ac1a1e3e9f
* Change request.infer to use param_names instead of request._input_list because it fix the bug * remove xfail mark * remove xfail_issue_35893 form tests/ __init__.py * Remove xfail_issue_35893. Add OnnxBackendNodeModelTest.test_convtranspose_3d_cpu to xfail_issue_38091 * Remove OnnxBackendNodeModelTest.test_convtranspose_3d_cpu from xfail_issue_38091 in test_backend.py
98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
# ******************************************************************************
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# Copyright 2018-2020 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ******************************************************************************
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import numpy as np
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import onnx
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from tests.test_onnx.utils import run_node
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def make_batch_norm_node(**node_attributes):
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return onnx.helper.make_node(
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"BatchNormalization", inputs=["X", "scale", "B", "mean", "var"], outputs=["Y"], **node_attributes
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)
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def test_batch_norm_test_node():
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data = np.arange(48).reshape((1, 3, 4, 4)).astype(np.float32)
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scale = np.ones((3,)).astype(np.float32) # Gamma
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bias = np.zeros((3,)).astype(np.float32) # Beta
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mean = np.mean(data, axis=(0, 2, 3))
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var = np.var(data, axis=(0, 2, 3))
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expected_output = np.array(
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[
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[
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[
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[-1.62694025, -1.41001487, -1.19308949, -0.97616416],
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[-0.75923878, -0.54231346, -0.32538807, -0.10846269],
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[0.10846269, 0.32538807, 0.54231334, 0.75923872],
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[0.9761641, 1.19308949, 1.41001487, 1.62694025],
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],
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[
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[-1.62694049, -1.41001511, -1.19308972, -0.97616434],
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[-0.7592392, -0.54231358, -0.32538843, -0.10846281],
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[0.10846233, 0.32538795, 0.5423131, 0.75923872],
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[0.97616386, 1.19308949, 1.41001463, 1.62694025],
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],
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[
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[-1.62694025, -1.41001511, -1.19308949, -0.97616434],
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[-0.75923872, -0.54231358, -0.32538795, -0.10846233],
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[0.10846233, 0.32538795, 0.54231358, 0.7592392],
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[0.97616386, 1.19308949, 1.41001511, 1.62694073],
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],
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]
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],
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dtype=np.float32,
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)
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node = make_batch_norm_node()
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result = run_node(node, [data, scale, bias, mean, var])[0]
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assert np.allclose(result, expected_output, rtol=1e-04, atol=1e-08)
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scale = np.broadcast_to(0.1, (3,)).astype(np.float32) # Gamma
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bias = np.broadcast_to(1, (3,)).astype(np.float32) # Beta
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expected_output = np.array(
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[
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[
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[
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[0.83730596, 0.85899848, 0.88069105, 0.90238357],
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[0.92407608, 0.94576865, 0.96746117, 0.98915374],
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[1.01084626, 1.03253877, 1.05423129, 1.07592392],
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[1.09761643, 1.11930895, 1.14100146, 1.16269398],
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],
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[
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[0.83730596, 0.85899854, 0.88069105, 0.90238357],
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[0.92407608, 0.94576865, 0.96746117, 0.98915374],
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[1.01084626, 1.03253877, 1.05423141, 1.07592392],
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[1.09761643, 1.11930895, 1.14100146, 1.16269398],
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],
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[
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[0.83730596, 0.85899848, 0.88069105, 0.90238357],
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[0.92407614, 0.94576865, 0.96746117, 0.98915374],
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[1.01084626, 1.03253877, 1.05423141, 1.07592392],
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[1.09761643, 1.11930895, 1.14100146, 1.16269398],
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],
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]
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],
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dtype=np.float32,
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)
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node = make_batch_norm_node()
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result = run_node(node, [data, scale, bias, mean, var])[0]
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assert np.allclose(result, expected_output, rtol=1e-04, atol=1e-08)
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