thresh_file=opt.thresh_file, \
add_gt=False, \
train_mode=False, \
jittering=False, \
nms_thresh=opt.nms_thresh)
""" Load the test triplets """
target_triplets = dset.get_zeroshottriplets(
) # uncomment to eval zeroshot triplets
#target_triplets = dset.visualphrases.words() # uncomment to eval all triplets
""" Keys to analyze """
keys = ['s-sro-o', 's-r-o-sro']
""" Aggregate csv result files (from official HICO eval code) """
# Logger path
logger_path = osp.join(opt.logger_dir, opt.exp_name)
detection_path = parser.get_res_dir(opt, 'detections_' + opt.embedding_type)
res_path = parser.get_res_dir(opt, 'res_' + opt.embedding_type)
for key in keys:
""" File out : 1 file for AP results : group all zeroshot triplets AP """
filename_out = osp.join(res_path, 'results_{}_{}_{}_{}.csv'.format(\
opt.cand_test,\
opt.test_split,\
opt.epoch_model,\
key))
with open(filename_out, 'wb') as f:
writer = csv.writer(f)
ap_triplets = []
use_precompobjectscore = opt.use_precompobjectscore)
loader = torch.utils.data.DataLoader(dset_loader,
batch_size=8,
shuffle=False,
num_workers=0,
collate_fn=dset_loader.collate_fn)
##################
""" Load model """
##################
# Logger path
logger_path = osp.join(opt.logger_dir, opt.exp_name)
save_dir = parser.get_res_dir(opt, 'detections_' + opt.embedding_type)
opt = parser.get_opts_from_dset(opt, dset) # additional opts from dset
# Model
model = models.get_model(opt)
model = nn.DataParallel(model).cuda()
checkpoint = torch.load(
osp.join(logger_path,
'model_' + opt.epoch_model + '.pth.tar')) #.module.state_dict()
# for k,v in checkpoint.items():
# print(k)
# print("*****************************************")
model.load_state_dict(checkpoint['model'], False)
lang_feats_precomp_sro = model.get_language_features(queries_sro, 'sro')
elif opt.use_analogy:
print('Precomputing query features in joint space with analogy...')
lang_feats_precomp_sro = model.get_language_features_analogy(queries_sro)
################
""" Evaluate """
################
keys = ['r','sro','s','o']
name_dir = 'scores_retrieval_' + subset_test + '_' + opt.embedding_type
save_dir = parser.get_res_dir(opt, name_dir)
sim_dir = parser.get_res_dir(opt, 'similarities_' + subset_test + '_' + opt.embedding_type)
# Print source triplets and simmilarities
if not opt.embedding_type=='target':
print_similarities(target_triplets, queries_sro)
print('Begin evaluation')
datasets = ['unrel','vrd']
num_cand_theoric = {'vrd_candidates':290974,'vrd_gt-candidates':51036, 'unrel_candidates':166368, 'unrel_gt-candidates':10308}
for cand_test in ['gt-candidates','candidates']:
#for cand_test in ['gt-candidates']:
for data_name in datasets:
# Target queries indices
queries_sro, triplet_queries_idx = model.precomp_target_queries(
triplet_queries)
# Pre-compute language features in joint sro space
print('Precomputing query features in joint space with analogy...')
lang_feats_precomp_sro = model.get_language_features_analogy(queries_sro)
################
""" Evaluate """
################
keys = opt.mixture_keys.split('_') if opt.mixture_keys else [
's-r-o', 's-r-o-sro', 's-sro-o'
]
save_dir = parser.get_res_dir(
opt, 'apRetrieval_{}_{}'.format(subset_test, opt.embedding_type))
##########################
""" Get the detections """
##########################
# Save the detections by key (group all triplets together)
det_path = osp.join(save_dir, 'detections_{}_{}_{}_{}_{}.pkl'.format(opt.cand_test,\
opt.epoch_model,\
opt.data_name,\
'%s',\
'%s'))
det_file = det_path % (subset_test, 's-r-o')
print('Begin evaluation')
