mmdetection/mmdet/models/mot/ocsort.py

# Copyright (c) OpenMMLab. All rights reserved.

from typing import Dict, Optional

from torch import Tensor

from mmdet.registry import MODELS
from mmdet.structures import TrackSampleList
from mmdet.utils import OptConfigType, OptMultiConfig
from .base import BaseMOTModel


@MODELS.register_module()
class OCSORT(BaseMOTModel):
    """OCOSRT: Observation-Centric SORT: Rethinking SORT for Robust
    Multi-Object Tracking

    This multi object tracker is the implementation of `OC-SORT
    <https://arxiv.org/abs/2203.14360>`_.

    Args:
        detector (dict): Configuration of detector. Defaults to None.
        tracker (dict): Configuration of tracker. Defaults to None.
        motion (dict): Configuration of motion. Defaults to None.
        init_cfg (dict): Configuration of initialization. Defaults to None.
    """

    def __init__(self,
                 detector: Optional[dict] = None,
                 tracker: Optional[dict] = None,
                 data_preprocessor: OptConfigType = None,
                 init_cfg: OptMultiConfig = None):
        super().__init__(data_preprocessor, init_cfg)

        if detector is not None:
            self.detector = MODELS.build(detector)

        if tracker is not None:
            self.tracker = MODELS.build(tracker)

    def loss(self, inputs: Tensor, data_samples: TrackSampleList,
             **kwargs) -> dict:
        """Calculate losses from a batch of inputs and data samples."""
        return self.detector.loss(inputs, data_samples, **kwargs)

    def predict(self, inputs: Dict[str, Tensor], data_samples: TrackSampleList,
                **kwargs) -> TrackSampleList:
        """Predict results from a video and data samples with post-processing.

        Args:
            inputs (Tensor): of shape (N, T, C, H, W) encoding
                input images. The N denotes batch size.
                The T denotes the number of frames in a video.
            data_samples (list[:obj:`TrackDataSample`]): The batch
                data samples. It usually includes information such
                as `video_data_samples`.
        Returns:
            TrackSampleList: Tracking results of the inputs.
        """
        assert inputs.dim() == 5, 'The img must be 5D Tensor (N, T, C, H, W).'
        assert inputs.size(0) == 1, \
            'OCSORT inference only support ' \
            '1 batch size per gpu for now.'

        assert len(data_samples) == 1, \
            'OCSORT inference only support 1 batch size per gpu for now.'

        track_data_sample = data_samples[0]
        video_len = len(track_data_sample)

        for frame_id in range(video_len):
            img_data_sample = track_data_sample[frame_id]
            single_img = inputs[:, frame_id].contiguous()
            # det_results List[DetDataSample]
            det_results = self.detector.predict(single_img, [img_data_sample])
            assert len(det_results) == 1, 'Batch inference is not supported.'

            pred_track_instances = self.tracker.track(
                data_sample=det_results[0], **kwargs)
            img_data_sample.pred_track_instances = pred_track_instances

        return [track_data_sample]