with open(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
else:
with open(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
if args.save_dir:
comp.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_bestaccuracy.dat"
)
else:
comp.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat"
)
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(
f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{args.arch_type})"
)
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test accuracy")
plt.xticks(a, comp, rotation="vertical")
plt.ylim(0, 100)
plt.legend()
plt.grid(color="gray")
if args.save_dir:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_AccuracyVsWeights.png",
dpi=1200)
else:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png",
dpi=1200)
plt.close()
def main(args, ITE=0):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
reinit = True if args.prune_type == "reinit" else False
# Data Loader
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
if args.dataset == "mnist":
traindataset = datasets.MNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.MNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar10":
traindataset = datasets.CIFAR10('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR10('../data',
train=False,
transform=transform)
from archs.cifar10 import AlexNet, LeNet5, fc1, vgg, resnet, densenet
elif args.dataset == "fashionmnist":
traindataset = datasets.FashionMNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.FashionMNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar100":
traindataset = datasets.CIFAR100('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR100('../data',
train=False,
transform=transform)
from archs.cifar100 import AlexNet, fc1, LeNet5, vgg, resnet
# If you want to add extra datasets paste here
else:
print("\nWrong Dataset choice \n")
exit()
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
#train_loader = cycle(train_loader)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
drop_last=True)
# Importing Network Architecture
global model
if args.arch_type == "fc1":
model = fc1.fc1().to(device)
elif args.arch_type == "lenet5":
model = LeNet5.LeNet5().to(device)
elif args.arch_type == "alexnet":
model = AlexNet.AlexNet().to(device)
elif args.arch_type == "vgg16":
model = vgg.vgg16().to(device)
elif args.arch_type == "resnet18":
model = resnet.resnet18().to(device)
elif args.arch_type == "densenet121":
model = densenet.densenet121().to(device)
# If you want to add extra model paste here
else:
print("\nWrong Model choice\n")
exit()
# Weight Initialization
model.apply(weight_init)
# Copying and Saving Initial State
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/")
torch.save(
model,
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar"
)
# Making Initial Mask
make_mask(model)
# Optimizer and Loss
optimizer = torch.optim.Adam(model.parameters(), weight_decay=1e-4)
criterion = nn.CrossEntropyLoss() # Default was F.nll_loss
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float)
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
for _ite in range(args.start_iter, ITERATION):
if not _ite == 0:
prune_by_percentile(args.prune_percent,
resample=resample,
reinit=reinit)
if reinit:
model.apply(weight_init)
#if args.arch_type == "fc1":
# model = fc1.fc1().to(device)
#elif args.arch_type == "lenet5":
# model = LeNet5.LeNet5().to(device)
#elif args.arch_type == "alexnet":
# model = AlexNet.AlexNet().to(device)
#elif args.arch_type == "vgg16":
# model = vgg.vgg16().to(device)
#elif args.arch_type == "resnet18":
# model = resnet.resnet18().to(device)
#elif args.arch_type == "densenet121":
# model = densenet.densenet121().to(device)
#else:
# print("\nWrong Model choice\n")
# exit()
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
pbar = tqdm(range(args.end_iter))
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, test_loader, criterion)
# Save Weights
if accuracy > best_accuracy:
best_accuracy = accuracy
utils.checkdir(
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/"
)
torch.save(
model,
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth.tar"
)
# Training
if _ite == 0:
loss = train(model, train_loader, optimizer, criterion)
#needed to be completed
#teacher_model = ...
else:
loss = train_with_distill(model, train_loader, optimizer,
teacher_model)
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%'
)
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_loss - np.min(all_loss)) /
np.ptp(all_loss).astype(float),
c="blue",
label="Loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
all_accuracy,
c="red",
label="Accuracy")
plt.title(
f"Loss Vs Accuracy Vs Iterations ({args.dataset},{args.arch_type})"
)
plt.xlabel("Iterations")
plt.ylabel("Loss and Accuracy")
plt.legend()
plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=1200)
plt.close()
# Dump Plot values
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
# Dumping mask
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
with open(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
utils.checkdir(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
comp.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat"
)
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(
f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{args.arch_type})"
)
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test accuracy")
plt.xticks(a, comp, rotation="vertical")
plt.ylim(0, 100)
plt.legend()
plt.grid(color="gray")
utils.checkdir(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png",
dpi=1200)
plt.close()
def main(args, ITE=0):
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
reinit = True if args.prune_type == "reinit" else False
# Data Loader
# Change data here
if args.dataset == 'LV':
# 1
X0 = torch.tensor([10., 5.])
