chore: import upstream snapshot with attribution

tools/script/torchsscriptTest.py

@@ -0,0 +1,52 @@
+#!/usr/bin/python
+import numpy as np
+
+mnn_module = '.tmp.mnn'
+ts_module = '.tmp.pt'
+onnx_module = '.tmp.onnx'
+input_file = '.input.txt'
+output_file = '.output.txt'
+
+def run_cmd(args):
+    from subprocess import Popen, PIPE, STDOUT
+    stdout, _ = Popen(args, stdout=PIPE, stderr=STDOUT).communicate()
+    return str(stdout)
+
+def run_torchscript():
+    import torchvision.models as models
+    import onnxruntime as ort
+    import torch
+    resnet18 = models.resnet18(pretrained=True)
+    x = torch.rand(1, 3, 224, 224)
+    resnet18_ts = torch.jit.trace(resnet18, x)
+    resnet18_ts.save(ts_module)
+    torch.onnx.export(resnet18, x, onnx_module)
+    on_module = ort.InferenceSession(onnx_module)
+    inputs = {}
+    for inp in on_module.get_inputs():
+        inputs[inp.name] = x.numpy()
+    y = on_module.run(None, inputs)[0]
+    nx = x.numpy().reshape(-1)
+    ny = y.reshape(-1)
+    np.savetxt(input_file, nx, fmt='%f')
+    np.savetxt(output_file, ny, fmt='%f')
+
+def run_mnn():
+    # convert to mnn module
+    conv_res = run_cmd(['./MNNConvert', '-f', 'TS', '--modelFile', ts_module, '--MNNModel', mnn_module, '--bizCode', 'mnn'])
+    if (str(conv_res).find('Done') == -1):
+        print('Convert Error!')
+        return
+    # mnn run
+    message = run_cmd(['./testModel.out', mnn_module, input_file, output_file, '0', '0.001'])
+    # message = run_cmd(['./testModel.out', mnn_module, '/Users/wangzhaode/x.txt', '/Users/wangzhaode/y.txt', '0', '0.001'])
+    if (str(message).find('Correct') == -1):
+        print('Run Error!')
+        # return
+    print(message)
+
+if __name__ == '__main__':
+    import os
+    import sys
+    run_torchscript()
+    run_mnn()