forked from PulseFocusPlatform/PulseFocusPlatform
183 lines
6.9 KiB
Python
183 lines
6.9 KiB
Python
# Copyright (c) 2021 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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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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__all__ = ['JDEDetectionLoss', 'JDEEmbeddingLoss', 'JDELoss']
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@register
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class JDEDetectionLoss(nn.Layer):
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__shared__ = ['num_classes']
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def __init__(self, num_classes=1):
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super(JDEDetectionLoss, self).__init__()
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self.num_classes = num_classes
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def det_loss(self, p_det, anchor, t_conf, t_box):
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pshape = paddle.shape(p_det)
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pshape.stop_gradient = True
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nB, nGh, nGw = pshape[0], pshape[-2], pshape[-1]
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nA = len(anchor)
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p_det = paddle.reshape(
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p_det, [nB, nA, self.num_classes + 5, nGh, nGw]).transpose(
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(0, 1, 3, 4, 2))
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# 1. loss_conf: cross_entropy
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p_conf = p_det[:, :, :, :, 4:6]
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p_conf_flatten = paddle.reshape(p_conf, [-1, 2])
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t_conf_flatten = t_conf.flatten()
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t_conf_flatten = paddle.cast(t_conf_flatten, dtype="int64")
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t_conf_flatten.stop_gradient = True
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loss_conf = F.cross_entropy(
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p_conf_flatten, t_conf_flatten, ignore_index=-1, reduction='mean')
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loss_conf.stop_gradient = False
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# 2. loss_box: smooth_l1_loss
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p_box = p_det[:, :, :, :, :4]
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p_box_flatten = paddle.reshape(p_box, [-1, 4])
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t_box_flatten = paddle.reshape(t_box, [-1, 4])
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fg_inds = paddle.nonzero(t_conf_flatten > 0).flatten()
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if fg_inds.numel() > 0:
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reg_delta = paddle.gather(p_box_flatten, fg_inds)
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reg_target = paddle.gather(t_box_flatten, fg_inds)
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else:
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reg_delta = paddle.to_tensor([0, 0, 0, 0], dtype='float32')
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reg_delta.stop_gradient = False
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reg_target = paddle.to_tensor([0, 0, 0, 0], dtype='float32')
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reg_target.stop_gradient = True
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loss_box = F.smooth_l1_loss(
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reg_delta, reg_target, reduction='mean', delta=1.0)
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loss_box.stop_gradient = False
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return loss_conf, loss_box
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def forward(self, det_outs, targets, anchors):
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"""
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Args:
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det_outs (list[Tensor]): output from detection head, each one
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is a 4-D Tensor with shape [N, C, H, W].
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targets (dict): contains 'im_id', 'gt_bbox', 'gt_ide', 'image',
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'im_shape', 'scale_factor' and 'tbox', 'tconf', 'tide' of
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each FPN level.
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anchors (list[list]): anchor setting of JDE model, N row M col, N is
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the anchor levels(FPN levels), M is the anchor scales each
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level.
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"""
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assert len(det_outs) == len(anchors)
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loss_confs = []
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loss_boxes = []
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for i, (p_det, anchor) in enumerate(zip(det_outs, anchors)):
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t_conf = targets['tconf{}'.format(i)]
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t_box = targets['tbox{}'.format(i)]
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loss_conf, loss_box = self.det_loss(p_det, anchor, t_conf, t_box)
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loss_confs.append(loss_conf)
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loss_boxes.append(loss_box)
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return {'loss_confs': loss_confs, 'loss_boxes': loss_boxes}
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@register
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class JDEEmbeddingLoss(nn.Layer):
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def __init__(self, ):
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super(JDEEmbeddingLoss, self).__init__()
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self.phony = self.create_parameter(shape=[1], dtype="float32")
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def emb_loss(self, p_ide, t_conf, t_ide, emb_scale, classifier):
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emb_dim = p_ide.shape[1]
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p_ide = p_ide.transpose((0, 2, 3, 1))
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p_ide_flatten = paddle.reshape(p_ide, [-1, emb_dim])
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mask = t_conf > 0
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mask = paddle.cast(mask, dtype="int64")
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mask.stop_gradient = True
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emb_mask = mask.max(1).flatten()
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emb_mask_inds = paddle.nonzero(emb_mask > 0).flatten()
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emb_mask_inds.stop_gradient = True
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# use max(1) to decide the id, TODO: more reseanable strategy
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t_ide_flatten = t_ide.max(1).flatten()
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t_ide_flatten = paddle.cast(t_ide_flatten, dtype="int64")
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valid_inds = paddle.nonzero(t_ide_flatten != -1).flatten()
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if emb_mask_inds.numel() == 0 or valid_inds.numel() == 0:
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# loss_ide = paddle.to_tensor([0]) # will be error in gradient backward
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loss_ide = self.phony * 0 # todo
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else:
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embedding = paddle.gather(p_ide_flatten, emb_mask_inds)
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embedding = emb_scale * F.normalize(embedding)
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logits = classifier(embedding)
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ide_target = paddle.gather(t_ide_flatten, emb_mask_inds)
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loss_ide = F.cross_entropy(
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logits, ide_target, ignore_index=-1, reduction='mean')
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loss_ide.stop_gradient = False
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return loss_ide
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def forward(self, ide_outs, targets, emb_scale, classifier):
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loss_ides = []
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for i, p_ide in enumerate(ide_outs):
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t_conf = targets['tconf{}'.format(i)]
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t_ide = targets['tide{}'.format(i)]
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loss_ide = self.emb_loss(p_ide, t_conf, t_ide, emb_scale,
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classifier)
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loss_ides.append(loss_ide)
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return loss_ides
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@register
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class JDELoss(nn.Layer):
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def __init__(self):
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super(JDELoss, self).__init__()
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def forward(self, loss_confs, loss_boxes, loss_ides, loss_params_cls,
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loss_params_reg, loss_params_ide, targets):
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assert len(loss_confs) == len(loss_boxes) == len(loss_ides)
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assert len(loss_params_cls) == len(loss_params_reg) == len(
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loss_params_ide)
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assert len(loss_confs) == len(loss_params_cls)
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batchsize = targets['gt_bbox'].shape[0]
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nTargets = paddle.nonzero(paddle.sum(targets['gt_bbox'], axis=2)).shape[
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0] / batchsize
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nTargets = paddle.to_tensor(nTargets, dtype='float32')
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nTargets.stop_gradient = True
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jde_losses = []
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for i, (loss_conf, loss_box, loss_ide, l_conf_p, l_box_p,
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l_ide_p) in enumerate(
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zip(loss_confs, loss_boxes, loss_ides, loss_params_cls,
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loss_params_reg, loss_params_ide)):
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jde_loss = l_conf_p(loss_conf) + l_box_p(loss_box) + l_ide_p(
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loss_ide)
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jde_losses.append(jde_loss)
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loss_all = {
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"loss_conf": sum(loss_confs),
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"loss_box": sum(loss_boxes),
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"loss_ide": sum(loss_ides),
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"loss": sum(jde_losses),
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"nTargets": nTargets,
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}
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return loss_all
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