更换文档检测模型

paddle_detection/ppdet/modeling/losses/probiou_loss.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2022 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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import paddle
+import paddle.nn.functional as F
+
+from ppdet.core.workspace import register, serializable
+
+__all__ = ['ProbIoULoss']
+
+
+def gbb_form(boxes):
+    xy, wh, angle = paddle.split(boxes, [2, 2, 1], axis=-1)
+    return paddle.concat([xy, wh.pow(2) / 12., angle], axis=-1)
+
+
+def rotated_form(a_, b_, angles):
+    cos_a = paddle.cos(angles)
+    sin_a = paddle.sin(angles)
+    a = a_ * paddle.pow(cos_a, 2) + b_ * paddle.pow(sin_a, 2)
+    b = a_ * paddle.pow(sin_a, 2) + b_ * paddle.pow(cos_a, 2)
+    c = (a_ - b_) * cos_a * sin_a
+    return a, b, c
+
+
+def probiou_loss(pred, target, eps=1e-3, mode='l1'):
+    """
+        pred    -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours predicted box ;in case of HBB angle == 0
+        target  -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours target    box ;in case of HBB angle == 0
+        eps     -> threshold to avoid infinite values
+        mode    -> ('l1' in [0,1] or 'l2' in [0,inf]) metrics according our paper
+
+    """
+
+    gbboxes1 = gbb_form(pred)
+    gbboxes2 = gbb_form(target)
+
+    x1, y1, a1_, b1_, c1_ = gbboxes1[:,
+                                     0], gbboxes1[:,
+                                                  1], gbboxes1[:,
+                                                               2], gbboxes1[:,
+                                                                            3], gbboxes1[:,
+                                                                                         4]
+    x2, y2, a2_, b2_, c2_ = gbboxes2[:,
+                                     0], gbboxes2[:,
+                                                  1], gbboxes2[:,
+                                                               2], gbboxes2[:,
+                                                                            3], gbboxes2[:,
+                                                                                         4]
+
+    a1, b1, c1 = rotated_form(a1_, b1_, c1_)
+    a2, b2, c2 = rotated_form(a2_, b2_, c2_)
+
+    t1 = 0.25 * ((a1 + a2) * (paddle.pow(y1 - y2, 2)) + (b1 + b2) * (paddle.pow(x1 - x2, 2))) + \
+         0.5 * ((c1+c2)*(x2-x1)*(y1-y2))
+    t2 = (a1 + a2) * (b1 + b2) - paddle.pow(c1 + c2, 2)
+    t3_ = (a1 * b1 - c1 * c1) * (a2 * b2 - c2 * c2)
+    t3 = 0.5 * paddle.log(t2 / (4 * paddle.sqrt(F.relu(t3_)) + eps))
+
+    B_d = (t1 / t2) + t3
+    # B_d = t1 + t2 + t3
+
+    B_d = paddle.clip(B_d, min=eps, max=100.0)
+    l1 = paddle.sqrt(1.0 - paddle.exp(-B_d) + eps)
+    l_i = paddle.pow(l1, 2.0)
+    l2 = -paddle.log(1.0 - l_i + eps)
+
+    if mode == 'l1':
+        probiou = l1
+    if mode == 'l2':
+        probiou = l2
+
+    return probiou
+
+
+@serializable
+@register
+class ProbIoULoss(object):
+    """ ProbIoU Loss, refer to https://arxiv.org/abs/2106.06072 for details """
+
+    def __init__(self, mode='l1', eps=1e-3):
+        super(ProbIoULoss, self).__init__()
+        self.mode = mode
+        self.eps = eps
+
+    def __call__(self, pred_rboxes, assigned_rboxes):
+        return probiou_loss(pred_rboxes, assigned_rboxes, self.eps, self.mode)