zhutian
120 lines
4.4 KiB
Python
120 lines
4.4 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 os
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import re
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import unittest
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import onnx.backend.test
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from onnx import defs
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from onnx_tf import opset_version
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from onnx_tf.backend import TensorflowBackend
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from onnx_tf.common.legacy import legacy_onnx_pre_ver
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from onnx_tf.common.legacy import legacy_opset_pre_ver
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def get_onnxtf_supported_ops():
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return opset_version.backend_opset_version
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def get_onnx_supported_ops():
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onnx_ops_dict = {}
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for schema in defs.get_all_schemas():
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onnx_ops_dict[schema.name] = {
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'version': schema.since_version,
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'deprecated': schema.deprecated
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}
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return onnx_ops_dict
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# This is a pytest magic variable to load extra plugins
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pytest_plugins = 'onnx.backend.test.report',
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backend_test = onnx.backend.test.BackendTest(TensorflowBackend, __name__)
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# The test cases excluded below should be considered permanent restrictions
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# based on the TensorFlow implementation. Unimplemented operators will raise
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# a BackendIsNotSupposedToImplementIt exception so that their test cases
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# will pass and show a verbose message stating it was effectively skipped.
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# https://github.com/onnx/onnx/issues/349
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backend_test.exclude(r'[a-z,_]*GLU[a-z,_]*')
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# TF does not support dialation and strides at the same time:
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# Will produce strides > 1 not supported in conjunction with dilation_rate > 1
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backend_test.exclude(r'[a-z,_]*dilated_strided[a-z,_]*')
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backend_test.exclude(r'[a-z,_]*Conv2d_dilated[a-z,_]*')
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# TF does not have column major max_pool_with_argmax
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backend_test.exclude(
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r'[a-z,_]*maxpool_with_argmax_2d_precomputed_strides[a-z,_]*')
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# PRelu OnnxBackendPyTorchConvertedModelTest has wrong dim for broadcasting
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backend_test.exclude(r'[a-z,_]*PReLU_[0-9]d_multiparam[a-z,_]*')
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# TF does not support int8, int16, uint8, uint16, uint32, uint64 for
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# tf.floormod and tf.truncatemod
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backend_test.exclude(r'test_mod_[a-z,_]*uint[0-9]+')
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backend_test.exclude(r'test_mod_[a-z,_]*int(8|(16))+')
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# TF only support uint8, int32, int64 for indices and int32 for depth in
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# tf.one_hot
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backend_test.exclude(r'test_onehot_[a-z,_]*')
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# TF doesn't support most of the attributes in resize op
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# test_node.py will cover the test
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backend_test.exclude(r'test_resize_[a-z,_]*')
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# range is using loop in the model test but all the outputs datatype are
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# missing in the body attribute of the loop
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backend_test.exclude(
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r'test_range_float_type_positive_delta_expanded[a-z,_]*')
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backend_test.exclude(
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r'test_range_int32_type_negative_delta_expanded[a-z,_]*')
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# skip all the cumsum testcases because all the axis in the testcases
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# are created as a 1-D 1 element tensor, but the spec clearly state
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# that axis should be a 0-D tensor(scalar)
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backend_test.exclude(r'test_cumsum_[a-z,_]*')
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if legacy_opset_pre_ver(7):
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backend_test.exclude(r'[a-z,_]*Upsample[a-z,_]*')
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if 'TRAVIS' in os.environ:
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backend_test.exclude('test_vgg19')
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backend_test.exclude('zfnet512')
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if legacy_onnx_pre_ver(1, 2):
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# These following tests fails by a tiny margin with onnx<1.2:
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backend_test.exclude('test_operator_add_broadcast_cpu')
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backend_test.exclude('test_operator_add_size1_broadcast_cpu')
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backend_test.exclude('test_operator_add_size1_right_broadcast_cpu')
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backend_test.exclude('test_operator_add_size1_singleton_broadcast_cpu')
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backend_test.exclude('test_averagepool_3d_default_cpu')
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# Do not support consumed flag:
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backend_test.exclude('test_batch_normalization')
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# Do not support RNN testing on onnx<1.2 due to incorrect tests:
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backend_test.exclude(r'test_operator_rnn_cpu')
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backend_test.exclude(r'test_operator_lstm_cpu')
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backend_test.exclude(r'test_operator_rnn_single_layer_cpu')
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# The onnx test for cast, float to string, does not work
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if not legacy_opset_pre_ver(9):
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backend_test.exclude(r'[a-z,_]*cast[a-z,_]*')
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if not legacy_opset_pre_ver(10):
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# Do not support dilations != 1 for ConvTranspose, test is added in opset 10
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backend_test.exclude(r'[a-z,_]*convtranspose_dilations[a-z,_]*')
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# some NLL test cases do not use the `NegativeLogLikelihoodLoss` operator
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# however they use the `where` operator which has some restrictions in TF 1.x
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# (x,y tensors must have same shape, broadcastable shapes not supported)
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backend_test.exclude(r'test_negative_log_likelihood_loss_[a-z,_]*')
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# import all test cases at global scope to make them visible to python.unittest
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globals().update(backend_test.enable_report().test_cases)
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if __name__ == '__main__':
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unittest.main()
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