802 lines
32 KiB
Python
802 lines
32 KiB
Python
# Copyright (c) 2020 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 numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ppdet.core.workspace import register
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from ppdet.modeling.bbox_utils import nonempty_bbox
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from .transformers import bbox_cxcywh_to_xyxy
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try:
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from collections.abc import Sequence
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except Exception:
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from collections import Sequence
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__all__ = [
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'BBoxPostProcess', 'MaskPostProcess', 'JDEBBoxPostProcess',
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'CenterNetPostProcess', 'DETRPostProcess', 'SparsePostProcess',
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'DETRBBoxSemiPostProcess'
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]
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@register
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class BBoxPostProcess(object):
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__shared__ = ['num_classes', 'export_onnx', 'export_eb']
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__inject__ = ['decode', 'nms']
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def __init__(self,
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num_classes=80,
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decode=None,
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nms=None,
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export_onnx=False,
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export_eb=False):
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super(BBoxPostProcess, self).__init__()
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self.num_classes = num_classes
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self.decode = decode
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self.nms = nms
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self.export_onnx = export_onnx
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self.export_eb = export_eb
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def __call__(self, head_out, rois, im_shape, scale_factor):
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"""
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Decode the bbox and do NMS if needed.
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Args:
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head_out (tuple): bbox_pred and cls_prob of bbox_head output.
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rois (tuple): roi and rois_num of rpn_head output.
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im_shape (Tensor): The shape of the input image.
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scale_factor (Tensor): The scale factor of the input image.
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export_onnx (bool): whether export model to onnx
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Returns:
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bbox_pred (Tensor): The output prediction with shape [N, 6], including
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labels, scores and bboxes. The size of bboxes are corresponding
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to the input image, the bboxes may be used in other branch.
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bbox_num (Tensor): The number of prediction boxes of each batch with
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shape [1], and is N.
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"""
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if self.nms is not None:
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bboxes, score = self.decode(head_out, rois, im_shape, scale_factor)
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bbox_pred, bbox_num, before_nms_indexes = self.nms(bboxes, score,
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self.num_classes)
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else:
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bbox_pred, bbox_num = self.decode(head_out, rois, im_shape,
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scale_factor)
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if self.export_onnx:
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# add fake box after postprocess when exporting onnx
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fake_bboxes = paddle.to_tensor(
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np.array(
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[[0., 0.0, 0.0, 0.0, 1.0, 1.0]], dtype='float32'))
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bbox_pred = paddle.concat([bbox_pred, fake_bboxes])
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bbox_num = bbox_num + 1
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if self.nms is not None:
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return bbox_pred, bbox_num, before_nms_indexes
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else:
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return bbox_pred, bbox_num
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def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
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"""
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Rescale, clip and filter the bbox from the output of NMS to
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get final prediction.
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Notes:
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Currently only support bs = 1.
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Args:
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bboxes (Tensor): The output bboxes with shape [N, 6] after decode
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and NMS, including labels, scores and bboxes.
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bbox_num (Tensor): The number of prediction boxes of each batch with
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shape [1], and is N.
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im_shape (Tensor): The shape of the input image.
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scale_factor (Tensor): The scale factor of the input image.
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Returns:
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pred_result (Tensor): The final prediction results with shape [N, 6]
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including labels, scores and bboxes.
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"""
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if self.export_eb:
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# enable rcnn models for edgeboard hw to skip the following postprocess.
