mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
141d81d095
* Move ONNX integration tests from onnx-fb-universe to PyTorch repo
* Switch to use torchvision
* Delete single rnn operator tests, they have been covered in e2e tests in test_caffe2.py
* Mirror the fix in onnx-fb-universe to bypass cuda check
667326d84b
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import glob
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import numpy as np
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import onnx.backend.test
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import caffe2.python.onnx.backend as c2
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import os
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from onnx import numpy_helper
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def load_tensor_as_numpy_array(f):
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tensor = onnx.TensorProto()
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with open(f, 'rb') as file:
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tensor.ParseFromString(file.read())
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return tensor
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def assert_similar(ref, real):
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np.testing.assert_equal(len(ref), len(real))
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for i in range(len(ref)):
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np.testing.assert_allclose(ref[i], real[i], rtol=1e-3)
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def run_generated_test(model_file, data_dir, device='CPU'):
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model = onnx.load(model_file)
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input_num = len(glob.glob(os.path.join(data_dir, "input_*.pb")))
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inputs = []
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for i in range(input_num):
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inputs.append(numpy_helper.to_array(load_tensor_as_numpy_array(
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os.path.join(data_dir, "input_{}.pb".format(i)))))
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output_num = len(glob.glob(os.path.join(data_dir, "output_*.pb")))
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outputs = []
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for i in range(output_num):
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outputs.append(numpy_helper.to_array(load_tensor_as_numpy_array(
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os.path.join(data_dir, "output_{}.pb".format(i)))))
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prepared = c2.prepare(model, device=device)
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c2_outputs = prepared.run(inputs)
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assert_similar(outputs, c2_outputs)
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