forked from mindspore-Ecosystem/mindspore
199 lines
8.1 KiB
Python
199 lines
8.1 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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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 pytest
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore as ms
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from mindspore import Tensor
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from mindspore.ops import functional as F
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from mindspore.ops.operations.nn_ops import UpsampleNearest3D
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class UpsampleNearest3DNet(nn.Cell):
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def __init__(self, output_size=None, scales=None):
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super(UpsampleNearest3DNet, self).__init__()
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self.upsample = UpsampleNearest3D(output_size, scales)
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def construct(self, x):
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out = self.upsample(x)
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return out
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_upsample_nearest_3d_dynamic_shape():
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"""
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Feature: Test UpsampleNearest3D op in gpu.
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Description: Test the ops in dynamic shape.
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Expectation: Expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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output_size = [3, 4, 5]
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net = UpsampleNearest3DNet(output_size=output_size)
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x_dyn = Tensor(shape=[None, 1, 2, 2, 4], dtype=ms.float32)
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net.set_inputs(x_dyn)
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x = Tensor(
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np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
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16]).reshape([1, 1, 2, 2, 4]), ms.float32)
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output = net(x)
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expect_shape = (1, 1, 3, 4, 5)
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assert expect_shape == output.asnumpy().shape
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('data_type', [np.float16, np.float32])
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def test_upsample_nearest_3d_output_size_float(data_type):
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"""
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Feature: UpsampleNearest3D
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Description: Test cases for UpsampleNearest3D operator with output_size.
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Expectation: The result match expected output.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_tensor = Tensor(
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np.array([[[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
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[[0.7, 0.8, 0.9], [1.0, 1.1, 1.2]]]]]).astype(data_type))
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expected = np.array([[[[[0.1000, 0.1000, 0.2000, 0.2000, 0.3000],
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[0.1000, 0.1000, 0.2000, 0.2000, 0.3000],
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[0.4000, 0.4000, 0.5000, 0.5000, 0.6000],
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[0.4000, 0.4000, 0.5000, 0.5000, 0.6000]],
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[[0.1000, 0.1000, 0.2000, 0.2000, 0.3000],
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[0.1000, 0.1000, 0.2000, 0.2000, 0.3000],
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[0.4000, 0.4000, 0.5000, 0.5000, 0.6000],
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[0.4000, 0.4000, 0.5000, 0.5000, 0.6000]],
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[[0.7000, 0.7000, 0.8000, 0.8000, 0.9000],
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[0.7000, 0.7000, 0.8000, 0.8000, 0.9000],
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[1.0000, 1.0000, 1.1000, 1.1000, 1.2000],
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[1.0000, 1.0000, 1.1000, 1.1000,
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1.2000]]]]]).astype(data_type)
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net = UpsampleNearest3DNet(output_size=[3, 4, 5])
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out = net(input_tensor)
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diff = abs(out.asnumpy() - expected)
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error = np.ones(shape=expected.shape) * 1.0e-5
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assert np.all(diff < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('data_type', [np.float16, np.float32])
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def test_upsample_nearest_3d_scales_float(data_type):
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"""
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Feature: UpsampleNearest3D
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Description: Test cases for UpsampleNearest3D operator with scales.
