更换文档检测模型
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paddle_detection/ppdet/modeling/heads/centertrack_head.py
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paddle_detection/ppdet/modeling/heads/centertrack_head.py
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# Copyright (c) 2022 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 .centernet_head import ConvLayer
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from ..keypoint_utils import get_affine_transform
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__all__ = ['CenterTrackHead']
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@register
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class CenterTrackHead(nn.Layer):
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"""
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Args:
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in_channels (int): the channel number of input to CenterNetHead.
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num_classes (int): the number of classes, 1 (MOT17 dataset) by default.
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head_planes (int): the channel number in all head, 256 by default.
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task (str): the type of task for regression, 'tracking' by default.
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loss_weight (dict): the weight of each loss.
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add_ltrb_amodal (bool): whether to add ltrb_amodal branch, False by default.
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"""
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__shared__ = ['num_classes']
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def __init__(self,
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in_channels,
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num_classes=1,
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head_planes=256,
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task='tracking',
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loss_weight={
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'tracking': 1.0,
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'ltrb_amodal': 0.1,
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},
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add_ltrb_amodal=True):
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super(CenterTrackHead, self).__init__()
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self.task = task
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self.loss_weight = loss_weight
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self.add_ltrb_amodal = add_ltrb_amodal
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# tracking head
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self.tracking = nn.Sequential(
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ConvLayer(
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in_channels, head_planes, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes, 2, kernel_size=1, stride=1, padding=0, bias=True))
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# ltrb_amodal head
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if self.add_ltrb_amodal and 'ltrb_amodal' in self.loss_weight:
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self.ltrb_amodal = nn.Sequential(
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ConvLayer(
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in_channels,
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head_planes,
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kernel_size=3,
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padding=1,
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bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes,
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4,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True))
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# TODO: add more tasks
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@classmethod
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def from_config(cls, cfg, input_shape):
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if isinstance(input_shape, (list, tuple)):
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input_shape = input_shape[0]
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return {'in_channels': input_shape.channels}
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def forward(self,
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feat,
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inputs,
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bboxes=None,
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bbox_inds=None,
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topk_clses=None,
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topk_ys=None,
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topk_xs=None):
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tracking = self.tracking(feat)
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head_outs = {'tracking': tracking}
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if self.add_ltrb_amodal and 'ltrb_amodal' in self.loss_weight:
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ltrb_amodal = self.ltrb_amodal(feat)
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head_outs.update({'ltrb_amodal': ltrb_amodal})
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if self.training:
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losses = self.get_loss(inputs, self.loss_weight, head_outs)
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return losses
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else:
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ret = self.generic_decode(head_outs, bboxes, bbox_inds, topk_ys,
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topk_xs)
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return ret
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def get_loss(self, inputs, weights, head_outs):
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index = inputs['index'].unsqueeze(2)
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mask = inputs['index_mask'].unsqueeze(2)
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batch_inds = list()
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for i in range(head_outs['tracking'].shape[0]):
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batch_ind = paddle.full(
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shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
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batch_inds.append(batch_ind)
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batch_inds = paddle.concat(batch_inds, axis=0)
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index = paddle.concat(x=[batch_inds, index], axis=2)
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# 1.tracking head loss: L1 loss
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tracking = head_outs['tracking'].transpose([0, 2, 3, 1])
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tracking_target = inputs['tracking']
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bs, _, _, c = tracking.shape
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tracking = tracking.reshape([bs, -1, c])
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pos_tracking = paddle.gather_nd(tracking, index=index)
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tracking_mask = paddle.cast(
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paddle.expand_as(mask, pos_tracking), dtype=pos_tracking.dtype)
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pos_num = tracking_mask.sum()
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tracking_mask.stop_gradient = True
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tracking_target.stop_gradient = True
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tracking_loss = F.l1_loss(
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pos_tracking * tracking_mask,
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tracking_target * tracking_mask,
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reduction='sum')