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return bboxes, bboxes, bbox_num
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if not self.export_onnx:
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bboxes_list = []
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bbox_num_list = []
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id_start = 0
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fake_bboxes = paddle.to_tensor(
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np.array(
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[[0., 0.0, 0.0, 0.0, 1.0, 1.0]], dtype='float32'))
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fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
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# add fake bbox when output is empty for each batch
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for i in range(bbox_num.shape[0]):
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if bbox_num[i] == 0:
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bboxes_i = fake_bboxes
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bbox_num_i = fake_bbox_num
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else:
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bboxes_i = bboxes[id_start:id_start + bbox_num[i], :]
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bbox_num_i = bbox_num[i:i + 1]
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id_start += bbox_num[i:i + 1]
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bboxes_list.append(bboxes_i)
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bbox_num_list.append(bbox_num_i)
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bboxes = paddle.concat(bboxes_list)
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bbox_num = paddle.concat(bbox_num_list)
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origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
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if not self.export_onnx:
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origin_shape_list = []
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scale_factor_list = []
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# scale_factor: scale_y, scale_x
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for i in range(bbox_num.shape[0]):
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expand_shape = paddle.expand(origin_shape[i:i + 1, :],
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[bbox_num[i:i + 1], 2])
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scale_y, scale_x = scale_factor[i, 0:1], scale_factor[i, 1:2]
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scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
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expand_scale = paddle.expand(scale, [bbox_num[i:i + 1], 4])
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origin_shape_list.append(expand_shape)
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scale_factor_list.append(expand_scale)
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self.origin_shape_list = paddle.concat(origin_shape_list)
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scale_factor_list = paddle.concat(scale_factor_list)
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else:
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# simplify the computation for bs=1 when exporting onnx
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scale_y, scale_x = scale_factor[0][0], scale_factor[0][1]
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scale = paddle.concat(
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[scale_x, scale_y, scale_x, scale_y]).unsqueeze(0)
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self.origin_shape_list = paddle.expand(origin_shape,
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[bbox_num[0:1], 2])
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scale_factor_list = paddle.expand(scale, [bbox_num[0:1], 4])
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# bboxes: [N, 6], label, score, bbox
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pred_label = bboxes[:, 0:1]
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pred_score = bboxes[:, 1:2]
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pred_bbox = bboxes[:, 2:]
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# rescale bbox to original image
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scaled_bbox = pred_bbox / scale_factor_list
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origin_h = self.origin_shape_list[:, 0]
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origin_w = self.origin_shape_list[:, 1]
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zeros = paddle.zeros_like(origin_h)
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# clip bbox to [0, original_size]
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x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
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y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
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x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
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y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
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pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
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# filter empty bbox
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keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
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keep_mask = paddle.unsqueeze(keep_mask, [1])
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pred_label = paddle.where(keep_mask, pred_label,
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paddle.ones_like(pred_label) * -1)
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pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
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return bboxes, pred_result, bbox_num
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def get_origin_shape(self, ):
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return self.origin_shape_list
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@register
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class MaskPostProcess(object):
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__shared__ = ['export_onnx', 'assign_on_cpu']
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"""
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refer to:
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https://github.com/facebookresearch/detectron2/layers/mask_ops.py
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Get Mask output according to the output from model
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"""
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def __init__(self,
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binary_thresh=0.5,
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export_onnx=False,
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assign_on_cpu=False):
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super(MaskPostProcess, self).__init__()
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self.binary_thresh = binary_thresh
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self.export_onnx = export_onnx
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self.assign_on_cpu = assign_on_cpu
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def __call__(self, mask_out, bboxes, bbox_num, origin_shape):
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"""
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Decode the mask_out and paste the mask to the origin image.
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Args:
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mask_out (Tensor): mask_head output with shape [N, 28, 28].
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bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode
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and NMS, including labels, scores and bboxes.
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bbox_num (Tensor): The number of prediction boxes of each batch with
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shape [1], and is N.
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origin_shape (Tensor): The origin shape of the input image, the tensor
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shape is [N, 2], and each row is [h, w].
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Returns:
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pred_result (Tensor): The final prediction mask results with shape
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[N, h, w] in binary mask style.
