Parameters heatmap tensor input heatmap the gaussian

Parameters
• heatmap ( Tensor ) – Input heatmap, the gaussian kernel will cover on it and maintain the
max value.
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• center ( list[int] ) – Coord of gaussian kernel’s center.
• radius ( int ) – Radius of gaussian kernel.
• k ( int ) – Coefficient of gaussian kernel. Default: 1.
Returns Updated heatmap covered by gaussian kernel.
Return type out_heatmap (Tensor)
mmdet.models.utils. get_uncertain_point_coords_with_randomness ( mask_pred , labels , num_points ,
oversample_ratio ,
importance_sample_ratio )
Get num_points most uncertain points with random points during train.
Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The uncertainties are calculated for
each point using ‘get_uncertainty()’ function that takes point’s logit prediction as input.
Parameters
• mask_pred ( Tensor ) – A tensor of shape (num_rois, num_classes, mask_height,
mask_width) for class-specific or class-agnostic prediction.
• labels ( list ) – The ground truth class for each instance.
• num_points ( int ) – The number of points to sample.
• oversample_ratio ( int ) – Oversampling parameter.
• importance_sample_ratio ( float ) – Ratio of points that are sampled via importnace
sampling.
Returns
A tensor of shape (num_rois, num_points, 2) that contains the coordinates sampled points.
Return type point_coords (Tensor)
mmdet.models.utils. get_uncertainty ( mask_pred , labels )
Estimate uncertainty based on pred logits.
We estimate uncertainty as L1 distance between 0.0 and the logits prediction in ‘mask_pred’ for the foreground
class in classes .
Parameters
• mask_pred ( Tensor ) – mask predication logits, shape (num_rois, num_classes,
mask_height, mask_width).
• labels ( list[Tensor] ) – Either predicted or ground truth label for each predicted mask,
of length num_rois.
Returns
Uncertainty scores with the most uncertain locations having the highest uncertainty score,
shape (num_rois, 1, mask_height, mask_width)
Return type scores (Tensor)
mmdet.models.utils. interpolate_as ( source , target , mode= bilinear , align_corners=False )
Interpolate the source to the shape of the target .
The source must be a Tensor, but the target can be a Tensor or a np.ndarray with the shape (..., target_h, target_w).
Parameters
• source ( Tensor ) – A 3D/4D Tensor with the shape (N, H, W) or (N, C, H, W).
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• target ( Tensor | np.ndarray ) – The interpolation target with the shape (..., target_h,
target_w).
• mode ( str ) – Algorithm used for interpolation. The options are the same as those in
F.interpolate(). Default: ' bilinear ' .
• align_corners ( bool ) – The same as the argument in F.interpolate().
Returns The interpolated source Tensor.
Return type Tensor
mmdet.models.utils. make_divisible ( value , divisor , min_value=None , min_ratio=0.9 )
Make divisible function.
This function rounds the channel number to the nearest value that can be divisible by the divisor. It is taken from
the original tf repo. It ensures that all layers have a channel number that is divisible by divisor. It can be seen
here: # noqa
Parameters
• value ( int ) – The original channel number.