You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
114 lines
3.5 KiB
114 lines
3.5 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
import paddle.v2.fluid as fluid
|
|
import paddle.v2.fluid.core as core
|
|
import paddle.v2.fluid.layers as layers
|
|
import paddle.v2.fluid.layers.detection as detection
|
|
from paddle.v2.fluid.framework import Program, program_guard
|
|
import unittest
|
|
import numpy as np
|
|
|
|
|
|
class TestBook(unittest.TestCase):
|
|
def test_detection_output(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
pb = layers.data(
|
|
name='prior_box',
|
|
shape=[10, 4],
|
|
append_batch_size=False,
|
|
dtype='float32')
|
|
pbv = layers.data(
|
|
name='prior_box_var',
|
|
shape=[10, 4],
|
|
append_batch_size=False,
|
|
dtype='float32')
|
|
loc = layers.data(
|
|
name='target_box',
|
|
shape=[20, 4],
|
|
append_batch_size=False,
|
|
dtype='float32')
|
|
scores = layers.data(
|
|
name='scores',
|
|
shape=[2, 20, 10],
|
|
append_batch_size=False,
|
|
dtype='float32')
|
|
out = layers.detection_output(
|
|
scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
|
|
class TestPriorBox(unittest.TestCase):
|
|
def test_prior_box(self):
|
|
data_shape = [3, 224, 224]
|
|
box, var = self.prior_box_output(data_shape)
|
|
|
|
assert len(box.shape) == 2
|
|
assert box.shape == var.shape
|
|
assert box.shape[1] == 4
|
|
|
|
def prior_box_output(self, data_shape):
|
|
images = fluid.layers.data(
|
|
name='pixel', shape=data_shape, dtype='float32')
|
|
conv1 = fluid.layers.conv2d(
|
|
input=images,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=2,
|
|
use_cudnn=False)
|
|
conv2 = fluid.layers.conv2d(
|
|
input=conv1,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=2,
|
|
use_cudnn=False)
|
|
conv3 = fluid.layers.conv2d(
|
|
input=conv2,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=2,
|
|
use_cudnn=False)
|
|
conv4 = fluid.layers.conv2d(
|
|
input=conv3,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=2,
|
|
use_cudnn=False)
|
|
conv5 = fluid.layers.conv2d(
|
|
input=conv4,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=2,
|
|
use_cudnn=False)
|
|
|
|
box, var = detection.prior_box(
|
|
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
|
|
image=images,
|
|
min_ratio=20,
|
|
max_ratio=90,
|
|
# steps=[8, 16, 32, 64, 100, 300],
|
|
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
|
|
base_size=300,
|
|
offset=0.5,
|
|
flip=True,
|
|
clip=True)
|
|
return box, var
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|