Source code for mmdet.models.detectors.kd_one_stage

import mmcv
import torch
from mmcv.runner import load_checkpoint

from .. import build_detector
from ..builder import DETECTORS
from .single_stage import SingleStageDetector

[docs]@DETECTORS.register_module() class KnowledgeDistillationSingleStageDetector(SingleStageDetector): r"""Implementation of `Distilling the Knowledge in a Neural Network. <>`_. Args: teacher_config (str | dict): Config file path or the config object of teacher model. teacher_ckpt (str, optional): Checkpoint path of teacher model. If left as None, the model will not load any weights. """ def __init__(self, backbone, neck, bbox_head, teacher_config, teacher_ckpt=None, eval_teacher=True, train_cfg=None, test_cfg=None, pretrained=None): super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg, pretrained) self.eval_teacher = eval_teacher # Build teacher model if isinstance(teacher_config, str): teacher_config = mmcv.Config.fromfile(teacher_config) self.teacher_model = build_detector(teacher_config['model']) if teacher_ckpt is not None: load_checkpoint( self.teacher_model, teacher_ckpt, map_location='cpu')
[docs] def forward_train(self, img, img_metas, gt_bboxes, gt_labels, gt_bboxes_ignore=None): """ Args: img (Tensor): Input images of shape (N, C, H, W). Typically these should be mean centered and std scaled. img_metas (list[dict]): A List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see :class:`mmdet.datasets.pipelines.Collect`. gt_bboxes (list[Tensor]): Each item are the truth boxes for each image in [tl_x, tl_y, br_x, br_y] format. gt_labels (list[Tensor]): Class indices corresponding to each box gt_bboxes_ignore (None | list[Tensor]): Specify which bounding boxes can be ignored when computing the loss. Returns: dict[str, Tensor]: A dictionary of loss components. """ x = self.extract_feat(img) with torch.no_grad(): teacher_x = self.teacher_model.extract_feat(img) out_teacher = self.teacher_model.bbox_head(teacher_x) losses = self.bbox_head.forward_train(x, out_teacher, img_metas, gt_bboxes, gt_labels, gt_bboxes_ignore) return losses
[docs] def cuda(self, device=None): """Since teacher_model is registered as a plain object, it is necessary to put the teacher model to cuda when calling cuda function.""" self.teacher_model.cuda(device=device) return super().cuda(device=device)
[docs] def train(self, mode=True): """Set the same train mode for teacher and student model.""" if self.eval_teacher: self.teacher_model.train(False) else: self.teacher_model.train(mode) super().train(mode)
def __setattr__(self, name, value): """Set attribute, i.e. = value This reloading prevent the teacher model from being registered as a nn.Module. The teacher module is registered as a plain object, so that the teacher parameters will not show up when calling ``self.parameters``, ``self.modules``, ``self.children`` methods. """ if name == 'teacher_model': object.__setattr__(self, name, value) else: super().__setattr__(name, value)