import tensorflow as tf
import os, sys
import numpy as np
import time
from dataprovider_supervise import dataprovider
from model_supervise import ground_model
from util.iou import calc_iou
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_name", type=str, default='grounder')
parser.add_argument("-g", "--gpu", type=str, default='0')
parser.add_argument("--restore_id", type=int, default=0)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
class Config(object):
batch_size = 40
img_feat_dir = './feature'
sen_dir = './annotation'
train_file_list = 'flickr30k_train_val.lst'
test_file_list = 'flickr30k_test.lst'
log_file = './log/ground_supervised'
save_path = './model/ground_supervised'
vocab_size = 17150
num_epoch = 3
max_step = 12000
optim='adam'
dropout = 0.5
lr = 0.001
weight_decay=0.0
def update_feed_dict(dataprovider, model, is_train):
img_feat, sen_feat, bbx_label = dataprovider.get_next_batch()
feed_dict = {
model.sen_data: sen_feat,
model.vis_data: img_feat,
model.bbx_label: bbx_label,
model.is_train: is_train}
return feed_dict
def eval_cur_batch(gt_label, cur_logits, is_train=True, num_sample=0):
res_prob = cur_logits
res_label = np.argmax(res_prob, axis=1)
accu = 0.0
if is_train:
accu = float(np.sum(res_label == gt_label)) / float(len(gt_label))
else:
for gt_id, cur_gt in enumerate(gt_label):
if res_label[gt_id] in cur_gt:
accu += 1.0
accu /= float(num_sample)
return accu
def load_img_id_list(file_list):
img_list = []
with open(file_list) as fin:
for img_id in fin.readlines():
img_list.append(int(img_id.strip()))
img_list = np.array(img_list).astype('int')
return img_list
def run_eval(sess, dataprovider, model, eval_op, feed_dict):
num_cnt = 0.0
num_cor = 0.0
for img_ind, img_id in enumerate(dataprovider.test_list):
img_feat_raw, sen_feat_batch, bbx_gt_batch, num_sample_all = dataprovider.get_test_feat(img_id)
# bbx_gt_batch = set(bbx_gt_batch)
if num_sample_all > 0:
num_corr = 0
num_sample = len(bbx_gt_batch)
img_feat = feed_dict[model.vis_data]
for i in range(num_sample):
img_feat[i] = img_feat_raw
sen_feat = feed_dict[model.sen_data]
sen_feat[:num_sample] = sen_feat_batch
bbx_label = feed_dict[model.bbx_label]
eval_feed_dict = {
model.sen_data: sen_feat,
model.vis_data: img_feat,
model.bbx_label: bbx_label,
model.is_train: False}
cur_att_logits = sess.run(eval_op, feed_dict=eval_feed_dict)
cur_att_logits = cur_att_logits[:num_sample]
cur_accuracy = eval_cur_batch(bbx_gt_batch, cur_att_logits, False, num_sample_all)
print '%d/%d: %d/%d, %.4f'%(img_ind, len(dataprovider.test_list), num_sample, num_sample_all, cur_accuracy)
num_cor += float(num_sample_all)*cur_accuracy
num_cnt += float(num_sample_all)
else:
print 'No gt for %d'%img_id
accu = num_cor/num_cnt
print 'Accuracy = %.4f'%accu
return accu
def run_evaluate():
train_list = []
test_list = []
config = Config()
train_list = load_img_id_list(config.train_file_list)
test_list = load_img_id_list(config.test_file_list)
config.save_path = config.save_path + '_' + args.model_name
assert(os.path.isdir(config.save_path))
restore_id = args.restore_id
assert(restore_id > 0)
cur_dataset = dataprovider(train_list, test_list, config.img_feat_dir, config.sen_dir, config.vocab_size,
batch_size=config.batch_size)
model = ground_model(config)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
with tf.Graph().as_default():
loss, train_op, loss_vec, logits = model.build_model()
# Create a session for running Ops on the Graph.
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# Run the Op to initialize the variables.
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver(max_to_keep=100)
feed_dict = update_feed_dict(cur_dataset, model, False)
print 'Restore model_%d'%restore_id
saver.restore(sess, './model/%s/model_%d.ckpt'%(config.save_path, restore_id))
print "-----------------------------------------------"
eval_accu = run_eval(sess, cur_dataset, model, logits, feed_dict)
print "-----------------------------------------------"
def main(_):
run_evaluate()
if __name__ == '__main__':
tf.app.run()
