dp92@dp92-System-Product-Name:~/Desktop/deep-stereo$ python Stereo_Online_Adaptation.py -l ~/Desktop/deep-stereo/data/kitti/list.txt -o ~/Desktop/deep-stereo/outputs --weights ~/Desktop/deep-stereo/pretrained/MADNet/kitti/weights.ckpt --modelName MADNet --blockConfig ~/Desktop/deep-stereo/block_config/MadNet_full.json --mode MAD --sampleMode PROBABILITY
/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
WARNING:tensorflow:From /home/dp92/Desktop/deep-stereo/Data_utils/data_reader.py:137: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From /home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py:188: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
WARNING:tensorflow:From /home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py:197: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py:197: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/dp92/Desktop/deep-stereo/Data_utils/data_reader.py:166: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).
==================================================
Starting Creation of MADNet
==================================================
WARNING: flag for trainign not setted, using default False
WARNING: warping flag not setted, setting default True value
WARNING: context_net flag not setted, setting default True value
WARNING: radius_d not setted, setting default value 2
WARNING: stride not setted, setting default value 1
Args Validated, setting up graph
Meta op to preprocess data created
Network ready
==================================================
Stereo Prediction Model:
 Layer left/conv1: (1, 160, 608, 16)
Layer left/conv2: (1, 160, 608, 16)
Layer left/conv3: (1, 80, 304, 32)
Layer left/conv4: (1, 80, 304, 32)
Layer left/conv5: (1, 40, 152, 64)
Layer left/conv6: (1, 40, 152, 64)
Layer left/conv7: (1, 20, 76, 96)
Layer left/conv8: (1, 20, 76, 96)
Layer left/conv9: (1, 10, 38, 128)
Layer left/conv10: (1, 10, 38, 128)
Layer left/conv11: (1, 5, 19, 192)
Layer left/conv12: (1, 5, 19, 192)
Layer right/conv1: (1, 160, 608, 16)
Layer right/conv2: (1, 160, 608, 16)
Layer right/conv3: (1, 80, 304, 32)
Layer right/conv4: (1, 80, 304, 32)
Layer right/conv5: (1, 40, 152, 64)
Layer right/conv6: (1, 40, 152, 64)
Layer right/conv7: (1, 20, 76, 96)
Layer right/conv8: (1, 20, 76, 96)
Layer right/conv9: (1, 10, 38, 128)
Layer right/conv10: (1, 10, 38, 128)
Layer right/conv11: (1, 5, 19, 192)
Layer right/conv12: (1, 5, 19, 192)
Layer fgc-volume-filtering-6/disp1: (1, 5, 19, 128)
Layer fgc-volume-filtering-6/disp2: (1, 5, 19, 128)
Layer fgc-volume-filtering-6/disp3: (1, 5, 19, 96)
Layer fgc-volume-filtering-6/disp4: (1, 5, 19, 64)
Layer fgc-volume-filtering-6/disp5: (1, 5, 19, 32)
Layer fgc-volume-filtering-6/disp6: (1, 5, 19, 1)
Layer fgc-volume-filtering-5/disp1: (1, 10, 38, 128)
Layer fgc-volume-filtering-5/disp2: (1, 10, 38, 128)
Layer fgc-volume-filtering-5/disp3: (1, 10, 38, 96)
Layer fgc-volume-filtering-5/disp4: (1, 10, 38, 64)
Layer fgc-volume-filtering-5/disp5: (1, 10, 38, 32)
Layer fgc-volume-filtering-5/disp6: (1, 10, 38, 1)
Layer fgc-volume-filtering-4/disp1: (1, 20, 76, 128)
Layer fgc-volume-filtering-4/disp2: (1, 20, 76, 128)
Layer fgc-volume-filtering-4/disp3: (1, 20, 76, 96)
Layer fgc-volume-filtering-4/disp4: (1, 20, 76, 64)
Layer fgc-volume-filtering-4/disp5: (1, 20, 76, 32)
Layer fgc-volume-filtering-4/disp6: (1, 20, 76, 1)
Layer fgc-volume-filtering-3/disp1: (1, 40, 152, 128)
Layer fgc-volume-filtering-3/disp2: (1, 40, 152, 128)
Layer fgc-volume-filtering-3/disp3: (1, 40, 152, 96)
Layer fgc-volume-filtering-3/disp4: (1, 40, 152, 64)
Layer fgc-volume-filtering-3/disp5: (1, 40, 152, 32)
Layer fgc-volume-filtering-3/disp6: (1, 40, 152, 1)
Layer fgc-volume-filtering-2/disp1: (1, 80, 304, 128)
Layer fgc-volume-filtering-2/disp2: (1, 80, 304, 128)
Layer fgc-volume-filtering-2/disp3: (1, 80, 304, 96)
Layer fgc-volume-filtering-2/disp4: (1, 80, 304, 64)
Layer fgc-volume-filtering-2/disp5: (1, 80, 304, 32)
Layer fgc-volume-filtering-2/disp6: (1, 80, 304, 1)
Layer context1: (1, 80, 304, 128)
Layer context2: (1, 80, 304, 128)
Layer context3: (1, 80, 304, 128)
Layer context4: (1, 80, 304, 96)
Layer context5: (1, 80, 304, 64)
Layer context6: (1, 80, 304, 32)
Layer context7: (1, 80, 304, 1)
Layer final_disp: (1, 80, 304, 1)
Prediction Layer rescaled_prediction: (1, 320, 1216, 1)

