distill_model.py

# Copyright (c) 2021 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import paddle
import paddle.nn as nn

from ppdet.core.workspace import register, create, load_config
from ppdet.utils.checkpoint import load_pretrain_weight
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)

__all__ = [
    'DistillModel',
    'FGDDistillModel',
    'CWDDistillModel',
    'LDDistillModel',
    'PPYOLOEDistillModel',
]


@register
class DistillModel(nn.Layer):
    """
    Build common distill model.
    Args:
        cfg: The student config.
        slim_cfg: The teacher and distill config.
    """

    def __init__(self, cfg, slim_cfg):
        super(DistillModel, self).__init__()
        self.arch = cfg.architecture

        self.stu_cfg = cfg
        self.student_model = create(self.stu_cfg.architecture)
        if 'pretrain_weights' in self.stu_cfg and self.stu_cfg.pretrain_weights:
            stu_pretrain = self.stu_cfg.pretrain_weights
        else:
            stu_pretrain = None

        slim_cfg = load_config(slim_cfg)
        self.tea_cfg = slim_cfg
        self.teacher_model = create(self.tea_cfg.architecture)
        if 'pretrain_weights' in self.tea_cfg and self.tea_cfg.pretrain_weights:
            tea_pretrain = self.tea_cfg.pretrain_weights
        else:
            tea_pretrain = None
        self.distill_cfg = slim_cfg

        # load pretrain weights
        self.is_inherit = False
        if stu_pretrain:
            if self.is_inherit and tea_pretrain:
                load_pretrain_weight(self.student_model, tea_pretrain)
                logger.debug(
                    "Inheriting! loading teacher weights to student model!")
            load_pretrain_weight(self.student_model, stu_pretrain)
            logger.info("Student model has loaded pretrain weights!")
        if tea_pretrain:
            load_pretrain_weight(self.teacher_model, tea_pretrain)
            logger.info("Teacher model has loaded pretrain weights!")

        self.teacher_model.eval()
        for param in self.teacher_model.parameters():
            param.trainable = False

        self.distill_loss = self.build_loss(self.distill_cfg)

    def build_loss(self, distill_cfg):
        if 'distill_loss' in distill_cfg and distill_cfg.distill_loss:
            return create(distill_cfg.distill_loss)
        else:
            return None

    def parameters(self):
        return self.student_model.parameters()

    def forward(self, inputs):
        if self.training:
            student_loss = self.student_model(inputs)
            with paddle.no_grad():
                teacher_loss = self.teacher_model(inputs)

            loss = self.distill_loss(self.teacher_model, self.student_model)
            student_loss['distill_loss'] = loss
            student_loss['teacher_loss'] = teacher_loss['loss']
            student_loss['loss'] += student_loss['distill_loss']
            return student_loss
        else:
            return self.student_model(inputs)


@register
class FGDDistillModel(DistillModel):
    """
    Build FGD distill model.
    Args:
        cfg: The student config.
        slim_cfg: The teacher and distill config.
    """

    def __init__(self, cfg, slim_cfg):
        super(FGDDistillModel, self).__init__(cfg=cfg, slim_cfg=slim_cfg)
        assert self.arch in ['RetinaNet', 'PicoDet'
                             ], 'Unsupported arch: {}'.format(self.arch)
        self.is_inherit = True

    def build_loss(self, distill_cfg):
        assert 'distill_loss_name' in distill_cfg and distill_cfg.distill_loss_name
        assert 'distill_loss' in distill_cfg and distill_cfg.distill_loss
        loss_func = dict()
        name_list = distill_cfg.distill_loss_name
        for name in name_list:
            loss_func[name] = create(distill_cfg.distill_loss)
        return loss_func

    def forward(self, inputs):
        if self.training:
            s_body_feats = self.student_model.backbone(inputs)
            s_neck_feats = self.student_model.neck(s_body_feats)
            with paddle.no_grad():
                t_body_feats = self.teacher_model.backbone(inputs)
                t_neck_feats = self.teacher_model.neck(t_body_feats)

            loss_dict = {}
            for idx, k in enumerate(self.distill_loss):
                loss_dict[k] = self.distill_loss[k](s_neck_feats[idx],
                                                    t_neck_feats[idx], inputs)
            if self.arch == "RetinaNet":
                loss = self.student_model.head(s_neck_feats, inputs)
            elif self.arch == "PicoDet":
                head_outs = self.student_model.head(
                    s_neck_feats, self.student_model.export_post_process)
                loss_gfl = self.student_model.head.get_loss(head_outs, inputs)
                total_loss = paddle.add_n(list(loss_gfl.values()))
                loss = {}
                loss.update(loss_gfl)
                loss.update({'loss': total_loss})
            else:
                raise ValueError(f"Unsupported model {self.arch}")

