forked from PulseFocusPlatform/PulseFocusPlatform
448 lines
18 KiB
Python
448 lines
18 KiB
Python
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# 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, rbox2poly
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from ppdet.modeling.layers import TTFBox
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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',
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'MaskPostProcess',
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'FCOSPostProcess',
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'S2ANetBBoxPostProcess',
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'JDEBBoxPostProcess',
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'CenterNetPostProcess',
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]
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@register
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class BBoxPostProcess(object):
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__shared__ = ['num_classes']
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__inject__ = ['decode', 'nms']
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def __init__(self, num_classes=80, decode=None, nms=None):
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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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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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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, _ = self.nms(bboxes, score, 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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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 bboxes.shape[0] == 0:
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bboxes = 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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bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
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origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
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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], 2])
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scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
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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], 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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# 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 pred_result
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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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def __init__(self, binary_thresh=0.5):
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super(MaskPostProcess, self).__init__()
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self.binary_thresh = binary_thresh
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def paste_mask(self, masks, boxes, im_h, im_w):
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"""
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Paste the mask prediction to the original image.
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"""
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x0, y0, x1, y1 = paddle.split(boxes, 4, axis=1)
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masks = paddle.unsqueeze(masks, [0, 1])
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img_y = paddle.arange(0, im_h, dtype='float32') + 0.5
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img_x = paddle.arange(0, im_w, dtype='float32') + 0.5
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img_y = (img_y - y0) / (y1 - y0) * 2 - 1
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img_x = (img_x - x0) / (x1 - x0) * 2 - 1
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img_x = paddle.unsqueeze(img_x, [1])
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img_y = paddle.unsqueeze(img_y, [2])
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N = boxes.shape[0]
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gx = paddle.expand(img_x, [N, img_y.shape[1], img_x.shape[2]])
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gy = paddle.expand(img_y, [N, img_y.shape[1], img_x.shape[2]])
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grid = paddle.stack([gx, gy], axis=3)
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img_masks = F.grid_sample(masks, grid, align_corners=False)
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return img_masks[:, 0]
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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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# TODO: support bs > 1 and mask output dtype is bool
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pred_result = paddle.zeros(
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[num_mask, origin_shape[0][0], origin_shape[0][1]], dtype='int32')
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if bbox_num == 1 and bboxes[0][0] == -1:
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return pred_result
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# TODO: optimize chunk paste
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pred_result = []
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for i in range(bboxes.shape[0]):
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im_h, im_w = origin_shape[i][0], origin_shape[i][1]
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pred_mask = self.paste_mask(mask_out[i], bboxes[i:i + 1, 2:], im_h,
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im_w)
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pred_mask = pred_mask >= self.binary_thresh
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pred_mask = paddle.cast(pred_mask, 'int32')
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pred_result.append(pred_mask)
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pred_result = paddle.concat(pred_result)
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return pred_result
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@register
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class FCOSPostProcess(object):
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__inject__ = ['decode', 'nms']
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def __init__(self, decode=None, nms=None):
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super(FCOSPostProcess, self).__init__()
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self.decode = decode
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self.nms = nms
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def __call__(self, fcos_head_outs, scale_factor):
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"""
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Decode the bbox and do NMS in FCOS.
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"""
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locations, cls_logits, bboxes_reg, centerness = fcos_head_outs
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bboxes, score = self.decode(locations, cls_logits, bboxes_reg,
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centerness, scale_factor)
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bbox_pred, bbox_num, _ = self.nms(bboxes, score)
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return bbox_pred, bbox_num
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@register
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class S2ANetBBoxPostProcess(nn.Layer):
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__shared__ = ['num_classes']
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__inject__ = ['nms']
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def __init__(self, num_classes=15, nms_pre=2000, min_bbox_size=0, nms=None):
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super(S2ANetBBoxPostProcess, self).__init__()
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self.num_classes = num_classes
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self.nms_pre = nms_pre
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self.min_bbox_size = min_bbox_size
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self.nms = nms
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self.origin_shape_list = []
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self.fake_pred_cls_score_bbox = paddle.to_tensor(
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np.array(
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[[-1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
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dtype='float32'))
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self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
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def forward(self, pred_scores, pred_bboxes):
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"""
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pred_scores : [N, M] score
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pred_bboxes : [N, 5] xc, yc, w, h, a
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im_shape : [N, 2] im_shape
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scale_factor : [N, 2] scale_factor
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"""
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pred_ploys0 = rbox2poly(pred_bboxes)
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pred_ploys = paddle.unsqueeze(pred_ploys0, axis=0)
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# pred_scores [NA, 16] --> [16, NA]
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pred_scores0 = paddle.transpose(pred_scores, [1, 0])
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pred_scores = paddle.unsqueeze(pred_scores0, axis=0)
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pred_cls_score_bbox, bbox_num, _ = self.nms(pred_ploys, pred_scores,
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self.num_classes)
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# Prevent empty bbox_pred from decode or NMS.
