MegEngine/sdk/load-and-run

• Load and Run
• • Build
  • • Build with cmake
  • Dump Model with Test Cases Using dump_with_testcase_mge.py
  • • Step 1
    • Step 2
    • Example
  • load_and_run --input the dumped mge file

Load and Run

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

Build

Build with cmake

Build MegEngine from source following README.md. It will also produce the executable, load_and_run, which loads a model and runs the test cases attached to the model.

Dump Model with Test Cases Using dump_with_testcase_mge.py

Step 1

Dump the model by calling the python API megengine.jit.trace.dump().

Step 2

Append the test cases to the dumped model using dump_with_testcase_mge.py.

The basic usage of dump_with_testcase_mge.py is

python3 dump_with_testcase_mge.py model -d input_description -o model_with_testcases

where model is the file dumped at step 1, input_description describes the input data of the test cases, and model_with_testcases is the saved model with test cases.

input_description can be provided in the following ways:

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.

For more usages, run

python3 dump_with_testcase_mge.py --help

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)"
   

3. Verify the correctness by running load_and_run at the target platform.

   load_and_run xornet.mge
   

load_and_run --input the dumped mge file

You can also use --input to set mge file's input, this argument support these 4 formats:

1. PPM/PGM image file.

   PPM/PGM is supported by OpenCV and simple to parse, you can easily use cv::imwrite to generate one.

   load_and_run model.mge --input "data:image.ppm"
   

   data is blob name and image.ppm is file path, we use : to seperate key and value. Please note that " is necessary in terminal.

2. npy file.

   npy is Numpy file format, here is a Python example

   import numpy as np
   import cv2
   mat = cv2.imread('file.jpg')
   np.save('image.npy', mat)
   arr = np.array([[[1.1, 1.2],[100, 200.0]]], dtype=np.float32)
   np.save('bbox.npy', arr)
   

   then load_and_run the model

   load_and_run model.mge --input data:image.npy;bbox.npy
   

3. json format.

   For json format, you have to identify data type and blob shape. Here is a Python example

   import numpy as np
   import json
   import cv2
   bbox = np.array([[[1.1, 1.2],[100, 200.0]]], dtype=np.float32)
   obj = dict()
   obj['shape'] = bbox.shape
   obj['raw'] = bbox.flatten().tolist()
   obj['type'] = str(bbox.dtype)
   json_object = dict()
   json_object['bbox'] = obj
   json_str = json.dumps(json_object)
   with open('bbox.json', 'w') as f:
   f.write(json_str)
   f.flush()
   f.close()
   

   The json loader in load_and_run is not fully implement RFC7159, it does not support boolean and utf string format which is useless during inference.

   Now let's load-and-run the model with json file

   load_and_run model.mge --input data:image.npy:bbox:bbox.json
   

   Mutiple key-value pair could be seperated with ;.

4. plain string format.

   Also, you can give the value directly

   load_and_run model.mge --input data:image.ppm --input "bbox:[0,0],[200.0,200.0]" --input "batchid:0"
   

   1. bbox shape is [1,2,2] for [0,0],[200.0,200.0]. In order to facilitate user experience, the string parser would add an extra axis for input, thus bbox:0 is correspond to [1] and bbox:[0] means that the shape is [1,1]

   2. Since we can only identify int32 and float32 from this format, don't forget . for float number.