mmdet will assert the type of dict/list. So convert omegaconf objects to dict/list.

Try select keys from cfg until the first key that exists. Otherwise return default.

Called after the backward pass of each iteration.

Build a box head defined by `cfg.MODEL.ROI_BOX_HEAD.NAME`.

3x3 convolution with padding

See get_caffe2_inputs() below.

Args: binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and W is the image width. E

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

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

Run gather on arbitrary picklable data (not necessarily tensors). Args: data: any picklable object dst (int): destination ra

Returns the field called `name`.

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.

Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. Args: names (str or list[str]): a data

Load LVIS metadata. Args: dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5"). Returns: di

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

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

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

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

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

Load a json file with COCO's instances annotation format. Currently supports instance detection, instance segmentation, and person keypoi

Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are treated as ground truth annotations and all files

Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``, such as "module.submodule.class_name". Rais

Args: predictions: return values of :meth:`forward()`. proposals (list[Instances]): proposals that match the features

Returns True when torch's version is at least `min_version`.

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x_center, y_center, width, height, an

Args: polygons (list[ndarray]): each array has shape (Nx2,) height, width (int) Returns: ndarray: a bool mask of sha

Args: predictions: return values of :meth:`forward()`. proposals (list[Instances]): proposals that match the features

Fake losses for fine segmentation and U/V coordinates. These are used when no suitable ground truth data was found in a batch. The lo

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

Put multiple scalars from keyword arguments. Examples: storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothin

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

Resample fine and coarse segmentation outputs from a predictor to the given bounding box and derive labels for each pixel of the bounding box

r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

Compute and display summary metrics for evaluation results given a custom value for max_dets_per_image

Args: img_size (int): Input image size. patch_kernel (tuple): kernel size for patch embedding. patch_stri

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 foreground classes (i.e. background is not incl

Args: cfg (CfgNode): torch_model (nn.Module): the detectron2 model (meta_arch) to be converted.

Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2".

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

Caffe2 implementation of preprocess_image, which is called inside each MetaArch's forward. It normalizes the input images, and the fi

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

Returns: bool: whether the arguments contain CfgNode and should be considered forwarded to from_config.

Run `_decode_per_level_predictions` for all feature levels and concat the results.

Transform anchors into proposals by applying the predicted anchor deltas. Returns: proposals (list[Tensor]): A list of L