def lemma_to_synset_key(keyin, keyout):
for line in keyin:
inst_id, lemma_ids = line.split(" ", 1)
keyout.write(inst_id)
for lemma_id in lemma_ids.split():
keyout.write(
" " + wordnet.ss2of(wordnet.lemma_from_key(lemma_id).synset()))
keyout.write("\n")
def get_in_id(wordnet_ss):
"""
Transforms a worndet synset into a imagenet id
Input: Synset
:param wordnet_ss:
:return: imagenet id (string)
"""
wn_id = wn.ss2of(wordnet_ss)
return wn_id[-1] + wn_id[:8]
def main():
global args, best_prec1, poinc_emb
global imgnet_poinc_wgt, imgnet_labels
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
if args.emb_name is None:
raise NameError('args.emb_name file not specified')
#load Poincare embedding
poinc_emb = torch.load(args.emb_dir + args.emb_name)
print('EMBEDDING TYPE:', poinc_emb['conf']['manifold'])
n_emb_dims = poinc_emb['embeddings'].shape[1]
args.n_emb_dims = n_emb_dims
print('NUM OF DIMENSIONS:', n_emb_dims)
#change labels from synset names into imagenet format
synset_list = [wn.synset(i) for i in poinc_emb['objects']]
offset_list = [wn.ss2of(j) for j in synset_list]
poinc_emb['objects'] = ['n' + i.split('-')[0] for i in offset_list]
#settings for distributed training
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()
if args.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the total world_size
# needs to be adjusted accordingly
args.world_size = ngpus_per_node * args.world_size
# Use torch.multiprocessing.spawn to launch distributed processes: the
# main_worker process function
mp.spawn(main_worker,
nprocs=ngpus_per_node,
args=(ngpus_per_node, args))
else:
# Simply call main_worker function
main_worker(args.gpu, ngpus_per_node, args)
def wordnet_batcher():
all_synsets = wordnet.all_synsets()
batch_size = get_batch_size()
while 1:
batch = islice(all_synsets, batch_size)
ids = []
defns = []
for synset in batch:
ids.append(wordnet.ss2of(synset))
defns.append(synset.definition())
if not ids:
return
yield ids, defns
def main(pred_matrix, pb_defns, out, reject_non_english, use_model, synset):
# Load mapping from English PropBank senses to English WordNet senses
mapping = {}
with open(pred_matrix, 'r') as matrix:
matrix = csv.DictReader(matrix, delimiter='\t')
for row in matrix:
if reject_non_english and row['1_ID_LANG'] != 'id:eng':
continue
if row['11_WN_SENSE'] != 'wn:NULL':
pb = row['16_PB_ROLESET'].split(':', 1)[1]
wn = row['11_WN_SENSE'].split(':', 1)[1]
mapping.setdefault(pb, set()).add(wn)
# Join with mapping from Finnish to English PropBank
with open(pb_defns, 'r') as propbank, open(out, 'w') as csvout:
propbank = csv.DictReader(propbank, delimiter='\t')
csvout = csv.writer(csvout)
csvout.writerow(['pb', 'wn'])
propbank = peekable(propbank)
for row in propbank:
pb_finn = "{}.{:0>2}".format(row['base'], row['number'])
if use_model:
match = MODEL_RE.match(row['note'])
if match:
pb = match.group(1)
else:
pb = None
else:
pb = row['link_original']
if pb == 'none.01':
pb = None
if pb is not None and pb in mapping:
for wn in mapping[pb]:
if synset:
csvout.writerow(
(pb_finn,
wordnet.ss2of(
wordnet.lemma_from_key(wn + "::").synset())))
else:
csvout.writerow((pb_finn, wn))
def main():
#parse args
global args
args = parser.parse_args()
if args.emb_file_name is None:
raise NameError('args.emb_file_name is not specified')
#GPU setting
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cudnn.benchmark = True
#import dataset
embedding = torch.load(args.emb_dir+args.emb_file_name)
print('EMBEDDING TYPE:', embedding['manifold'])
n_emb_dims = embedding['embeddings'].shape[1]
args.n_emb_dims = n_emb_dims
print('NUM OF DIMENSIONS:', n_emb_dims)
#change labels from synset names into imagenet format
synset_list = [wn.synset(i) for i in embedding['objects']]
offset_list = [wn.ss2of(j) for j in synset_list]
embedding['objects'] = ['n'+i.split('-')[0] for i in offset_list]
#load the CNN part of the model
print("=>using pre-trained model '{}'".format(args.arch))
orig_vgg = models.__dict__[args.arch](pretrained=True)
#change the model to project into desired embedding space
if embedding['manifold'] == 'poincare':
model = PoincareEmbVGG(orig_vgg, args.n_emb_dims)
elif embedding['manifold'] == 'euclidean':
model = EuclidEmbVGG(orig_vgg, args.n_emb_dims)
model.to(device, non_blocking=True)
model.features = torch.nn.DataParallel(model.features)
#load weights from training on 1K classes
if os.path.isfile(args.saved_weights):
print("=> loading checkpoint '{}'".format(args.saved_weights))
checkpoint = torch.load(args.saved_weights)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(args.saved_weights,
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.saved_weights))
#data loading
evaldir = '/mnt/fast-data15/datasets/imagenet/fa2011'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