            for k in loss_dict:
                loss['loss'] += loss_dict[k]
                loss[k] = loss_dict[k]
            return loss
        else:
            body_feats = self.student_model.backbone(inputs)
            neck_feats = self.student_model.neck(body_feats)
            head_outs = self.student_model.head(neck_feats)
            if self.arch == "RetinaNet":
                bbox, bbox_num = self.student_model.head.post_process(
                    head_outs, inputs['im_shape'], inputs['scale_factor'])
                return {'bbox': bbox, 'bbox_num': bbox_num}
            elif self.arch == "PicoDet":
                head_outs = self.student_model.head(
                    neck_feats, self.student_model.export_post_process)
                scale_factor = inputs['scale_factor']
                bboxes, bbox_num = self.student_model.head.post_process(
                    head_outs,
                    scale_factor,
                    export_nms=self.student_model.export_nms)
                return {'bbox': bboxes, 'bbox_num': bbox_num}
            else:
                raise ValueError(f"Unsupported model {self.arch}")


@register
class CWDDistillModel(DistillModel):
    """                                                                                                                                                    
    Build CWD distill model.                                                                                                                               
    Args:                                                                                                                                                  
        cfg: The student config.                                                                                                                           
        slim_cfg: The teacher and distill config.                                                                                                          
    """

    def __init__(self, cfg, slim_cfg):
        super(CWDDistillModel, self).__init__(cfg=cfg, slim_cfg=slim_cfg)
        assert self.arch in ['GFL', 'RetinaNet'], 'Unsupported arch: {}'.format(
            self.arch)

    def build_loss(self, distill_cfg):
        assert 'distill_loss_name' in distill_cfg and distill_cfg.distill_loss_name
        assert 'distill_loss' in distill_cfg and distill_cfg.distill_loss
        loss_func = dict()
        name_list = distill_cfg.distill_loss_name
        for name in name_list:
            loss_func[name] = create(distill_cfg.distill_loss)
        return loss_func

    def get_loss_retinanet(self, stu_fea_list, tea_fea_list, inputs):
        loss = self.student_model.head(stu_fea_list, inputs)
        loss_dict = {}
        for idx, k in enumerate(self.distill_loss):
            loss_dict[k] = self.distill_loss[k](stu_fea_list[idx],
                                                tea_fea_list[idx])

            loss['loss'] += loss_dict[k]
            loss[k] = loss_dict[k]
        return loss

    def get_loss_gfl(self, stu_fea_list, tea_fea_list, inputs):
        loss = {}
        head_outs = self.student_model.head(stu_fea_list)
        loss_gfl = self.student_model.head.get_loss(head_outs, inputs)
        loss.update(loss_gfl)
        total_loss = paddle.add_n(list(loss.values()))
        loss.update({'loss': total_loss})

        feat_loss = {}
        loss_dict = {}
        s_cls_feat, t_cls_feat = [], []
        for s_neck_f, t_neck_f in zip(stu_fea_list, tea_fea_list):
            conv_cls_feat, _ = self.student_model.head.conv_feat(s_neck_f)
            cls_score = self.student_model.head.gfl_head_cls(conv_cls_feat)
            t_conv_cls_feat, _ = self.teacher_model.head.conv_feat(t_neck_f)
            t_cls_score = self.teacher_model.head.gfl_head_cls(t_conv_cls_feat)
            s_cls_feat.append(cls_score)
            t_cls_feat.append(t_cls_score)

        for idx, k in enumerate(self.distill_loss):
            loss_dict[k] = self.distill_loss[k](s_cls_feat[idx],
                                                t_cls_feat[idx])
            feat_loss[f"neck_f_{idx}"] = self.distill_loss[k](stu_fea_list[idx],
                                                              tea_fea_list[idx])

        for k in feat_loss:
            loss['loss'] += feat_loss[k]
            loss[k] = feat_loss[k]

        for k in loss_dict:
            loss['loss'] += loss_dict[k]
            loss[k] = loss_dict[k]
        return loss

    def forward(self, inputs):
        if self.training:
            s_body_feats = self.student_model.backbone(inputs)
            s_neck_feats = self.student_model.neck(s_body_feats)
            with paddle.no_grad():
                t_body_feats = self.teacher_model.backbone(inputs)
                t_neck_feats = self.teacher_model.neck(t_body_feats)

            if self.arch == "RetinaNet":
                loss = self.get_loss_retinanet(s_neck_feats, t_neck_feats,
                                               inputs)
            elif self.arch == "GFL":
                loss = self.get_loss_gfl(s_neck_feats, t_neck_feats, inputs)
            else:
                raise ValueError(f"unsupported arch {self.arch}")
            return loss
        else:
            body_feats = self.student_model.backbone(inputs)
            neck_feats = self.student_model.neck(body_feats)
            head_outs = self.student_model.head(neck_feats)
            if self.arch == "RetinaNet":
                bbox, bbox_num = self.student_model.head.post_process(
                    head_outs, inputs['im_shape'], inputs['scale_factor'])
                return {'bbox': bbox, 'bbox_num': bbox_num}
            elif self.arch == "GFL":
                bbox_pred, bbox_num = head_outs
                output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
                return output
            else:
                raise ValueError(f"unsupported arch {self.arch}")


@register
class LDDistillModel(DistillModel):
    """
    Build LD distill model.
    Args:
        cfg: The student config.
        slim_cfg: The teacher and distill config.
    """

    def __init__(self, cfg, slim_cfg):
        super(LDDistillModel, self).__init__(cfg=cfg, slim_cfg=slim_cfg)
        assert self.arch in ['GFL'], 'Unsupported arch: {}'.format(self.arch)

    def forward(self, inputs):
        if self.training:
            s_body_feats = self.student_model.backbone(inputs)
            s_neck_feats = self.student_model.neck(s_body_feats)
            s_head_outs = self.student_model.head(s_neck_feats)
            with paddle.no_grad():
                t_body_feats = self.teacher_model.backbone(inputs)
                t_neck_feats = self.teacher_model.neck(t_body_feats)
                t_head_outs = self.teacher_model.head(t_neck_feats)

            soft_label_list = t_head_outs[0]
            soft_targets_list = t_head_outs[1]
            student_loss = self.student_model.head.get_loss(
                s_head_outs, inputs, soft_label_list, soft_targets_list)
            total_loss = paddle.add_n(list(student_loss.values()))
            student_loss['loss'] = total_loss
            return student_loss
        else:
            return self.student_model(inputs)


@register
class PPYOLOEDistillModel(DistillModel):
    """
    Build PPYOLOE distill model, only used in PPYOLOE
    Args:
        cfg: The student config.
        slim_cfg: The teacher and distill config.
    """

    def __init__(self, cfg, slim_cfg):
        super(PPYOLOEDistillModel, self).__init__(cfg=cfg, slim_cfg=slim_cfg)
        assert self.arch in ['PPYOLOE'], 'Unsupported arch: {}'.format(
            self.arch)

    def forward(self, inputs, alpha=0.125):
        if self.training:
            with paddle.no_grad():
                teacher_loss = self.teacher_model(inputs)
            if hasattr(self.teacher_model.yolo_head, "assigned_labels"):
                self.student_model.yolo_head.assigned_labels, self.student_model.yolo_head.assigned_bboxes, self.student_model.yolo_head.assigned_scores, self.student_model.yolo_head.mask_positive = \
                    self.teacher_model.yolo_head.assigned_labels, self.teacher_model.yolo_head.assigned_bboxes, self.teacher_model.yolo_head.assigned_scores, self.teacher_model.yolo_head.mask_positive
                delattr(self.teacher_model.yolo_head, "assigned_labels")
                delattr(self.teacher_model.yolo_head, "assigned_bboxes")
                delattr(self.teacher_model.yolo_head, "assigned_scores")
                delattr(self.teacher_model.yolo_head, "mask_positive")
            student_loss = self.student_model(inputs)

            logits_loss, feat_loss = self.distill_loss(self.teacher_model,
                                                       self.student_model)
            det_total_loss = student_loss['loss']
            total_loss = alpha * (det_total_loss + logits_loss + feat_loss)
            student_loss['loss'] = total_loss
            student_loss['det_loss'] = det_total_loss
            student_loss['logits_loss'] = logits_loss
            student_loss['feat_loss'] = feat_loss
            return student_loss
        else:
            return self.student_model(inputs)