def __init__(self):
self.detector = model.SegLinkDetector()
# global TF variables
with tf.device('/cpu:0'):
self.global_step = tf.Variable(0, trainable=False, name='global_step', dtype=tf.int64)
tf.summary.scalar('global_step', self.global_step, collections=['brief'])
# setup training graphs and summaries
self._setup_train_net_multigpu()
# if true the training process will be terminated in the next iteration
self.should_stop = False
def evaluate():
with tf.device('/cpu:0'):
# input data
streams = data.input_stream(FLAGS.test_dataset)
pstreams = data.test_preprocess(streams)
if FLAGS.test_resize_method == 'dynamic':
# each test image is resized to a different size
# test batch size must be 1
assert(FLAGS.test_batch_size == 1)
batches = tf.train.batch(pstreams,
FLAGS.test_batch_size,
capacity=1000,
num_threads=1,
dynamic_pad=True)
else:
# resize every image to the same size
batches = tf.train.batch(pstreams,
FLAGS.test_batch_size,
capacity=1000,
num_threads=1)
image_size = tf.shape(batches['image'])[1:3]
fetches = {}
fetches['images'] = batches['image']
fetches['image_name'] = batches['image_name']
fetches['resize_size'] = batches['resize_size']
fetches['orig_size'] = batches['orig_size']
# detector
detector = model.SegLinkDetector()
all_maps = detector.build_model(batches['image'])
# decode local predictions
all_nodes, all_links, all_reg = [], [], []
for i, maps in enumerate(all_maps):
cls_maps, lnk_maps, reg_maps = maps
reg_maps = tf.multiply(reg_maps, data.OFFSET_VARIANCE)
# segments classification
cls_prob = tf.nn.softmax(tf.reshape(cls_maps, [-1, 2]))
cls_pos_prob = cls_prob[:, model.POS_LABEL]
cls_pos_prob_maps = tf.reshape(cls_pos_prob, tf.shape(cls_maps)[:3])
# node status is 1 where probability is higher than threshold
node_labels = tf.cast(tf.greater_equal(cls_pos_prob_maps, FLAGS.node_threshold),
tf.int32)
# link classification
lnk_prob = tf.nn.softmax(tf.reshape(lnk_maps, [-1, 2]))
lnk_pos_prob = lnk_prob[:, model.POS_LABEL]
lnk_shape = tf.shape(lnk_maps)
lnk_pos_prob_maps = tf.reshape(lnk_pos_prob,
[lnk_shape[0], lnk_shape[1], lnk_shape[2], -1])
# link status is 1 where probability is higher than threshold
link_labels = tf.cast(tf.greater_equal(lnk_pos_prob_maps, FLAGS.link_threshold),
tf.int32)
all_nodes.append(node_labels)
all_links.append(link_labels)
all_reg.append(reg_maps)
fetches['link_labels_%d' % i] = link_labels
# decode segments and links
segments, group_indices, segment_counts = ops.decode_segments_links(
image_size, all_nodes, all_links, all_reg,
anchor_sizes=list(detector.anchor_sizes))
fetches['segments'] = segments
fetches['group_indices'] = group_indices
fetches['segment_counts'] = segment_counts
# combine segments
combined_rboxes, combined_counts = ops.combine_segments(
segments, group_indices, segment_counts)
fetches['combined_rboxes'] = combined_rboxes
fetches['combined_counts'] = combined_counts
sess_config = tf.ConfigProto()
with tf.Session(config=sess_config) as sess:
# load model
model_loader = tf.train.Saver()
model_loader.restore(sess, FLAGS.test_model)
batch_size = FLAGS.test_batch_size
n_batches = int(math.ceil(FLAGS.num_test / batch_size))
# result directory
result_dir = os.path.join(FLAGS.log_dir, 'results' + FLAGS.result_suffix)
utils.mkdir_if_not_exist(result_dir)
intermediate_result_path = os.path.join(FLAGS.log_dir, 'intermediate.pkl')
if FLAGS.load_intermediate:
all_batches = joblib.load(intermediate_result_path)
logging.info('Intermediate result loaded from {}'.format(intermediate_result_path))
else:
# run all batches and store results in a list
all_batches = []
with slim.queues.QueueRunners(sess):
for i in range(n_batches):
if i % 10 == 0:
logging.info('Evaluating batch %d/%d' % (i+1, n_batches))
sess_outputs = sess.run(fetches)
all_batches.append(sess_outputs)
if FLAGS.save_intermediate:
joblib.dump(all_batches, intermediate_result_path, compress=5)
logging.info('Intermediate result saved to {}'.format(intermediate_result_path))
# # visualize local rboxes (TODO)
# if FLAGS.save_vis:
# vis_save_prefix = os.path.join(save_dir, 'localpred_batch_%d_' % i)
# pred_rboxes_counts = []
# for j in range(len(all_maps)):
# pred_rboxes_counts.append((sess_outputs['segments_det_%d' % j],
# sess_outputs['segment_counts_det_%d' % j]))
# _visualize_layer_det(sess_outputs['images'],
# pred_rboxes_counts,
# vis_save_prefix)
# # visualize joined rboxes (TODO)
# if FLAGS.save_vis:
# vis_save_prefix = os.path.join(save_dir, 'batch_%d_' % i)
# # _visualize_linked_det(sess_outputs, save_prefix)
# _visualize_combined_rboxes(sess_outputs, vis_save_prefix)
if FLAGS.result_format == 'icdar_2015_inc':
postprocess_and_write_results_ic15(all_batches, result_dir)
elif FLAGS.result_format == 'icdar_2013':
postprocess_and_write_results_ic13(all_batches, result_dir)
else:
logging.critical('Unknown result format: {}'.format(FLAGS.result_format))
sys.exit(1)
logging.info('Evaluation done.')