Sets the learning rate to the initial LR decayed by 10 at every specified step # Adapted from PyTorch Imagenet example: # https://github.com/p

Decode locations from predictions using priors to undo the encoding we did for offset regression at train time. Args: loc (tensor): lo

Applies network layers and ops on input image(s) x. Args: x: input image or batch of images. Shape: [batch,3*batch,300,300].

Return the directory where experimental artifacts are placed. If the directory does not exist, it is created. A canonical path is built using

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

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

Apply non-maximum suppression at test time to avoid detecting too many overlapping bounding boxes for a given object. Args: boxes: (te

Encode the variances from the priorbox layers into the ground truth boxes we have matched (based on jaccard overlap) with the prior boxes. Arg

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. E.g.: A ∩ B

Parse a PASCAL VOC xml file

Convert prior_boxes to (xmin, ymin, xmax, ymax) representation for comparison to point form ground truth data. Args: boxes: (tensor)

Returns the original image object at index in PIL form Note: not using self.__getitem__(), as any transformations passed in could mes

Test a Fast R-CNN network on an image database.

ap = voc_ap(rec, prec, [use_07_metric]) Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11 point met

rec, prec, ap = voc_eval(detpath, annopath, imagesetfile, classname,

Args: image (Tensor): image tensor to be transformed Return: a tensor with channels swapped according to swap

Arguments: target (annotation) : the target annotation to be made usable will be an ET.Element Returns: