Draw N samples from multinomial. Args: N (int): Number of samples. Returns: Tensor: Samples.

Draw annotations/segmentaions in Detectron2 Dataset format. Args: dic (dict): annotation/segmentation data of one image,

Draw panoptic prediction results on an image. Args: panoptic_seg (Tensor): of shape (height, width) where the values are

Args: text (str): class label position (tuple): a tuple of the x and y coordinates to place text on image.

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

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

Implement operator-level flops counting using jit. This is a wrapper of fvcore.nn.flop_count, that supports standard detection models in

Forward backbone. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these should be mean centered

Forward backbone. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these should be mean centered

Forward backbone. Args: img (Tensor): Input images of shape (N, C, H, W). Typically these should be mean centered

Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. Args: features: same as in `forward()`

Return dict from versioned blob to list of (i, j), where i is index of consumer op, j is the index of input of that op.

like `get_fields` in the Instances object, but return each field in tensor representations

Return dict from versioned blob to (i, j), where i is index of producer op, j is the index of output of that op.

Return the device type ("cpu" or "gpu"/"cuda") of each (versioned) blob

Arguments: pred_logits, pred_deltas, pred_masks: Same as the output of: meth:`TensorMaskHead.forward`

Run model on the data_loader and evaluate the metrics with evaluator. Also benchmark the inference speed of `model.forward` accurately. T

Prepare some proposals to be used to train the ROI heads. It performs box matching between `proposals` and `targets`, and assigns

Return the losses from a set of RPN predictions and their associated ground-truth. Args: anchors (list[Boxes or RotatedB

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

Parse the package dependencies listed in a requirements file but strips specific versioning information. Args: fname (str): path

Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image. The location, height, and width for pasting each mask is d

Get features from feature maps in `features_list` that correspond to specific point coordinates inside each bounding box from `boxes`.

Deprecated

Print main metrics in a format similar to Detectron, so that they are easy to copypaste into a spreadsheet. Args: results (Order

Add an `img_tensor` associated with `img_name`, to be shown on tensorboard. Args: img_name (str): The name of th

Args: box_scores (N, 5): boxes in corner-form and probabilities. (Note here 5 == 4 + 1, i.e., 4-dim horizontal bo

Rename the op_id-th operator in predict_net, change it's output_id-th input's name to the new_name. It also does automatic re-route and c

Args: bottom_up (Backbone): module representing the bottom up subnetwork. Must be a subclass of :class:`Backbone`

NOTE: this interface is experimental. Args: num_classes (int): number of classes. Used to label background proposals.

Naive tracking heuristics to assign same color to the same instance, will update the internal state of tracked instances. Re

Calculate proper im2col step size, which should be divisible by input_size and not larger than prefer_size. Meanwhile the step size s

Compute all non-empty centroids.

Derive the desired score numbers from summarized COCOeval. Args: coco_eval (None or COCOEval): None represents no predic

Forward logic of the box prediction branch. If `self.train_on_pred_boxes is True`, the function puts predicted boxes in the `prop

Args: features, targets: the same as in Same as in :meth:`ROIHeads.forward`. proposals (list[Instance

Forward logic of the densepose prediction branch. Args: features (dict[str, Tensor]): input data as a mapping from featu

Forward logic of the coarse mask head.

Forward logic of the mask point head.

Return a process group based on gloo backend, containing all the ranks The result is cached.

Load and return the configuration from the specified file (relative to the base configuration directory)

Group elements of an iterable by chunks of size `n`, e.g. grouper(range(9), 4) -> (0, 1, 2, 3), (4, 5, 6, 7), (8, None, None, None)

A context which ignores CUDA OOM exception from pytorch.

Computes utility values for linear interpolation at points v. The points are given as normalized offsets in the source interval (v0_src,

Returns: list[int]: size of the tensor, on each rank Tensor: padded tensor that has the max size

Returns: Tensor: A Tensors of predicted class-specific or class-agnostic boxes for all images in a batch. Element

Prepare ._gts and ._dts for evaluation based on params :return: None

Based on the matching between N proposals and M groundtruth, sample the proposals and set their classification labels. Args: