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rtdetr_paddle/ppdet/modeling/post_process.py
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rtdetr_paddle/ppdet/modeling/post_process.py
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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.functional as F
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from ppdet.core.workspace import register
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from .transformers import bbox_cxcywh_to_xyxy
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__all__ = [
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'DETRPostProcess',
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]
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@register
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class DETRPostProcess(object):
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__shared__ = ['num_classes', 'use_focal_loss', 'with_mask']
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__inject__ = []
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def __init__(self,
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num_classes=80,
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num_top_queries=100,
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dual_queries=False,
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dual_groups=0,
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use_focal_loss=False,
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with_mask=False,
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mask_threshold=0.5,
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use_avg_mask_score=False,
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bbox_decode_type='origin'):
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super(DETRPostProcess, self).__init__()
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assert bbox_decode_type in ['origin', 'pad']
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self.num_classes = num_classes
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self.num_top_queries = num_top_queries
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self.dual_queries = dual_queries
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self.dual_groups = dual_groups
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self.use_focal_loss = use_focal_loss
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self.with_mask = with_mask
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self.mask_threshold = mask_threshold
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self.use_avg_mask_score = use_avg_mask_score
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self.bbox_decode_type = bbox_decode_type
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def _mask_postprocess(self, mask_pred, score_pred, index):
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mask_score = F.sigmoid(paddle.gather_nd(mask_pred, index))
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mask_pred = (mask_score > self.mask_threshold).astype(mask_score.dtype)
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if self.use_avg_mask_score:
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avg_mask_score = (mask_pred * mask_score).sum([-2, -1]) / (
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mask_pred.sum([-2, -1]) + 1e-6)
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score_pred *= avg_mask_score
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return mask_pred[0].astype('int32'), score_pred
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def __call__(self, head_out, im_shape, scale_factor, pad_shape):
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"""
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Decode the bbox and mask.
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Args:
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head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
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im_shape (Tensor): The shape of the input image without padding.
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scale_factor (Tensor): The scale factor of the input image.
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pad_shape (Tensor): The shape of the input image with padding.
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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 [bs], and is N.
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"""
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bboxes, logits, masks = head_out
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if self.dual_queries:
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num_queries = logits.shape[1]
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logits, bboxes = logits[:, :int(num_queries // (self.dual_groups + 1)), :], \
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bboxes[:, :int(num_queries // (self.dual_groups + 1)), :]
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bbox_pred = bbox_cxcywh_to_xyxy(bboxes)
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# calculate the original shape of the image
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origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
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img_h, img_w = paddle.split(origin_shape, 2, axis=-1)
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if self.bbox_decode_type == 'pad':
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# calculate the shape of the image with padding
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out_shape = pad_shape / im_shape * origin_shape
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out_shape = out_shape.flip(1).tile([1, 2]).unsqueeze(1)
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elif self.bbox_decode_type == 'origin':
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out_shape = origin_shape.flip(1).tile([1, 2]).unsqueeze(1)
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else:
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raise Exception(
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f'Wrong `bbox_decode_type`: {self.bbox_decode_type}.')
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bbox_pred *= out_shape
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scores = F.sigmoid(logits) if self.use_focal_loss else F.softmax(
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logits)[:, :, :-1]
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if not self.use_focal_loss:
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scores, labels = scores.max(-1), scores.argmax(-1)
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if scores.shape[1] > self.num_top_queries:
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scores, index = paddle.topk(
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scores, self.num_top_queries, axis=-1)
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batch_ind = paddle.arange(
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end=scores.shape[0]).unsqueeze(-1).tile(
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[1, self.num_top_queries])
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index = paddle.stack([batch_ind, index], axis=-1)
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labels = paddle.gather_nd(labels, index)
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bbox_pred = paddle.gather_nd(bbox_pred, index)
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else:
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scores, index = paddle.topk(
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scores.flatten(1), self.num_top_queries, axis=-1)
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labels = index % self.num_classes
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index = index // self.num_classes
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batch_ind = paddle.arange(end=scores.shape[0]).unsqueeze(-1).tile(
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[1, self.num_top_queries])
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index = paddle.stack([batch_ind, index], axis=-1)
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bbox_pred = paddle.gather_nd(bbox_pred, index)
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mask_pred = None
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if self.with_mask:
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assert masks is not None
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masks = F.interpolate(
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masks, scale_factor=4, mode="bilinear", align_corners=False)
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# TODO: Support prediction with bs>1.
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# remove padding for input image
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h, w = im_shape.astype('int32')[0]
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masks = masks[..., :h, :w]
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# get pred_mask in the original resolution.
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img_h = img_h[0].astype('int32')
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img_w = img_w[0].astype('int32')
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masks = F.interpolate(
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masks,
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size=(img_h, img_w),
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mode="bilinear",
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align_corners=False)
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mask_pred, scores = self._mask_postprocess(masks, scores, index)
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bbox_pred = paddle.concat(
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[
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labels.unsqueeze(-1).astype('float32'), scores.unsqueeze(-1),
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bbox_pred
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],
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axis=-1)
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bbox_num = paddle.to_tensor(
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self.num_top_queries, dtype='int32').tile([bbox_pred.shape[0]])
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bbox_pred = bbox_pred.reshape([-1, 6])
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return bbox_pred, bbox_num, mask_pred
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def paste_mask(masks, boxes, im_h, im_w, assign_on_cpu=False):
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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_int, y0_int = 0, 0
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x1_int, y1_int = im_w, im_h
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x0, y0, x1, y1 = paddle.split(boxes, 4, axis=1)
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N = masks.shape[0]
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img_y = paddle.arange(y0_int, y1_int) + 0.5
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img_x = paddle.arange(x0_int, x1_int) + 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, img_y have shapes (N, w), (N, h)
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if assign_on_cpu:
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paddle.set_device('cpu')
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gx = img_x[:, None, :].expand(
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[N, paddle.shape(img_y)[1], paddle.shape(img_x)[1]])
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gy = img_y[:, :, None].expand(
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[N, paddle.shape(img_y)[1], paddle.shape(img_x)[1]])
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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 multiclass_nms(bboxs, num_classes, match_threshold=0.6, match_metric='iou'):
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final_boxes = []
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for c in range(num_classes):
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idxs = bboxs[:, 0] == c
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if np.count_nonzero(idxs) == 0: continue
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r = nms(bboxs[idxs, 1:], match_threshold, match_metric)
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final_boxes.append(np.concatenate([np.full((r.shape[0], 1), c), r], 1))
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return final_boxes
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def nms(dets, match_threshold=0.6, match_metric='iou'):
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""" Apply NMS to avoid detecting too many overlapping bounding boxes.
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Args:
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dets: shape [N, 5], [score, x1, y1, x2, y2]
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match_metric: 'iou' or 'ios'
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match_threshold: overlap thresh for match metric.
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"""
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if dets.shape[0] == 0:
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return dets[[], :]
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scores = dets[:, 0]
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x1 = dets[:, 1]
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y1 = dets[:, 2]
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x2 = dets[:, 3]
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y2 = dets[:, 4]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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ndets = dets.shape[0]
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suppressed = np.zeros((ndets), dtype=np.int32)
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for _i in range(ndets):
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i = order[_i]
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if suppressed[i] == 1:
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continue
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ix1 = x1[i]
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iy1 = y1[i]
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ix2 = x2[i]
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iy2 = y2[i]
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iarea = areas[i]
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for _j in range(_i + 1, ndets):
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j = order[_j]
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if suppressed[j] == 1:
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continue
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xx1 = max(ix1, x1[j])
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yy1 = max(iy1, y1[j])
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xx2 = min(ix2, x2[j])
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yy2 = min(iy2, y2[j])
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w = max(0.0, xx2 - xx1 + 1)
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h = max(0.0, yy2 - yy1 + 1)
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inter = w * h
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if match_metric == 'iou':
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union = iarea + areas[j] - inter
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match_value = inter / union
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elif match_metric == 'ios':
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smaller = min(iarea, areas[j])
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match_value = inter / smaller
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else:
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raise ValueError()
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if match_value >= match_threshold:
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suppressed[j] = 1
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keep = np.where(suppressed == 0)[0]
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dets = dets[keep, :]
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return dets
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