def main(**kwargs):
exp_base_dir = coco_paths['exp_dir']
if kwargs['dataset'] == 'flickr':
exp_base_dir = flickr_paths['exp_dir']
exp_const = ExpConstants(kwargs['exp_name'], exp_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.seed = 0
exp_const.contextualize = not kwargs['no_context']
exp_const.random_lang = kwargs['random_lang']
data_const = FlickrDatasetConstants(kwargs['subset'])
model_const = Constants()
model_const.model_num = kwargs['model_num']
model_const.object_encoder = ObjectEncoderConstants()
model_const.object_encoder.context_layer.output_attentions = True
model_const.object_encoder.object_feature_dim = 2048
model_const.cap_encoder = CapEncoderConstants()
model_const.cap_encoder.output_attentions = True
model_const.cap_info_nce_layers = kwargs['cap_info_nce_layers']
if model_const.model_num == -100:
filename = os.path.join(exp_const.exp_dir, f'results_val_best.json')
results = io.load_json_object(filename)
model_const.model_num = results['model_num']
print('Selected model num:', model_const.model_num)
model_const.object_encoder_path = os.path.join(
exp_const.model_dir, f'object_encoder_{model_const.model_num}')
model_const.lang_sup_criterion_path = os.path.join(
exp_const.model_dir, f'lang_sup_criterion_{model_const.model_num}')
if exp_const.random_lang is True:
model_const.cap_encoder_path = os.path.join(
exp_const.model_dir, f'cap_encoder_{model_const.model_num}')
eval_flickr_phrase_loc.main(exp_const, data_const, model_const)
def main(**kwargs):
exp_base_dir = coco_paths['exp_dir']
if kwargs['dataset'] == 'flickr':
exp_base_dir = flickr_paths['exp_dir']
exp_const = ExpConstants(kwargs['exp_name'], exp_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.seed = 0
exp_const.contextualize = not kwargs['no_context']
exp_const.random_lang = kwargs['random_lang']
data_const = FlickrDatasetConstants(kwargs['subset'])
model_const = Constants()
model_const.object_encoder = ObjectEncoderConstants()
model_const.object_encoder.context_layer.output_attentions = True
model_const.object_encoder.object_feature_dim = 2048
model_const.cap_encoder = CapEncoderConstants()
model_const.cap_encoder.output_attentions = True
model_const.cap_info_nce_layers = kwargs['cap_info_nce_layers']
model_nums = find_all_model_numbers(exp_const.model_dir)
for num in model_nums:
continue
if num <= 3000:
continue
model_const.model_num = num
model_const.object_encoder_path = os.path.join(
exp_const.model_dir, f'object_encoder_{model_const.model_num}')
model_const.lang_sup_criterion_path = os.path.join(
exp_const.model_dir, f'lang_sup_criterion_{model_const.model_num}')
if exp_const.random_lang is True:
model_const.cap_encoder_path = os.path.join(
exp_const.model_dir, f'cap_encoder_{model_const.model_num}')
filename = os.path.join(exp_const.exp_dir,
f'results_{data_const.subset}_{num}.json')
if os.path.exists(filename):
print(io.load_json_object(filename))
continue
eval_flickr_phrase_loc.main(exp_const, data_const, model_const)
best_model_num = -1
best_pt_recall = 0
best_results = None
for num in model_nums:
filename = os.path.join(exp_const.exp_dir,
f'results_{data_const.subset}_{num}.json')
if not os.path.exists(filename):
continue
results = io.load_json_object(filename)
results['model_num'] = num
print(results)
if results['pt_recall'] >= best_pt_recall:
best_results = results
best_pt_recall = results['pt_recall']
best_model_num = num
print('-' * 80)
best_results['model_num'] = best_model_num
print(best_results)
filename = os.path.join(exp_const.exp_dir,
f'results_{data_const.subset}_best.json')
io.dump_json_object(best_results, filename)
