# 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 sys import math from op_test import OpTest def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0): pb_w = p_box[:, 2] - p_box[:, 0] + (norm == False) pb_h = p_box[:, 3] - p_box[:, 1] + (norm == False) pb_x = pb_w * 0.5 + p_box[:, 0] pb_y = pb_h * 0.5 + p_box[:, 1] shape = (1, p_box.shape[0]) if axis == 0 else (p_box.shape[0], 1) pb_w = pb_w.reshape(shape) pb_h = pb_h.reshape(shape) pb_x = pb_x.reshape(shape) pb_y = pb_y.reshape(shape) if pb_v.ndim == 2: pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1]) if pb_v.ndim == 1: tb_x = pb_v[0] * t_box[:, :, 0] * pb_w + pb_x tb_y = pb_v[1] * t_box[:, :, 1] * pb_h + pb_y tb_w = np.exp(pb_v[2] * t_box[:, :, 2]) * pb_w tb_h = np.exp(pb_v[3] * t_box[:, :, 3]) * pb_h else: tb_x = pb_v[:, :, 0] * t_box[:, :, 0] * pb_w + pb_x tb_y = pb_v[:, :, 1] * t_box[:, :, 1] * pb_h + pb_y tb_w = np.exp(pb_v[:, :, 2] * t_box[:, :, 2]) * pb_w tb_h = np.exp(pb_v[:, :, 3] * t_box[:, :, 3]) * pb_h output_box[:, :, 0] = tb_x - tb_w / 2 output_box[:, :, 1] = tb_y - tb_h / 2 output_box[:, :, 2] = tb_x + tb_w / 2 - (not norm) output_box[:, :, 3] = tb_y + tb_h / 2 - (not norm) def box_encoder(t_box, p_box, pb_v, output_box, norm): pb_w = p_box[:, 2] - p_box[:, 0] + (norm == False) pb_h = p_box[:, 3] - p_box[:, 1] + (norm == False) pb_x = pb_w * 0.5 + p_box[:, 0] pb_y = pb_h * 0.5 + p_box[:, 1] shape = (1, p_box.shape[0]) pb_w = pb_w.reshape(shape) pb_h = pb_h.reshape(shape) pb_x = pb_x.reshape(shape) pb_y = pb_y.reshape(shape) if pb_v.ndim == 2: pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1]) tb_x = ((t_box[:, 2] + t_box[:, 0]) / 2).reshape(t_box.shape[0], 1) tb_y = ((t_box[:, 3] + t_box[:, 1]) / 2).reshape(t_box.shape[0], 1) tb_w = (t_box[:, 2] - t_box[:, 0]).reshape(t_box.shape[0], 1) + (not norm) tb_h = (t_box[:, 3] - t_box[:, 1]).reshape(t_box.shape[0], 1) + (not norm) if pb_v.ndim == 1: output_box[:, :, 0] = (tb_x - pb_x) / pb_w / pb_v[0] output_box[:, :, 1] = (tb_y - pb_y) / pb_h / pb_v[1] output_box[:, :, 2] = np.log(np.fabs(tb_w / pb_w)) / pb_v[2] output_box[:, :, 3] = np.log(np.fabs(tb_h / pb_h)) / pb_v[3] else: output_box[:, :, 0] = (tb_x - pb_x) / pb_w / pb_v[:, :, 0] output_box[:, :, 1] = (tb_y - pb_y) / pb_h / pb_v[:, :, 1] output_box[:, :, 2] = np.log(np.fabs(tb_w / pb_w)) / pb_v[:, :, 2] output_box[:, :, 3] = np.log(np.fabs(tb_h / pb_h)) / pb_v[:, :, 3] def batch_box_coder(p_box, pb_v, t_box, lod, code_type, norm, axis=0): n = t_box.shape[0] m = p_box.shape[0] if code_type == "DecodeCenterSize": m = t_box.shape[1] output_box = np.zeros((n, m, 4), dtype=np.float32) cur_offset = 0 for i in range(len(lod)): if (code_type == "EncodeCenterSize"): box_encoder(t_box[cur_offset:(cur_offset + lod[i]), :], p_box, pb_v, output_box[cur_offset:(cur_offset + lod[i]), :, :], norm) elif (code_type == "DecodeCenterSize"): box_decoder(t_box, p_box, pb_v, output_box, norm, axis) cur_offset += lod[i] return output_box class TestBoxCoderOp(OpTest): def test_check_output(self): self.check_output() def setUp(self): self.op_type = "box_coder" lod = [[1, 1, 1, 1, 1]] prior_box = np.random.random((81, 4)).astype('float32') prior_box_var = np.random.random((81, 4)).astype('float32') target_box = np.random.random((20, 81, 4)).astype('float32') code_type = "DecodeCenterSize" box_normalized = False output_box = batch_box_coder(prior_box, prior_box_var, target_box, lod[0], code_type, box_normalized) self.inputs = { 'PriorBox': prior_box, 'PriorBoxVar': prior_box_var, 'TargetBox': target_box, } self.attrs = { 'code_type': 'decode_center_size', 'box_normalized': False } self.outputs = {'OutputBox': output_box} class TestBoxCoderOpWithOneRankVar(OpTest): def test_check_output(self): self.check_output() def setUp(self): self.op_type = "box_coder" lod = [[1, 1, 1, 1, 1]] prior_box = np.random.random((81, 4)).astype('float32') prior_box_var = np.random.random((4)).astype('float32') target_box = np.random.random((20, 81, 4)).astype('float32') code_type = "DecodeCenterSize" box_normalized = False output_box = batch_box_coder(prior_box, prior_box_var, target_box, lod[0], code_type, box_normalized) self.inputs = { 'PriorBox': prior_box, 'PriorBoxVar': prior_box_var, 'TargetBox': target_box, } self.attrs = { 'code_type': 'decode_center_size', 'box_normalized': False } self.outputs = {'OutputBox': output_box} class TestBoxCoderOpWithoutBoxVar(OpTest): def test_check_output(self): self.check_output() def setUp(self): self.op_type = "box_coder" lod = [[0, 1, 2, 3, 4, 5]] prior_box = np.random.random((81, 4)).astype('float32') prior_box_var = np.ones((81, 4)).astype('float32') target_box = np.random.random((20, 81, 4)).astype('float32') code_type = "DecodeCenterSize" box_normalized = False output_box = batch_box_coder(prior_box, prior_box_var, target_box, lod[0], code_type, box_normalized) self.inputs = { 'PriorBox': prior_box, 'TargetBox': target_box, } self.attrs = { 'code_type': 'decode_center_size', 'box_normalized': False } self.outputs = {'OutputBox': output_box} class TestBoxCoderOpWithLoD(OpTest): def test_check_output(self): self.check_output() def setUp(self): self.op_type = "box_coder" lod = [[10, 20, 20]] prior_box = np.random.random((20, 4)).astype('float32') prior_box_var = np.random.random((20, 4)).astype('float32') target_box = np.random.random((50, 4)).astype('float32') code_type = "EncodeCenterSize" box_normalized = True output_box = batch_box_coder(prior_box, prior_box_var, target_box, lod[0], code_type, box_normalized) self.inputs = { 'PriorBox': prior_box, 'PriorBoxVar': prior_box_var, 'TargetBox': (target_box, lod), } self.attrs = {'code_type': 'encode_center_size', 'box_normalized': True} self.outputs = {'OutputBox': output_box} class TestBoxCoderOpWithAxis(OpTest): def test_check_output(self): self.check_output() def setUp(self): self.op_type = "box_coder" lod = [[1, 1, 1, 1, 1]] prior_box = np.random.random((30, 4)).astype('float32') prior_box_var = np.random.random((4)).astype('float32') target_box = np.random.random((30, 81, 4)).astype('float32') code_type = "DecodeCenterSize" box_normalized = False axis = 1 output_box = batch_box_coder(prior_box, prior_box_var, target_box, lod[0], code_type, box_normalized, axis) self.inputs = { 'PriorBox': prior_box, 'PriorBoxVar': prior_box_var, 'TargetBox': target_box, } self.attrs = { 'code_type': 'decode_center_size', 'box_normalized': False, 'axis': axis } self.outputs = {'OutputBox': output_box} class TestBoxCoderOpWithVariance(OpTest): def test_check_output(self): self.check_output() def setUp(self): self.op_type = "box_coder" lod = [[1, 1, 1, 1, 1]] prior_box = np.random.random((30, 4)).astype('float32') prior_box_var = np.random.random((4)).astype('float32') target_box = np.random.random((30, 81, 4)).astype('float32') code_type = "DecodeCenterSize" box_normalized = False axis = 1 output_box = batch_box_coder(prior_box, prior_box_var, target_box, lod[0], code_type, box_normalized, axis) self.inputs = { 'PriorBox': prior_box, 'TargetBox': target_box, } self.attrs = { 'code_type': 'decode_center_size', 'box_normalized': False, 'variance': prior_box_var.astype(np.float).flatten(), 'axis': axis } self.outputs = {'OutputBox': output_box} if __name__ == '__main__': unittest.main()