Custom collate fn for dealing with batches of images that have a different number of associated object annotations (bounding boxes). Argument

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

Args: loc_data: (tensor) Loc preds from loc layers Shape: [batch,num_priors*4] conf_data: (tensor) Sh

weight initalizer: truncated normal and random normal.

Multibox Loss Args: predictions (tuple): A tuple containing loc preds, conf preds, and prior boxes from SSD net.

Returns the original annotation of image at index Note: not using self.__getitem__(), as any transformations passed in could mess up

Returns the original annotation of image at index Note: not using self.__getitem__(), as any transformations passed in could mess up

Returns the original image at an index in tensor form Note: not using self.__getitem__(), as any transformations passed in could mess

Returns the original image at an index in tensor form Note: not using self.__getitem__(), as any transformations passed in could mess

Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

Draw detected bounding boxes.