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