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"""
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num_mask = mask_out.shape[0]
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origin_shape = paddle.cast(origin_shape, 'int32')
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device = paddle.device.get_device()
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if self.export_onnx:
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h, w = origin_shape[0][0], origin_shape[0][1]
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mask_onnx = paste_mask(mask_out[:, None, :, :], bboxes[:, 2:], h, w,
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self.assign_on_cpu)
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mask_onnx = mask_onnx >= self.binary_thresh
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pred_result = paddle.cast(mask_onnx, 'int32')
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else:
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max_h = paddle.max(origin_shape[:, 0])
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max_w = paddle.max(origin_shape[:, 1])
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pred_result = paddle.zeros(
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[num_mask, max_h, max_w], dtype='int32') - 1
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id_start = 0
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for i in range(paddle.shape(bbox_num)[0]):
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bboxes_i = bboxes[id_start:id_start + bbox_num[i], :]
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mask_out_i = mask_out[id_start:id_start + bbox_num[i], :, :]
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im_h = origin_shape[i, 0]
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im_w = origin_shape[i, 1]
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pred_mask = paste_mask(mask_out_i[:, None, :, :],
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bboxes_i[:, 2:], im_h, im_w,
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self.assign_on_cpu)
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pred_mask = paddle.cast(pred_mask >= self.binary_thresh,
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'int32')
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pred_result[id_start:id_start + bbox_num[i], :im_h, :
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im_w] = pred_mask
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id_start += bbox_num[i]
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if self.assign_on_cpu:
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paddle.set_device(device)
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return pred_result
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@register
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class JDEBBoxPostProcess(nn.Layer):
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__shared__ = ['num_classes']
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__inject__ = ['decode', 'nms']
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def __init__(self, num_classes=1, decode=None, nms=None, return_idx=True):
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super(JDEBBoxPostProcess, self).__init__()
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self.num_classes = num_classes
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self.decode = decode
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self.nms = nms
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self.return_idx = return_idx
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self.fake_bbox_pred = paddle.to_tensor(
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np.array(
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[[-1, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype='float32'))
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self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
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self.fake_nms_keep_idx = paddle.to_tensor(
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np.array(
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[[0]], dtype='int32'))
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self.fake_yolo_boxes_out = paddle.to_tensor(
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np.array(
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[[[0.0, 0.0, 0.0, 0.0]]], dtype='float32'))
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self.fake_yolo_scores_out = paddle.to_tensor(
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np.array(
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[[[0.0]]], dtype='float32'))
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self.fake_boxes_idx = paddle.to_tensor(np.array([[0]], dtype='int64'))
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def forward(self, head_out, anchors):
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"""
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Decode the bbox and do NMS for JDE model.
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Args:
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head_out (list): Bbox_pred and cls_prob of bbox_head output.
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anchors (list): Anchors of JDE model.
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Returns:
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boxes_idx (Tensor): The index of kept bboxes after decode 'JDEBox'.
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bbox_pred (Tensor): The output is the prediction with shape [N, 6]
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including labels, scores and bboxes.
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bbox_num (Tensor): The number of prediction of each batch with shape [N].
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nms_keep_idx (Tensor): The index of kept bboxes after NMS.
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"""
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boxes_idx, yolo_boxes_scores = self.decode(head_out, anchors)
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if len(boxes_idx) == 0:
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boxes_idx = self.fake_boxes_idx
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yolo_boxes_out = self.fake_yolo_boxes_out
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yolo_scores_out = self.fake_yolo_scores_out
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else:
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yolo_boxes = paddle.gather_nd(yolo_boxes_scores, boxes_idx)
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# TODO: only support bs=1 now
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yolo_boxes_out = paddle.reshape(
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yolo_boxes[:, :4], shape=[1, len(boxes_idx), 4])
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yolo_scores_out = paddle.reshape(
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yolo_boxes[:, 4:5], shape=[1, 1, len(boxes_idx)])
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boxes_idx = boxes_idx[:, 1:]
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if self.return_idx:
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bbox_pred, bbox_num, nms_keep_idx = self.nms(
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yolo_boxes_out, yolo_scores_out, self.num_classes)
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if bbox_pred.shape[0] == 0:
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bbox_pred = self.fake_bbox_pred
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bbox_num = self.fake_bbox_num
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nms_keep_idx = self.fake_nms_keep_idx
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return boxes_idx, bbox_pred, bbox_num, nms_keep_idx
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else:
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bbox_pred, bbox_num, _ = self.nms(yolo_boxes_out, yolo_scores_out,
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self.num_classes)
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if bbox_pred.shape[0] == 0:
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bbox_pred = self.fake_bbox_pred
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bbox_num = self.fake_bbox_num
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return _, bbox_pred, bbox_num, _
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@register
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class CenterNetPostProcess(object):
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"""
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Postprocess the model outputs to get final prediction:
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1. Do NMS for heatmap to get top `max_per_img` bboxes.
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2. Decode bboxes using center offset and box size.
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3. Rescale decoded bboxes reference to the origin image shape.
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Args:
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max_per_img(int): the maximum number of predicted objects in a image,
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500 by default.
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down_ratio(int): the down ratio from images to heatmap, 4 by default.
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regress_ltrb (bool): whether to regress left/top/right/bottom or
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width/height for a box, true by default.
