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class mmtrack.models.aggregators.EmbedAggregator(num_convs=1, channels=256, kernel_size=3, norm_cfg=None, act_cfg={'type': 'ReLU'}, init_cfg=None)[source]

Embedding convs to aggregate multi feature maps.

This module is proposed in “Flow-Guided Feature Aggregation for Video Object Detection”. FGFA.

Parameters
  • num_convs (int) – Number of embedding convs.

  • channels (int) – Channels of embedding convs. Defaults to 256.

  • kernel_size (int) – Kernel size of embedding convs, Defaults to 3.

  • norm_cfg (dict) – Configuration of normlization method after each conv. Defaults to None.

  • act_cfg (dict) – Configuration of activation method after each conv. Defaults to dict(type=’ReLU’).

  • init_cfg (dict or list[dict], optional) – Initialization config dict. Defaults to None.

forward(x, ref_x)[source]

Aggregate reference feature maps ref_x.

The aggregation mainly contains two steps: 1. Computing the cos similarity between x and ref_x. 2. Use the normlized (i.e. softmax) cos similarity to weightedly sum ref_x.

Parameters
  • x (Tensor) – of shape [1, C, H, W]

  • ref_x (Tensor) – of shape [N, C, H, W]. N is the number of reference feature maps.

Returns

The aggregated feature map with shape [1, C, H, W].

Return type

Tensor

class mmtrack.models.aggregators.SelsaAggregator(in_channels, num_attention_blocks=16, init_cfg=None)[source]

Selsa aggregator module.

This module is proposed in “Sequence Level Semantics Aggregation for Video Object Detection”. SELSA.

Parameters
  • in_channels (int) – The number of channels of the features of proposal.

  • num_attention_blocks (int) – The number of attention blocks used in selsa aggregator module. Defaults to 16.

  • init_cfg (dict or list[dict], optional) – Initialization config dict. Defaults to None.

forward(x, ref_x)[source]

Aggregate the features ref_x of reference proposals.

The aggregation mainly contains two steps: 1. Use multi-head attention to computing the weight between x and ref_x. 2. Use the normlized (i.e. softmax) weight to weightedly sum ref_x.

Parameters
  • x (Tensor) – of shape [N, C]. N is the number of key frame proposals.

  • ref_x (Tensor) – of shape [M, C]. M is the number of reference frame proposals.

Returns

The aggregated features of key frame proposals with shape [N, C].

Return type

Tensor

backbones

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motion

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mmtrack.utils

mmtrack.utils.collect_env()[source]

Collect the information of the running environments.

mmtrack.utils.get_root_logger(log_file=None, log_level=20)[source]

Get root logger.

Parameters
  • log_file (str) – File path of log. Defaults to None.

  • log_level (int) – The level of logger. Defaults to logging.INFO.

Returns

The obtained logger

Return type

logging.Logger

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