theta = [1.0, 0.1, 1.5, 0.75]
datfunc = Dat.LotkaVolterra(theta)
t_train = torch.linspace(0., 25., args.train_size)
t_eval = torch.linspace(0., 100., args.eval_size)
t_test = torch.linspace(0, 200, args.test_size)
elif args.dataset == 'FHN':
#2
X0 = torch.tensor([-1.0, 1.0])
theta = [0.2, 0.2, 3.0]
datfunc = Dat.FHN(theta)
t_train = torch.linspace(0., 25., args.train_size)
t_eval = torch.linspace(0., 100., args.eval_size)
t_test = torch.linspace(0, 200, args.test_size)
elif args.dataset == 'Lorenz63':
#3
X0 = torch.tensor([1.0, 1.0, 1.0])
theta = [10.0, 28.0, 8.0 / 3.0]
datfunc = Dat.Lorenz63(theta)
t_train = torch.linspace(
0., 25.,
args.train_size) # Need to ask about extents for test case Lorenz
t_eval = torch.linspace(0., 50., args.eval_size)
t_test = torch.linspace(0., 100., args.test_size)
# Need X0 and parameters
# elif args.dataset == 'Lorenz96':
# 4
# X0 = torch.tensor([])
# theta =
# datfunc = Lorenz96(theta)
elif args.dataset == 'ChemicalReactionSimple':
#5
X0 = torch.tensor([1., 1.])
theta = [.5, .8, .4]
datfunc = Dat.ChemicalReactionSimple(theta)
t_train = torch.linspace(0., 25., args.train_size)
t_eval = torch.linspace(0., 100., args.eval_size)
t_test = torch.linspace(0, 200, args.test_size)
elif args.dataset == 'Chemostat':
#6
X0 = torch.tensor([1., 2., 3., 4., 5., 6., 10.])
Cetas = np.linspace(2., 3., 6, dtype=float)
VMs = np.linspace(1., 2., 6, dtype=float)
KMs = np.ones(6, dtype=float)
theta = np.squeeze(
np.concatenate([
Cetas.reshape([1, -1]),
VMs.reshape([1, -1]),
KMs.reshape([1, -1])
],
axis=1))
flowrate = 2.
feedConc = 3.
datfunc = Dat.Chemostat(6, flowrate, feedConc, theta)
t_train = torch.linspace(0., 1.,
args.train_size) # Ask about the extent here
t_eval = torch.linspace(0., 2., args.eval_size)
t_test = torch.linspace(0, 5, args.test_size)
elif args.dataset == 'Clock':
#7
X0 = torch.tensor([1, 1.2, 1.9, .3, .8, .98, .8])
theta = np.asarray([
.8, .05, 1.2, 1.5, 1.4, .13, 1.5, .33, .18, .26, .28, .5, .089,
.52, 2.1, .052, .72
])
datfunc = Dat.Clock(theta)
t_train = torch.linspace(0., 5., args.train_size)
t_eval = torch.linspace(0., 10., args.eval_size)
t_test = torch.linspace(0, 20, args.test_size)
elif args.dataset == 'ProteinTransduction':
#8
X0 = torch.tensor([1., 0., 1., 0., 0.])
theta = [0.07, 0.6, 0.05, 0.3, 0.017, 0.3]
datfunc = Dat.ProteinTransduction(theta)
t_train = torch.linspace(0., 25., args.train_size)
t_eval = torch.linspace(0., 100., args.eval_size)
t_test = torch.linspace(0, 200, args.test_size)
X_train = Dat.generate_data(datfunc,
X0,
t_train,
method=args.integrate_method)
X_eval = Dat.generate_data(datfunc,
X0,
t_eval,
method=args.integrate_method)
X_test = Dat.generate_data(datfunc,
X0,
t_test,
method=args.integrate_method)
dx_dt_train = datfunc(t=None, x=X_train.numpy().T)
dx_dt_eval = datfunc(t=None, x=X_eval.numpy().T)
dx_dt_test = datfunc(t=None, x=X_test.numpy().T)
train_queue = (X_train, dx_dt_train.T)
valid_queue = (X_eval, dx_dt_eval.T)
# Importing Network Architecture/ Import only one model here
global model
if args.arch_type == 'network':
model = network.fc1().to(device)
# If you want to add extra model paste here
else:
print("\nWrong Model choice\n")
exit()
# Weight Initialization
model.apply(weight_init)
# Copying and Saving Initial State
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/")
torch.save(
model,
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar"
)
# Making Initial Mask
make_mask(model)
# Optimizer and Loss
optimizer = torch.optim.Adam(model.parameters(), weight_decay=1e-4)
# criterion = nn.CrossEntropyLoss() # Default was F.nll_loss
# Change loss to MSE
criterion = nn.MSELoss()
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
# Pruning
# NOTE First Pruning Iteration is of No Compression
# Test accuracy needs to turn in MAE
bestacc = 0.0
best_accuracy = 0.3
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float) # What is this?
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
plt.ion()
fig, ax = plt.subplots(figsize=(20, 20))
for _ite in range(args.start_iter, ITERATION):
if not _ite == 0:
prune_by_percentile(args.prune_percent,
resample=resample,
reinit=reinit)
if reinit:
model.apply(weight_init)
#if args.arch_type == "fc1":
# model = fc1.fc1().to(device)
#elif args.arch_type == "lenet5":
# model = LeNet5.LeNet5().to(device)
#elif args.arch_type == "alexnet":
# model = AlexNet.AlexNet().to(device)
#elif args.arch_type == "vgg16":
# model = vgg.vgg16().to(device)
#elif args.arch_type == "resnet18":
# model = resnet.resnet18().to(device)
#elif args.arch_type == "densenet121":
# model = densenet.densenet121().to(device)
#else:
# print("\nWrong Model choice\n")
# exit()
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
pbar = tqdm(range(args.end_iter))
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, valid_queue, criterion, t_eval, ax)
# Save Weights
if accuracy < best_accuracy:
best_accuracy = accuracy
utils.checkdir(
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/"
)
torch.save(
model,
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pt"
)
# Training
loss = train(model, train_queue, optimizer, criterion)
all_loss[iter_] = loss
all_accuracy[iter_] = accuracy
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%'
)
plt.draw()
plt.pause(0.1)
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
# plt.plot(np.arange(1,(args.end_iter)+1), 100*(all_loss - np.min(all_loss))/np.ptp(all_loss).astype(float), c="blue", label="Loss")
# plt.plot(np.arange(1,(args.end_iter)+1), all_accuracy, c="red", label="Accuracy")
# plt.title(f"Loss Vs Accuracy Vs Iterations ({args.dataset},{args.arch_type})")
# plt.xlabel("Iterations")
# plt.ylabel("Loss and Accuracy")
# plt.legend()
# plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
# plt.savefig(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png", dpi=1200)
# plt.close()
# Dump Plot values
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
# Dumping mask
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
with open(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
utils.checkdir(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
comp.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat"
)
# Plotting
plt.ioff()
plt.show()
a = np.arange(args.prune_iterations)
# plt.plot(a, bestacc, c="blue", label="Winning tickets")
# plt.title(f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{args.arch_type})")
# plt.xlabel("Unpruned Weights Percentage")
# plt.ylabel("test accuracy")
# plt.xticks(a, comp, rotation ="vertical")
# plt.ylim(0,100)
# plt.legend()
# plt.grid(color="gray")
utils.checkdir(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
def main(args, ITE=0):
reinit = True if args.prune_type == "reinit" else False
traindataset, testdataset = get_split(args.dataset)
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2,
drop_last=False)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
drop_last=True)
'''
### testing whether cifar10 is getting normalized correctly
mean = 0.0
for images, _ in train_loader:
batch_samples = images.size(0)
images = images.view(batch_samples, images.size(1), -1)
# print(images[0]);exit()
mean += images.mean(2).sum(0)
