x_list contains a list of tensors to nest together and run

x_list contains a list of tensors to nest together and run

x_list contains a list of tensors to nest together and run

x_list contains a list of tensors to nest together and run

Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. This is useful for downstream tasks where a box is kn

Look for keys with the following patterns: 1) Starts with "fpn.inner." Example: "fpn.inner.res2.2.sum.lateral.weight"

Freeze the first several stages of the ResNet. Commonly used in fine-tuning. Layers that produce the same feature map spatia

Freeze the first several stages of the model. Commonly used in fine-tuning. Layers that produce the same feature map spatial size ar

Args: polygon_masks (list[list[ndarray]] or PolygonMasks) height, width (int)

Remove AliasWithName placeholder and rename the input/output of it

In-place fuse extra copy ops between cpu/gpu for the following case: a -CopyAToB-> b -CopyBToA> c1 -NextOp1-> d1

Helper for building a global average pooling layer.

Generate a tensor storing canonical anchor boxes, which are all anchor boxes of different sizes and aspect_ratios centered at (0, 0).

Generate a tensor storing canonical anchor boxes, which are all anchor boxes of different sizes, aspect_ratios, angles centered at (0

Generates per stage widths and depths from RegNet parameters.

Convert pytorch-style structured inputs to caffe2-style inputs that are tuples of tensors. Args: batched_inputs

Returns path to a builtin config file. Args: config_path (str): config file name relative to detectron2's "configs/" dir

Get all the configuration files (relative to the base configuration directory)

Get box regression transformation deltas (dx, dy, dw, dh, da) that can be used to transform the `src_boxes` into the `target_boxes`.

Get box regression transformation deltas (dx1, dy1, dx2, dy2) that can be used to transform the `src_boxes` into the `target_boxes`.

Version is not available for import through Python since it is above the top level of the package. Instead, we parse it from the file with a r

Create evaluator(s) for a given dataset. This uses the special metadata "evaluator_type" associated with each builtin dataset. For your o

Get all the evolution configuration files (relative to the base configuration directory)

Args: gt_classes: a long tensor of shape R that contains the gt class label of each proposal. num_fed_loss_classes: m

Args: anchors (list[list[Boxes]]): a list of N=#image elements. Each is a list of #feature level Boxes. The Boxes

Get all the HRNet configuration files (relative to the base configuration directory)

Post-processing for instance segmentation, gets class agnostic instance id. Args: sem_seg: A Tensor of shape [1, H, W], predicted sem

Get the model from the specified file (relative to the base configuration directory)

Return a list of configs to include in package for model zoo. Copy over these configs inside detectron2/model_zoo.

Creates a function that converts outputs of the caffe2 model to detectron2's standard format. The function uses information i

Post-processing for panoptic segmentation. Args: sem_seg: A Tensor of shape [1, H, W] of predicted semantic label. center_hea

Take the output blobs from init_net by running it. Outputs: params: dict from blob name to numpy array device_options: dict f

Convert image-level absolute coordinates to box-normalized [0, 1] x [0, 1] point cooordinates. Args: boxes_coords (Tensor): A tensor

Get all the quick schedules configuration files (relative to the base configuration directory)

create a Tuple[Dict[str, Tensor]] which is the input type of scripted model

json_str is a JSON string representing a mesh_name -> texture_atlas_path dictionary

Execute the policy based on input data, and decide what transform to apply to inputs. Args: args: Any fixed-length posit