forked from mindspore-Ecosystem/mindspore
100 lines
3.7 KiB
Python
100 lines
3.7 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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from mindspore import Tensor
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from mindspore.nn import Cell
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from mindspore.ops.operations.nn_ops import GridSampler2D, GridSampler3D
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class Net2D(Cell):
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def __init__(self, mode, padding_mode, align_corners):
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super(Net2D, self).__init__()
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self.grid_sampler_2d = GridSampler2D(mode, padding_mode, align_corners)
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def construct(self, x0, x1):
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return self.grid_sampler_2d(x0, x1)
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class Net3D(Cell):
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def __init__(self, mode, padding_mode, align_corners):
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super(Net3D, self).__init__()
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self.grid_sampler_3d = GridSampler3D(mode, padding_mode, align_corners)
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def construct(self, x0, x1):
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return self.grid_sampler_3d(x0, x1)
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def run_net2d(dtype):
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in_tensor = Tensor(np.arange(36).reshape((2, 3, 3, 2)).astype(dtype))
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grid = Tensor(np.arange(0.2, 1, 0.1).reshape((2, 2, 1, 2)).astype(dtype))
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if dtype == np.float32:
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expect_out = np.array([[[[3.2], [3.7]], [[9.2], [9.7]], [[15.200001], [15.7]]],
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[[[22.2], [22.699999]], [[28.2], [28.699999]], [[34.2], [34.7]]]])
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elif dtype == np.float64:
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expect_out = np.array([[[[3.2], [3.7]], [[9.2], [9.7]], [[15.2], [15.7]]],
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[[[22.2], [22.7]], [[28.2], [28.7]], [[34.2], [34.7]]]])
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error_out = np.ones(shape=expect_out.shape) * 1.0e-6
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net = Net2D('bilinear', 'border', True)
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output = net(in_tensor, grid)
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diff_out = output.asnumpy() - expect_out
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assert np.all(np.abs(diff_out) < error_out)
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def run_net3d(dtype):
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in_tensor = Tensor(np.arange(32).reshape((2, 2, 2, 2, 2)).astype(dtype))
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grid = Tensor(np.arange(-0.2, 1, 0.1).reshape((2, 2, 1, 1, 3)).astype(dtype))
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if dtype == np.float32:
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expect_out = np.array([[[[[3.3]], [[4.35]]], [[[11.300001]], [[12.349999]]]],
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[[[[21.4]], [[22.449999]]], [[[29.4]], [[30.449999]]]]])
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elif dtype == np.float64:
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expect_out = np.array([[[[[3.3]], [[4.35]]], [[[11.3]], [[12.35]]]],
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[[[[21.4]], [[22.45]]], [[[29.4]], [[30.45]]]]])
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error_out = np.ones(shape=expect_out.shape) * 1.0e-6
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net = Net3D('bilinear', 'zeros', True)
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output = net(in_tensor, grid)
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diff_out = output.asnumpy() - expect_out
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assert np.all(np.abs(diff_out) < error_out)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_gridsampler2d():
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"""
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Feature: GridSampler2D op.
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Description: test data type is float32 and float64 in GPU.
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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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run_net2d(np.float32)
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run_net2d(np.float64)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_gridsampler3d():
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"""
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Feature: GridSampler3D op.
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Description: test data type is float32 and float64 in GPU.
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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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run_net3d(np.float32)
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run_net3d(np.float64)
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