# Copyright (c) OpenMMLab. All rights reserved. from typing import Union from mmengine.config import ConfigDict from mmengine.structures import InstanceData from torch import Tensor from mmdet.registry import MODELS from mmdet.structures import SampleList from mmdet.structures.bbox import BaseBoxes from mmdet.structures.mask import BitmapMasks, PolygonMasks from mmdet.utils import ConfigType from .base import BaseDetector try: import detectron2 from detectron2.config import get_cfg from detectron2.modeling import build_model from detectron2.structures.masks import BitMasks as D2_BitMasks from detectron2.structures.masks import PolygonMasks as D2_PolygonMasks from detectron2.utils.events import EventStorage except ImportError: detectron2 = None def _to_cfgnode_list(cfg: ConfigType, config_list: list = [], father_name: str = 'MODEL') -> tuple: """Convert the key and value of mmengine.ConfigDict into a list. Args: cfg (ConfigDict): The detectron2 model config. config_list (list): A list contains the key and value of ConfigDict. Defaults to []. father_name (str): The father name add before the key. Defaults to "MODEL". Returns: tuple: - config_list: A list contains the key and value of ConfigDict. - father_name (str): The father name add before the key. Defaults to "MODEL". """ for key, value in cfg.items(): name = f'{father_name}.{key.upper()}' if isinstance(value, ConfigDict) or isinstance(value, dict): config_list, fater_name = \ _to_cfgnode_list(value, config_list, name) else: config_list.append(name) config_list.append(value) return config_list, father_name def convert_d2_pred_to_datasample(data_samples: SampleList, d2_results_list: list) -> SampleList: """Convert the Detectron2's result to DetDataSample. Args: 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`. d2_results_list (list): The list of the results of Detectron2's model. Returns: list[:obj:`DetDataSample`]: Detection results of the input images. Each DetDataSample 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). """ assert len(data_samples) == len(d2_results_list) for data_sample, d2_results in zip(data_samples, d2_results_list): d2_instance = d2_results['instances'] results = InstanceData() results.bboxes = d2_instance.pred_boxes.tensor results.scores = d2_instance.scores results.labels = d2_instance.pred_classes if d2_instance.has('pred_masks'): results.masks = d2_instance.pred_masks data_sample.pred_instances = results return data_samples @MODELS.register_module() class Detectron2Wrapper(BaseDetector): """Wrapper of a Detectron2 model. Input/output formats of this class follow MMDetection's convention, so a Detectron2 model can be trained and evaluated in MMDetection. Args: detector (:obj:`ConfigDict` or dict): The module config of Detectron2. bgr_to_rgb (bool): whether to convert image from BGR to RGB. Defaults to False. rgb_to_bgr (bool): whether to convert image from RGB to BGR. Defaults to False. """ def __init__(self, detector: ConfigType, bgr_to_rgb: bool = False, rgb_to_bgr: bool = False) -> None: if detectron2 is None: raise ImportError('Please install Detectron2 first') assert not (bgr_to_rgb and rgb_to_bgr), ( '`bgr2rgb` and `rgb2bgr` cannot be set to True at the same time') super().__init__() self._channel_conversion = rgb_to_bgr or bgr_to_rgb cfgnode_list, _ = _to_cfgnode_list(detector) self.cfg = get_cfg() self.cfg.merge_from_list(cfgnode_list) self.d2_model = build_model(self.cfg) self.storage = EventStorage() def init_weights(self) -> None: """Initialization Backbone. NOTE: The initialization of other layers are in Detectron2, if users want to change the initialization way, please change the code in Detectron2. """ from detectron2.checkpoint import DetectionCheckpointer checkpointer = DetectionCheckpointer(model=self.d2_model) checkpointer.load(self.cfg.MODEL.WEIGHTS, checkpointables=[]) def loss(self, batch_inputs: Tensor, batch_data_samples: SampleList) -> Union[dict, tuple]: """Calculate losses from a batch of inputs and data samples. The inputs will first convert to the Detectron2 type and feed into D2 models. 