# 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_box_coder_api(self): program = Program() with program_guard(program): x = layers.data(name='x', shape=[4], dtype='float32') y = layers.data(name='z', shape=[4], dtype='float32', lod_level=1) bcoder = layers.box_coder( prior_box=x, prior_box_var=[0.1, 0.2, 0.1, 0.2], target_box=y, code_type='encode_center_size') self.assertIsNotNone(bcoder) 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): program = Program() with program_guard(program): 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 TestDensityPriorBox(unittest.TestCase): def test_density_prior_box(self): program = Program() with program_guard(program): 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.density_prior_box( input=conv1, image=images, densities=[3, 4], fixed_sizes=[50., 60.], fixed_ratios=[1.0], clip=True) assert len(box.shape) == 4 assert box.shape == var.shape assert box.shape[-1] == 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 TestGenerateProposalLabels(unittest.TestCase): def test_generate_proposal_labels(self): program = Program() with program_guard(program): rpn_rois = layers.data( name='rpn_rois', shape=[4, 4], dtype='float32', lod_level=1, append_batch_size=False) gt_classes = layers.data( name='gt_classes', shape=[6], dtype='int32', lod_level=1, append_batch_size=False) is_crowd = layers.data( name='is_crowd', shape=[6], dtype='int32', lod_level=1, append_batch_size=False) gt_boxes = layers.data( name='gt_boxes', shape=[6, 4], dtype='float32', lod_level=1, append_batch_size=False) im_info = layers.data( name='im_info', shape=[1, 3], dtype='float32', lod_level=1, append_batch_size=False) class_nums = 5 outs = fluid.layers.generate_proposal_labels( rpn_rois=rpn_rois, gt_classes=gt_classes, is_crowd=is_crowd, gt_boxes=gt_boxes, im_info=im_info, batch_size_per_im=2, fg_fraction=0.5, fg_thresh=0.5, bg_thresh_hi=0.5, bg_thresh_lo=0.0, bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], class_nums=class_nums) rois = outs[0] labels_int32 = outs[1] bbox_targets = outs[2] bbox_inside_weights = outs[3] bbox_outside_weights = outs[4] assert rois.shape[1] == 4 assert rois.shape[0] == labels_int32.shape[0] assert rois.shape[0] == bbox_targets.shape[0] assert rois.shape[0] == bbox_inside_weights.shape[0] assert rois.shape[0] == bbox_outside_weights.shape[0] assert bbox_targets.shape[1] == 4 * class_nums assert bbox_inside_weights.shape[1] == 4 * class_nums assert bbox_outside_weights.shape[1] == 4 * class_nums class TestGenerateMaskLabels(unittest.TestCase): def test_generate_mask_labels(self): program = Program() with program_guard(program): im_info = layers.data( name='im_info', shape=[1, 3], dtype='float32', lod_level=1, append_batch_size=False) gt_classes = layers.data( name='gt_classes', shape=[2, 1], dtype='int32', lod_level=1, append_batch_size=False) is_crowd = layers.data( name='is_crowd', shape=[2, 1], dtype='int32', lod_level=1, append_batch_size=False) gt_segms = layers.data( name='gt_segms', shape=[20, 2], dtype='float32', lod_level=3, append_batch_size=False) rois = layers.data( name='rois', shape=[4, 4], dtype='float32', lod_level=1, append_batch_size=False) labels_int32 = layers.data( name='labels_int32', shape=[4, 1], dtype='int32', lod_level=1, append_batch_size=False) num_classes = 5 resolution = 14 outs = fluid.layers.generate_mask_labels( im_info=im_info, gt_classes=gt_classes, is_crowd=is_crowd, gt_segms=gt_segms, rois=rois, labels_int32=labels_int32, num_classes=num_classes, resolution=resolution) mask_rois, roi_has_mask_int32, mask_int32 = outs assert mask_rois.shape[1] == 4 assert mask_int32.shape[1] == num_classes * resolution * resolution 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)) class TestRpnTargetAssign(unittest.TestCase): def test_rpn_target_assign(self): program = Program() with program_guard(program): bbox_pred_shape = [10, 50, 4] cls_logits_shape = [10, 50, 2] anchor_shape = [50, 4] bbox_pred = layers.data( name='bbox_pred', shape=bbox_pred_shape, append_batch_size=False, dtype='float32') cls_logits = layers.data( name='cls_logits', shape=cls_logits_shape, append_batch_size=False, dtype='float32') anchor_box = layers.data( name='anchor_box', shape=anchor_shape, append_batch_size=False, dtype='float32') anchor_var = layers.data( name='anchor_var', shape=anchor_shape, append_batch_size=False, dtype='float32') gt_boxes = layers.data( name='gt_boxes', shape=[4], lod_level=1, dtype='float32') is_crowd = layers.data( name='is_crowd', shape=[1, 10], dtype='int32', lod_level=1, append_batch_size=False) im_info = layers.data( name='im_info', shape=[1, 3], dtype='float32', lod_level=1, append_batch_size=False) outs = layers.rpn_target_assign( bbox_pred=bbox_pred, cls_logits=cls_logits, anchor_box=anchor_box, anchor_var=anchor_var, gt_boxes=gt_boxes, is_crowd=is_crowd, im_info=im_info, rpn_batch_size_per_im=256, rpn_straddle_thresh=0.0, rpn_fg_fraction=0.5, rpn_positive_overlap=0.7, rpn_negative_overlap=0.3, use_random=False) pred_scores = outs[0] pred_loc = outs[1] tgt_lbl = outs[2] tgt_bbox = outs[3] bbox_inside_weight = outs[4] self.assertIsNotNone(pred_scores) self.assertIsNotNone(pred_loc) self.assertIsNotNone(tgt_lbl) self.assertIsNotNone(tgt_bbox) self.assertIsNotNone(bbox_inside_weight) assert pred_scores.shape[1] == 1 assert pred_loc.shape[1] == 4 assert pred_loc.shape[1] == tgt_bbox.shape[1] print(str(program)) class TestGenerateProposals(unittest.TestCase): def test_generate_proposals(self): program = Program() with program_guard(program): data_shape = [20, 64, 64] images = fluid.layers.data( name='images', shape=data_shape, dtype='float32') im_info = fluid.layers.data( name='im_info', shape=[3], dtype='float32') anchors, variances = fluid.layers.anchor_generator( name='anchor_generator', input=images, anchor_sizes=[32, 64], aspect_ratios=[1.0], variance=[0.1, 0.1, 0.2, 0.2], stride=[16.0, 16.0], offset=0.5) num_anchors = anchors.shape[2] scores = fluid.layers.data( name='scores', shape=[num_anchors, 8, 8], dtype='float32') bbox_deltas = fluid.layers.data( name='bbox_deltas', shape=[num_anchors * 4, 8, 8], dtype='float32') rpn_rois, rpn_roi_probs = fluid.layers.generate_proposals( name='generate_proposals', scores=scores, bbox_deltas=bbox_deltas, im_info=im_info, anchors=anchors, variances=variances, pre_nms_top_n=6000, post_nms_top_n=1000, nms_thresh=0.5, min_size=0.1, eta=1.0) self.assertIsNotNone(rpn_rois) self.assertIsNotNone(rpn_roi_probs) print(rpn_rois.shape) class TestYoloDetection(unittest.TestCase): def test_yolov3_loss(self): program = Program() with program_guard(program): x = layers.data(name='x', shape=[30, 7, 7], dtype='float32') gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32') gt_label = layers.data(name='gt_label', shape=[10], dtype='int32') gt_score = layers.data(name='gt_score', shape=[10], dtype='float32') loss = layers.yolov3_loss( x, gt_box, gt_label, [10, 13, 30, 13], [0, 1], 10, 0.7, 32, gt_score=gt_score, use_label_smooth=False) self.assertIsNotNone(loss) def test_yolo_box(self): program = Program() with program_guard(program): x = layers.data(name='x', shape=[30, 7, 7], dtype='float32') img_size = layers.data(name='img_size', shape=[2], dtype='int32') boxes, scores = layers.yolo_box(x, img_size, [10, 13, 30, 13], 10, 0.01, 32) self.assertIsNotNone(boxes) self.assertIsNotNone(scores) class TestBoxClip(unittest.TestCase): def test_box_clip(self): program = Program() with program_guard(program): input_box = layers.data( name='input_box', shape=[7, 4], dtype='float32', lod_level=1) im_info = layers.data(name='im_info', shape=[3], dtype='float32') out = layers.box_clip(input_box, im_info) self.assertIsNotNone(out) class TestMulticlassNMS(unittest.TestCase): def test_multiclass_nms(self): program = Program() with program_guard(program): bboxes = layers.data( name='bboxes', shape=[-1, 10, 4], dtype='float32') scores = layers.data(name='scores', shape=[-1, 10], dtype='float32') output = layers.multiclass_nms(bboxes, scores, 0.3, 400, 200, 0.7) self.assertIsNotNone(output) class TestCollectFpnPropsals(unittest.TestCase): def test_collect_fpn_proposals(self): program = Program() with program_guard(program): multi_bboxes = [] multi_scores = [] for i in range(4): bboxes = layers.data( name='rois' + str(i), shape=[10, 4], dtype='float32', lod_level=1, append_batch_size=False) scores = layers.data( name='scores' + str(i), shape=[10, 1], dtype='float32', lod_level=1, append_batch_size=False) multi_bboxes.append(bboxes) multi_scores.append(scores) fpn_rois = layers.collect_fpn_proposals(multi_bboxes, multi_scores, 2, 5, 10) self.assertIsNotNone(fpn_rois) class TestDistributeFpnProposals(unittest.TestCase): def test_distribute_fpn_proposals(self): program = Program() with program_guard(program): fpn_rois = fluid.layers.data( name='data', shape=[4], dtype='float32', lod_level=1) multi_rois, restore_ind = layers.distribute_fpn_proposals( fpn_rois=fpn_rois, min_level=2, max_level=5, refer_level=4, refer_scale=224) self.assertIsNotNone(multi_rois) self.assertIsNotNone(restore_ind) if __name__ == '__main__': unittest.main()