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110 lines
4.5 KiB
110 lines
4.5 KiB
# 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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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 paddlers.models.ppdet.core.workspace import register, create, load_config |
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from paddlers.models.ppdet.modeling import ops |
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from paddlers.models.ppdet.utils.checkpoint import load_pretrain_weight |
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from paddlers.models.ppdet.utils.logger import setup_logger |
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logger = setup_logger(__name__) |
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class DistillModel(nn.Layer): |
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def __init__(self, cfg, slim_cfg): |
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super(DistillModel, self).__init__() |
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self.student_model = create(cfg.architecture) |
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logger.debug('Load student model pretrain_weights:{}'.format( |
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cfg.pretrain_weights)) |
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load_pretrain_weight(self.student_model, cfg.pretrain_weights) |
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slim_cfg = load_config(slim_cfg) |
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self.teacher_model = create(slim_cfg.architecture) |
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self.distill_loss = create(slim_cfg.distill_loss) |
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logger.debug('Load teacher model pretrain_weights:{}'.format( |
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slim_cfg.pretrain_weights)) |
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load_pretrain_weight(self.teacher_model, slim_cfg.pretrain_weights) |
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for param in self.teacher_model.parameters(): |
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param.trainable = False |
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def parameters(self): |
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return self.student_model.parameters() |
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def forward(self, inputs): |
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if self.training: |
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teacher_loss = self.teacher_model(inputs) |
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student_loss = self.student_model(inputs) |
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loss = self.distill_loss(self.teacher_model, self.student_model) |
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student_loss['distill_loss'] = loss |
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student_loss['teacher_loss'] = teacher_loss['loss'] |
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student_loss['loss'] += student_loss['distill_loss'] |
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return student_loss |
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else: |
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return self.student_model(inputs) |
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@register |
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class DistillYOLOv3Loss(nn.Layer): |
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def __init__(self, weight=1000): |
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super(DistillYOLOv3Loss, self).__init__() |
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self.weight = weight |
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def obj_weighted_reg(self, sx, sy, sw, sh, tx, ty, tw, th, tobj): |
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loss_x = ops.sigmoid_cross_entropy_with_logits(sx, F.sigmoid(tx)) |
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loss_y = ops.sigmoid_cross_entropy_with_logits(sy, F.sigmoid(ty)) |
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loss_w = paddle.abs(sw - tw) |
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loss_h = paddle.abs(sh - th) |
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loss = paddle.add_n([loss_x, loss_y, loss_w, loss_h]) |
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weighted_loss = paddle.mean(loss * F.sigmoid(tobj)) |
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return weighted_loss |
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def obj_weighted_cls(self, scls, tcls, tobj): |
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loss = ops.sigmoid_cross_entropy_with_logits(scls, F.sigmoid(tcls)) |
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weighted_loss = paddle.mean(paddle.multiply(loss, F.sigmoid(tobj))) |
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return weighted_loss |
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def obj_loss(self, sobj, tobj): |
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obj_mask = paddle.cast(tobj > 0., dtype="float32") |
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obj_mask.stop_gradient = True |
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loss = paddle.mean( |
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ops.sigmoid_cross_entropy_with_logits(sobj, obj_mask)) |
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return loss |
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def forward(self, teacher_model, student_model): |
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teacher_distill_pairs = teacher_model.yolo_head.loss.distill_pairs |
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student_distill_pairs = student_model.yolo_head.loss.distill_pairs |
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distill_reg_loss, distill_cls_loss, distill_obj_loss = [], [], [] |
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for s_pair, t_pair in zip(student_distill_pairs, |
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teacher_distill_pairs): |
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distill_reg_loss.append( |
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self.obj_weighted_reg(s_pair[0], s_pair[1], s_pair[2], s_pair[ |
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3], t_pair[0], t_pair[1], t_pair[2], t_pair[3], t_pair[4])) |
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distill_cls_loss.append( |
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self.obj_weighted_cls(s_pair[5], t_pair[5], t_pair[4])) |
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distill_obj_loss.append(self.obj_loss(s_pair[4], t_pair[4])) |
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distill_reg_loss = paddle.add_n(distill_reg_loss) |
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distill_cls_loss = paddle.add_n(distill_cls_loss) |
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distill_obj_loss = paddle.add_n(distill_obj_loss) |
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loss = (distill_reg_loss + distill_cls_loss + distill_obj_loss |
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) * self.weight |
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return loss
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