# 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 paddle.fluid as fluid import paddle.fluid.layers as layers from paddle.fluid.framework import Program, program_guard import unittest class TestDetection(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=[2, 10, 4], append_batch_size=False, dtype='float32') scores = layers.data( name='scores', shape=[2, 10, 20], append_batch_size=False, dtype='float32') out = layers.detection_output( scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv) self.assertIsNotNone(out) self.assertEqual(out.shape[-1], 6) print(str(program)) def test_detection_api(self): program = Program() with program_guard(program): x = layers.data(name='x', shape=[4], dtype='float32') y = layers.data(name='y', shape=[4], dtype='float32') z = layers.data(name='z', shape=[4], dtype='float32', lod_level=1) iou = layers.iou_similarity(x=x, y=y) bcoder = layers.box_coder( prior_box=x, prior_box_var=y, target_box=z, code_type='encode_center_size') self.assertIsNotNone(iou) self.assertIsNotNone(bcoder) matched_indices, matched_dist = layers.bipartite_match(iou) self.assertIsNotNone(matched_indices) self.assertIsNotNone(matched_dist) gt = layers.data( name='gt', shape=[1, 1], dtype='int32', lod_level=1) trg, trg_weight = layers.target_assign( gt, matched_indices, mismatch_value=0) self.assertIsNotNone(trg) self.assertIsNotNone(trg_weight) gt2 = layers.data( name='gt2', shape=[10, 4], dtype='float32', lod_level=1) trg, trg_weight = layers.target_assign( gt2, matched_indices, mismatch_value=0) self.assertIsNotNone(trg) self.assertIsNotNone(trg_weight) print(str(program)) def test_ssd_loss(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=[10, 4], dtype='float32') scores = layers.data(name='scores', shape=[10, 21], dtype='float32') gt_box = layers.data( name='gt_box', shape=[4], lod_level=1, dtype='float32') gt_label = layers.data( name='gt_label', shape=[1], lod_level=1, dtype='int32') loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv) self.assertIsNotNone(loss) self.assertEqual(loss.shape[-1], 1) print(str(program)) class TestPriorBox(unittest.TestCase): def test_prior_box(self): data_shape = [3, 224, 224] images = fluid.layers.data( name='pixel', shape=data_shape, dtype='float32') conv1 = fluid.layers.conv2d(images, 3, 3, 2) box, var = layers.prior_box( input=conv1, image=images, min_sizes=[100.0], aspect_ratios=[1.], flip=True, clip=True) assert len(box.shape) == 4 assert box.shape == var.shape assert box.shape[3] == 4 class TestAnchorGenerator(unittest.TestCase): def test_anchor_generator(self): data_shape = [3, 224, 224] images = fluid.layers.data( name='pixel', shape=data_shape, dtype='float32') conv1 = fluid.layers.conv2d(images, 3, 3, 2) anchor, var = fluid.layers.anchor_generator( input=conv1, anchor_sizes=[64, 128, 256, 512], aspect_ratios=[0.5, 1.0, 2.0], variance=[0.1, 0.1, 0.2, 0.2], stride=[16.0, 16.0], offset=0.5) assert len(anchor.shape) == 4 assert anchor.shape == var.shape assert anchor.shape[3] == 4 class TestMultiBoxHead(unittest.TestCase): def test_multi_box_head(self): data_shape = [3, 224, 224] mbox_locs, mbox_confs, box, var = self.multi_box_head_output(data_shape) assert len(box.shape) == 2 assert box.shape == var.shape assert box.shape[1] == 4 assert mbox_locs.shape[1] == mbox_confs.shape[1] def multi_box_head_output(self, data_shape): images = fluid.layers.data( name='pixel', shape=data_shape, dtype='float32') conv1 = fluid.layers.conv2d(images, 3, 3, 2) conv2 = fluid.layers.conv2d(conv1, 3, 3, 2) conv3 = fluid.layers.conv2d(conv2, 3, 3, 2) conv4 = fluid.layers.conv2d(conv3, 3, 3, 2) conv5 = fluid.layers.conv2d(conv4, 3, 3, 2) mbox_locs, mbox_confs, box, var = layers.multi_box_head( inputs=[conv1, conv2, conv3, conv4, conv5, conv5], image=images, num_classes=21, min_ratio=20, max_ratio=90, aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]], base_size=300, offset=0.5, flip=True, clip=True) return mbox_locs, mbox_confs, box, var class TestDetectionMAP(unittest.TestCase): def test_detection_map(self): program = Program() with program_guard(program): detect_res = layers.data( name='detect_res', shape=[10, 6], append_batch_size=False, dtype='float32') label = layers.data( name='label', shape=[10, 6], append_batch_size=False, dtype='float32') map_out = layers.detection_map(detect_res, label, 21) self.assertIsNotNone(map_out) self.assertEqual(map_out.shape, (1, )) print(str(program)) if __name__ == '__main__': unittest.main()