更换文档检测模型

paddle_detection/ppdet/modeling/ssod/utils.py

@@ -0,0 +1,104 @@
+#   Copyright (c) 2023 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.
+
+import paddle
+import paddle.nn.functional as F
+
+
+def align_weak_strong_shape(data_weak, data_strong):
+    max_shape_x = max(data_strong['image'].shape[2],
+                      data_weak['image'].shape[2])
+    max_shape_y = max(data_strong['image'].shape[3],
+                      data_weak['image'].shape[3])
+
+    scale_x_s = max_shape_x / data_strong['image'].shape[2]
+    scale_y_s = max_shape_y / data_strong['image'].shape[3]
+    scale_x_w = max_shape_x / data_weak['image'].shape[2]
+    scale_y_w = max_shape_y / data_weak['image'].shape[3]
+    target_size = [max_shape_x, max_shape_y]
+
+    if scale_x_s != 1 or scale_y_s != 1:
+        data_strong['image'] = F.interpolate(
+            data_strong['image'],
+            size=target_size,
+            mode='bilinear',
+            align_corners=False)
+        if 'gt_bbox' in data_strong:
+            gt_bboxes = data_strong['gt_bbox'].numpy()
+            for i in range(len(gt_bboxes)):
+                if len(gt_bboxes[i]) > 0:
+                    gt_bboxes[i][:, 0::2] = gt_bboxes[i][:, 0::2] * scale_x_s
+                    gt_bboxes[i][:, 1::2] = gt_bboxes[i][:, 1::2] * scale_y_s
+            data_strong['gt_bbox'] = paddle.to_tensor(gt_bboxes)
+
+    if scale_x_w != 1 or scale_y_w != 1:
+        data_weak['image'] = F.interpolate(
+            data_weak['image'],
+            size=target_size,
+            mode='bilinear',
+            align_corners=False)
+        if 'gt_bbox' in data_weak:
+            gt_bboxes = data_weak['gt_bbox'].numpy()
+            for i in range(len(gt_bboxes)):
+                if len(gt_bboxes[i]) > 0:
+                    gt_bboxes[i][:, 0::2] = gt_bboxes[i][:, 0::2] * scale_x_w
+                    gt_bboxes[i][:, 1::2] = gt_bboxes[i][:, 1::2] * scale_y_w
+            data_weak['gt_bbox'] = paddle.to_tensor(gt_bboxes)
+    return data_weak, data_strong
+
+
+def QFLv2(pred_sigmoid,
+          teacher_sigmoid,
+          weight=None,
+          beta=2.0,
+          reduction='mean'):
+    pt = pred_sigmoid
+    zerolabel = paddle.zeros_like(pt)
+    loss = F.binary_cross_entropy(
+        pred_sigmoid, zerolabel, reduction='none') * pt.pow(beta)
+    pos = weight > 0
+
+    pt = teacher_sigmoid[pos] - pred_sigmoid[pos]
+    loss[pos] = F.binary_cross_entropy(
+        pred_sigmoid[pos], teacher_sigmoid[pos],
+        reduction='none') * pt.pow(beta)
+
+    valid = weight >= 0
+    if reduction == "mean":
+        loss = loss[valid].mean()
+    elif reduction == "sum":
+        loss = loss[valid].sum()
+    return loss
+
+
+def filter_invalid(bbox, label=None, score=None, thr=0.0, min_size=0):
+    if score.numel() > 0:
+        soft_score = score.max(-1)
+        valid = soft_score >= thr
+        bbox = bbox[valid]
+
+        if label is not None:
+            label = label[valid]
+        score = score[valid]
+    if min_size is not None and bbox.shape[0] > 0:
+        bw = bbox[:, 2]
+        bh = bbox[:, 3]
+        valid = (bw > min_size) & (bh > min_size)
+        bbox = bbox[valid]
+
+        if label is not None:
+            label = label[valid]
+            score = score[valid]
+
+    return bbox, label, score