def __init__(self, batch_size=None):
net_params, train_params = parser_cfg_file('./net.cfg')
self._model_save_path = str(train_params['model_save_path'])
self.input_img_height = int(net_params['input_height'])
self.input_img_width = int(net_params['input_width'])
if batch_size is None:
self.test_batch_size = int(net_params['test_batch_size'])
else:
self.test_batch_size = batch_size
# 加载label onehot
f = open('./data/word_onehot.txt', 'r')
data = f.read()
words_onehot_dict = eval(data)
self.words_list = list(words_onehot_dict.keys())
self.words_onehot_list = [words_onehot_dict[self.words_list[i]] for i in range(len(self.words_list))]
# 构建网络
self.inputs_tensor = tf.placeholder(tf.float32, [self.test_batch_size, self.input_img_height, self.input_img_width, 1])
self.seq_len_tensor = tf.placeholder(tf.int32, [None], name='seq_len')
crnn_net = CRNN(net_params, self.inputs_tensor, self.seq_len_tensor, self.test_batch_size, True)
net_output, decoded, self.max_char_count = crnn_net.construct_graph()
self.dense_decoded = tf.sparse_tensor_to_dense(decoded[0], default_value=-1)
self.sess = tf.Session()
saver = tf.train.Saver()
saver.restore(self.sess, "./model/ckpt")
def __init__(self, pre_train=False):
net_params, train_params = parser_cfg_file('./net.cfg')
self.input_height = int(net_params['input_height'])
self.input_width = int(net_params['input_width'])
self.batch_size = int(train_params['batch_size'])
self._learning_rate = float(train_params['learning_rate'])
self._max_iterators = int(train_params['max_iterators'])
self._train_logger_init()
self._pre_train = pre_train
self._model_save_path = str(train_params['model_save_path'])
if self._pre_train:
ckpt = tf.train.checkpoint_exists(self._model_save_path)
if ckpt:
print('Checkpoint is valid...')
f = open('./model/train_step.txt', 'r')
step = f.readline()
self._start_step = int(step)
f.close()
else:
assert 0, print('Checkpoint is invalid...')
else:
self._start_step = 0
self._inputs = tf.placeholder(
tf.float32, [self.batch_size, 32, self.input_width, 1])
# label
self._label = tf.sparse_placeholder(tf.int32, name='label')
# The length of the sequence [32] * 64
self._seq_len = tf.placeholder(tf.int32, [None], name='seq_len')
crnn_net = CRNN(net_params, self._inputs, self._seq_len,
self.batch_size, True)
self._net_output, self._decoded, self._max_char_count = crnn_net.construct_graph(
)
self.dense_decoded = tf.sparse_tensor_to_dense(self._decoded[0],
default_value=-1)
