def vidvrd_generator(self): feature_type = 'train' # for each_ins in vid_data.gen_vrd_instance(feature_type): # yield { # "image/encoded": np.array2string(each_ins.get_my_feature(feature_type).ravel()), # "image/format": ["jpeg"], # "image/class/label": [vid_relation.load_relation('first')[each_ins.predicate.split('_')[0]]], # "image/height": [each_ins.height], # "image/width": [each_ins.width] # } ins_list = vid_data.gen_vrd_instance(feature_type) for i in range(3): yield { "image/encoded": np.array2string( ins_list[i].get_my_feature(feature_type).ravel()), "image/format": ["jpeg"], "image/class/label": [ vid_relation.load_relation('first')[ ins_list[i].predicate.split('_')[0]] ], "image/height": [ins_list[i].height], "image/width": [ins_list[i].width] }
def feature_encoders(self, data_dir): del data_dir feature_type = 'train' for each_ins in vid_data.gen_vrd_instance(feature_type): yield { "inputs": each_ins.get_my_feature(feature_type), "targets": vid_relation.load_relation('first')[each_ins.predicate.split('_')[0]] }
def vidvrd_generator(self): feature_type = 'train' for each_ins in vid_data.gen_vrd_instance(feature_type): yield { "image/encoded": np.array2string(each_ins.get_my_feature(feature_type).ravel()), "image/format": ["png"], "image/class/label": [vid_relation.load_relation('first')[each_ins.predicate.split('_')[0]]], "image/height": [each_ins.height], "image/width": [each_ins.width] }
def vidvrd_generator(self): feature_type = 'train' # for each_ins in vid_data.gen_vrd_instance(feature_type): # features = {"image/encoded": np.array2string(each_ins.get_my_feature(feature_type).ravel()), # "image/format": ["png"], # "image/height": [each_ins.height], # "image/width": [each_ins.width]} # yield features ins_list = vid_data.gen_vrd_instance(feature_type) for i in range(3): features = {"image/encoded": np.array2string(ins_list[i].get_my_feature(feature_type).ravel()), "image/format": ["jpg"], "image/height": [ins_list[i].height], "image/width": [ins_list[i].width]} yield features
def get_vvrd_truth_list(data_type, relation_type): pos = 0 if relation_type == 'first' else 1 file_path = '../data/vvrd_truth_' + data_type + '_' + relation_type + '_list.json' vrd_json = {} with open(file_path, 'w+') as f: for each_vrd in get_data.gen_vrd_instance(data_type): if pos == 1: if len(each_vrd.predicate.split('_')) > 1: rel = load_relation(relation_type)[ each_vrd.predicate.split('_')[pos]] else: rel = -1 else: rel = load_relation(relation_type)[each_vrd.predicate.split( '_')[pos]] vrd_json[each_vrd.video_id + '_' + str(each_vrd.begin_fid) + '_' + str(each_vrd.end_fid)] = rel # each_vrd_json = {each_vrd.video_id + '_' # + str(each_vrd.begin_fid) + '_' # + str(each_vrd.end_fid): rel} f.write(json.dumps(vrd_json)) return 'Successfully get VVRD Truth List: ' + file_path
"targets": vid_relation.load_relation('first')[each_ins.predicate.split('_')[0]] } def vidvrd_generator(self): feature_type = 'train' for each_ins in vid_data.gen_vrd_instance(feature_type): yield { "image/encoded": np.array2string(each_ins.get_my_feature(feature_type).ravel()), "image/format": ["png"], "image/class/label": [vid_relation.load_relation('first')[each_ins.predicate.split('_')[0]]], "image/height": [each_ins.height], "image/width": [each_ins.width] } # ins_list = vid_data.gen_vrd_instance(feature_type) # for i in range(3): # yield { # "image/encoded": np.array2string(ins_list[i].get_my_feature(feature_type).ravel()), # "image/format": ["png"], # "image/class/label": [vid_relation.load_relation('first')[ins_list[i].predicate.split('_')[0]]], # "image/height": [ins_list[i].height], # "image/width": [ins_list[i].width] # } if __name__ == '__main__': feature_type = 'train' each_ins = vid_data.gen_vrd_instance(feature_type)[0] print(each_ins.get_my_feature(feature_type)) print(vid_relation.load_relation('first')[each_ins.predicate.split('_')[0]])