forked from mindspore-Ecosystem/mindspore
123 lines
4.9 KiB
Python
123 lines
4.9 KiB
Python
# Copyright 2020-2022 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
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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class NetBoundingBoxDecode(nn.Cell):
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def __init__(self, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
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super(NetBoundingBoxDecode, self).__init__()
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self.decode = P.BoundingBoxDecode(max_shape=(768, 1280), means=means, stds=stds,
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wh_ratio_clip=0.016)
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def construct(self, anchor, groundtruth):
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return self.decode(anchor, groundtruth)
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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_boundingbox_decode():
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anchor = np.array([[4, 1, 2, 1], [2, 2, 2, 3]], np.float32)
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deltas = np.array([[3, 1, 2, 2], [1, 2, 1, 4]], np.float32)
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means = (0.1, 0.1, 0.2, 0.2)
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stds = (2.0, 2.0, 3.0, 3.0)
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anchor_box = Tensor(anchor, mindspore.float32)
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deltas_box = Tensor(deltas, mindspore.float32)
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expect_deltas = np.array([[28.6500, 0.0000, 0.0000, 33.8500],
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[0.0000, 0.0000, 15.8663, 72.7000]], np.float32)
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error = np.ones(shape=[2, 4]) * 1.0e-4
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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boundingbox_decode = NetBoundingBoxDecode(means, stds)
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output = boundingbox_decode(anchor_box, deltas_box)
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diff = output.asnumpy() - expect_deltas
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assert np.all(abs(diff) < error)
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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boundingbox_decode = NetBoundingBoxDecode(means, stds)
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output = boundingbox_decode(anchor_box, deltas_box)
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diff = output.asnumpy() - expect_deltas
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assert np.all(abs(diff) < error)
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def test_bounding_box_decode_functional():
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"""
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Feature: test bounding_box_decode functional API.
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Description: test case for bounding_box_decode functional API.
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Expectation: the result match with expected result.
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"""
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anchor_box = Tensor([[4, 1, 2, 1], [2, 2, 2, 3]], mstype.float32)
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deltas = Tensor([[3, 1, 2, 2], [1, 2, 1, 4]], mstype.float32)
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output = F.bounding_box_decode(anchor_box, deltas, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
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max_shape=(768, 1280), wh_ratio_clip=0.016)
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expected = np.array([[4.1953125, 0., 0., 5.1953125], [2.140625, 0., 3.859375, 60.59375]]).astype(np.float32)
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np.testing.assert_array_almost_equal(output.asnumpy(), expected, decimal=2)
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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_bounding_box_decode_functional_modes():
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"""
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Feature: test bounding_box_decode functional API in PyNative and Graph modes.
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Description: test case for bounding_box_decode functional API.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_bounding_box_decode_functional()
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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test_bounding_box_decode_functional()
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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_dynamic_shape_boundingbox_decode():
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"""
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Feature: Test dynamic shape of BoundingBoxDecode operator
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Description: dynamic input
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Expectation: success.
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"""
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anchor = np.array([[4, 1, 2, 1], [2, 2, 2, 3]], np.float32)
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deltas = np.array([[3, 1, 2, 2], [1, 2, 1, 4]], np.float32)
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means = (0.1, 0.1, 0.2, 0.2)
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stds = (2.0, 2.0, 3.0, 3.0)
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anchor_box = Tensor(anchor, mindspore.float32)
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deltas_box = Tensor(deltas, mindspore.float32)
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expect_deltas = np.array([[28.6500, 0.0000, 0.0000, 33.8500],
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[0.0000, 0.0000, 15.8663, 72.7000]], np.float32)
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error = np.ones(shape=[2, 4]) * 1.0e-4
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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boundingbox_decode = NetBoundingBoxDecode(means, stds)
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input_dyn = Tensor(shape=[None, 4], dtype=anchor_box.dtype)
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deltas_box_dyn = Tensor(shape=[None, 4], dtype=deltas_box.dtype)
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boundingbox_decode.set_inputs(input_dyn, deltas_box_dyn)
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output = boundingbox_decode(anchor_box, deltas_box)
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diff = output.asnumpy() - expect_deltas
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assert np.all(abs(diff) < error)
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