mean = mean / len(train_loader.dataset)
var = 0.0
for images, _ in train_loader:
batch_samples = images.size(0)
images = images.view(batch_samples, images.size(1), -1)
var += ((images - mean.unsqueeze(1))**2).sum([0,2])
std = torch.sqrt(var / (len(train_loader.dataset)*images.size(-1)))
print("mean, std: ", mean, std)
exit()
'''
# Importing Network Architecture
global model
model = get_model(args.arch_type)
# Weight Initialization
model.apply(weight_init)
# Copying and Saving Initial State
initial_state_dict = copy.deepcopy(model.state_dict())
tar_dir = f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.exp_name}/"
utils.checkdir(tar_dir)
torch.save(model,
tar_dir + f"initial_state_dict_{args.prune_type}.pth.tar")
if not args.rlt:
# Making Initial Mask
make_mask(model, None)
# Optimizer and Loss
optimizer = torch.optim.Adam(model.parameters(), weight_decay=1e-4)
criterion = nn.CrossEntropyLoss() # Default was F.nll_loss
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
dump_dir = f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.exp_name}/"
utils.checkdir(dump_dir)
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float)
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
for _ite in range(args.start_iter, ITERATION):
# random lotter ticket
if args.rlt:
# percent of weights to prune
percent = 1 - ((1 - args.prune_percent / 100)**_ite)
make_mask(model, percent)
# same original initialized weights, with different random masks
original_initialization(mask, initial_state_dict)
else:
# first net is unpruned!
if _ite != 0:
prune_by_percentile(args.prune_percent, reinit=reinit)
if reinit:
model.apply(weight_init)
step = 0
for name, param in model.named_parameters():
if 'weight' in name and 'classifier' in name:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
pbar = tqdm(range(args.end_iter))
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, test_loader)
# Save Weights
if accuracy > best_accuracy:
best_accuracy = accuracy
torch.save(
model,
tar_dir + f"{_ite}_model_{args.prune_type}.pth.tar")
# Training
loss = train(model, train_loader, optimizer, criterion)
all_loss[iter_] = loss
all_accuracy[iter_] = accuracy
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%'
)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_loss - np.min(all_loss)) /
np.ptp(all_loss).astype(float),
c="blue",
label="Loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
all_accuracy,
c="red",
label="Accuracy")
plt.title(
f"Loss Vs Accuracy Vs Iterations ({args.dataset},{args.arch_type})"
)
plt.xlabel("Iterations")
plt.ylabel("Loss and Accuracy")
plt.legend()
plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=1200)
plt.close()
# Dump Plot values
dump_dir = f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.exp_name}/"
utils.checkdir(dump_dir)
all_loss.dump(dump_dir + f"{args.prune_type}_all_loss_{comp1}.dat")
all_accuracy.dump(dump_dir +
f"{args.prune_type}_all_accuracy_{comp1}.dat")
# Dumping mask
with open(dump_dir + f"{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
comp.dump(dump_dir + f"{args.prune_type}_compression.dat")
bestacc.dump(dump_dir + f"{args.prune_type}_bestaccuracy.dat")
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(
f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{args.arch_type})"
)
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test accuracy")
plt.xticks(a, comp, rotation="vertical")
plt.ylim(0, 100)
plt.legend()
plt.grid(color="gray")
utils.checkdir(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png",
dpi=1200)
plt.close()
def main(args, ITE=0):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Data Loader
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
if args.dataset == "mnist":
traindataset = datasets.MNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.MNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc3, fc5, vgg, resnet
elif args.dataset == "cifar10":
traindataset = datasets.CIFAR10('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR10('../data',
train=False,
transform=transform)
from archs.cifar10 import AlexNet, LeNet5, fc1, vgg, resnet, densenet
elif args.dataset == "fashionmnist":
traindataset = datasets.FashionMNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.FashionMNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar100":
traindataset = datasets.CIFAR100('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR100('../data',
train=False,
transform=transform)
from archs.cifar100 import AlexNet, fc1, LeNet5, vgg, resnet
else:
print("\nWrong Dataset choice \n")
exit()
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
#train_loader = cycle(train_loader)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
drop_last=True)
model = fc5.fc5().to(device)
model.apply(weight_init)
# Copying and Saving Initial State
output_dir = args.output_dir if args.output_dir else f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/"
