mmdetection/mmdet/models/detectors/semi_base.py

# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
from torch import Tensor

from mmdet.models.utils import (filter_gt_instances, rename_loss_dict,
                                reweight_loss_dict)
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.structures.bbox import bbox_project
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .base import BaseDetector


@MODELS.register_module()
class SemiBaseDetector(BaseDetector):
    """Base class for semi-supervised detectors.

    Semi-supervised detectors typically consisting of a teacher model
    updated by exponential moving average and a student model updated
    by gradient descent.

    Args:
        detector (:obj:`ConfigDict` or dict): The detector config.
        semi_train_cfg (:obj:`ConfigDict` or dict, optional):
            The semi-supervised training config.
        semi_test_cfg (:obj:`ConfigDict` or dict, optional):
            The semi-supervised testing config.
        data_preprocessor (:obj:`ConfigDict` or dict, optional): Config of
            :class:`DetDataPreprocessor` to process the input data.
            Defaults to None.
        init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
            list[dict], optional): Initialization config dict.
            Defaults to None.
    """

    def __init__(self,
                 detector: ConfigType,
                 semi_train_cfg: OptConfigType = None,
                 semi_test_cfg: OptConfigType = None,
                 data_preprocessor: OptConfigType = None,
                 init_cfg: OptMultiConfig = None) -> None:
        super().__init__(
            data_preprocessor=data_preprocessor, init_cfg=init_cfg)
        self.student = MODELS.build(detector)
        self.teacher = MODELS.build(detector)
        self.semi_train_cfg = semi_train_cfg
        self.semi_test_cfg = semi_test_cfg
        if self.semi_train_cfg.get('freeze_teacher', True) is True:
            self.freeze(self.teacher)

    @staticmethod
    def freeze(model: nn.Module):
        """Freeze the model."""
        model.eval()
        for param in model.parameters():
            param.requires_grad = False

    def loss(self, multi_batch_inputs: Dict[str, Tensor],
             multi_batch_data_samples: Dict[str, SampleList]) -> dict:
        """Calculate losses from multi-branch inputs and data samples.

        Args:
            multi_batch_inputs (Dict[str, Tensor]): The dict of multi-branch
                input images, each value with shape (N, C, H, W).
                Each value should usually be mean centered and std scaled.
            multi_batch_data_samples (Dict[str, List[:obj:`DetDataSample`]]):
                The dict of multi-branch data samples.

        Returns:
            dict: A dictionary of loss components
        """
        losses = dict()
        losses.update(**self.loss_by_gt_instances(
            multi_batch_inputs['sup'], multi_batch_data_samples['sup']))

        origin_pseudo_data_samples, batch_info = self.get_pseudo_instances(
            multi_batch_inputs['unsup_teacher'],
            multi_batch_data_samples['unsup_teacher'])
        multi_batch_data_samples[
            'unsup_student'] = self.project_pseudo_instances(
                origin_pseudo_data_samples,
                multi_batch_data_samples['unsup_student'])
        losses.update(**self.loss_by_pseudo_instances(
            multi_batch_inputs['unsup_student'],
            multi_batch_data_samples['unsup_student'], batch_info))
        return losses

    def loss_by_gt_instances(self, batch_inputs: Tensor,
                             batch_data_samples: SampleList) -> dict:
        """Calculate losses from a batch of inputs and ground-truth data
        samples.

        Args:
            batch_inputs (Tensor): Input images of shape (N, C, H, W).
                These should usually be mean centered and std scaled.
            batch_data_samples (List[:obj:`DetDataSample`]): The batch
                data samples. It usually includes information such
                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.

        Returns:
            dict: A dictionary of loss components
        """

        losses = self.student.loss(batch_inputs, batch_data_samples)
        sup_weight = self.semi_train_cfg.get('sup_weight', 1.)
        return rename_loss_dict('sup_', reweight_loss_dict(losses, sup_weight))

    def loss_by_pseudo_instances(self,
                                 batch_inputs: Tensor,
                                 batch_data_samples: SampleList,
                                 batch_info: Optional[dict] = None) -> dict:
        """Calculate losses from a batch of inputs and pseudo data samples.

        Args:
            batch_inputs (Tensor): Input images of shape (N, C, H, W).
                These should usually be mean centered and std scaled.
            batch_data_samples (List[:obj:`DetDataSample`]): The batch
                data samples. It usually includes information such
                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`,
                which are `pseudo_instance` or `pseudo_panoptic_seg`
                or `pseudo_sem_seg` in fact.
            batch_info (dict): Batch information of teacher model
                forward propagation process. Defaults to None.

        Returns:
            dict: A dictionary of loss components
        """
        batch_data_samples = filter_gt_instances(
            batch_data_samples, score_thr=self.semi_train_cfg.cls_pseudo_thr)
        losses = self.student.loss(batch_inputs, batch_data_samples)
        pseudo_instances_num = sum([
            len(data_samples.gt_instances)
            for data_samples in batch_data_samples
        ])
        unsup_weight = self.semi_train_cfg.get(
            'unsup_weight', 1.) if pseudo_instances_num > 0 else 0.
        return rename_loss_dict('unsup_',
                                reweight_loss_dict(losses, unsup_weight))

    @torch.no_grad()
    def get_pseudo_instances(
            self, batch_inputs: Tensor, batch_data_samples: SampleList
    ) -> Tuple[SampleList, Optional[dict]]:
        """Get pseudo instances from teacher model."""
        self.teacher.eval()
        results_list = self.teacher.predict(
            batch_inputs, batch_data_samples, rescale=False)
        batch_info = {}
        for data_samples, results in zip(batch_data_samples, results_list):
            data_samples.gt_instances = results.pred_instances
            data_samples.gt_instances.bboxes = bbox_project(
                data_samples.gt_instances.bboxes,
                torch.from_numpy(data_samples.homography_matrix).inverse().to(
                    self.data_preprocessor.device), data_samples.ori_shape)
        return batch_data_samples, batch_info

    def project_pseudo_instances(self, batch_pseudo_instances: SampleList,
                                 batch_data_samples: SampleList) -> SampleList:
        """Project pseudo instances."""
        for pseudo_instances, data_samples in zip(batch_pseudo_instances,
                                                  batch_data_samples):
            data_samples.gt_instances = copy.deepcopy(
                pseudo_instances.gt_instances)
            data_samples.gt_instances.bboxes = bbox_project(
                data_samples.gt_instances.bboxes,
                torch.tensor(data_samples.homography_matrix).to(
                    self.data_preprocessor.device), data_samples.img_shape)
        wh_thr = self.semi_train_cfg.get('min_pseudo_bbox_wh', (1e-2, 1e-2))
        return filter_gt_instances(batch_data_samples, wh_thr=wh_thr)

    def predict(self, batch_inputs: Tensor,
                batch_data_samples: SampleList) -> SampleList:
        """Predict results from a batch of inputs and data samples with post-
        processing.

        Args:
            batch_inputs (Tensor): Inputs with shape (N, C, H, W).
            batch_data_samples (List[:obj:`DetDataSample`]): The Data
                Samples. It usually includes information such as
                `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
            rescale (bool): Whether to rescale the results.
                Defaults to True.

        Returns:
            list[:obj:`DetDataSample`]: Return the detection results of the
            input images. The returns value is DetDataSample,
            which usually contain 'pred_instances'. And the
            ``pred_instances`` usually contains following keys.

                - scores (Tensor): Classification scores, has a shape
                    (num_instance, )
                - labels (Tensor): Labels of bboxes, has a shape
                    (num_instances, ).
                - bboxes (Tensor): Has a shape (num_instances, 4),
                    the last dimension 4 arrange as (x1, y1, x2, y2).
                - masks (Tensor): Has a shape (num_instances, H, W).
        """
        if self.semi_test_cfg.get('predict_on', 'teacher') == 'teacher':
            return self.teacher(
                batch_inputs, batch_data_samples, mode='predict')
        else:
            return self.student(
                batch_inputs, batch_data_samples, mode='predict')

    def _forward(self, batch_inputs: Tensor,
                 batch_data_samples: SampleList) -> SampleList:
        """Network forward process. Usually includes backbone, neck and head
        forward without any post-processing.

        Args:
            batch_inputs (Tensor): Inputs with shape (N, C, H, W).

        Returns:
            tuple: A tuple of features from ``rpn_head`` and ``roi_head``
            forward.
        """
        if self.semi_test_cfg.get('forward_on', 'teacher') == 'teacher':
            return self.teacher(
                batch_inputs, batch_data_samples, mode='tensor')
        else:
            return self.student(
                batch_inputs, batch_data_samples, mode='tensor')

    def extract_feat(self, batch_inputs: Tensor) -> Tuple[Tensor]:
        """Extract features.

        Args:
            batch_inputs (Tensor): Image tensor with shape (N, C, H ,W).

        Returns:
            tuple[Tensor]: Multi-level features that may have
            different resolutions.
        """
        if self.semi_test_cfg.get('extract_feat_on', 'teacher') == 'teacher':
            return self.teacher.extract_feat(batch_inputs)
        else:
            return self.student.extract_feat(batch_inputs)

    def _load_from_state_dict(self, state_dict: dict, prefix: str,
                              local_metadata: dict, strict: bool,
                              missing_keys: Union[List[str], str],
                              unexpected_keys: Union[List[str], str],
                              error_msgs: Union[List[str], str]) -> None:
        """Add teacher and student prefixes to model parameter names."""
        if not any([
                'student' in key or 'teacher' in key
                for key in state_dict.keys()
        ]):
            keys = list(state_dict.keys())
            state_dict.update({'teacher.' + k: state_dict[k] for k in keys})
            state_dict.update({'student.' + k: state_dict[k] for k in keys})
            for k in keys:
                state_dict.pop(k)
        return super()._load_from_state_dict(
            state_dict,
            prefix,
            local_metadata,
            strict,
            missing_keys,
            unexpected_keys,
            error_msgs,
        )