58856e2b8b766e076d13afa935324d517fa9388f,research/lstm_object_detection/models/lstm_ssd_mobilenet_v1_feature_extractor.py,LSTMMobileNetV1FeatureExtractor,extract_features,#LSTMMobileNetV1FeatureExtractor#Any#Any#Any#Any#Any#,79

Before Change

                state_name, net.shape[0].value / unroll_length, tf.float32)
          else:
            c = state_saver.state("%s_c" % state_name)
            h = state_saver.state("%s_h" % state_name)
            init_state = (c, h)

          // Identities added for inputing state tensors externally.
          c_ident = tf.identity(init_state[0], name="lstm_state_in_c")

After Change

        // ConvLSTM layers.
        batch_size = net.shape[0].value / unroll_length
        with tf.variable_scope("LSTM", reuse=self._reuse_weights) as lstm_scope:
          lstm_cell, init_state, _ = self.create_lstm_cell(
              batch_size, (net.shape[1].value, net.shape[2].value), state_saver,
              state_name)
          net_seq = list(tf.split(net, unroll_length))

Instances