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195 lines
6.8 KiB
195 lines
6.8 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest
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def sigmoid(x):
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return 1.0 / (1.0 + np.exp(-1.0 * x))
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def mse(x, y, num):
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return ((y - x)**2).sum() / num
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def bce(x, y, mask):
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x = x.reshape((-1))
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y = y.reshape((-1))
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mask = mask.reshape((-1))
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error_sum = 0.0
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count = 0
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for i in range(x.shape[0]):
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if mask[i] > 0:
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error_sum += y[i] * np.log(x[i]) + (1 - y[i]) * np.log(1 - x[i])
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count += 1
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return error_sum / (-1.0 * count)
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def box_iou(box1, box2):
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b1_x1 = box1[0] - box1[2] / 2
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b1_x2 = box1[0] + box1[2] / 2
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b1_y1 = box1[1] - box1[3] / 2
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b1_y2 = box1[1] + box1[3] / 2
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b2_x1 = box2[0] - box2[2] / 2
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b2_x2 = box2[0] + box2[2] / 2
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b2_y1 = box2[1] - box2[3] / 2
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b2_y2 = box2[1] + box2[3] / 2
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b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
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b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
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inter_rect_x1 = max(b1_x1, b2_x1)
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inter_rect_y1 = max(b1_y1, b2_y1)
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inter_rect_x2 = min(b1_x2, b2_x2)
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inter_rect_y2 = min(b1_y2, b2_y2)
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inter_area = max(inter_rect_x2 - inter_rect_x1, 0) * max(
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inter_rect_y2 - inter_rect_y1, 0)
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return inter_area / (b1_area + b2_area + inter_area)
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def build_target(gtboxs, attrs, grid_size):
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n, b, _ = gtboxs.shape
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ignore_thresh = attrs["ignore_thresh"]
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img_height = attrs["img_height"]
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anchors = attrs["anchors"]
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class_num = attrs["class_num"]
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an_num = len(anchors) / 2
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obj_mask = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
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noobj_mask = np.ones((n, an_num, grid_size, grid_size)).astype('float32')
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tx = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
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ty = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
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tw = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
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th = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
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tconf = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
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tcls = np.zeros(
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(n, an_num, grid_size, grid_size, class_num)).astype('float32')
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for i in range(n):
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for j in range(b):
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if gtboxs[i, j, :].sum() == 0:
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continue
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gt_label = int(gtboxs[i, j, 0])
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gx = gtboxs[i, j, 1] * grid_size
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gy = gtboxs[i, j, 2] * grid_size
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gw = gtboxs[i, j, 3] * grid_size
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gh = gtboxs[i, j, 4] * grid_size
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gi = int(gx)
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gj = int(gy)
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gtbox = [0, 0, gw, gh]
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max_iou = 0
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for k in range(an_num):
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anchor_box = [0, 0, anchors[2 * k], anchors[2 * k + 1]]
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iou = box_iou(gtbox, anchor_box)
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if iou > max_iou:
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max_iou = iou
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best_an_index = k
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if iou > ignore_thresh:
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noobj_mask[i, best_an_index, gj, gi] = 0
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obj_mask[i, best_an_index, gj, gi] = 1
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noobj_mask[i, best_an_index, gj, gi] = 0
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tx[i, best_an_index, gj, gi] = gx - gi
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ty[i, best_an_index, gj, gi] = gy - gj
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tw[i, best_an_index, gj, gi] = np.log(gw / anchors[2 *
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best_an_index])
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th[i, best_an_index, gj, gi] = np.log(
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gh / anchors[2 * best_an_index + 1])
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tconf[i, best_an_index, gj, gi] = 1
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tcls[i, best_an_index, gj, gi, gt_label] = 1
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return (tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask)
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def YoloV3Loss(x, gtbox, attrs):
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n, c, h, w = x.shape
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an_num = len(attrs['anchors']) / 2
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class_num = attrs["class_num"]
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x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
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pred_x = sigmoid(x[:, :, :, :, 0])
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pred_y = sigmoid(x[:, :, :, :, 1])
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pred_w = x[:, :, :, :, 2]
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pred_h = x[:, :, :, :, 3]
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pred_conf = sigmoid(x[:, :, :, :, 4])
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pred_cls = sigmoid(x[:, :, :, :, 5:])
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tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
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gtbox, attrs, x.shape[2])
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obj_mask_expand = np.tile(
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np.expand_dims(obj_mask, 4), (1, 1, 1, 1, int(attrs['class_num'])))
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loss_x = mse(pred_x * obj_mask, tx * obj_mask, obj_mask.sum())
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loss_y = mse(pred_y * obj_mask, ty * obj_mask, obj_mask.sum())
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loss_w = mse(pred_w * obj_mask, tw * obj_mask, obj_mask.sum())
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loss_h = mse(pred_h * obj_mask, th * obj_mask, obj_mask.sum())
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loss_conf_obj = bce(pred_conf * obj_mask, tconf * obj_mask, obj_mask)
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loss_conf_noobj = bce(pred_conf * noobj_mask, tconf * noobj_mask,
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noobj_mask)
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loss_class = bce(pred_cls * obj_mask_expand, tcls * obj_mask_expand,
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obj_mask_expand)
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# print "loss_x: ", loss_x
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# print "loss_y: ", loss_y
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# print "loss_w: ", loss_w
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# print "loss_h: ", loss_h
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# print "loss_conf_obj: ", loss_conf_obj
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# print "loss_conf_noobj: ", loss_conf_noobj
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# print "loss_class: ", loss_class
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return loss_x + loss_y + loss_w + loss_h + loss_conf_obj + loss_conf_noobj + loss_class
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class TestYolov3LossOp(OpTest):
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def setUp(self):
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self.initTestCase()
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self.op_type = 'yolov3_loss'
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x = np.random.random(size=self.x_shape).astype('float32')
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gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
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gtbox[:, :, 0] = np.random.randint(0, self.class_num,
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self.gtbox_shape[:2])
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self.attrs = {
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"img_height": self.img_height,
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"anchors": self.anchors,
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"class_num": self.class_num,
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"ignore_thresh": self.ignore_thresh,
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}
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self.inputs = {'X': x, 'GTBox': gtbox}
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self.outputs = {'Loss': np.array([YoloV3Loss(x, gtbox, self.attrs)])}
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print self.outputs
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def test_check_output(self):
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self.check_output(atol=1e-3)
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# def test_check_grad_normal(self):
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# self.check_grad(['X', 'Grid'], 'Output', max_relative_error=0.61)
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def initTestCase(self):
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self.img_height = 608
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self.anchors = [10, 13, 16, 30, 33, 23]
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self.class_num = 10
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self.ignore_thresh = 0.5
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self.x_shape = (5, len(self.anchors) / 2 * (5 + self.class_num), 7, 7)
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self.gtbox_shape = (5, 10, 5)
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if __name__ == "__main__":
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unittest.main()
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