ResNet101_DUC_HDC can have invalid test_data.

During testing models from model zoo I realized that onnxruntime and ngraph give similar results on ResNet101_DUC_HDC but still have 80% missmatch.
Is it possible that test data for ResNet101_DUC_HDC are invalid?

onnxruntime:
E           AssertionError:
E           Not equal to tolerance rtol=0.001, atol=1e-07
E
E           Mismatch: 80.2%
E           Max absolute difference: 0.9990995
E           Max relative difference: 1.0197404e+09
E            x: array([[[3.535608e-04, 1.008540e-03, 6.378655e-04, ..., 5.859119e-02,
E                    3.697309e-02, 4.621094e-02],
E                   [1.310240e-04, 3.463278e-05, 2.679980e-05, ..., 2.111239e-02,...
E            y: array([[[8.367564e-12, 4.105623e-11, 2.043494e-11, ..., 1.814237e-10,
E                    6.354865e-11, 6.036995e-11],
E                   [3.100877e-12, 1.409852e-12, 8.585701e-13, ..., 6.537316e-11,...

nGraph:
E           AssertionError:
E           Not equal to tolerance rtol=0.001, atol=1e-07
E
E           Mismatch: 80.2%
E           Max absolute difference: 0.99909943
E           Max relative difference: 1.0189351e+09
E            x: array([[[3.535608e-04, 1.008540e-03, 6.378655e-04, ..., 5.859119e-02,
E                    3.697309e-02, 4.621094e-02],
E                   [1.310240e-04, 3.463278e-05, 2.679980e-05, ..., 2.111239e-02,...
E            y: array([[[8.374161e-12, 4.108864e-11, 2.045105e-11, ..., 1.815672e-10,
E                    6.359892e-11, 6.041759e-11],
E                   [3.103328e-12, 1.410971e-12, 8.592453e-13, ..., 6.542469e-11,...

x - reference output
y - actual framework output

The issue identifies the ResNet101_DUC_HDC model and reports mismatches from onnxruntime and nGraph against the reference output. Start by reproducing the reported comparison and checking how this model's test data and reference output are produced. Done means establishing whether the test data are invalid and documenting or correcting the affected validation path.