""" Load dataset """
data_path = '{}/{}'.format(opt.data_path, 'hico')
image_path = '{}/{}/{}'.format(opt.data_path, 'hico', 'images')
cand_dir = '{}/{}/{}'.format(opt.data_path, 'hico', 'detections')
dset = Hico(data_path, \
image_path, \
opt.test_split, \
cand_dir=cand_dir,\
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,\
# print("*****************************************")
model.load_state_dict(checkpoint['model'], False)
if isinstance(model, torch.nn.DataParallel):
model = model.module
model.eval()
######################
""" Query triplets """
######################
if opt.train_split == 'trainval' or opt.train_split == 'train':
triplet_queries = dset.visualphrases.words()
else:
triplet_queries = dset.get_zeroshottriplets()
###############
""" Analogy """
###############
if opt.use_analogy:
model.precomp_language_features() # pre-compute unigram emb
model.precomp_sim_tables() # pre-compute similarity tables for speed-up
# Target queries indices
queries_sro, triplet_queries_idx = model.precomp_target_queries(
triplet_queries)
# Pre-compute language features in joint sro space
image_path = '{}/{}/{}'.format(opt.data_path, 'hico', 'images')
cand_dir = '{}/{}/{}'.format(opt.data_path, 'hico', 'detections')
dset = Hico(data_path, \
image_path, \
opt.test_split, \
cand_dir=cand_dir,\
thresh_file=opt.thresh_file, \
add_gt=False, \
train_mode=False, \
jittering=False, \
nms_thresh=opt.nms_thresh)
""" Key types """
keys = ['s-r-o', 's-sro-o', 's-r-o-sro']
""" Load the test triplets """
target_triplets = dset.get_zeroshottriplets()
subset = 'zeroshottriplet'
for key in keys:
""" Load ap results for all triplets """
filename_in = osp.join(opt.logger_dir, opt.exp_name, 'results_{}_{}_{}_{}_def.csv'.format(\
opt.cand_test,\
opt.test_split,\
opt.epoch_model,\
key))
with open(filename_in) as f:
reader = csv.DictReader(f)
ap_results = [r for r in reader]
""" Write csv subset of triplets """
filename_out = osp.join(opt.logger_dir, opt.exp_name, 'results_{}_{}_{}_{}_{}_def.csv'.format(\
import os.path as osp
from datasets.hico_api import Hico
import csv
import pickle
# Load vocabulary of triplets
root_path = './data'
data_path = '{}/{}'.format(root_path, 'hico')
image_path = '{}/{}/{}'.format(root_path, 'hico', 'images')
cand_dir = '{}/{}/{}'.format(root_path, 'hico', 'detections')
split = 'trainval' #'train','trainval'
dset = Hico(data_path, image_path, split, cand_dir)
# Get set of triplets
triplets_remove = dset.get_zeroshottriplets()
triplet_cat_remove = []
for l in range(len(triplets_remove)):
triplet_cat_remove.append(dset.visualphrases.word2idx[triplets_remove[l]])
# Build a new set of candidates excluding the triplet categories
cand_positives = pickle.load(open(osp.join(data_path, 'cand_positives_' + split + '.pkl'),'rb'))
idx_keep = []
for j in range(cand_positives.shape[0]):
if j%100000==0:
print('Done {}/{}'.format(j, cand_positives.shape[0]))
im_id = cand_positives[j,0]
cand_id = cand_positives[j,1]
# Load the gt label of visualphrase