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

Args: target (dict): COCO target json annotation as a python dict height (int): height width (int): width

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

Args: index (int): Index Returns: tuple: Tuple (image, (target, masks, num_crowds)). targe

Add a downsample layer to the backbone as per what SSD does.

Add a downsample layer to the backbone as per what SSD does.

Checks the log for the valid keys for this input.

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

Disables the given function name fom being considered for the average or outputted in print_stats.

Returns the elapsed time between two entries based on the given conditionals. If a query isn't specified, the first / last entry will

Does the same as above but instead of pairwise, elementwise along the outer dimension.

Enables function names disabled by disable.

Args: - x: The convOut from a layer in the backbone network Size: [batch_size, in_channels, conv_h, conv_w])

Args: - convouts (list): A list of convouts for the corresponding layers in in_channels. Returns: - A list of

The input should be of size [batch_size, 3, img_h, img_w]

Returns a list of convouts for each layer.