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(output_dir)
torch.save(
initial_state_dict,
os.path.join(output_dir,
f"initial_state_dict_{args.prune_type}.pth.tar"))
optimizer = torch.optim.Adam(model.parameters(),
weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss()
# Pruning
# NOTE First Pruning Iteration is of No Compression
dump_dir = args.dump_dir if args.dump_dir else f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/"
for _ite in range(args.start_iter, args.prune_iterations + 1):
best_accuracy = 0
if not _ite == 0:
utils.pruning_generate(model, 0.2, method=args.pruning_method)
model_state_dict = model.state_dict()
model_orig_weight = utils.rewind_weight(initial_state_dict,
model_state_dict.keys())
model_state_dict.update(model_orig_weight)
model.load_state_dict(model_state_dict)
torch.save(
model.state_dict(),
os.path.join(output_dir,
f"{_ite}_model_init_{args.prune_type}.pth.tar"))
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
pbar = tqdm(range(args.end_iter))
for iter_ in pbar:
if iter_ % args.valid_freq == 0:
if _ite > 0:
print(model.classifier[8].weight_orig[0, :5])
print(model.classifier[8].weight_mask[0, :5])
else:
print(model.classifier[8].weight[0, :5])
accuracy = test(model, test_loader, criterion)
if accuracy > best_accuracy:
best_accuracy = accuracy
utils.checkdir(output_dir)
torch.save(
model.state_dict(),
os.path.join(
output_dir,
f"{_ite}_model_{args.prune_type}.pth.tar"))
# Training
if not args.pruning_method == 'random':
loss = train(model, train_loader, optimizer, criterion)
else:
loss = 100
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%'
)
def main(args, ITE=0):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
reinit = True if args.prune_type=="reinit" else False
#carregar dataset
MAX_LEN = 60 # NOTE: we filter out a lot of sentences for speed
train_data, valid_data, test_data = text_datasets.IWSLT.splits(exts=('.de', '.en'),
fields=(SRC, TRG),
filter_pred=lambda x: len(vars(x)['src']) <= MAX_LEN and len(vars(x)['trg']) <= MAX_LEN)
MIN_FREQ = 2 # NOTE: we limit the vocabulary to frequent words for speed
SRC.build_vocab(train_data.src, min_freq=MIN_FREQ)
TRG.build_vocab(train_data.trg, min_freq=MIN_FREQ)
PAD_INDEX = TRG.vocab.stoi[PAD_TOKEN]
print_data_info(train_data, valid_data, test_data, SRC, TRG)
train_iter = data.BucketIterator(train_data, batch_size=64, train=True,
sort_within_batch=True,
sort_key=lambda x: (len(x.src), len(x.trg)), repeat=False,
device=DEVICE)
valid_iter = data.Iterator(valid_data, batch_size=1, train=False, sort=False, repeat=False, device=DEVICE)
# Importing Network Architecture
global model
model = make_model(len(SRC.vocab), len(TRG.vocab),
emb_size=256, hidden_size=256,
num_layers=1, dropout=0.2)
print(isinstance(model,nn.Linear))
# Weight Initialization
model.apply(weight_init)
# Copying and Saving Initial State
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/")
torch.save(model, f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar")
# Making Initial Mask
make_mask(model)
# Optimizer and Loss
optim = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.NLLLoss(reduction="sum", ignore_index=PAD_INDEX)
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION,float)
bestacc = np.zeros(ITERATION,float)
step = 0
all_loss = np.zeros(args.end_iter,float)
all_accuracy = np.zeros(args.end_iter,float)
for _ite in range(args.start_iter, ITERATION):
if not _ite == 0:
prune_by_percentile(args.prune_percent, resample=resample, reinit=reinit)
if reinit:
model.apply(weight_init)
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
weight_dev = param.device
param.data = torch.from_numpy(param.data.cpu().numpy() * mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
pbar = tqdm(range(args.end_iter))
for iter_ in pbar:
# Training
loss = train(model, pad_index=PAD_INDEX,train_iter=train_iter,valid_iter=valid_iter,args=args)
accuracy = test(model,valid_iter=valid_iter,pad_index=PAD_INDEX)
if accuracy > best_accuracy:
best_accuracy = accuracy
utils.checkdir(f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/")
torch.save(model,f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth.tar")
all_loss[iter_] = loss
all_accuracy[iter_] = accuracy
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%')
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite]=best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
# plt.plot(np.arange(1,(args.end_iter)+1), 100*(all_loss - np.min(all_loss))/np.ptp(all_loss).astype(float), c="blue", label="Loss")
# plt.plot(np.arange(1,(args.end_iter)+1), all_accuracy, c="red", label="Accuracy")
# plt.title(f"Loss Vs Accuracy Vs Iterations ({args.dataset},{args.arch_type})")
# plt.xlabel("Iterations")
# plt.ylabel("Loss and Accuracy")
# plt.legend()
# plt.grid(color="gray")
# utils.checkdir(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
# plt.savefig(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png", dpi=1200)
# plt.close()
# Dump Plot values
utils.checkdir(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