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"""
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__shared__ = ['down_ratio']
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def __init__(self, max_per_img=500, down_ratio=4, regress_ltrb=True):
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super(CenterNetPostProcess, self).__init__()
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self.max_per_img = max_per_img
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self.down_ratio = down_ratio
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self.regress_ltrb = regress_ltrb
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# _simple_nms() _topk() are same as TTFBox in ppdet/modeling/layers.py
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def _simple_nms(self, heat, kernel=3):
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""" Use maxpool to filter the max score, get local peaks. """
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pad = (kernel - 1) // 2
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hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad)
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keep = paddle.cast(hmax == heat, 'float32')
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return heat * keep
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def _topk(self, scores):
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""" Select top k scores and decode to get xy coordinates. """
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k = self.max_per_img
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shape_fm = paddle.shape(scores)
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shape_fm.stop_gradient = True
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cat, height, width = shape_fm[1], shape_fm[2], shape_fm[3]
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# batch size is 1
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scores_r = paddle.reshape(scores, [cat, -1])
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topk_scores, topk_inds = paddle.topk(scores_r, k)
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topk_ys = topk_inds // width
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topk_xs = topk_inds % width
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topk_score_r = paddle.reshape(topk_scores, [-1])
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topk_score, topk_ind = paddle.topk(topk_score_r, k)
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k_t = paddle.full(paddle.shape(topk_ind), k, dtype='int64')
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topk_clses = paddle.cast(paddle.floor_divide(topk_ind, k_t), 'float32')
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topk_inds = paddle.reshape(topk_inds, [-1])
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topk_ys = paddle.reshape(topk_ys, [-1, 1])
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topk_xs = paddle.reshape(topk_xs, [-1, 1])
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topk_inds = paddle.gather(topk_inds, topk_ind)
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topk_ys = paddle.gather(topk_ys, topk_ind)
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topk_xs = paddle.gather(topk_xs, topk_ind)
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return topk_score, topk_inds, topk_clses, topk_ys, topk_xs
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def __call__(self, hm, wh, reg, im_shape, scale_factor):
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# 1.get clses and scores, note that hm had been done sigmoid
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heat = self._simple_nms(hm)
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scores, inds, topk_clses, ys, xs = self._topk(heat)
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clses = topk_clses.unsqueeze(1)
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scores = scores.unsqueeze(1)
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# 2.get bboxes, note only support batch_size=1 now
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reg_t = paddle.transpose(reg, [0, 2, 3, 1])
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reg = paddle.reshape(reg_t, [-1, reg_t.shape[-1]])
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reg = paddle.gather(reg, inds)
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xs = paddle.cast(xs, 'float32')
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ys = paddle.cast(ys, 'float32')
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xs = xs + reg[:, 0:1]
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ys = ys + reg[:, 1:2]
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wh_t = paddle.transpose(wh, [0, 2, 3, 1])
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wh = paddle.reshape(wh_t, [-1, wh_t.shape[-1]])