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. """ d2_batched_inputs = self._convert_to_d2_inputs( batch_inputs=batch_inputs, batch_data_samples=batch_data_samples, training=True) with self.storage as storage: # noqa losses = self.d2_model(d2_batched_inputs) # storage contains some training information, such as cls_accuracy. # you can use storage.latest() to get the detail information return losses def predict(self, batch_inputs: Tensor, batch_data_samples: SampleList) -> SampleList: """Predict results from a batch of inputs and data samples with post- processing. The inputs will first convert to the Detectron2 type and feed into D2 models. And the results will convert back to the MMDet type. 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: list[:obj:`DetDataSample`]: Detection results of the input images. Each DetDataSample 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). """ d2_batched_inputs = self._convert_to_d2_inputs( batch_inputs=batch_inputs, batch_data_samples=batch_data_samples, training=False) # results in detectron2 has already rescale d2_results_list = self.d2_model(d2_batched_inputs) batch_data_samples = convert_d2_pred_to_datasample( data_samples=batch_data_samples, d2_results_list=d2_results_list) return batch_data_samples def _forward(self, *args, **kwargs): """Network forward process. Usually includes backbone, neck and head forward without any post- processing. """ raise NotImplementedError( f'`_forward` is not implemented in {self.__class__.__name__}') def extract_feat(self, *args, **kwargs): """Extract features from images. `extract_feat` will not be used in obj:``Detectron2Wrapper``. """ pass def _convert_to_d2_inputs(self, batch_inputs: Tensor, batch_data_samples: SampleList, training=True) -> list: """Convert inputs type to support Detectron2's model. 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`. training (bool): Whether to enable training time processing. Returns: list[dict]: A list of dict, which will be fed into Detectron2's model. And the dict usually contains following keys. - image (Tensor): Image in (C, H, W) format. - instances (Instances): GT Instance. - height (int): the output height resolution of the model - width (int): the output width resolution of the model """ from detectron2.data.detection_utils import filter_empty_instances from detectron2.structures import Boxes, Instances batched_d2_inputs = [] for image, data_samples in zip(batch_inputs, batch_data_samples): d2_inputs = dict() # deal with metainfo meta_info = data_samples.metainfo d2_inputs['file_name'] = meta_info['img_path'] d2_inputs['height'], d2_inputs['width'] = meta_info['ori_shape'] d2_inputs['image_id'] = meta_info['img_id'] # deal with image if self._channel_conversion: image = image[[2, 1, 0], ...] d2_inputs['image'] = image # deal with gt_instances gt_instances = data_samples.gt_instances d2_instances = Instances(meta_info['img_shape']) gt_boxes = gt_instances.bboxes # TODO: use mmdet.structures.box.get_box_tensor after PR 8658 # has merged if isinstance(gt_boxes, BaseBoxes): gt_boxes = gt_boxes.tensor d2_instances.gt_boxes = Boxes(gt_boxes) d2_instances.gt_classes = gt_instances.labels if gt_instances.get('masks', None) is not None: gt_masks = gt_instances.masks if isinstance(gt_masks, PolygonMasks): d2_instances.gt_masks = D2_PolygonMasks(gt_masks.masks) elif isinstance(gt_masks, BitmapMasks): d2_instances.gt_masks = D2_BitMasks(gt_masks.masks) else: raise TypeError('The type of `gt_mask` can be ' '`PolygonMasks` or `BitMasks`, but get ' f'{type(gt_masks)}.') # convert to cpu and convert back to cuda to avoid # some potential error if training: device = gt_boxes.device d2_instances = filter_empty_instances( d2_instances.to('cpu')).to(device) d2_inputs['instances'] = d2_instances batched_d2_inputs.append(d2_inputs) return batched_d2_inputs