eval_dataset = datasets.ImageFolder(evaldir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,]))
eval_loader = torch.utils.data.DataLoader(
eval_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
#sort embedding to match image labels
img_labels = eval_dataset.classes
img2emb_idx = [embedding['objects'].index(i)
for i in img_labels]
emb_wgts = embedding['embeddings'][img2emb_idx]
emb_wgts = emb_wgts.float().to(device, non_blocking=True)
n_classes = emb_wgts.shape[0]
#load 21k class distance matrix
class_distance_mat = torch.load('class_dist_mat.pt').to(device,
non_blocking=True)
class_distance_mat = class_distance_mat+torch.t(class_distance_mat)
#trackers
batch_time = AverageMeter('Time', ':6.3f')
top5_pos_track = AverageMeter('Top5+', ':6.2f')
top5_neg_track = AverageMeter('Top5-', ':6.2f')
progress = ProgressMeter(
len(eval_loader),
[batch_time, top5_pos_track, top5_neg_track],
prefix='Eval: ')
#evaluate
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(eval_loader):
print(i)
#if i <= 25329:
# continue
if i == 40:
pdb.set_trace()
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
#compute output
output = model(images)
#evaluate
preds = prediction(output, emb_wgts, 5, embedding['manifold'])
target_dist_mat = class_distance_mat[target]
top5_pos, top5_neg = calc_top5_pos_neg(preds, target_dist_mat)
#track evaluation
top5_pos_track.update(top5_pos, preds.shape[0])
top5_neg_track.update(top5_neg, preds.shape[0])
#measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
print(
' * Top5+ {top5_pos_track.avg: .3f} Top5- {top5_neg_track.avg:.3f}'.format(top5_pos_track=top5_pos_track, top5_neg_track=top5_neg_track))
def ss2of(self, key, synset):
"""Convert synset to offset
"""
off = 'n' + wn.ss2of(synset)[:8]
self._labels[key] = off
return off
def main():
global args, best_prec1, poinc_emb
global imgnet_poinc_wgt, imgnet_poinc_labels
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
if args.emb_name is None:
raise NameError('args.emb_name file not specified')
#load Lorentzian embedding
lorentz_emb = torch.load(args.emb_dir + args.emb_name)
print('EMBEDDING TYPE:', poinc_emb['manifold'])
n_emb_dims = poinc_emb['embeddings'].shape[1] - 1
print('NUM OF DIMENSIONS:', n_emb_dims)
#change labels from synset names into imagenet format
synset_list = [wn.synset(i) for i in poinc_emb['objects']]
offset_list = [wn.ss2of(j) for j in synset_list]
poinc_emb['objects'] = ['n' + i.split('-')[0] for i in offset_list]
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
orig_vgg = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
orig_vgg = models.__dict__[args.arch]()
#Change model to project into poincare space
model = PoincareVGG(orig_vgg, n_emb_dims, args.unfreeze)
if args.gpu is not None:
model = model.cuda(args.gpu)
else:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
# define loss function (criterion) and optimizer
criterion = LorentzXEntropyLoss()
if args.unfreeze:
optimizer = torch.optim.SGD([{
'params': model.features.parameters(),
'lr': args.lr * 10**-1
}, {
'params': model.fc.parameters()
}, {
'params': model.classifier.parameters()
}],
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
lr_sched = torch.optim.lr_scheduler.StepLR(
optimizer, step_size=args.lr_decay_interval, gamma=args.lr_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_sched.load_state_dict(checkpoint['scheduler'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val_white')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
#create poincare embedding that only contains imagenet synsets
imgnet_poinc_labels = train_dataset.classes
imgnet2poinc_idx = [
poinc_emb['objects'].index(i) for i in imgnet_poinc_labels
]
imgnet_poinc_wgt = poinc_emb['embeddings'][imgnet2poinc_idx]
imgnet_poinc_wgt = imgnet_poinc_wgt.float().cuda(non_blocking=True)
#create train and val data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
sampler=None)
val_loader = torch.utils.data.DataLoader(datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
validate(val_loader, model, criterion)
return
for epoch in range(args.start_epoch, args.epochs):
lr_sched.step()
# train the model
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
'scheduler': lr_sched.state_dict(),
}, is_best, args.emb_name + '_checkp.pth.tar')
def get_synset_id(ss):
if not ss:
return None
return wn.ss2of(ss)
def lemma_id_to_synset_id(lemma_id):
from nltk.corpus import wordnet
return wordnet.ss2of(wordnet.lemma_from_key(lemma_id + "::").synset())
# -*- coding: utf-8 -*-
"""
Get definition and examples from WordNet ID
"""
from nltk.corpus import wordnet as wn
word = input("WordNet ID : ")
word_wn = wn.of2ss(word.replace('-', ''))
print(word_wn.definition())
print(word_wn.examples())
print("WordNet ID : " + wn.ss2of(word_wn))