Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Returns: iterable It calls :func:`detectron2.data.build_detection_train_loader` with a customized DatasetMapper,

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

3x3 convolution with padding

default_evaluation_params: Default parameters to use for the validation and evaluation.

Method evaluate_method: evaluate method and returns the results Results. Dictionary with the following values: - method (required

see

Arguments: images (list[Tensor] or ImageList): images to be processed targets (list[BoxList]): ground-truth boxes pre

Args: input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to feature map tensor for each feature lev

Args: input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to feature map tensor for each feature lev

Args: batched_inputs: a list, batched outputs of :class:`DetectionTransform` . Each item in the list contains the

Arguments: features (list[Tensor]): FPN feature map tensors in high to low resolution. Each tensor in the list co

Arguments: features (list[Tensor]): FPN feature map tensors in high to low resolution. Each tensor in the list co

see detectron2.modeling.ROIHeads

hidden: 1 x n x self.hidden_size encoder_outputs: time_step x n x self.hidden_size (T,N,C)

Args: l_feature: (N, T, E) where T is length, N is batch size and d is dim of model v_feature: (N, T, E) shape the sa

Args: tokens: (N, T, C) where T is length, N is batch size and C is classes number lengths: (N,)

see detectron2.modeling.ROIHeads

Args: batched_inputs: a list, batched outputs of :class:`DatasetMapper` . Each item in the list contains the inpu

r""" Args: query, key, value: map a query and a set of key-value pairs to an output. See "Attention Is All You Need" for more

r"""Take in and process masked source/target sequences. Args: src: the sequence to the encoder (required). tgt: the s

r"""Pass the input through the encoder layers in turn. Args: src: the sequence to the encoder (required). mask: the m

r"""Pass the inputs (and mask) through the decoder layer in turn. Args: tgt: the sequence to the decoder (required).

r"""Pass the input through the encoder layer. Args: src: the sequence to the encoder layer (required). src_mask: the

r"""Pass the inputs (and mask) through the decoder layer. Args: tgt: the sequence to the decoder layer (required). me

r"""Inputs of forward function Args: x: the sequence fed to the positional encoder model (required). Shape: x: