# Copyright (c) 2020 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 numpy as np import paddle import paddle.nn as nn import paddle.nn.functional as F from ppdet.core.workspace import register from ppdet.modeling.bbox_utils import nonempty_bbox, rbox2poly from ppdet.modeling.layers import TTFBox try: from collections.abc import Sequence except Exception: from collections import Sequence __all__ = [ 'BBoxPostProcess', 'MaskPostProcess', 'FCOSPostProcess', 'S2ANetBBoxPostProcess', 'JDEBBoxPostProcess', 'CenterNetPostProcess', ] @register class BBoxPostProcess(object): __shared__ = ['num_classes'] __inject__ = ['decode', 'nms'] def __init__(self, num_classes=80, decode=None, nms=None): super(BBoxPostProcess, self).__init__() self.num_classes = num_classes self.decode = decode self.nms = nms def __call__(self, head_out, rois, im_shape, scale_factor): """ Decode the bbox and do NMS if needed. Args: head_out (tuple): bbox_pred and cls_prob of bbox_head output. rois (tuple): roi and rois_num of rpn_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 [1], and is N. """ if self.nms is not None: bboxes, score = self.decode(head_out, rois, im_shape, scale_factor) bbox_pred, bbox_num, _ = self.nms(bboxes, score, self.num_classes) else: bbox_pred, bbox_num = self.decode(head_out, rois, im_shape, scale_factor) return bbox_pred, bbox_num def get_pred(self, bboxes, bbox_num, im_shape, scale_factor): """ Rescale, clip and filter the bbox from the output of NMS to get final prediction. Notes: Currently only support bs = 1. Args: bboxes (Tensor): The output bboxes with shape [N, 6] after decode and NMS, including labels, scores and bboxes. bbox_num (Tensor): The number of prediction boxes of each batch with shape [1], and is N. im_shape (Tensor): The shape of the input image. scale_factor (Tensor): The scale factor of the input image. Returns: pred_result (Tensor): The final prediction results with shape [N, 6] including labels, scores and bboxes. """ if bboxes.shape[0] == 0: bboxes = 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')) origin_shape = paddle.floor(im_shape / scale_factor + 0.5) origin_shape_list = [] scale_factor_list = [] # scale_factor: scale_y, scale_x for i in range(bbox_num.shape[0]): expand_shape = paddle.expand(origin_shape[i:i + 1, :], [bbox_num[i], 2]) scale_y, scale_x = scale_factor[i][0], scale_factor[i][1] scale = paddle.concat([scale_x, scale_y, scale_x, scale_y]) expand_scale = paddle.expand(scale, [bbox_num[i], 4]) origin_shape_list.append(expand_shape) scale_factor_list.append(expand_scale) self.origin_shape_list = paddle.concat(origin_shape_list) scale_factor_list = paddle.concat(scale_factor_list) # bboxes: [N, 6], label, score, bbox pred_label = bboxes[:, 0:1] pred_score = bboxes[:, 1:2] pred_bbox = bboxes[:, 2:] # rescale bbox to original image scaled_bbox = pred_bbox / scale_factor_list origin_h = self.origin_shape_list[:, 0] origin_w = self.origin_shape_list[:, 1] zeros = paddle.zeros_like(origin_h) # clip bbox to [0, original_size] x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros) y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros) x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros) y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros) pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1) # filter empty bbox keep_mask = nonempty_bbox(pred_bbox, return_mask=True) keep_mask = paddle.unsqueeze(keep_mask, [1]) pred_label = paddle.where(keep_mask, pred_label, paddle.ones_like(pred_label) * -1) pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1) return pred_result def get_origin_shape(self, ): return self.origin_shape_list @register class MaskPostProcess(object): def __init__(self, binary_thresh=0.5): super(MaskPostProcess, self).__init__() self.binary_thresh = binary_thresh def paste_mask(self, masks, boxes, im_h, im_w): """ Paste the mask prediction to the original image. """ x0, y0, x1, y1 = paddle.split(boxes, 4, axis=1) masks = paddle.unsqueeze(masks, [0, 1]) img_y = paddle.arange(0, im_h, dtype='float32') + 0.5 img_x = paddle.arange(0, im_w, dtype='float32') + 0.5 img_y = (img_y - y0) / (y1 - y0) * 2 - 1 img_x = (img_x - x0) / (x1 - x0) * 2 - 1 img_x = paddle.unsqueeze(img_x, [1]) img_y = paddle.unsqueeze(img_y, [2]) N = boxes.shape[0] gx = paddle.expand(img_x, [N, img_y.shape[1], img_x.shape[2]]) gy = paddle.expand(img_y, [N, img_y.shape[1], img_x.shape[2]]) grid = paddle.stack([gx, gy], axis=3) img_masks = F.grid_sample(masks, grid, align_corners=False) return img_masks[:, 0] def __call__(self, mask_out, bboxes, bbox_num, origin_shape): """ Decode the mask_out and paste the mask to the origin image. Args: mask_out (Tensor): mask_head output with shape [N, 28, 28]. bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode and NMS, including labels, scores and bboxes. bbox_num (Tensor): The number of prediction