D_LEARNING_RATE = 0.01
G_LEARNING_RATE = 0.01
BETA = OUTPUT_DIM / 8.0
GAMMA = 0.1
WORKDIR = '/cache/'
DIS_MODEL_BEST_FILE = '/cache/flickr_dis_teacher_modaLoss_' + str(
OUTPUT_DIM) + '.model'
DIS_MODEL_PRETRAIN_FILE = '/cache/dis_baseline_pretrain_' + str(
OUTPUT_DIM) + '.model'
train_i2t, train_i2t_pos, train_i2t_neg, train_t2i, train_t2i_pos, train_t2i_neg = ut.load_all_query_url(
)
feature_dict = ut.load_all_feature(WORKDIR)
label_dict = ut.load_all_label(WORKDIR)
def generate_samples(sess, generator, train_list, train_pos, train_neg, flag):
data = []
for query in train_pos:
pos_list = train_pos[query]
candidate_neg_list = train_neg[query]
candidate_list = train_list[query]
random.shuffle(pos_list)
random.shuffle(candidate_neg_list)
random.shuffle(candidate_list)
sample_size = int(len(candidate_list) / SAMPLERATIO)
candidate_list = candidate_list[0:sample_size]
BETA = OUTPUT_DIM / 8.0
GAMMA = 0.1
# WORKDIR = '../mir/'
DIS_MODEL_BEST_FILE = '/....../teacher_best_pretrain' + str(
OUTPUT_DIM) + '.model'
#DIS_MODEL_BEST_I2I_FILE = '/home/huhengtong/UKD/teacher_UGACH/OL_teacher_best_i2i_' + str(OUTPUT_DIM) + '.model'
# DIS_MODEL_NEWEST_FILE = './model/dis_newest_nn_' + str(OUTPUT_DIM) + '.model'
train_i2t, train_i2t_pos, train_i2t_neg, train_t2i, train_t2i_pos, train_t2i_neg = ut.load_all_query_url(
)
# pdb.set_trace()
feature_dict = ut.load_all_feature()
label_dict = ut.load_all_label()
#print(len(feature_dict), len(label_dict))
def generate_samples(train_pos, train_neg, flag):
data = []
for query in train_pos:
pos_list = train_pos[query]
candidate_neg_list = train_neg[query]
random.shuffle(pos_list)
random.shuffle(candidate_neg_list)
for i in range(SELECTNUM):
data.append((query, pos_list[i], candidate_neg_list[i]))
WORKDIR = '../mir/'
GEN_MODEL_BEST_FILE = './model/gan_best_nn_' + str(OUTPUT_DIM) + '.model'
DIS_MODEL_BEST_FILE = './model/dis_best_nn_' + str(OUTPUT_DIM) + '.model'
GEN_MODEL_NEWEST_FILE = './model/gan_newest_nn_' + str(OUTPUT_DIM) + '.model'
DIS_MODEL_NEWEST_FILE = './model/dis_newest_nn_' + str(OUTPUT_DIM) + '.model'
DIS_MODEL_PRETRAIN_FILE = './model/dis_pretrain_nn_' + str(
OUTPUT_DIM) + '.model'
train_i2t, train_i2t_pos, train_i2t_neg, train_t2i, train_t2i_pos, train_t2i_neg, test_i2t, test_i2t_pos, test_t2i, test_t2i_pos = ut.load_all_query_url(
WORKDIR + 'list/', CLASS_DIM)
feature_dict = ut.load_all_feature(WORKDIR + 'list/', WORKDIR + 'feature/')
label_dict = ut.load_all_label(WORKDIR + 'list/')
record_file = open('record_' + str(OUTPUT_DIM) + '.txt', 'w')
record_file.close()
def generate_samples(sess, generator, train_list, train_pos, train_neg, flag):
data = []
for query in train_pos:
pos_list = train_pos[query]
candidate_neg_list = train_neg[query]
candidate_list = train_list[query]
random.shuffle(pos_list)
random.shuffle(candidate_neg_list)
random.shuffle(candidate_list)
