GPU training does not work about faceswap HOT 13 CLOSED

2018-01-28 20:05:43.939681: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this T
ensorFlow binary was not compiled to use: AVX
2018-01-28 20:05:44.539616: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Found device 0 with properties:
name: Quadro K2000M major: 3 minor: 0 memoryClockRate(GHz): 0.745
pciBusID: 0000:01:00.0
totalMemory: 2.00GiB freeMemory: 1.92GiB
2018-01-28 20:05:44.540210: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1120] Creating TensorFlow device (/device:GP
U:0) -> (device: 0, name: Quadro K2000M, pci bus id: 0000:01:00.0, compute capability: 3.0)
2018-01-28 20:05:47.504003: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:47.566201: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.07GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:47.603648: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.04GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:47.730635: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.04GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:47.838415: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.03GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:47.860781: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.07GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:48.012480: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:48.050733: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:48.051200: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2018-01-28 20:05:48.108983: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory
trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
0.15305635 0.11783895
save model weights
usage: faceswap.py [-h] {extract,train,convert} ...

positional arguments:
  {extract,train,convert}
    extract             Extract the faces from a pictures.
    train               This command trains the model for the two faces A and
                        B.
    convert             Convert a source image to a new one with the face
                        swapped.

optional arguments:
  -h, --help            show this help message and exit