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# Bboxes and score before NMS may be empty due to the score threshold.
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if pred_cls_score_bbox.shape[0] <= 0 or pred_cls_score_bbox.shape[
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1] <= 1:
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pred_cls_score_bbox = self.fake_pred_cls_score_bbox
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bbox_num = self.fake_bbox_num
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pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [-1, 10])
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return pred_cls_score_bbox, 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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Args:
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bboxes(Tensor): bboxes [N, 10]
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bbox_num(Tensor): bbox_num
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im_shape(Tensor): [1 2]
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scale_factor(Tensor): [1 2]
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Returns:
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bbox_pred(Tensor): The output is the prediction with shape [N, 8]
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including labels, scores and bboxes. The size of
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bboxes are corresponding to the original image.
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"""
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origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
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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], 2])
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scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
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scale = paddle.concat([
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scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x,
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scale_y
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])
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expand_scale = paddle.expand(scale, [bbox_num[i], 8])
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origin_shape_list.append(expand_shape)
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scale_factor_list.append(expand_scale)
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origin_shape_list = paddle.concat(origin_shape_list)
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scale_factor_list = paddle.concat(scale_factor_list)
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# bboxes: [N, 10], label, score, bbox
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pred_label_score = bboxes[:, 0:2]
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pred_bbox = bboxes[:, 2:]
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# rescale bbox to original image
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pred_bbox = pred_bbox.reshape([-1, 8])
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scaled_bbox = pred_bbox / scale_factor_list
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origin_h = origin_shape_list[:, 0]
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origin_w = origin_shape_list[:, 1]
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bboxes = scaled_bbox
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zeros = paddle.zeros_like(origin_h)
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x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros)
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y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros)
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x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros)
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y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros)
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x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros)
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y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros)
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x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros)
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y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros)
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pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1)
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|
|
pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
|
||
|
|
return pred_result
|
||
|
|
|
||
|
|
|
||
|
|
@register
|
||
|
|
class JDEBBoxPostProcess(BBoxPostProcess):
|
||
|
|
def __call__(self, head_out, anchors):
|
||
|
|
"""
|
||
|
|
Decode the bbox and do NMS for JDE model.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
head_out (list): Bbox_pred and cls_prob of bbox_head output.
|
||
|
|
anchors (list): Anchors of JDE model.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
boxes_idx (Tensor): The index of kept bboxes after decode 'JDEBox'.
|
||
|
|
bbox_pred (Tensor): The output is the prediction with shape [N, 6]
|
||
|
|
including labels, scores and bboxes.
|
||
|
|
bbox_num (Tensor): The number of prediction of each batch with shape [N].
|
||
|
|
nms_keep_idx (Tensor): The index of kept bboxes after NMS.
|
||
|
|
"""
|
||
|
|
boxes_idx, bboxes, score = self.decode(head_out, anchors)
|
||
|
|
bbox_pred, bbox_num, nms_keep_idx = self.nms(bboxes, score,
|
||
|
|
self.num_classes)
|
||
|
|
if bbox_pred.shape[0] == 0:
|
||
|
|
bbox_pred = paddle.to_tensor(
|
||
|
|
np.array(
|
||
|
|
[[-1, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype='float32'))
|
||
|
|
bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
|
||
|
|
nms_keep_idx = paddle.to_tensor(np.array([[0]], dtype='int32'))
|
||
|
|
|
||
|
|
return boxes_idx, bbox_pred, bbox_num, nms_keep_idx
|
||
|
|
|
||
|
|
|
||
|
|
@register
|
||
|
|
class CenterNetPostProcess(TTFBox):
|
||
|
|
"""
|
||
|
|
Postprocess the model outputs to get final prediction:
|
||
|
|
1. Do NMS for heatmap to get top `max_per_img` bboxes.
|
||
|
|
2. Decode bboxes using center offset and box size.
|
||
|
|
3. Rescale decoded bboxes reference to the origin image shape.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
max_per_img(int): the maximum number of predicted objects in a image,
|
||
|
|
500 by default.
