NOTE: this interface is experimental. Args: in_channels: channels of the input features mask_classification:

NOTE: this interface is experimental. Args: in_channels: channels of the input features mask_classification:

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features n

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features n

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features t

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features c

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features t

Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

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: crop_instance (bool): if False, extend cropping boxes to avoid cropping instances

Args: h, w (int): original image size new_h, new_w (int): new image size interp: PIL interpolation method

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

For each row of the matrix, find the smallest element and subtract it from every element in its row. Go to Step 2.

Find a zero (Z) in the resulting matrix. If there is no starred zero in its row or column, star Z. Repeat for each element in the

Cover each column containing a starred zero. If K columns are covered, the starred zeros describe a complete set of unique as

Find a noncovered zero and prime it. If there is no starred zero in the row containing this primed zero, Go to Step 5. Otherwise,

Construct a series of alternating primed and starred zeros as follows. Let Z0 represent the uncovered primed zero found in Step 4.

Add the value found in Step 4 to every element of each covered row, and subtract it from every element of each uncovered column.

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

Add config for MASK_FORMER.

Distort depth image with multiplicative gamma noise. This is adapted from the DexNet 2.0 codebase. Their code: https://github.com/Ber

Add (approximate) Gaussian Process noise to ordered point cloud. This is adapted from the DexNet 2.0 codebase. @param xyz_img: a [H

Converts a numpy.ndarray (N x H x W x C) to a torch.FloatTensor of shape (N x C x H x W) OR converts a nump.ndarray (H x W x C) to a

Converts a numpy.ndarray (N x H x W x C) to a torch.FloatTensor of shape (N x C x H x W) OR converts a nump.ndarray (H x W x C) to a

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

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

Combine several bit masks [N, H, W] into a mask [H,W], e.g. 8*480*640 tensor becomes a numpy array of 480*640. [[1,0,0], [0,1,0]] = > [1,

Combine several bit masks [N, H, W] into a mask [H,W], e.g. 8*480*640 tensor becomes a numpy array of 480*640. [[1,0,0], [0,1,0]] = > [1,

Adds x,y coordinates as channels to feature map @param feature_map: a [T x C x H x W] torch tensor

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

Randomly drop a few ellipses in the image for robustness. This is adapted from the DexNet 2.0 codebase. Their code: https://github.co