ap_triplets.append(ap)
# Write mAP
writer.writerow(['mAP', np.nanmean(ap_triplets)])
"""
Get the similarities used for predictions : in detection_dir. 1 file for each target triplet (more readable)
"""
if not opt.embedding_type == 'target':
occurrences_train = dset.get_occurrences_precomp(
opt.train_split.split('_')[0])
opt.occurrences = dset.get_occurrences_precomp(
opt.train_split.split('_')[0])
opt = parser.get_opts_from_dset(opt, dset) # additional opts from dset
model = models.get_model(opt)
checkpoint = torch.load(osp.join(logger_path,
'model_' + opt.epoch_model + '.pth.tar'),
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['model'])
model.eval()
if torch.cuda.is_available():
model.cuda()
""" Pre-compute language feats """
if opt.use_analogy:
model.precomp_language_features()
""" Get the triplets to retrieve """
####################################
subset_test = 'unrel'
target_triplets = pickle.load(open('/sequoia/data2/jpeyre/datasets/unrel_iccv17/unrel-dataset/triplet_queries.pkl', 'rb')) # Unrel queries in VG vocab
print('Computing retrieval on {} triplet queries'.format(len(target_triplets)))
##################
""" Load model """
##################
logger_path = osp.join(opt.logger_dir, opt.exp_name)
opt = parser.get_opts_from_dset(opt, dset)
# Load model
print('Loading model')
model = models.get_model(opt)
checkpoint = torch.load(osp.join(logger_path, 'model_' + opt.epoch_model + '.pth.tar'), map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['model'])
model.eval()
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
if torch.cuda.is_available():