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wh = paddle.gather(wh, inds)
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if self.regress_ltrb:
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x1 = xs - wh[:, 0:1]
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y1 = ys - wh[:, 1:2]
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x2 = xs + wh[:, 2:3]
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y2 = ys + wh[:, 3:4]
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else:
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x1 = xs - wh[:, 0:1] / 2
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y1 = ys - wh[:, 1:2] / 2
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x2 = xs + wh[:, 0:1] / 2
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y2 = ys + wh[:, 1:2] / 2
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n, c, feat_h, feat_w = paddle.shape(hm)
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padw = (feat_w * self.down_ratio - im_shape[0, 1]) / 2
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padh = (feat_h * self.down_ratio - im_shape[0, 0]) / 2
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x1 = x1 * self.down_ratio
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y1 = y1 * self.down_ratio
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x2 = x2 * self.down_ratio
|
|
y2 = y2 * self.down_ratio
|
|
x1 = x1 - padw
|
|
y1 = y1 - padh
|
|
x2 = x2 - padw
|
|
y2 = y2 - padh
|
|
bboxes = paddle.concat([x1, y1, x2, y2], axis=1)
|
|
scale_y = scale_factor[:, 0:1]
|
|
scale_x = scale_factor[:, 1:2]
|
|
scale_expand = paddle.concat(
|
|
[scale_x, scale_y, scale_x, scale_y], axis=1)
|
|
boxes_shape = bboxes.shape[:]
|
|
scale_expand = paddle.expand(scale_expand, shape=boxes_shape)
|
|
bboxes = paddle.divide(bboxes, scale_expand)
|
|
|
|
results = paddle.concat([clses, scores, bboxes], axis=1)
|
|
return results, paddle.shape(results)[0:1], inds, topk_clses, ys, xs
|
|
|
|
|
|
@register
|
|
class DETRPostProcess(object):
|
|
__shared__ = ['num_classes', 'use_focal_loss', 'with_mask']
|
|
__inject__ = []
|
|
|
|
def __init__(self,
|
|
num_classes=80,
|
|
num_top_queries=100,
|
|
dual_queries=False,
|
|
dual_groups=0,
|
|
use_focal_loss=False,
|
|
with_mask=False,
|
|
mask_threshold=0.5,
|
|
use_avg_mask_score=False,
|
|
bbox_decode_type='origin'):
|
|
super(DETRPostProcess, self).__init__()
|
|
assert bbox_decode_type in ['origin', 'pad']
|
|
|
|
self.num_classes = num_classes
|
|
self.num_top_queries = num_top_queries
|
|
self.dual_queries = dual_queries
|
|
self.dual_groups = dual_groups
|
|
self.use_focal_loss = use_focal_loss
|
|
self.with_mask = with_mask
|
|
self.mask_threshold = mask_threshold
|
|
self.use_avg_mask_score = use_avg_mask_score
|
|
self.bbox_decode_type = bbox_decode_type
|
|
|
|
def _mask_postprocess(self, mask_pred, score_pred, index):
|
|
mask_score = F.sigmoid(paddle.gather_nd(mask_pred, index))
|
|
mask_pred = (mask_score > self.mask_threshold).astype(mask_score.dtype)
|
|
if self.use_avg_mask_score:
|
|
avg_mask_score = (mask_pred * mask_score).sum([-2, -1]) / (
|
|
mask_pred.sum([-2, -1]) + 1e-6)
|
|
score_pred *= avg_mask_score
|
|
|
|
return mask_pred[0].astype('int32'), score_pred
|
|
|
|
def __call__(self, head_out, im_shape, scale_factor, pad_shape):
|
|
"""
|
|
Decode the bbox and mask.
|
|
|
|
Args:
|
|
head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
|
|
im_shape (Tensor): The shape of the input image without padding.
|
|
scale_factor (Tensor): The scale factor of the input image.
|
|
pad_shape (Tensor): The shape of the input image with padding.
|
|
Returns:
|
|
bbox_pred (Tensor): The output prediction with shape [N, 6], including
|
|
labels, scores and bboxes. The size of bboxes are corresponding
|
|
to the input image, the bboxes may be used in other branch.
|
|
bbox_num (Tensor): The number of prediction boxes of each batch with
|
|
shape [bs], and is N.
|
|
"""
|
|
bboxes, logits, masks = head_out
|
|
if self.dual_queries:
|
|
num_queries = logits.shape[1]
|
|
logits, bboxes = logits[:, :int(num_queries // (self.dual_groups + 1)), :], \
|
|
bboxes[:, :int(num_queries // (self.dual_groups + 1)), :]
|
|
|
|
bbox_pred = bbox_cxcywh_to_xyxy(bboxes)
|
|
# calculate the original shape of the image
|
|
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
|
|
img_h, img_w = paddle.split(origin_shape, 2, axis=-1)
|
|
if self.bbox_decode_type == 'pad':
|
|
# calculate the shape of the image with padding
|
|
out_shape = pad_shape / im_shape * origin_shape
|
|
out_shape = out_shape.flip(1).tile([1, 2]).unsqueeze(1)
|
|
elif self.bbox_decode_type == 'origin':
|
|
out_shape = origin_shape.flip(1).tile([1, 2]).unsqueeze(1)
|
|
else:
|
|
raise Exception(
|
|
f'Wrong `bbox_decode_type`: {self.bbox_decode_type}.')