boxes of each batch with shape [1], and is N. origin_shape (Tensor): The origin shape of the input image, the tensor shape is [N, 2], and each row is [h, w]. Returns: pred_result (Tensor): The final prediction mask results with shape [N, h, w] in binary mask style. """ num_mask = mask_out.shape[0] origin_shape = paddle.cast(origin_shape, 'int32') # TODO: support bs > 1 and mask output dtype is bool pred_result = paddle.zeros( [num_mask, origin_shape[0][0], origin_shape[0][1]], dtype='int32') if bbox_num == 1 and bboxes[0][0] == -1: return pred_result # TODO: optimize chunk paste pred_result = [] for i in range(bboxes.shape[0]): im_h, im_w = origin_shape[i][0], origin_shape[i][1] pred_mask = self.paste_mask(mask_out[i], bboxes[i:i + 1, 2:], im_h, im_w) pred_mask = pred_mask >= self.binary_thresh pred_mask = paddle.cast(pred_mask, 'int32') pred_result.append(pred_mask) pred_result = paddle.concat(pred_result) return pred_result @register class FCOSPostProcess(object): __inject__ = ['decode', 'nms'] def __init__(self, decode=None, nms=None): super(FCOSPostProcess, self).__init__() self.decode = decode self.nms = nms def __call__(self, fcos_head_outs, scale_factor): """ Decode the bbox and do NMS in FCOS. """ locations, cls_logits, bboxes_reg, centerness = fcos_head_outs bboxes, score = self.decode(locations, cls_logits, bboxes_reg, centerness, scale_factor) bbox_pred, bbox_num, _ = self.nms(bboxes, score) return bbox_pred, bbox_num @register class S2ANetBBoxPostProcess(nn.Layer): __shared__ = ['num_classes'] __inject__ = ['nms'] def __init__(self, num_classes=15, nms_pre=2000, min_bbox_size=0, nms=None): super(S2ANetBBoxPostProcess, self).__init__() self.num_classes = num_classes self.nms_pre = nms_pre self.min_bbox_size = min_bbox_size self.nms = nms self.origin_shape_list = [] self.fake_pred_cls_score_bbox = paddle.to_tensor( np.array( [[-1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype='float32')) self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32')) def forward(self, pred_scores, pred_bboxes): """ pred_scores : [N, M] score pred_bboxes : [N, 5] xc, yc, w, h, a im_shape : [N, 2] im_shape scale_factor : [N, 2] scale_factor """ pred_ploys0 = rbox2poly(pred_bboxes) pred_ploys = paddle.unsqueeze(pred_ploys0, axis=0) # pred_scores [NA, 16] --> [16, NA] pred_scores0 = paddle.transpose(pred_scores, [1, 0]) pred_scores = paddle.unsqueeze(pred_scores0, axis=0) pred_cls_score_bbox, bbox_num, _ = self.nms(pred_ploys, pred_scores, self.num_classes) # Prevent empty bbox_pred from decode or NMS. # Bboxes and score before NMS may be empty due to the score threshold. if pred_cls_score_bbox.shape[0] <= 0 or pred_cls_score_bbox.shape[ 1] <= 1: pred_cls_score_bbox = self.fake_pred_cls_score_bbox bbox_num = self.fake_bbox_num pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [-1, 10]) return pred_cls_score_bbox, bbox_num def get_pred(self, bboxes, bbox_num, im_shape, scale_factor): """ Rescale, clip and filter the bbox from the output of NMS to get final prediction. Args: bboxes(Tensor): bboxes [N, 10] bbox_num(Tensor): bbox_num im_shape(Tensor): [1 2] scale_factor(Tensor): [1 2] Returns: bbox_pred(Tensor): The output is the prediction with shape [N, 8] including labels, scores and bboxes. The size of bboxes are corresponding to the original image. """ origin_shape = paddle.floor(im_shape / scale_factor + 0.5) origin_shape_list = [] scale_factor_list = [] # scale_factor: scale_y, scale_x for i in range(bbox_num.shape[0]): expand_shape = paddle.expand(origin_shape[i:i + 1, :], [bbox_num[i], 2]) scale_y, scale_x = scale_factor[i][0], scale_factor[i][1] scale = paddle.concat([ scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x, scale_y ]) expand_scale = paddle.expand(scale, [bbox_num[i], 8]) origin_shape_list.append(expand_shape) scale_factor_list.append(expand_scale) origin_shape_list = paddle.concat(origin_shape_list) scale_factor_list = paddle.concat(scale_factor_list) # bboxes: [N, 10], label, score, bbox pred_label_score = bboxes[:, 0:2] pred_bbox = bboxes[:, 2:] # rescale bbox to original image pred_bbox = pred_bbox.reshape([-1, 8]) scaled_bbox = pred_bbox / scale_factor_list origin_h = origin_shape_list[:, 0] origin_w = origin_shape_list[:, 1] bboxes = scaled_bbox zeros = paddle.zeros_like(origin_h) x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros) y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros) x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros) y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros) x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros) y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros) x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros) y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros) pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1) 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]