Caffe2 - Python API
A deep learning, cross platform ML framework
test_caffe2_common.py
1 from __future__ import absolute_import
2 from __future__ import division
3 from __future__ import print_function
4 from __future__ import unicode_literals
5 
6 import glob
7 import numpy as np
8 import onnx.backend.test
10 import os
11 from onnx import numpy_helper
12 
13 
14 def load_tensor_as_numpy_array(f):
15  tensor = onnx.TensorProto()
16  with open(f, 'rb') as file:
17  tensor.ParseFromString(file.read())
18  return tensor
19 
20 
21 def assert_similar(ref, real):
22  np.testing.assert_equal(len(ref), len(real))
23  for i in range(len(ref)):
24  np.testing.assert_allclose(ref[i], real[i], rtol=1e-3)
25 
26 
27 def run_generated_test(model_file, data_dir, device='CPU'):
28  model = onnx.load(model_file)
29  input_num = len(glob.glob(os.path.join(data_dir, "input_*.pb")))
30  inputs = []
31  for i in range(input_num):
32  inputs.append(numpy_helper.to_array(load_tensor_as_numpy_array(
33  os.path.join(data_dir, "input_{}.pb".format(i)))))
34  output_num = len(glob.glob(os.path.join(data_dir, "output_*.pb")))
35  outputs = []
36  for i in range(output_num):
37  outputs.append(numpy_helper.to_array(load_tensor_as_numpy_array(
38  os.path.join(data_dir, "output_{}.pb".format(i)))))
39  prepared = c2.prepare(model, device=device)
40  c2_outputs = prepared.run(inputs)
41  assert_similar(outputs, c2_outputs)