add unit test

python/paddle/v2/fluid/tests/object_detection/test_prior_boxes.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 numpy as np
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers.detection as detection
+import paddle.v2.fluid.core as core
+import unittest
+
+
+def prior_box_output(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_boxes(
+        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
+
+
+def main(use_cuda):
+    if use_cuda:  # prior_box only support CPU.
+        return
+
+    box, var = prior_box_output(data_shape=[3, 224, 224])
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+    batch = [128]
+
+    for i in range(1):
+        # print("iteration : %d" % i)
+        x = np.random.random(batch + data_shape).astype("float32")
+        tensor_x = core.LoDTensor()
+        tensor_x.set(x, place)
+        box, var = exe.run(fluid.default_main_program(),
+                           feed={'pixel': tensor_x},
+                           fetch_list=[box, var])
+        box_arr = np.array(box)
+        var_arr = np.array(var)
+        assert box_arr.shape[1] == 4
+        assert var_arr.shape[1] == 4
+        assert box_arr.shape[0] == var_arr.shape[0]
+
+
+class TestFitALine(unittest.TestCase):
+    def test_cpu(self):
+        with self.program_scope_guard():
+            main(use_cuda=False)
+
+    def test_cuda(self):
+        with self.program_scope_guard():
+            main(use_cuda=True)
+
+
+if __name__ == '__main__':
+    unittest.main()