all_loss.dump(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat")
all_accuracy.dump(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat")
# Dumping mask
utils.checkdir(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
with open(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl", 'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter,float)
all_accuracy = np.zeros(args.end_iter,float)
# Dumping Values for Plotting
utils.checkdir(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
comp.dump(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat")
bestacc.dump(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat")
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(f"Test BLEU vs Unpruned Weights Percentage ({args.dataset},{args.arch_type})")
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test BLEU")
plt.xticks(a, comp, rotation ="vertical")
plt.ylim(0,100)
plt.legend()
plt.grid(color="gray")
utils.checkdir(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png", dpi=1200)
plt.close()
model = resnet.resnet18().to(device)
elif args.arch_type == 'Conv2':
model = SmallVGG.Conv2().to(device)
elif args.arch_type == 'Conv4':
model = SmallVGG.Conv4().to(device)
elif args.arch_type == 'Conv6':
model = SmallVGG.Conv6().to(device)
elif args.arch_type == 'Conv8':
model = SmallVGG.Conv8().to(device)
else:
raise Exception('\nWrong Model choice\n')
# Weight Initialization
model.apply(weight_init)
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(f'{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/')
torch.save(
model,
f'{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/initial_state_dict.pth.tar'
)
# Making Initial Mask
mask = []
score = []
for name, p in model.named_parameters():
if 'weight' in name:
tensor = p.data.cpu().numpy()
mask.append(np.ones_like(tensor))
if p.data.dim() > 1:
score.append(init.xavier_normal_(torch.ones_like(p.data)))
else:
def main(args, ITE=0):
import pandas as pd
pd.set_option('display.width', 400)
pd.set_option('display.max_columns', 10)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
reinit = True if args.prune_type == "reinit" else False
layerwise = True if args.prune_type == "layerwise" else False
# Data Loader
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
transform_cifar10 = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == "mnist":
traindataset = datasets.MNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.MNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar10":
traindataset = datasets.CIFAR10('../data',
train=True,
download=True,
transform=transform_cifar10)
testdataset = datasets.CIFAR10('../data',
train=False,
transform=transform_cifar10)
from archs.cifar10 import AlexNet, LeNet5, fc1, vgg, resnet, densenet, minivgg
elif args.dataset == "fashionmnist":
traindataset = datasets.FashionMNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.FashionMNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar100":
traindataset = datasets.CIFAR100('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR100('../data',
train=False,
transform=transform)
from archs.cifar100 import AlexNet, fc1, LeNet5, vgg, resnet
# If you want to add extra datasets paste here
else:
print("\nWrong Dataset choice \n")
exit()
# obtain training indices that will be used for validation
if args.early_stopping:
print(' Splitting Validation sets ')
trainset_size = int((1 - args.valid_size) * len(traindataset))
valset_size = len(traindataset) - trainset_size
trainset, valset = torch.utils.data.random_split(
traindataset, [trainset_size, valset_size])
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
valid_loader = torch.utils.data.DataLoader(valset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
else:
print(' Eww, no validation set? ')
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
# train_loader = cycle(train_loader)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
drop_last=True)
# Importing Network Architecture
global model
if args.arch_type == "fc1":
model = fc1.fc1().to(device)
elif args.arch_type == "lenet5":
model = LeNet5.LeNet5().to(device)
elif args.arch_type == "alexnet":
model = AlexNet.AlexNet().to(device)
elif args.arch_type == "vgg16":
model = vgg.vgg16().to(device)
elif args.arch_type == "resnet18":
model = resnet.resnet18().to(device)
elif args.arch_type == "densenet121":
model = densenet.densenet121().to(device)
# If you want to add extra model paste here
elif args.arch_type == "conv2":
model = minivgg.conv2().to(device)
elif args.arch_type == "conv4":
model = minivgg.conv4().to(device)
elif args.arch_type == "conv6":
model = minivgg.conv6().to(device)
else:
print("\nWrong Model choice\n")
exit()
