MegEngine/sdk/load-and-run

• Load and Run
• • Build
  • Dump Model
  • • Input File for dump_with_testcase.py
    • Input File for dump_with_testcase_mge.py
  • Example

Load and Run

Load a model and run, for testing/debugging/profiling.

Build

megvii3 build

bazel build //brain/megbrain:load_and_run

See mnist-example for detailed explanations on build.

Dump Model

There are two methods to dump model:

1. Dump by MegHair/utils/debug/load_network_and_run.py --dump-cpp-model /path/to/output, to test on random inputs. Useful for profiling.
2. Pack model as specified by
   dump_with_testcase.py, and use
   that script to dump model. This is useful for checking correctness on
   different platforms.

Input File for dump_with_testcase.py

The input file must be a python pickle. It can be in one of the following two
formats:

1. Contain a network that can be loaded by meghair.utils.io.load_network; in
   such case, --data must be given and network output evaulated on current
   computing device is used as groundtruth. All output vars would be checked.
   The input data can be one of the following:
   1. In the format var0:file0;var1:file1... meaning that var0 should use
      image file file0, var1 should use image file1 and so on. If there
      is only one input var, the var name can be omitted. This can be combined
      with --resize-input option.
   2. In the format var0:#rand(min, max, shape...);var1:#rand(min, max)...
      meaning to fill the corresponding input vars with uniform random numbers
      in the range [min, max), optionally overriding its shape.
2. Contain a dict in the format {"outputs": [], "testcases": []}, where
   outputs is a list of output VarNodes and testcases is a list of test
   cases. Each test case should be a dict that maps input var names to
   corresponding values as numpy.ndarray. The expected outputs should also be
   provided as inputs, and correctness should be checked by AssertEqual. You
   can find more details in dump_with_testcase.py.

Input File for dump_with_testcase_mge.py

The input file is obtained by calling megengine.jit.trace.dump().
--data must be given.

Example

1. Obtain the model file by running xornet.py

2. Dump the file with test cases attached to the model.

   python3 dump_with_testcase_mge.py xornet_deploy.mge -o xornet.mge -d "#rand(0.1, 0.8, 4, 2)"
   

   The dumped file xornet.mge can be loaded by load_and_run.