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tracking_loss = tracking_loss / (pos_num + 1e-4)
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# 2.ltrb_amodal head loss(optinal): L1 loss
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if self.add_ltrb_amodal and 'ltrb_amodal' in self.loss_weight:
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ltrb_amodal = head_outs['ltrb_amodal'].transpose([0, 2, 3, 1])
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ltrb_amodal_target = inputs['ltrb_amodal']
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bs, _, _, c = ltrb_amodal.shape
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ltrb_amodal = ltrb_amodal.reshape([bs, -1, c])
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pos_ltrb_amodal = paddle.gather_nd(ltrb_amodal, index=index)
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ltrb_amodal_mask = paddle.cast(
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paddle.expand_as(mask, pos_ltrb_amodal),
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dtype=pos_ltrb_amodal.dtype)
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pos_num = ltrb_amodal_mask.sum()
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ltrb_amodal_mask.stop_gradient = True
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ltrb_amodal_target.stop_gradient = True
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ltrb_amodal_loss = F.l1_loss(
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pos_ltrb_amodal * ltrb_amodal_mask,
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ltrb_amodal_target * ltrb_amodal_mask,
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reduction='sum')
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ltrb_amodal_loss = ltrb_amodal_loss / (pos_num + 1e-4)
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losses = {'tracking_loss': tracking_loss, }
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plugin_loss = weights['tracking'] * tracking_loss
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if self.add_ltrb_amodal and 'ltrb_amodal' in self.loss_weight:
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losses.update({'ltrb_amodal_loss': ltrb_amodal_loss})
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plugin_loss += weights['ltrb_amodal'] * ltrb_amodal_loss
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losses.update({'plugin_loss': plugin_loss})
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return losses
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def generic_decode(self, head_outs, bboxes, bbox_inds, topk_ys, topk_xs):
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topk_ys = paddle.floor(topk_ys) # note: More accurate
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topk_xs = paddle.floor(topk_xs)
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cts = paddle.concat([topk_xs, topk_ys], 1)
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ret = {'bboxes': bboxes, 'cts': cts}
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regression_heads = ['tracking'] # todo: add more tasks
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for head in regression_heads:
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if head in head_outs:
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ret[head] = _tranpose_and_gather_feat(head_outs[head],
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bbox_inds)
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if 'ltrb_amodal' in head_outs:
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ltrb_amodal = head_outs['ltrb_amodal']
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ltrb_amodal = _tranpose_and_gather_feat(ltrb_amodal, bbox_inds)
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bboxes_amodal = paddle.concat(
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[
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topk_xs * 1.0 + ltrb_amodal[..., 0:1],
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topk_ys * 1.0 + ltrb_amodal[..., 1:2],
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topk_xs * 1.0 + ltrb_amodal[..., 2:3],
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topk_ys * 1.0 + ltrb_amodal[..., 3:4]
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],
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axis=1)
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ret['bboxes'] = paddle.concat([bboxes[:, 0:2], bboxes_amodal], 1)
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# cls_id, score, x0, y0, x1, y1
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return ret
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def centertrack_post_process(self, dets, meta, out_thresh):
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if not ('bboxes' in dets):
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return [{}]
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preds = []
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c, s = meta['center'].numpy(), meta['scale'].numpy()
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h, w = meta['out_height'].numpy(), meta['out_width'].numpy()
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trans = get_affine_transform(
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center=c[0],
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input_size=s[0],
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rot=0,
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output_size=[w[0], h[0]],
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shift=(0., 0.),
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inv=True).astype(np.float32)
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for i, dets_bbox in enumerate(dets['bboxes']):
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if dets_bbox[1] < out_thresh:
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break
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item = {}
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item['score'] = dets_bbox[1]
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item['class'] = int(dets_bbox[0]) + 1
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item['ct'] = transform_preds_with_trans(
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dets['cts'][i].reshape([1, 2]), trans).reshape(2)
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if 'tracking' in dets:
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tracking = transform_preds_with_trans(
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(dets['tracking'][i] + dets['cts'][i]).reshape([1, 2]),
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trans).reshape(2)
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item['tracking'] = tracking - item['ct']
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if 'bboxes' in dets:
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bbox = transform_preds_with_trans(
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dets_bbox[2:6].reshape([2, 2]), trans).reshape(4)
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item['bbox'] = bbox
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preds.append(item)
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return preds
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def transform_preds_with_trans(coords, trans):
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target_coords = np.ones((coords.shape[0], 3), np.float32)
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target_coords[:, :2] = coords
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target_coords = np.dot(trans, target_coords.transpose()).transpose()
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return target_coords[:, :2]
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def _tranpose_and_gather_feat(feat, bbox_inds):
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feat = feat.transpose([0, 2, 3, 1])
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feat = feat.reshape([-1, feat.shape[3]])
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feat = paddle.gather(feat, bbox_inds)
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return feat
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