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88 lines
2.9 KiB
88 lines
2.9 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import numpy as np
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import paddle.v2.fluid as fluid
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import paddle.v2.fluid.layers.detection as detection
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import paddle.v2.fluid.core as core
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import unittest
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def prior_box_output(data_shape):
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images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
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conv1 = fluid.layers.conv2d(
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input=images, num_filters=3, filter_size=3, stride=2, use_cudnn=False)
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conv2 = fluid.layers.conv2d(
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input=conv1, num_filters=3, filter_size=3, stride=2, use_cudnn=False)
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conv3 = fluid.layers.conv2d(
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input=conv2, num_filters=3, filter_size=3, stride=2, use_cudnn=False)
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conv4 = fluid.layers.conv2d(
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input=conv3, num_filters=3, filter_size=3, stride=2, use_cudnn=False)
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conv5 = fluid.layers.conv2d(
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input=conv4, num_filters=3, filter_size=3, stride=2, use_cudnn=False)
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box, var = detection.prior_box(
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inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
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image=images,
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min_ratio=20,
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max_ratio=90,
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# steps=[8, 16, 32, 64, 100, 300],
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aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
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base_size=300,
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offset=0.5,
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flip=True,
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clip=True)
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return box, var
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def main(use_cuda):
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if use_cuda: # prior_box only support CPU.
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return
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data_shape = [3, 224, 224]
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box, var = prior_box_output(data_shape)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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batch = [4] # batch is not used in the prior_box.
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assert box.shape[1] == 4
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assert var.shape[1] == 4
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assert box.shape == var.shape
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assert len(box.shape) == 2
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for _ in range(1):
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x = np.random.random(batch + data_shape).astype("float32")
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tensor_x = core.LoDTensor()
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tensor_x.set(x, place)
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boxes, vars = exe.run(fluid.default_main_program(),
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feed={'pixel': tensor_x},
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fetch_list=[box, var])
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assert vars.shape == var.shape
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assert boxes.shape == box.shape
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class TestFitALine(unittest.TestCase):
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def test_cpu(self):
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main(use_cuda=False)
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def test_cuda(self):
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main(use_cuda=True)
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if __name__ == '__main__':
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unittest.main()
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