# 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 unittest import numpy as np import math import copy from op_test import OpTest from test_anchor_generator_op import anchor_generator_in_python from test_multiclass_nms_op import iou from test_multiclass_nms_op import nms import paddle.fluid as fluid from paddle.fluid import Program, program_guard import paddle def multiclass_nms(prediction, class_num, keep_top_k, nms_threshold): selected_indices = {} num_det = 0 for c in range(class_num): if c not in prediction.keys(): continue cls_dets = prediction[c] all_scores = np.zeros(len(cls_dets)) for i in range(all_scores.shape[0]): all_scores[i] = cls_dets[i][4] indices = nms(cls_dets, all_scores, 0.0, nms_threshold, -1, False, 1.0) selected_indices[c] = indices num_det += len(indices) score_index = [] for c, indices in selected_indices.items(): for idx in indices: score_index.append((prediction[c][idx][4], c, idx)) sorted_score_index = sorted( score_index, key=lambda tup: tup[0], reverse=True) if keep_top_k > -1 and num_det > keep_top_k: sorted_score_index = sorted_score_index[:keep_top_k] num_det = keep_top_k nmsed_outs = [] for s, c, idx in sorted_score_index: xmin = prediction[c][idx][0] ymin = prediction[c][idx][1] xmax = prediction[c][idx][2] ymax = prediction[c][idx][3] nmsed_outs.append([c + 1, s, xmin, ymin, xmax, ymax]) return nmsed_outs, num_det def retinanet_detection_out(boxes_list, scores_list, anchors_list, im_info, score_threshold, nms_threshold, nms_top_k, keep_top_k): class_num = scores_list[0].shape[-1] im_height, im_width, im_scale = im_info num_level = len(scores_list) prediction = {} for lvl in range(num_level): scores_per_level = scores_list[lvl] scores_per_level = scores_per_level.flatten() bboxes_per_level = boxes_list[lvl] bboxes_per_level = bboxes_per_level.flatten() anchors_per_level = anchors_list[lvl] anchors_per_level = anchors_per_level.flatten() thresh = score_threshold if lvl < (num_level - 1) else 0.0 selected_indices = np.argwhere(scores_per_level > thresh) scores = scores_per_level[selected_indices] sorted_indices = np.argsort(-scores, axis=0, kind='mergesort') if nms_top_k > -1 and nms_top_k < sorted_indices.shape[0]: sorted_indices = sorted_indices[:nms_top_k] for i in range(sorted_indices.shape[0]): idx = selected_indices[sorted_indices[i]] idx = idx[0][0] a = int(idx / class_num) c = int(idx % class_num) box_offset = a * 4 anchor_box_width = anchors_per_level[ box_offset + 2] - anchors_per_level[box_offset] + 1 anchor_box_height = anchors_per_level[ box_offset + 3] - anchors_per_level[box_offset + 1] + 1 anchor_box_center_x = anchors_per_level[ box_offset] + anchor_box_width / 2 anchor_box_center_y = anchors_per_level[box_offset + 1] + anchor_box_height / 2 target_box_center_x = bboxes_per_level[ box_offset] * anchor_box_width + anchor_box_center_x target_box_center_y = bboxes_per_level[ box_offset + 1] * anchor_box_height + anchor_box_center_y target_box_width = math.exp(bboxes_per_level[box_offset + 2]) * anchor_box_width target_box_height = math.exp(bboxes_per_level[ box_offset + 3]) * anchor_box_height pred_box_xmin = target_box_center_x - target_box_width / 2 pred_box_ymin = target_box_center_y - target_box_height / 2 pred_box_xmax = target_box_center_x + target_box_width / 2 - 1 pred_box_ymax = target_box_center_y + target_box_height / 2 - 1 pred_box_xmin = pred_box_xmin / im_scale pred_box_ymin = pred_box_ymin / im_scale pred_box_xmax = pred_box_xmax / im_scale pred_box_ymax = pred_box_ymax / im_scale pred_box_xmin = max( min(pred_box_xmin, np.round(im_width / im_scale) - 1), 0.) pred_box_ymin = max( min(pred_box_ymin, np.round(im_height / im_scale) - 1), 0.) pred_box_xmax = max( min(pred_box_xmax, np.round(im_width / im_scale) - 1), 0.) pred_box_ymax = max( min(pred_box_ymax, np.round(im_height / im_scale) - 1), 0.) if c not in prediction.keys(): prediction[c] = [] prediction[c].append([ pred_box_xmin, pred_box_ymin, pred_box_xmax, pred_box_ymax, scores_per_level[idx] ]) nmsed_outs, nmsed_num = multiclass_nms(prediction, class_num, keep_top_k, nms_threshold) return nmsed_outs, nmsed_num def batched_retinanet_detection_out(boxes, scores, anchors, im_info, score_threshold, nms_threshold, nms_top_k, keep_top_k): batch_size = scores[0].shape[0] det_outs = [] lod = [] for n in range(batch_size): boxes_per_batch = [] scores_per_batch = [] num_level = len(scores) for lvl in range(num_level): boxes_per_batch.append(boxes[lvl][n]) scores_per_batch.append(scores[lvl][n]) nmsed_outs, nmsed_num = retinanet_detection_out( boxes_per_batch, scores_per_batch, anchors, im_info[n], score_threshold, nms_threshold, nms_top_k, keep_top_k) lod.append(nmsed_num) if nmsed_num == 0: continue det_outs.extend(nmsed_outs) return det_outs, lod class TestRetinanetDetectionOutOp1(OpTest): def set_argument(self): self.score_threshold = 0.05 self.min_level = 3 self.max_level = 7 self.nms_threshold = 0.3 self.nms_top_k = 1000 self.keep_top_k = 200 self.scales_per_octave = 3 self.aspect_ratios = [1.0, 2.0, 0.5] self.anchor_scale = 4 self.anchor_strides = [8, 16, 32, 64, 128] self.box_size = 4 self.class_num = 80 self.batch_size = 1 self.input_channels = 20 self.layer_h = [] self.layer_w = [] num_levels = self.max_level - self.min_level + 1 for i in range(num_levels): self.layer_h.append(2**(num_levels - i)) self.layer_w.append(2**(num_levels - i)) def init_test_input(self): anchor_num = len(self.aspect_ratios) * self.scales_per_octave num_levels = self.max_level - self.min_level + 1 self.scores_list = [] self.bboxes_list = [] self.anchors_list = [] for i in range(num_levels): layer_h = self.layer_h[i] layer_w = self.layer_w[i] input_feat = np.random.random((self.batch_size, self.input_channels, layer_h, layer_w)).astype('float32') score = np.random.random( (self.batch_size, self.class_num * anchor_num, layer_h, layer_w)).astype('float32') score = np.transpose(score, [0, 2, 3, 1]) score = score.reshape((self.batch_size, -1, self.class_num)) box = np.random.random((self.batch_size, self.box_size * anchor_num, layer_h, layer_w)).astype('float32') box = np.transpose(box, [0, 2, 3, 1]) box = box.reshape((self.batch_size, -1, self.box_size)) anchor_sizes = [] for octave in range(self.scales_per_octave): anchor_sizes.append( float(self.anchor_strides[i] * (2**octave)) / float(self.scales_per_octave) * self.anchor_scale) anchor, var = anchor_generator_in_python( input_feat=input_feat, anchor_sizes=anchor_sizes, aspect_ratios=self.aspect_ratios, variances=[1.0, 1.0, 1.0, 1.0], stride=[self.anchor_strides[i], self.anchor_strides[i]], offset=0.5) anchor = np.reshape(anchor, [-1, 4]) self.scores_list.append(score.astype('float32')) self.bboxes_list.append(box.astype('float32')) self.anchors_list.append(anchor.astype('float32')) self.im_info = np.array([[256., 256., 1.5]]).astype( 'float32') #im_height, im_width, scale def setUp(self): self.set_argument() self.init_test_input() nmsed_outs, lod = batched_retinanet_detection_out( self.bboxes_list, self.scores_list, self.anchors_list, self.im_info, self.score_threshold, self.nms_threshold, self.nms_top_k, self.keep_top_k) nmsed_outs = np.array(nmsed_outs).astype('float32') self.op_type = 'retinanet_detection_output' self.inputs = { 'BBoxes': [('b0', self.bboxes_list[0]), ('b1', self.bboxes_list[1]), ('b2', self.bboxes_list[2]), ('b3', self.bboxes_list[3]), ('b4', self.bboxes_list[4])], 'Scores': [('s0', self.scores_list[0]), ('s1', self.scores_list[1]), ('s2', self.scores_list[2]), ('s3', self.scores_list[3]), ('s4', self.scores_list[4])], 