Build train ops for disparity 0
Going to train on ['fgc-volume-filtering-6/disp1', 'fgc-volume-filtering-6/disp2', 'fgc-volume-filtering-6/disp3', 'fgc-volume-filtering-6/disp4', 'fgc-volume-filtering-6/disp5', 'fgc-volume-filtering-6/disp6', 'left/conv12', 'left/conv11']
Number of variable to train: 16
Done
==================================================
Build train ops for disparity 1
Going to train on ['fgc-volume-filtering-5/disp1', 'fgc-volume-filtering-5/disp2', 'fgc-volume-filtering-5/disp3', 'fgc-volume-filtering-5/disp4', 'fgc-volume-filtering-5/disp5', 'fgc-volume-filtering-5/disp6', 'left/conv10', 'left/conv9']
Number of variable to train: 16
Done
==================================================
Build train ops for disparity 2
Going to train on ['fgc-volume-filtering-4/disp1', 'fgc-volume-filtering-4/disp2', 'fgc-volume-filtering-4/disp3', 'fgc-volume-filtering-4/disp4', 'fgc-volume-filtering-4/disp5', 'fgc-volume-filtering-4/disp6', 'left/conv8', 'left/conv7']
Number of variable to train: 16
Done
==================================================
Build train ops for disparity 3
Going to train on ['fgc-volume-filtering-3/disp1', 'fgc-volume-filtering-3/disp2', 'fgc-volume-filtering-3/disp3', 'fgc-volume-filtering-3/disp4', 'fgc-volume-filtering-3/disp5', 'fgc-volume-filtering-3/disp6', 'left/conv6', 'left/conv5']
Number of variable to train: 16
Done
==================================================
Build train ops for disparity 4
Going to train on ['fgc-volume-filtering-2/disp1', 'fgc-volume-filtering-2/disp2', 'fgc-volume-filtering-2/disp3', 'fgc-volume-filtering-2/disp4', 'fgc-volume-filtering-2/disp5', 'fgc-volume-filtering-2/disp6', 'left/conv4', 'left/conv3', 'left/conv2', 'left/conv1', 'context1', 'context2', 'context3', 'context4', 'context5', 'context6', 'context7']
Number of variable to train: 34
Done
==================================================
2018-12-02 16:00:52.566854: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-12-02 16:00:52.660504: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-12-02 16:00:52.660863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.7845
pciBusID: 0000:01:00.0
totalMemory: 7.93GiB freeMemory: 7.34GiB
2018-12-02 16:00:52.660891: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2018-12-02 16:00:52.840854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-12-02 16:00:52.840895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2018-12-02 16:00:52.840901: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2018-12-02 16:00:52.841054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7081 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING:tensorflow:From Stereo_Online_Adaptation.py:151: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
Disparity Net Restored?: True, number of restored variables: 98
Step:   0	bad3:0.95	EPE:71.89	SSIM:0.10	f/b time:0.025095	Missing time:0:00:00.552091
Exception catched FIFOQueue '_1_input_reader/batch/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
	 [[node input_reader/batch (defined at /home/dp92/Desktop/deep-stereo/Data_utils/data_reader.py:166)  = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_reader/batch/fifo_queue, input_reader/batch/n)]]

Caused by op 'input_reader/batch', defined at:
  File "Stereo_Online_Adaptation.py", line 328, in <module>
    main(args)
  File "Stereo_Online_Adaptation.py", line 44, in main
    shuffle=False
  File "/home/dp92/Desktop/deep-stereo/Data_utils/data_reader.py", line 126, in __init__
    self._build_input_pipeline()
  File "/home/dp92/Desktop/deep-stereo/Data_utils/data_reader.py", line 166, in _build_input_pipeline
    self._left_batch, self._right_batch, self._gt_batch = tf.train.batch([left_image, right_image, gt_image], self._batch_size, capacity=self._batch_size*20, num_threads=tt)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 306, in new_func
    return func(*args, **kwargs)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py", line 1017, in batch
    name=name)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/training/input.py", line 787, in _batch
    dequeued = queue.dequeue_many(batch_size, name=name)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/ops/data_flow_ops.py", line 478, in dequeue_many
    self._queue_ref, n=n, component_types=self._dtypes, name=name)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 3487, in queue_dequeue_many_v2
    component_types=component_types, timeout_ms=timeout_ms, name=name)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
    return func(*args, **kwargs)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
    op_def=op_def)
  File "/home/dp92/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
    self._traceback = tf_stack.extract_stack()

OutOfRangeError (see above for traceback): FIFOQueue '_1_input_reader/batch/fifo_queue' is closed and has insufficient elements (requested 1, current size 0)
	 [[node input_reader/batch (defined at /home/dp92/Desktop/deep-stereo/Data_utils/data_reader.py:166)  = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](input_reader/batch/fifo_queue, input_reader/batch/n)]]

All Done, Bye Bye!