NOTE: this interface is experimental. Args: is_train: for training or inference augmentations: a list of augm

NOTE: this interface is experimental. Args: is_train: for training or inference augmentations: a list of augm

NOTE: this interface is experimental. Args: is_train: whether it's used in training or inference augmentatio

NOTE: this interface is experimental. Args: is_train: for training or inference augmentations: a list of augm

NOTE: this interface is experimental. Args: is_train: for training or inference augmentations: a list of augm

NOTE: this interface is experimental. Args: is_train: for training or inference augmentations: a list of augm

Args: dataset_name (str): name of the dataset to be evaluated. distributed (bool): if True, will collect results from

Args: dataset_name: name of the dataset output_dir: output directory to save results for evaluation.

Evaluate predictions. Fill self._results with the metrics of the tasks.

Uses the given `dataset_name` argument (instead of the names in cfg), because the standard practice is to evaluate each test set individually

Expects a numpy array shape Bx4. Requires the original image size in (H, W) format.

Expects a torch tensor with shape Bx4. Requires the original image size in (H, W) format.

Expects a numpy array with shape HxWxC in uint8 format.

Expects batched images with shape BxCxHxW and float format. This transformation may not exactly match apply_image. apply_image is

Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred

Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred

Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with t

Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with t

result: [xc,yc,w,h] range [0,1] to [x1,y1,x2,y2] range [0,w], [0,h]

build model

Decorate a function or a class's __init__ method so that it can be called with a :class:`CfgNode` object using a :func:`from_config` function

Resize the output instances. The input images are often resized when entering an object detector. As a result, we often need the outputs

Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred

Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred

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

Draw instance-level prediction results on an image. Args: predictions (Instances): the output of an instance detection/s

Args: soft_mask (ndarray): float array of shape (H, W), each value in [0, 1]. color: color of the mask. Refer to `mat

"whole" for part means there is no part category for it, filter these ground truth (for paco)

Forward function. Args: x: input features with shape of (B, H, W, C)

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function.

Forward function.

Args: x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. Returns: dict[str-

Forward function. Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_w

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function.

Forward function.

Args: x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. Returns: dict[str-

Forward function. Args: x: input features with shape of (B, H, W, C)

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function.

Forward function.

Args: x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. Returns: dict[str-

Forward function. Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_w

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function.

Forward function.

Args: x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. Returns: dict[str-

Subclasses must override this method, but adhere to the same return type. Returns: dict[str->Tensor]: mapping from featu

This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the forma

This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the forma

Performs the matching Params: outputs: This is a dict that contains at least these entries: "pred_logits": Tenso