|
||
|
|
down_ratio(int): the down ratio from images to heatmap, 4 by default.
|
||
|
|
regress_ltrb (bool): whether to regress left/top/right/bottom or
|
||
|
|
width/height for a box, true by default.
|
||
|
|
for_mot (bool): whether return other features used in tracking model.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
__shared__ = ['down_ratio']
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
max_per_img=500,
|
||
|
|
down_ratio=4,
|
||
|
|
regress_ltrb=True,
|
||
|
|
for_mot=False):
|
||
|
|
super(TTFBox, self).__init__()
|
||
|
|
self.max_per_img = max_per_img
|
||
|
|
self.down_ratio = down_ratio
|
||
|
|
self.regress_ltrb = regress_ltrb
|
||
|
|
self.for_mot = for_mot
|
||
|
|
|
||
|
|
def __call__(self, hm, wh, reg, im_shape, scale_factor):
|
||
|
|
heat = self._simple_nms(hm)
|
||
|
|
scores, inds, clses, ys, xs = self._topk(heat)
|
||
|
|
scores = paddle.tensor.unsqueeze(scores, [1])
|
||
|
|
clses = paddle.tensor.unsqueeze(clses, [1])
|
||
|
|
|
||
|
|
reg_t = paddle.transpose(reg, [0, 2, 3, 1])
|
||
|
|
# Like TTFBox, batch size is 1.
|
||
|
|
# TODO: support batch size > 1
|
||
|
|
reg = paddle.reshape(reg_t, [-1, paddle.shape(reg_t)[-1]])
|
||
|
|
reg = paddle.gather(reg, inds)
|
||
|
|
xs = paddle.cast(xs, 'float32')
|
||
|
|
ys = paddle.cast(ys, 'float32')
|
||
|
|
xs = xs + reg[:, 0:1]
|
||
|
|
ys = ys + reg[:, 1:2]
|
||
|
|
|
||
|
|
wh_t = paddle.transpose(wh, [0, 2, 3, 1])
|
||
|
|
wh = paddle.reshape(wh_t, [-1, paddle.shape(wh_t)[-1]])
|
||
|
|
wh = paddle.gather(wh, inds)
|
||
|
|
|
||
|
|
if self.regress_ltrb:
|
||
|
|
x1 = xs - wh[:, 0:1]
|
||
|
|
y1 = ys - wh[:, 1:2]
|
||
|
|
x2 = xs + wh[:, 2:3]
|
||
|
|
y2 = ys + wh[:, 3:4]
|
||
|
|
else:
|
||
|
|
x1 = xs - wh[:, 0:1] / 2
|
||
|
|
y1 = ys - wh[:, 1:2] / 2
|
||
|
|
x2 = xs + wh[:, 0:1] / 2
|
||
|
|
y2 = ys + wh[:, 1:2] / 2
|
||
|
|
|
||
|
|
n, c, feat_h, feat_w = paddle.shape(hm)
|
||
|
|
padw = (feat_w * self.down_ratio - im_shape[0, 1]) / 2
|
||
|
|
padh = (feat_h * self.down_ratio - im_shape[0, 0]) / 2
|
||
|
|
x1 = x1 * self.down_ratio
|
||
|
|
y1 = y1 * self.down_ratio
|
||
|
|
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 = paddle.shape(bboxes)
|
||
|
|
boxes_shape.stop_gradient = True
|
||
|
|
scale_expand = paddle.expand(scale_expand, shape=boxes_shape)
|
||
|
|
bboxes = paddle.divide(bboxes, scale_expand)
|
||
|
|
if self.for_mot:
|
||
|
|
results = paddle.concat([bboxes, scores, clses], axis=1)
|
||
|
|
return results, inds
|
||
|
|
else:
|
||
|
|
results = paddle.concat([clses, scores, bboxes], axis=1)
|
||
|
|
return results, paddle.shape(results)[0:1]
|