Apply the transforms on the input. Args: x: input to apply the transform operations. meth (str): meth.

Applies the exif orientation correctly. This code exists per the bug: https://github.com/python-pillow/Pillow/issues/3973 with the

Given two lists of boxes of N ground truth boxes and M anchor boxes, compute the assignment between the two, following the assignment rules i

Batch shuffle, for making use of BatchNorm. *** Only support DistributedDataParallel (DDP) model. ***

Undo batch shuffle. *** Only support DistributedDataParallel (DDP) model. ***

Build a single dataset according to dataset_name. Args: config (BaseConfig): config. dataset_name (str): dat

Args: sizes (list[list[int]]): sizes[i] is the list of anchor sizes to use for the i-th feature map. If len(sizes

Checks that `replacement`, which is intended to replace `original` is of the right type. The type is correct if it matches exactly or is one

Clears the ground truth so that it's ready for another run.

Configurations for some libraries.

Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.

Convert different format of masks or polygons to a tuple of masks and polygons. Returns: list[GenericMask]:

Creates gradient clipping closure to clip by value or by norm, according to the provided config.

Args: boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4 image_sizes (list[tuple]): list of image

Decodes a raw config value (e.g., from a yaml config files or command line argument) into a Python object. If the value is a dict, it wil

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

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

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

Derive the desired score numbers from summarized LVISEval. Args: lvis_eval (None or LVISEval): None represents no predic

Args: masks: N, 1, H, W boxes: N, 4 img_h, img_w (int): skip_empty (bool): only paste masks within the region tha

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Dump a Markdown file that records the model evaluation metrics and corresponding scores to the current working directory. Args:

Evaluate the box proposals in self._predictions. Fill self._results with the metrics for "box_proposals" task.

Evaluate the box proposals in self._predictions. Fill self._results with the metrics for "box_proposals" task.

Evaluate self._predictions on the classification task. Fill self._results with the metrics of the tasks.

Evaluate self._predictions on the classification task. Fill self._results with the metrics of the tasks.

Evaluate self._predictions on the given tasks. Fill self._results with the metrics of the tasks.

Evaluate self._predictions on the given tasks. Fill self._results with the metrics of the tasks.

Evaluate self._predictions on the given tasks. Fill self._results with the metrics of the tasks.

Evaluate self._predictions on the given tasks. Fill self._results with the metrics of the tasks.

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official COCO API recall evaluation code. Howev

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official COCO API recall evaluation code. Howev

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official LVIS API recall evaluation code. Howev

Evaluate the coco results using COCOEval API.

Evaluate the coco results using COCOEval API.

Evaluate the coco results using COCOEval API.

Evaluate the coco results using COCOEval API.

Evaluate the coco results using COCOEval API.

Evaluate the lvis results using LVISEval API.

Find an available port of current machine / node.

Dynamically creates a new type that inherits the type of a given instance and overrides the `step` method to add gradient clipping

This is a generic jit handle that counts the number of activations for any operation given the output shape. Args:

Return an activation function given a string

Returns metadata for "separated" version of the panoptic segmentation dataset.

Create a list of dataset indices (with repeats) to use for one epoch. Args: generator (torch.Generator): pseudo random n

Returns: gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the total number of anchor

Compute (fractional) per-image repeat factors. Args: See __init__. Returns: torch.Tensor: the i-th

Args: input (dict): one dataset dict Returns: dict: one output dict