def create_generate_model(opts): assert isinstance(opts, options) with tf.variable_scope(tf.get_variable_scope(), reuse=True): model = inference_wrapper(opts, reuse=True) vocab = vocabulary(opts) generator = CaptionGenerator(model, vocab, beam_size=4) return generator
'the place of h5 file that contains image attributes information') tf.flags.DEFINE_string('train_dir', './model', 'the directory for training the model') tf.flags.DEFINE_string('caption_file', 'result/caption_', 'the file which save results') tf.flags.DEFINE_string('id', 'test', 'the id to specified') opts = options() opts.batch_size = 1 opts.data_h5 = FLAGS.data_h5 opts.data_json = FLAGS.data_json opts.attributes_h5 = FLAGS.attributes_h5 opts.train_dir = FLAGS.train_dir dataloader = DataLoader(opts, 'test') model = inference_wrapper(opts) vocab = vocabulary(opts) generator = CaptionGenerator(model, vocab, beam_size=4) nImage = len(dataloader.test_ix) sess = tf.Session() checkpoint = tf.train.latest_checkpoint(opts.train_dir) ema = tf.train.ExponentialMovingAverage(decay=0.999) variables_to_restore = ema.variables_to_restore() saver = tf.train.Saver(var_list=variables_to_restore, max_to_keep=5) saver.restore(sess, checkpoint) print('restoring from checkpoint {:s}'.format(checkpoint)) res = [] for i in xrange(nImage):
def create_val_fn(): FLAGS = namedtuple( 'FLAGS', ['data_h5', 'data_json', 'attributes_h5', 'train_dir', 'caption_file']) FLAGS.data_h5 = './data/data.h5' FLAGS.data_json = './data/data.json' FLAGS.attributes_h5 = './data/tag_feats.h5' FLAGS.train_dir = './model' FLAGS.caption_file = 'result/caption.json' opts = options() opts.batch_size = 1 opts.data_h5 = FLAGS.data_h5 opts.data_json = FLAGS.data_json opts.attributes_h5 = FLAGS.attributes_h5 opts.train_dir = FLAGS.train_dir dataloader = DataLoader(opts, 'val') # reuse variables with tf.variable_scope(tf.get_variable_scope(), reuse=True): model = inference_wrapper(opts, reuse=True) vocab = vocabulary(opts) generator = CaptionGenerator(model, vocab, beam_size=4) nImage = len(dataloader.val_ix) def val(sess): res = [] t1 = 0 for i in xrange(nImage): attributes, features, image_feature, image_ids, img = dataloader.get_batch( 1) caption = generator.beam_search(sess, image_feature, attributes, features) caption = caption[0].sentence caption = [vocab.to_word(w) for w in caption] caption = ' '.join(caption) res.append({'image_id': image_ids[0], 'caption': caption}) print('processing image:{:d}/{:d},time {:f}s'.format( i, nImage, time.time() - t1)) t1 = time.time() current_dir = os.getcwd() caption_file_full_path = os.path.join(current_dir, FLAGS.caption_file) with open(caption_file_full_path, 'w') as f: json.dump(res, f) #evaluation.myeval(caption_file_full_path) os.system('./eval.sh {:s}'.format(caption_file_full_path)) result_file = caption_file_full_path + '.json_out.json' with open(result_file) as f: result = json.load(f) for metric, val in result.iteritems(): print('{:s}:{:f}'.format(metric, val)) # return the cider metric return result['CIDEr'] return val