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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| from typing import Dict, Optional | |
| from torch import Tensor | |
| from mmdet.registry import MODELS | |
| from mmdet.structures import SampleList, TrackSampleList | |
| from mmdet.utils import OptConfigType, OptMultiConfig | |
| from .base import BaseMOTModel | |
| class ByteTrack(BaseMOTModel): | |
| """ByteTrack: Multi-Object Tracking by Associating Every Detection Box. | |
| This multi object tracker is the implementation of `ByteTrack | |
| <https://arxiv.org/abs/2110.06864>`_. | |
| Args: | |
| detector (dict): Configuration of detector. Defaults to None. | |
| tracker (dict): Configuration of tracker. Defaults to None. | |
| data_preprocessor (dict or ConfigDict, optional): The pre-process | |
| config of :class:`TrackDataPreprocessor`. it usually includes, | |
| ``pad_size_divisor``, ``pad_value``, ``mean`` and ``std``. | |
| init_cfg (dict or list[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: SampleList, **kwargs) -> dict: | |
| """Calculate losses from a batch of inputs and data samples. | |
| Args: | |
| inputs (Tensor): of shape (N, C, H, W) encoding | |
| input images. Typically these should be mean centered and std | |
| scaled. The N denotes batch size | |
| data_samples (list[:obj:`DetDataSample`]): The batch | |
| data samples. It usually includes information such | |
| as `gt_instance`. | |
| Returns: | |
| dict: A dictionary of loss components. | |
| """ | |
| 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, \ | |
| 'Bytetrack inference only support ' \ | |
| '1 batch size per gpu for now.' | |
| assert len(data_samples) == 1, \ | |
| 'Bytetrack 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] | |