|
|
bbox_pred *= out_shape
|
|
|
|
scores = F.sigmoid(logits) if self.use_focal_loss else F.softmax(
|
|
logits)[:, :, :-1]
|
|
|
|
if not self.use_focal_loss:
|
|
scores, labels = scores.max(-1), scores.argmax(-1)
|
|
if scores.shape[1] > self.num_top_queries:
|
|
scores, index = paddle.topk(
|
|
scores, self.num_top_queries, axis=-1)
|
|
batch_ind = paddle.arange(
|
|
end=scores.shape[0]).unsqueeze(-1).tile(
|
|
[1, self.num_top_queries])
|
|
index = paddle.stack([batch_ind, index], axis=-1)
|
|
labels = paddle.gather_nd(labels, index)
|
|
bbox_pred = paddle.gather_nd(bbox_pred, index)
|
|
else:
|
|
scores, index = paddle.topk(
|
|
scores.flatten(1), self.num_top_queries, axis=-1)
|
|
labels = index % self.num_classes
|
|
index = index // self.num_classes
|
|
batch_ind = paddle.arange(end=scores.shape[0]).unsqueeze(-1).tile(
|
|
[1, self.num_top_queries])
|
|
index = paddle.stack([batch_ind, index], axis=-1)
|
|
bbox_pred = paddle.gather_nd(bbox_pred, index)
|
|
|
|
mask_pred = None
|
|
if self.with_mask:
|
|
assert masks is not None
|
|
masks = F.interpolate(
|
|
masks, scale_factor=4, mode="bilinear", align_corners=False)
|
|
# TODO: Support prediction with bs>1.
|
|
# remove padding for input image
|
|
h, w = im_shape.astype('int32')[0]
|
|
masks = masks[..., :h, :w]
|
|
# get pred_mask in the original resolution.
|
|
img_h = img_h[0].astype('int32')
|
|
img_w = img_w[0].astype('int32')
|
|
masks = F.interpolate(
|
|
masks,
|
|
size=(img_h, img_w),
|
|
mode="bilinear",
|
|
align_corners=False)
|
|
mask_pred, scores = self._mask_postprocess(masks, scores, index)
|
|
|
|
bbox_pred = paddle.concat(
|
|
[
|
|
labels.unsqueeze(-1).astype('float32'), scores.unsqueeze(-1),
|
|
bbox_pred
|
|
],
|
|
axis=-1)
|
|
bbox_num = paddle.to_tensor(
|
|
self.num_top_queries, dtype='int32').tile([bbox_pred.shape[0]])
|
|
bbox_pred = bbox_pred.reshape([-1, 6])
|
|
return bbox_pred, bbox_num, mask_pred
|
|
|
|
|
|
@register
|
|
class SparsePostProcess(object):
|
|
__shared__ = ['num_classes', 'assign_on_cpu']
|
|
|
|
def __init__(self,
|
|
num_proposals,
|
|
num_classes=80,
|
|
binary_thresh=0.5,
|
|
assign_on_cpu=False):
|
|
super(SparsePostProcess, self).__init__()
|
|
self.num_classes = num_classes
|
|
self.num_proposals = num_proposals
|
|
self.binary_thresh = binary_thresh
|
|
self.assign_on_cpu = assign_on_cpu
|
|
|
|
def __call__(self, scores, bboxes, scale_factor, ori_shape, masks=None):
|
|
assert len(scores) == len(bboxes) == \
|
|
len(ori_shape) == len(scale_factor)
|
|
device = paddle.device.get_device()
|
|
batch_size = len(ori_shape)
|
|
|
|
scores = F.sigmoid(scores)
|
|
has_mask = masks is not None
|
|
if has_mask:
|
|
masks = F.sigmoid(masks)
|
|
masks = masks.reshape([batch_size, -1, *masks.shape[1:]])
|
|
|
|
bbox_pred = []
|
|
mask_pred = [] if has_mask else None
|
|
bbox_num = paddle.zeros([batch_size], dtype='int32')
|
|
for i in range(batch_size):
|
|
score = scores[i]
|
|
bbox = bboxes[i]
|
|
score, indices = score.flatten(0, 1).topk(
|
|
self.num_proposals, sorted=False)
|
|
label = indices % self.num_classes
|
|
if has_mask:
|
|
mask = masks[i]
|
|
mask = mask.flatten(0, 1)[indices]
|
|
|
|
H, W = ori_shape[i][0], ori_shape[i][1]
|
|
bbox = bbox[paddle.cast(indices / self.num_classes, indices.dtype)]
|
|
bbox /= scale_factor[i]
|
|
bbox[:, 0::2] = paddle.clip(bbox[:, 0::2], 0, W)
|
|
bbox[:, 1::2] = paddle.clip(bbox[:, 1::2], 0, H)
|
|
|
|
keep = ((bbox[:, 2] - bbox[:, 0]).numpy() > 1.) & \
|
|
((bbox[:, 3] - bbox[:, 1]).numpy() > 1.)
