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81 lines
3.0 KiB
81 lines
3.0 KiB
# Copyright 2020 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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class NetBoundingBoxEncode(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(NetBoundingBoxEncode, self).__init__()
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self.encode = P.BoundingBoxEncode(means=means, stds=stds)
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def construct(self, anchor, groundtruth):
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return self.encode(anchor, groundtruth)
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def bbox2delta(proposals, gt, means, stds):
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px = (proposals[..., 0] + proposals[..., 2]) * 0.5
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py = (proposals[..., 1] + proposals[..., 3]) * 0.5
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pw = proposals[..., 2] - proposals[..., 0] + 1.0
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ph = proposals[..., 3] - proposals[..., 1] + 1.0
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gx = (gt[..., 0] + gt[..., 2]) * 0.5
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gy = (gt[..., 1] + gt[..., 3]) * 0.5
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gw = gt[..., 2] - gt[..., 0] + 1.0
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gh = gt[..., 3] - gt[..., 1] + 1.0
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dx = (gx - px) / pw
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dy = (gy - py) / ph
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dw = np.log(gw / pw)
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dh = np.log(gh / ph)
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means = np.array(means, np.float32)
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stds = np.array(stds, np.float32)
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deltas = np.stack([(dx - means[0]) / stds[0], (dy - means[1]) / stds[1],
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(dw - means[2]) / stds[2], (dh - means[3]) / stds[3]], axis=-1)
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return deltas
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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_boundingbox_encode():
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anchor = np.array([[4, 1, 6, 9], [2, 5, 5, 9]]).astype(np.float32)
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gt = np.array([[3, 2, 7, 7], [1, 5, 5, 8]]).astype(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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groundtruth_box = Tensor(gt, mindspore.float32)
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expect_deltas = bbox2delta(anchor, gt, means, stds)
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error = np.ones(shape=[2, 4]) * 1.0e-6
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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boundingbox_encode = NetBoundingBoxEncode(means, stds)
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output = boundingbox_encode(anchor_box, groundtruth_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_encode = NetBoundingBoxEncode(means, stds)
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output = boundingbox_encode(anchor_box, groundtruth_box)
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diff = output.asnumpy() - expect_deltas
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assert np.all(abs(diff) < error)
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