wehub-resource-sync
121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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import paddle
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from paddle.vision.ops import RoIPool, roi_pool
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class TestRoIPool(unittest.TestCase):
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def setUp(self):
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self.data = np.random.rand(1, 256, 32, 32).astype('float32')
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boxes = np.random.rand(3, 4)
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boxes[:, 2] += boxes[:, 0] + 3
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boxes[:, 3] += boxes[:, 1] + 4
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self.boxes = boxes.astype('float32')
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self.boxes_num = np.array([3], dtype=np.int32)
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def roi_pool_functional(self, output_size):
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if isinstance(output_size, int):
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output_shape = (3, 256, output_size, output_size)
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else:
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output_shape = (3, 256, output_size[0], output_size[1])
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if paddle.in_dynamic_mode():
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data = paddle.to_tensor(self.data)
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boxes = paddle.to_tensor(self.boxes)
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boxes_num = paddle.to_tensor(self.boxes_num)
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pool_out = roi_pool(
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data, boxes, boxes_num=boxes_num, output_size=output_size
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)
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np.testing.assert_equal(pool_out.shape, output_shape)
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else:
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data = paddle.static.data(
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shape=self.data.shape, dtype=self.data.dtype, name='data'
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)
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boxes = paddle.static.data(
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shape=self.boxes.shape, dtype=self.boxes.dtype, name='boxes'
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)
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boxes_num = paddle.static.data(
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shape=self.boxes_num.shape,
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dtype=self.boxes_num.dtype,
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name='boxes_num',
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)
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pool_out = roi_pool(
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data, boxes, boxes_num=boxes_num, output_size=output_size
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)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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pool_out = exe.run(
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paddle.static.default_main_program(),
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feed={
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'data': self.data,
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'boxes': self.boxes,
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'boxes_num': self.boxes_num,
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},
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fetch_list=[pool_out],
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)
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np.testing.assert_equal(pool_out[0].shape, output_shape)
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def test_roi_pool_functional_dynamic(self):
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self.roi_pool_functional(3)
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self.roi_pool_functional(output_size=(3, 4))
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def test_roi_pool_functional_static(self):
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paddle.enable_static()
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self.roi_pool_functional(3)
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paddle.disable_static()
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def test_RoIPool(self):
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roi_pool_c = RoIPool(output_size=(4, 3))
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data = paddle.to_tensor(self.data)
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boxes = paddle.to_tensor(self.boxes)
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boxes_num = paddle.to_tensor(self.boxes_num)
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pool_out = roi_pool_c(data, boxes, boxes_num)
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np.testing.assert_equal(pool_out.shape, (3, 256, 4, 3))
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def test_value(
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self,
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):
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data = (
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np.array(list(range(1, 17))).reshape(1, 1, 4, 4).astype(np.float32)
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)
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boxes = np.array([[1.0, 1.0, 2.0, 2.0], [1.5, 1.5, 3.0, 3.0]]).astype(
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np.float32
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)
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boxes_num = np.array([2]).astype(np.int32)
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output = np.array([[[[11.0]]], [[[16.0]]]], dtype=np.float32)
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data = paddle.to_tensor(data)
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boxes = paddle.to_tensor(boxes)
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boxes_num = paddle.to_tensor(boxes_num)
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roi_pool_c = RoIPool(output_size=1)
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pool_out = roi_pool_c(data, boxes, boxes_num)
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np.testing.assert_almost_equal(pool_out.numpy(), output)
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if __name__ == '__main__':
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unittest.main()
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