i run tf-faster-rcnn on jeston nano,but test_faster_rcnn.sh fails running the following:
Loaded.
2019-09-02 17:16:52.083757: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 14.32MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-09-02 17:17:04.198378: W tensorflow/core/common_runtime/bfc_allocator.cc:267] Allocator (GPU_0_bfc) ran out of memory trying to allocate 33.94MiB. Current allocation summary follows.
2019-09-02 17:17:04.199946: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (256): Total Chunks: 28, Chunks in use: 28. 7.0KiB allocated for chunks. 7.0KiB in use in bin. 3.3KiB client-requested in use in bin.
2019-09-02 17:17:04.200113: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (512): Total Chunks: 17, Chunks in use: 16. 9.0KiB allocated for chunks. 8.2KiB in use in bin. 8.0KiB client-requested in use in bin.
2019-09-02 17:17:04.200757: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (1024): Total Chunks: 18, Chunks in use: 18. 18.5KiB allocated for chunks. 18.5KiB in use in bin. 18.1KiB client-requested in use in bin.
2019-09-02 17:17:04.204144: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (2048): Total Chunks: 51, Chunks in use: 50. 105.2KiB allocated for chunks. 102.5KiB in use in bin. 100.2KiB client-requested in use in bin.
......
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[1,64,278,500] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node MobilenetV1/Conv2d_1_pointwise/Conv2D (defined at /home/a/archiconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/contrib/layers/python/layers/layers.py:1060) = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](MobilenetV1/Conv2d_1_depthwise/Relu6, MobilenetV1/Conv2d_1_pointwise/weights/read)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[{{node MobilenetV1_2/rois/stack_1/_313}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_622_MobilenetV1_2/rois/stack_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Am new to TF, would appreciate any advice on how to resolve the error.
Thank you in advance!