Focal loss as described in https://arxiv.org/pdf/1708.02002.pdf Adapted from https://github.com/clcarwin/focal_loss_pytorch/blob/mast

Instead of using softmax, use class[0] to be the objectness score and do sigmoid focal loss on that. Then for the rest of the classes

Focal loss but using sigmoid like the original paper. Note: To make things mesh easier, the network still predicts 81 class confidenc

Warning: result not cached.

transformed_cat_id is [0,80) as indices in cfg.dataset.class_names

Initializes the backbone weights for training.

Initialize the console to be able to use ANSI escape characters on Windows.

We resize both tensors to [A,B,2] without new malloc: [A,2] -> [A,1,2] -> [A,B,2] [B,2] -> [1,B,2] -> [A,B,2] Then we compute the area of

We resize both tensors to [A,B,2] without new malloc: [A,2] -> [A,1,2] -> [A,B,2] [B,2] -> [1,B,2] -> [A,B,2] Then we compute the area of

We resize both tensors to [A,B,2] without new malloc: [A,2] -> [A,1,2] -> [A,B,2] [B,2] -> [1,B,2] -> [A,B,2] Then we compute the area of

Compute the jaccard overlap of two sets of boxes. The jaccard overlap is simply the intersection over union of two boxes. Here we operate on

Compute the jaccard overlap of two sets of boxes. The jaccard overlap is simply the intersection over union of two boxes. Here we operate on

Compute the jaccard overlap of two sets of boxes. The jaccard overlap is simply the intersection over union of two boxes. E.g.: A ∩ B

Compute the jaccard overlap of two sets of boxes. The jaccard overlap is simply the intersection over union of two boxes. E.g.: A ∩ B

Utility function for computing log_sum_exp while determining This will be used to determine unaveraged confidence loss across all examples in

Note that priors are [x,y,width,height] where (x,y) is the center of the box.

Computes the pariwise mask IoU between two sets of masks of size [a, h, w] and [b, h, w]. The output is of size [a, b]. Wait I thought t

Inputs inputs are matricies of size _ x N. Output is size _1 x _2. Note: if iscrowd is True, then mask2 should be the crowd.

Match each prior box with the ground truth box of the highest jaccard overlap, encode the bounding boxes, then return the matched indices corr

Returns whether or not nvidia-smi is present in this system's PATH.

Prepare inverted table for category id lookup given a coco cats object.

Returns a list of APs for this image, with each element being for a class

Returns the total amount accumulated across all functions in seconds.

Transform e.g. features.24.bias to layers.4.1.bias

Takes a transformed image tensor and returns a numpy ndarray that is untransformed. Arguments w and h are the original height and width of th

Returns a list of the indexes of all the gpus visible to pytorch.