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Expectation: The result match expected output.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_tensor = Tensor(
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np.array([[[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
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[[0.7, 0.8, 0.9], [1.0, 1.1, 1.2]]]]]).astype(data_type))
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expected = np.array(
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[[[[[0.1000, 0.1000, 0.1000, 0.2000, 0.2000, 0.3000, 0.3000],
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[0.1000, 0.1000, 0.1000, 0.2000, 0.2000, 0.3000, 0.3000],
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[0.4000, 0.4000, 0.4000, 0.5000, 0.5000, 0.6000, 0.6000],
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[0.4000, 0.4000, 0.4000, 0.5000, 0.5000, 0.6000, 0.6000]],
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[[0.1000, 0.1000, 0.1000, 0.2000, 0.2000, 0.3000, 0.3000],
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[0.1000, 0.1000, 0.1000, 0.2000, 0.2000, 0.3000, 0.3000],
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[0.4000, 0.4000, 0.4000, 0.5000, 0.5000, 0.6000, 0.6000],
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[0.4000, 0.4000, 0.4000, 0.5000, 0.5000, 0.6000, 0.6000]],
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[[0.7000, 0.7000, 0.7000, 0.8000, 0.8000, 0.9000, 0.9000],
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[0.7000, 0.7000, 0.7000, 0.8000, 0.8000, 0.9000, 0.9000],
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[1.0000, 1.0000, 1.0000, 1.1000, 1.1000, 1.2000, 1.2000],
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[1.0000, 1.0000, 1.0000, 1.1000, 1.1000, 1.2000,
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1.2000]]]]]).astype(data_type)
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net = UpsampleNearest3DNet(scales=[1.5, 2.0, 2.5])
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out = net(input_tensor)
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diff = abs(out.asnumpy() - expected)
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error = np.ones(shape=expected.shape) * 1.0e-5
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assert np.all(diff < error)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_upsample_nearest_3d_error():
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"""
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Feature: UpsampleNearest3D
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Description: Test cases for UpsampleNearest3D operator with errors.
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Expectation: Raise expected error type.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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with pytest.raises(ValueError):
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input_tensor = Tensor(np.ones((2, 2, 2, 2), dtype=np.float32))
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net = UpsampleNearest3DNet(output_size=[3, 4, 5])
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net(input_tensor)
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with pytest.raises(TypeError):
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input_tensor = Tensor(np.ones((2, 2, 2, 2, 2), dtype=np.int32))
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net = UpsampleNearest3DNet(output_size=[3, 4, 5])
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net(input_tensor)
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with pytest.raises(TypeError):
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input_tensor = Tensor(np.ones((2, 2, 2, 2, 2), dtype=np.float32))
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net = UpsampleNearest3DNet(scales=[1, 2, 3])
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net(input_tensor)
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with pytest.raises(ValueError):
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input_tensor = Tensor(np.ones((2, 2, 2, 2, 2), dtype=np.float32))
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net = UpsampleNearest3DNet(output_size=[3, 4])
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net(input_tensor)
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with pytest.raises(ValueError):
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input_tensor = Tensor(np.ones((2, 2, 2, 2, 2), dtype=np.float32))
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net = UpsampleNearest3DNet(scales=[1.0, 2.0, 3.0, 4.0])
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net(input_tensor)
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with pytest.raises(ValueError):
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input_tensor = Tensor(np.ones((2, 2, 2, 2, 2), dtype=np.float32))
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net = UpsampleNearest3DNet(output_size=[3, 4, 5],
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scales=[1.0, 2.0, 3.0])
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net(input_tensor)
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with pytest.raises(ValueError):
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input_tensor = Tensor(np.ones((2, 2, 2, 2, 2), dtype=np.float32))
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net = UpsampleNearest3DNet()
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net(input_tensor)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_vmap_upsample_nearest3d():
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"""
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Feature: UpsampleNearest3D GPU op vmap feature.
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Description: test the vmap feature of UpsampleNearest3D.
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Expectation: success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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# 3 batches
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input_tensor = Tensor(
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np.arange(0, 4.8, 0.1).reshape([3, 1, 1, 2, 2, 4]).astype(np.float32))
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net = UpsampleNearest3DNet(output_size=[3, 2, 2])
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expect = np.array([[[[[[0.0, 0.2], [0.4, 0.6]], [[0.0, 0.2], [0.4, 0.6]],
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[[0.8, 1.0], [1.2, 1.4]]]]],
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[[[[[1.6, 1.8], [2.0, 2.2]], [[1.6, 1.8], [2.0, 2.2]],
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[[2.4, 2.6], [2.8, 3.0]]]]],
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[[[[[3.2, 3.4], [3.6, 3.8]], [[3.2, 3.4], [3.6, 3.8]],
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[[4.0, 4.2], [4.4, 4.6]]]]]])
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out_vmap = F.vmap(net, in_axes=(0))(input_tensor)
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error = np.ones(shape=expect.shape) * 1.0e-6
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assert np.all(abs(out_vmap.asnumpy() - expect) < error)
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