"Modified cost for logarithmic updates" "$M_{ij} = (-c_{ij} + u_i + v_j) / epsilon$"

"Modified cost for logarithmic updates" "$M_{ij} = (-c_{ij} + u_i + v_j) / epsilon$"

"Modified cost for logarithmic updates" "$M_{ij} = (-c_{ij} + u_i + v_j) / epsilon$"

"Modified cost for logarithmic updates" "$M_{ij} = (-c_{ij} + u_i + v_j) / epsilon$"

Arguments: box_cls, box_delta, box_center: Same as the output of :meth:`FCOSHead.forward` shifts (list[list[Tensor]):

Arguments: box_cls, box_delta, box_center: Same as the output of :meth:`FCOSHead.forward` shifts (list[list[Tensor]):

Arguments: box_cls, box_delta, box_center: Same as the output of :meth:`FCOSHead.forward` shifts (list[list[Tensor]):

Arguments: box_cls, box_delta, box_center: Same as the output of :meth:`FCOSHead.forward` shifts (list[list[Tensor]):

Normalize, pad and batch the input images.

Normalize, pad and batch the input images.

Normalize, pad and batch the input images.

Normalize, pad and batch the input images.

Compute iou loss of type ['iou', 'giou', 'linear_iou'] Args: inputs (tensor): pred values targets (tensor): target values

Compute iou loss of type ['iou', 'giou', 'linear_iou'] Args: inputs (tensor): pred values targets (tensor): target values

Compute iou loss of type ['iou', 'giou', 'linear_iou'] Args: inputs (tensor): pred values targets (tensor): target values

Compute iou loss of type ['iou', 'giou', 'linear_iou'] Args: inputs (tensor): pred values targets (tensor): target values

Single-image inference. Return bounding-box detection results by thresholding on scores and applying non-maximum suppression (NMS).

Single-image inference. Return bounding-box detection results by thresholding on scores and applying non-maximum suppression (NMS).

Single-image inference. Return bounding-box detection results by thresholding on scores and applying non-maximum suppression (NMS).

Single-image inference. Return bounding-box detection results by thresholding on scores and applying non-maximum suppression (NMS).

Args: For `gt_classes`, `gt_shifts_deltas` and `gt_ious` parameters, see :meth:`FCOS.get_ground_truth`.

Args: For `gt_classes`, `gt_shifts_deltas` and `gt_ious` parameters, see :meth:`FCOS.get_ground_truth`.

Args: For `gt_classes`, `gt_shifts_deltas` and `gt_ious` parameters, see :meth:`FCOS.get_ground_truth`.

Args: For `gt_classes`, `gt_shifts_deltas` and `gt_ious` parameters, see :meth:`FCOS.get_ground_truth`.

Rearrange the tensor layout from the network output, i.e.: list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi) to per-image prediction

Rearrange the tensor layout from the network output, i.e.: list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi) to per-image prediction

Rearrange the tensor layout from the network output, i.e.: list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi) to per-image prediction

Rearrange the tensor layout from the network output, i.e.: list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi) to per-image prediction

function used for multiscale test, will be refactor in the future. The same input with `forward` function.

function used for multiscale test, will be refactor in the future. The same input with `forward` function.

function used for multiscale test, will be refactor in the future. The same input with `forward` function.

function used for multiscale test, will be refactor in the future. The same input with `forward` function.

Build a backbone from `cfg.MODEL.BACKBONE.NAME`. Returns: an instance of :class:`Backbone`

Build a backbone from `cfg.MODEL.BACKBONE.NAME`. Returns: an instance of :class:`Backbone`

Build a backbone from `cfg.MODEL.BACKBONE.NAME`. Returns: an instance of :class:`Backbone`

Build a backbone from `cfg.MODEL.BACKBONE.NAME`. Returns: an instance of :class:`Backbone`

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

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

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

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

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

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

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

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