# Weight Initialization. Warning! This drops test acc, so i'm examining this function.
model.apply(weight_init)
# get time for file path
import datetime
now = datetime.datetime.now()
now_ = now.strftime("%02m%02d%02H%02M_")
# Copying and Saving Initial State
print(' saving initial model... ')
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(
f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/")
torch.save(
model,
f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar"
)
print(
" initial model saved in ",
f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar"
)
# Making Initial Mask
make_mask(model)
# Optimizer and Loss
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
criterion = nn.CrossEntropyLoss() # Default was F.nll_loss
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float)
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_vloss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
for _ite in range(args.start_iter, ITERATION):
# Early stopping parameter for each pruning iteration
early_stopping = EarlyStopping(
patience=99,
verbose=True) ######### we don't stop, party all night
if not _ite == 0:
prune_by_percentile(args.prune_percent,
args.fc_prune_percent,
resample=resample,
reinit=reinit,
layerwise=layerwise,
if_split=args.split_conv_and_fc)
if reinit:
model.apply(weight_init)
#if args.arch_type == "fc1":
# model = fc1.fc1().to(device)
#elif args.arch_type == "lenet5":
# model = LeNet5.LeNet5().to(device)
#elif args.arch_type == "alexnet":
# model = AlexNet.AlexNet().to(device)
#elif args.arch_type == "vgg16":
# model = vgg.vgg16().to(device)
#elif args.arch_type == "resnet18":
# model = resnet.resnet18().to(device)
#elif args.arch_type == "densenet121":
# model = densenet.densenet121().to(device)
#else:
# print("\nWrong Model choice\n")
# exit()
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
time.sleep(0.25)
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
# pbar = range(args.end_iter)
pbar = tqdm(range(args.end_iter))
stop_flag = False
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, test_loader, criterion)
# Save Weights
if accuracy > best_accuracy:
best_accuracy = accuracy
# We don't save model per test-acc, will use validation-acc!
# utils.checkdir(f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/")
# torch.save(model,f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth.tar")
# Training
loss = train(model, train_loader, optimizer, criterion)
all_loss[iter_] = loss
all_accuracy[iter_] = accuracy
# Validating
valid_loss, loss_v = validate(model, valid_loader, optimizer,
criterion)
all_vloss[iter_] = valid_loss #loss_v
# early stopping
checkpoint_path = f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/"
save_path = f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth.tar"
# msg = early_stopping(valid_loss, model, checkpoint_path, save_path)
early_stopping(valid_loss, model, checkpoint_path, save_path)
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
time.sleep(0.25)
pbar.set_description(
# f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}% \t' + msg)
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} V-Loss: {valid_loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%'
)
if iter_ % 5 == 4:
print('')
if early_stopping.early_stop and not stop_flag:
print("Early stopping")
stop_flag = True
# break
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_loss - np.min(all_loss)) /
np.ptp(all_loss).astype(float),
c="blue",
label="Train loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_vloss - np.min(all_vloss)) /
np.ptp(all_vloss).astype(float),
c="green",
label="Valid loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
all_accuracy,
c="red",
label="Accuracy")
plt.title(
f"Loss Vs Accuracy Vs Iterations ({args.dataset},{now_}{args.arch_type})"
)
plt.xlabel("Iterations")
plt.ylabel("Loss and Accuracy")
plt.legend()
plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=300)
plt.close()
# Dump Plot values
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/")
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
# Dumping mask
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/")
with open(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/")
comp.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat"
)
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(
f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{now_}{args.arch_type})"
)
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test accuracy")
plt.xticks(a, comp, rotation="vertical")
plt.ylim(0, 100)
plt.legend()
plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png",
dpi=300)
plt.close()
print('Training ended~~~')