|
|
if keep.sum() == 0:
|
|
bbox = paddle.zeros([1, 6], dtype='float32')
|
|
if has_mask:
|
|
mask = paddle.zeros([1, H, W], dtype='uint8')
|
|
else:
|
|
label = paddle.to_tensor(label.numpy()[keep]).astype(
|
|
'float32').unsqueeze(-1)
|
|
score = paddle.to_tensor(score.numpy()[keep]).astype(
|
|
'float32').unsqueeze(-1)
|
|
bbox = paddle.to_tensor(bbox.numpy()[keep]).astype('float32')
|
|
if has_mask:
|
|
mask = paddle.to_tensor(mask.numpy()[keep]).astype(
|
|
'float32').unsqueeze(1)
|
|
mask = paste_mask(mask, bbox, H, W, self.assign_on_cpu)
|
|
mask = paddle.cast(mask >= self.binary_thresh, 'uint8')
|
|
bbox = paddle.concat([label, score, bbox], axis=-1)
|
|
|
|
bbox_num[i] = bbox.shape[0]
|
|
bbox_pred.append(bbox)
|
|
if has_mask:
|
|
mask_pred.append(mask)
|
|
|
|
bbox_pred = paddle.concat(bbox_pred)
|
|
mask_pred = paddle.concat(mask_pred) if has_mask else None
|
|
|
|
if self.assign_on_cpu:
|
|
paddle.set_device(device)
|
|
|
|
if has_mask:
|
|
return bbox_pred, bbox_num, mask_pred
|
|
else:
|
|
return bbox_pred, bbox_num
|
|
|
|
|
|
def paste_mask(masks, boxes, im_h, im_w, assign_on_cpu=False):
|
|
"""
|
|
Paste the mask prediction to the original image.
|
|
"""
|
|
x0_int, y0_int = 0, 0
|
|
x1_int, y1_int = im_w, im_h
|
|
x0, y0, x1, y1 = paddle.split(boxes, 4, axis=1)
|
|
N = masks.shape[0]
|
|
img_y = paddle.arange(y0_int, y1_int) + 0.5
|
|
img_x = paddle.arange(x0_int, x1_int) + 0.5
|
|
|
|
img_y = (img_y - y0) / (y1 - y0) * 2 - 1
|
|
img_x = (img_x - x0) / (x1 - x0) * 2 - 1
|
|
# img_x, img_y have shapes (N, w), (N, h)
|
|
|
|
if assign_on_cpu:
|
|
paddle.set_device('cpu')
|
|
gx = img_x[:, None, :].expand(
|
|
[N, paddle.shape(img_y)[1], paddle.shape(img_x)[1]])
|
|
gy = img_y[:, :, None].expand(
|
|
[N, paddle.shape(img_y)[1], paddle.shape(img_x)[1]])
|
|
grid = paddle.stack([gx, gy], axis=3)
|
|
img_masks = F.grid_sample(masks, grid, align_corners=False)
|
|
return img_masks[:, 0]
|
|
|
|
|
|
def multiclass_nms(bboxs, num_classes, match_threshold=0.6, match_metric='iou'):
|
|
final_boxes = []
|
|
for c in range(num_classes):
|
|
idxs = bboxs[:, 0] == c
|
|
if np.count_nonzero(idxs) == 0: continue
|
|
r = nms(bboxs[idxs, 1:], match_threshold, match_metric)
|
|
final_boxes.append(np.concatenate([np.full((r.shape[0], 1), c), r], 1))
|
|
return final_boxes
|
|
|
|
|
|
def nms(dets, match_threshold=0.6, match_metric='iou'):
|
|
""" Apply NMS to avoid detecting too many overlapping bounding boxes.
|
|
Args:
|
|
dets: shape [N, 5], [score, x1, y1, x2, y2]
|
|
match_metric: 'iou' or 'ios'
|
|
match_threshold: overlap thresh for match metric.