'Anchors': [('a0', self.anchors_list[0]), ('a1', self.anchors_list[1]), ('a2', self.anchors_list[2]), ('a3', self.anchors_list[3]), ('a4', self.anchors_list[4])], 'ImInfo': (self.im_info, [[1, ]]) } self.outputs = {'Out': (nmsed_outs, [lod])} self.attrs = { 'score_threshold': self.score_threshold, 'nms_top_k': self.nms_top_k, 'nms_threshold': self.nms_threshold, 'keep_top_k': self.keep_top_k, 'nms_eta': 1., } def test_check_output(self): self.check_output() class TestRetinanetDetectionOutOp2(OpTest): def set_argument(self): self.score_threshold = 0.05 self.min_level = 3 self.max_level = 7 self.nms_threshold = 0.3 self.nms_top_k = 1000 self.keep_top_k = 200 self.scales_per_octave = 3 self.aspect_ratios = [1.0, 2.0, 0.5] self.anchor_scale = 4 self.anchor_strides = [8, 16, 32, 64, 128] self.box_size = 4 self.class_num = 80 self.batch_size = 1 self.input_channels = 20 # Here test the case there the shape of each FPN level # is irrelevant. self.layer_h = [1, 4, 8, 8, 16] self.layer_w = [1, 4, 8, 8, 16] class TestRetinanetDetectionOutOpNo3(TestRetinanetDetectionOutOp1): def set_argument(self): # Here set 2.0 to test the case there is no outputs. # In practical use, 0.0 < score_threshold < 1.0 self.score_threshold = 2.0 self.min_level = 3 self.max_level = 7 self.nms_threshold = 0.3 self.nms_top_k = 1000 self.keep_top_k = 200 self.scales_per_octave = 3 self.aspect_ratios = [1.0, 2.0, 0.5] self.anchor_scale = 4 self.anchor_strides = [8, 16, 32, 64, 128] self.box_size = 4 self.class_num = 80 self.batch_size = 1 self.input_channels = 20 self.layer_h = [] self.layer_w = [] num_levels = self.max_level - self.min_level + 1 for i in range(num_levels): self.layer_h.append(2**(num_levels - i)) self.layer_w.append(2**(num_levels - i)) class TestRetinanetDetectionOutOpNo4(TestRetinanetDetectionOutOp1): def set_argument(self): self.score_threshold = 0.05 self.min_level = 2 self.max_level = 5 self.nms_threshold = 0.3 self.nms_top_k = 1000 self.keep_top_k = 200 self.scales_per_octave = 3 self.aspect_ratios = [1.0, 2.0, 0.5] self.anchor_scale = 4 self.anchor_strides = [8, 16, 32, 64, 128] self.box_size = 4 self.class_num = 80 self.batch_size = 1 self.input_channels = 20 self.layer_h = [] self.layer_w = [] num_levels = self.max_level - self.min_level + 1 for i in range(num_levels): self.layer_h.append(2**(num_levels - i)) self.layer_w.append(2**(num_levels - i)) def setUp(self): self.set_argument() self.init_test_input() nmsed_outs, lod = batched_retinanet_detection_out( self.bboxes_list, self.scores_list, self.anchors_list, self.im_info, self.score_threshold, self.nms_threshold, self.nms_top_k, self.keep_top_k) nmsed_outs = np.array(nmsed_outs).astype('float32') self.op_type = 'retinanet_detection_output' self.inputs = { 'BBoxes': [('b0', self.bboxes_list[0]), ('b1', self.bboxes_list[1]), ('b2', self.bboxes_list[2]), ('b3', self.bboxes_list[3])], 'Scores': [('s0', self.scores_list[0]), ('s1', self.scores_list[1]), ('s2', self.scores_list[2]), ('s3', self.scores_list[3])], 'Anchors': [('a0', self.anchors_list[0]), ('a1', self.anchors_list[1]), ('a2', self.anchors_list[2]), ('a3', self.anchors_list[3])], 'ImInfo': (self.im_info, [[1, ]]) } self.outputs = {'Out': (nmsed_outs, [lod])} self.attrs = { 'score_threshold': self.score_threshold, 'nms_top_k': self.nms_top_k, 'nms_threshold': self.nms_threshold, 'keep_top_k': self.keep_top_k, 'nms_eta': 1., } def test_check_output(self): self.check_output() class TestRetinanetDetectionOutOpNo5(TestRetinanetDetectionOutOp1): def set_argument(self): self.score_threshold = 0.05 self.min_level = 3 self.max_level = 7 self.nms_threshold = 0.3 self.nms_top_k = 100 self.keep_top_k = 10 self.scales_per_octave = 3 self.aspect_ratios = [1.0, 2.0, 0.5] self.anchor_scale = 4 self.anchor_strides = [8, 16, 32, 64, 128] self.box_size = 4 self.class_num = 80 self.batch_size = 1 self.input_channels = 20 self.layer_h = [] self.layer_w = [] num_levels = self.max_level - self.min_level + 1 for i in range(num_levels): self.layer_h.append(2**(num_levels - i)) self.layer_w.append(2**(num_levels - i)) class TestRetinanetDetectionOutOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): bboxes_low1 = fluid.data( name='bboxes_low1', shape=[1, 44, 4], dtype='float32') bboxes_high1 = fluid.data( name='bboxes_high1', shape=[1, 11, 4], dtype='float32') scores_low1 = fluid.data( name='scores_low1', shape=[1, 44, 10], dtype='float32') scores_high1 = fluid.data( name='scores_high1', shape=[1, 11, 10], dtype='float32') anchors_low1 = fluid.data( name='anchors_low1', shape=[44, 4], dtype='float32') anchors_high1 = fluid.data( name='anchors_high1', shape=[11, 4], dtype='float32') im_info1 = fluid.data( name="im_info1", shape=[1, 3], dtype='float32') # The `bboxes` must be list, each element must be Variable and # its Tensor data type must be one of float32 and float64. def test_bboxes_type(): fluid.layers.retinanet_detection_output( bboxes=bboxes_low1, scores=[scores_low1, scores_high1], anchors=[anchors_low1, anchors_high1], im_info=im_info1) self.assertRaises(TypeError, test_bboxes_type) def test_bboxes_tensor_dtype(): bboxes_high2 = fluid.data( name='bboxes_high2', shape=[1, 11, 4], dtype='int32') fluid.layers.retinanet_detection_output( bboxes=[bboxes_high2, 5], scores=[scores_low1, scores_high1], anchors=[anchors_low1, anchors_high1], im_info=im_info1) self.assertRaises(TypeError, test_bboxes_tensor_dtype) # The `scores` must be list, each element must be Variable and its # Tensor data type must be one of float32 and float64. def test_scores_type(): fluid.layers.retinanet_detection_output( bboxes=[bboxes_low1, bboxes_high1], scores=scores_low1, anchors=[anchors_low1, anchors_high1], im_info=im_info1) self.assertRaises(TypeError, test_scores_type) def test_scores_tensor_dtype(): scores_high2 = fluid.data( name='scores_high2', shape=[1, 11, 10], dtype='int32') fluid.layers.retinanet_detection_output( bboxes=[bboxes_low1, bboxes_high1], scores=[scores_high2, 5], anchors=[anchors_low1, anchors_high1], im_info=im_info1) self.assertRaises(TypeError, test_scores_tensor_dtype) # The `anchors` must be list, each element must be Variable and its # Tensor data type must be one of float32 and float64. def test_anchors_type(): fluid.layers.retinanet_detection_output( bboxes=[bboxes_low1, bboxes_high1], scores=[scores_low1, scores_high1], anchors=anchors_low1, im_info=im_info1) self.assertRaises(TypeError, test_anchors_type) def test_anchors_tensor_dtype(): anchors_high2 = fluid.data( name='anchors_high2', shape=[11, 4], dtype='int32') fluid.layers.retinanet_detection_output( bboxes=[bboxes_low1, bboxes_high1], scores=[scores_low1, scores_high1], anchors=[anchors_high2, 5], im_info=im_info1) self.assertRaises(TypeError, test_anchors_tensor_dtype) # The `im_info` must be Variable and the data type of `im_info` # Tensor must be one of float32 and float64. def test_iminfo_type(): fluid.layers.retinanet_detection_output( bboxes=[bboxes_low1, bboxes_high1], scores=[scores_low1, scores_high1], anchors=[anchors_low1, anchors_high1], im_info=[2, 3, 4]) self.assertRaises(TypeError, test_iminfo_type) def test_iminfo_tensor_dtype(): im_info2 = fluid.data( name='im_info2', shape=[1, 3], dtype='int32') fluid.layers.retinanet_detection_output( bboxes=[bboxes_low1, bboxes_high1], scores=[scores_low1, scores_high1], anchors=[anchors_low1, anchors_high1], im_info=im_info2) self.assertRaises(TypeError, test_iminfo_tensor_dtype) if __name__ == '__main__': paddle.enable_static() unittest.main()