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From GitBox <...@apache.org>
Subject [GitHub] [singa] chrishkchris commented on pull request #763: tensor operation type lang pair - kInt CPU, cuda
Date Mon, 13 Jul 2020 09:11:11 GMT

chrishkchris commented on pull request #763:
URL: https://github.com/apache/singa/pull/763#issuecomment-657429882


   This PR is a problem (bug) fix to issue #744 , and can be merged after I complied and tested
it with GPU.
   
   1. test_onnx_backend.py
   
   ```
   root@71ac539cda77:~/dcsysh/singa/test/python# python3 test_onnx_backend.py
   ss............................ssssssssssssssssssssssssssssssss................ssss..ss..ss............ssssssssss..ssssssssssssssss............ssss....................ssssssss........................ssss....ssssssssssssssssssssssssss........ssssssssssssssssssssssssssss..........ssssssssssssss.......s.sssssss....ssssss..................ssss....ssssss..ssss..........ss.s............ssss....ssssssssssssssssssss........ssssssssssss..............ssssssssssssssssssssssss......ss......ssss..ss..............ssssssssss............ssssssssssssssssssssssssss......ssss....ssssssssssssssssss..................................ss........ssssssssssssssss....ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss................ssssssssssssssssssssssssssssssss................ssssssssssssssssss....................ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss..........ssss......ssss........ssss..............ss..........................................ssssssssssss....................ssssssssssssssssssss....ssssssssssssssssssssssssssssss..................ssss................
   ----------------------------------------------------------------------
   Ran 1114 tests in 1.256s
   
   OK (skipped=603)
   ```
   
   
   2. simple training test (just to see if it introduces other serious bug)
   
   ```
   root@71ac539cda77:~/dcsysh/singa/examples/cnn# python3 train_cnn.py cnn mnist
   Starting Epoch 0:
   Training loss = 582.622375, training accuracy = 0.792789
   Evaluation accuracy = 0.933393, Elapsed Time = 3.197757s
   Starting Epoch 1:
   Training loss = 234.050247, training accuracy = 0.921675
   Evaluation accuracy = 0.956430, Elapsed Time = 3.176632s
   Starting Epoch 2:
   Training loss = 167.452026, training accuracy = 0.944854
   Evaluation accuracy = 0.969351, Elapsed Time = 3.174831s
   Starting Epoch 3:
   Training loss = 141.909851, training accuracy = 0.952591
   Evaluation accuracy = 0.969752, Elapsed Time = 3.174302s
   Starting Epoch 4:
   Training loss = 121.488564, training accuracy = 0.959212
   Evaluation accuracy = 0.970853, Elapsed Time = 3.173884s
   Starting Epoch 5:
   Training loss = 106.590233, training accuracy = 0.965181
   Evaluation accuracy = 0.976362, Elapsed Time = 3.271060s
   Starting Epoch 6:
   Training loss = 97.972565, training accuracy = 0.967399
   Evaluation accuracy = 0.976863, Elapsed Time = 3.177941s
   Starting Epoch 7:
   Training loss = 89.360428, training accuracy = 0.970484
   Evaluation accuracy = 0.973658, Elapsed Time = 3.177838s
   Starting Epoch 8:
   Training loss = 82.499229, training accuracy = 0.972719
   Evaluation accuracy = 0.977865, Elapsed Time = 3.312110s
   Starting Epoch 9:
   Training loss = 79.484062, training accuracy = 0.973252
   Evaluation accuracy = 0.981070, Elapsed Time = 3.180323s
   
   ```


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