|
|
"""
|
|
if dets.shape[0] == 0:
|
|
return dets[[], :]
|
|
scores = dets[:, 0]
|
|
x1 = dets[:, 1]
|
|
y1 = dets[:, 2]
|
|
x2 = dets[:, 3]
|
|
y2 = dets[:, 4]
|
|
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
|
|
order = scores.argsort()[::-1]
|
|
|
|
ndets = dets.shape[0]
|
|
suppressed = np.zeros((ndets), dtype=np.int32)
|
|
|
|
for _i in range(ndets):
|
|
i = order[_i]
|
|
if suppressed[i] == 1:
|
|
continue
|
|
ix1 = x1[i]
|
|
iy1 = y1[i]
|
|
ix2 = x2[i]
|
|
iy2 = y2[i]
|
|
iarea = areas[i]
|
|
for _j in range(_i + 1, ndets):
|
|
j = order[_j]
|
|
if suppressed[j] == 1:
|
|
continue
|
|
xx1 = max(ix1, x1[j])
|
|
yy1 = max(iy1, y1[j])
|
|
xx2 = min(ix2, x2[j])
|
|
yy2 = min(iy2, y2[j])
|
|
w = max(0.0, xx2 - xx1 + 1)
|
|
h = max(0.0, yy2 - yy1 + 1)
|
|
inter = w * h
|
|
if match_metric == 'iou':
|
|
union = iarea + areas[j] - inter
|
|
match_value = inter / union
|
|
elif match_metric == 'ios':
|
|
smaller = min(iarea, areas[j])
|
|
match_value = inter / smaller
|
|
else:
|
|
raise ValueError()
|
|
if match_value >= match_threshold:
|
|
suppressed[j] = 1
|
|
keep = np.where(suppressed == 0)[0]
|
|
dets = dets[keep, :]
|
|
return dets
|
|
|
|
|
|
@register
|
|
class DETRBBoxSemiPostProcess(object):
|
|
__shared__ = ['num_classes', 'use_focal_loss']
|
|
__inject__ = []
|
|
|
|
def __init__(self,
|
|
num_classes=80,
|
|
num_top_queries=100,
|
|
use_focal_loss=False):
|
|
super(DETRBBoxSemiPostProcess, self).__init__()
|
|
self.num_classes = num_classes
|
|
self.num_top_queries = num_top_queries
|
|
self.use_focal_loss = use_focal_loss
|
|
|
|
def __call__(self, head_out):
|
|
"""
|
|
Decode the bbox.
|
|
Args:
|
|
head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
|
|
im_shape (Tensor): The shape of the input image.
|
|
scale_factor (Tensor): The scale factor of the input image.
|
|
Returns:
|
|
bbox_pred (Tensor): The output prediction with shape [N, 6], including
|
|
labels, scores and bboxes. The size of bboxes are corresponding
|
|
to the input image, the bboxes may be used in other branch.
|
|
bbox_num (Tensor): The number of prediction boxes of each batch with
|
|
shape [bs], and is N.
|
|
"""
|
|
bboxes, logits, masks = head_out
|
|
bbox_pred = bboxes
|
|
|
|
scores = F.softmax(logits, axis=2)
|
|
|
|
import copy
|
|
soft_scores = copy.deepcopy(scores)
|
|
scores, index = paddle.topk(scores.max(-1), 300, axis=-1)
|
|
|
|
batch_ind = paddle.arange(end=scores.shape[0]).unsqueeze(-1).tile(
|
|
[1, 300])
|
|
index = paddle.stack([batch_ind, index], axis=-1)
|
|
labels = paddle.gather_nd(soft_scores.argmax(-1), index).astype('int32')
|
|
score_class = paddle.gather_nd(soft_scores, index)
|
|
bbox_pred = paddle.gather_nd(bbox_pred, index)
|
|
bbox_pred = paddle.concat(
|
|
[
|
|
labels.unsqueeze(-1).astype('float32'), score_class,
|
|
scores.unsqueeze(-1), bbox_pred
|
|
],
|
|
axis=-1)
|
|
bbox_num = paddle.to_tensor(
|
|
bbox_pred.shape[1], dtype='int32').tile([bbox_pred.shape[0]])
|
|
bbox_pred = bbox_pred.reshape([-1, bbox_pred.shape[-1]])
|
|
return bbox_pred, bbox_num
|