It now calls :func:`detectron2.solver.build_lr_scheduler`. Overwrite it if you'd like a different scheduler.

Returns: torch.nn.Module: It now calls :func:`detectron2.modeling.build_model`. Overwrite it if you'd like a dif

Returns: torch.optim.Optimizer: It now calls :func:`detectron2.solver.build_optimizer`. Overwrite it if you'd li

Returns: torch.optim.Optimizer: It now calls :func:`detectron2.solver.build_optimizer`. Overwrite it if you'd li

Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.

Build a mask head defined by `cfg.MODEL.ROI_VISIBLE_MASK_HEAD.NAME`.

Build a list of writers to be used. By default it contains writers that write metrics to the screen, a json file, and a tenso

Build a list of writers to be used. By default it contains writers that write metrics to the screen, a json file, and a tenso

Concatenates a list of BitMasks into a single BitMasks Arguments: bitmasks_list (list[BitMasks]) Returns:

Delete all the stored images for visualization. This should be called after images are written to tensorboard.

Clone the RotatedBoxes. Returns: RotatedBoxes

Construct the init_net from params dictionary

Apply some basic name conversion to names in C2 weights. It only deals with typical backbone models. Args: original_keys (list[s

Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops (see description under Returns). Args: box_list

Map Caffe2 Detectron weight names to Detectron2 names. Args: weights (dict): name -> tensor Returns: dict: detectron2 n

Convert a dataset in detectron2's standard format into COCO json format Generic dataset description can be found here: https://detectron

Given a blob object, return the Caffe2 operator that creates this blob as constant. Currently support NumPy tensor and Caffe2 Int8Tensor.

Draws keypoints of an instance and follows the rules for keypoint connections to draw lines between appropriate keypoints. This follo

Args: box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0 are the coordinates of th

Args: circle_coord (list(int) or tuple(int)): contains the x and y coordinates of the center of the circle.

Args: rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle), where cnt_x and cnt_y are the center c

Save extra metadata that will be used by inference in the output protobuf.

Convert semantic segmentation to COCO stuff format with segments encoded as RLEs. See http://cocodataset.org/#format-results

Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None

Export a Detectron2 model via ONNX. Arg: model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py

Export a detectron2 model to caffe2 format. Args: cfg (CfgNode): a detectron2 config, with extra export-related options

Call `fast_rcnn_inference_single_image_rotated` for all images. Args: boxes (list[Tensor]): A list of Tensors of predicted class-spe

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

Filter out images with none annotations or only crowd annotations (i.e., images without non-crowd annotations). A common training-time pr

For each feature map, select the `pre_nms_topk` highest scoring proposals, apply NMS, clip proposals, and remove small boxes. Return the `pos

For each feature map, select the `pre_nms_topk` highest scoring proposals, apply NMS, clip proposals, and remove small boxes. Return the `pos

Expand a hierarchical dict of scalars into a flat dict of scalars. If results[k1][k2][k3] = v, the returned dict will have the entry {"k1

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

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"

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

Generate a tensor storing anchor boxes, which are continuous geometric rectangles centered on one feature map point sample. We can la

Generate a tensor storing anchor boxes, which are continuous geometric rectangles centered on one feature map point sample. We can la

Returns the field called `name`.

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

Returns the URL to the model trained using the given config Args: config_path (str): config file name relative to detectron2's "conf

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

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

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

Returns: dict: a dict which maps names (str) to data of the fields Modifying the returned dict will modify this instance

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

Like model_zoo.get, but do not load any weights (even pretrained)

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

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

For ONNX exported model, GroupNorm will be represented as ATen op, this can be a drop in replacement from ATen to GroupNorm

Guess the version of a partial config where the VERSION field is not specified. Returns the version, or the latest if cannot make a guess.

Extract predicted keypoint locations from heatmaps. Args: maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap

Returns: dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars

Idenfity the reshape sub-graph in a protobuf. The reshape sub-graph is defined as matching the following pattern: (input_blob) -> Op_1 -

Args: score_thresh (float): same as `fast_rcnn_inference_rotated`. nms_thresh (float): same as `fast_rcnn_inference_r

Run inference on the given inputs. Args: batched_inputs (list[dict]): same as in :meth:`forward` detected_in

A context where the model is temporarily changed to eval mode, and restored to previous mode afterwards. Args: model: a torch Mo

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

Dump an "Instances" object to a COCO-format json that's used for evaluation. Args: instances (Instances): img_id (int): the

Convert pred_mask_logits to estimated foreground probability masks while also extracting only the masks for the predicted classes in pred_ins

Compute the mask prediction loss defined in the Mask R-CNN paper. Args: pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, W

Post process each predicted keypoint heatmap in `pred_keypoint_logits` into (x, y, score) and add it to the `pred_instances` as a `pred_k