DocumentAIKit/ppdet/modeling/ssod_utils.py

#   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.

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']
            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'] = 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']
            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'] = gt_bboxes
    return data_weak, data_strong


def permute_to_N_HWA_K(tensor, K):
    """
    Transpose/reshape a tensor from (N, (A x K), H, W) to (N, (HxWxA), K)
    """
    assert tensor.dim() == 4, tensor.shape
    N, _, H, W = tensor.shape
    tensor = tensor.reshape([N, -1, K, H, W]).transpose([0, 3, 4, 1, 2])
    tensor = tensor.reshape([N, -1, K])
    return tensor


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