Closed ZetaRing closed 1 year ago
great!
Hi, there is the data loss:checksum does no match
2019-02-01 10:40:18.741628: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at save_restore_v2_ops.cc:184 : Data loss: Checksum does no t match: stored 3218031918 vs. calculated on the restored bytes 599589009 Traceback (most recent call last): File "C:\Users\85395\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1292, in _do_call return fn(*args) File "C:\Users\85395\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1277, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "C:\Users\85395\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1367, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.DataLossError: Checksum does not match: stored 3218031918 vs. calculated on the restored bytes 599589009 [[{{node save/RestoreV2}} = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DTFLOAT, DT FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]] [[{{node save/RestoreV2/_5}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:loca lhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_12_save/RestoreV2", tensor_type=DT_FLOAT, _device="/job:localhost/repl ica:0/task:0/device:GPU:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "evaluate.py", line 231, in
Caused by op 'save/RestoreV2', defined at:
File "evaluate.py", line 231, in
DataLossError (see above for traceback): Checksum does not match: stored 3218031918 vs. calculated on the restored bytes 599589009 [[{{node save/RestoreV2}} = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DTFLOAT, DT FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]] [[{{node save/RestoreV2/_5}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:loca lhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_12_save/RestoreV2", tensor_type=DT_FLOAT, _device="/job:localhost/repl ica:0/task:0/device:GPU:0"]()]]
Realize abstracting critical points and upper-bound points from point cloud and then visualize them based on a well-trained model.