Memory leak issue

[GraceBoston]

tensorflow-gpu 1.3.0
tensorflow-tensorboard 0.1.8
Keras 2.0.6
Keras-Applications 1.0.6

I downloaded model_zoo link and successfully running 2 models ( ssd_mobilenet_v1_coco, ssd_inception_v2_coco) without any problems.But when i try faster_rcnn_inception_resnet_v2_atrous_coco, and rfcn_resnet101_coco models, models start working properly, but it consumes almost 62.8G/62.8G memory, so I couldn't run it. Have you ever got this issues?

I have 2394*3062 png image files for training, so i resizing the image as 600 * 767(config file).
i also using 'os.environ['CUDA_VISIBLE_DEVICES'] = '1'' and it works well.

Total memory: 10.91GiB
Free memory: 10.75GiB
2018-10-23 10:55:29.290302: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 
2018-10-23 10:55:29.290311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y 
2018-10-23 10:55:29.290323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:06:00.0)
2018-10-23 10:55:31.938505: I tensorflow/core/common_runtime/simple_placer.cc:697] Ignoring device specification /device:GPU:0 for node 'prefetch_queue_Dequeue' because the input edge from 'prefetch_queue' is a reference connection and already has a device field set to /device:CPU:0
INFO:tensorflow:Restoring parameters from model.ckpt
INFO:tensorflow:Starting Session.
INFO:tensorflow:Saving checkpoint to path train/model.ckpt
INFO:tensorflow:Starting Queues.
INFO:tensorflow:global_step/sec: 0
INFO:tensorflow:Recording summary at step 0.
INFO:tensorflow:global step 1: loss = 1.5886 (24.888 sec/step)
INFO:tensorflow:global step 2: loss = 1.4227 (0.812 sec/step)
INFO:tensorflow:global step 3: loss = 1.2016 (0.875 sec/step)

model {
  faster_rcnn {
    num_classes: 1
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 767
      }
    }
    feature_extractor {
      type: 'faster_rcnn_inception_resnet_v2'
      first_stage_features_stride: 8
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 8
        width_stride: 8
      }
    }
    first_stage_atrous_rate: 2
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 300
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 17
    maxpool_kernel_size: 1
    maxpool_stride: 1
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: false
        dropout_keep_probability: 1.0
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}

train_config: {
  batch_size: 1
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        manual_step_learning_rate {
          initial_learning_rate: 0.0003
          schedule {
            step: 0
            learning_rate: .0003
          }
          schedule {
            step: 900000
            learning_rate: .00003
          }
          schedule {
            step: 1200000
            learning_rate: .000003
          }
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "model.ckpt"
  from_detection_checkpoint: true
  # Note: The below line limits the training process to 200K steps, which we
  # empirically found to be sufficient enough to train the pets dataset. This
  # effectively bypasses the learning rate schedule (the learning rate will
  # never decay). Remove the below line to train indefinitely.
  num_steps: 200000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "train.record"
  }
  label_map_path: "annotations/label_map.pbtxt"
}

eval_config: {
  num_examples: 170
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "val.record"
  }
  label_map_path: "annotations/label_map.pbtxt"
  shuffle: false
  num_readers: 1
}

[Nick Bourdakos]

Hi @GraceBoston, this repo relies on a dated version of the TensorFlow api. We've moved to a more future proof version here: https://github.com/cloud-annotations/training
I encourage you to try it out and reopen this issue there if you are still running into problems