brails.processors.garage_detector.lib.efficientdet.utils module
- class brails.processors.garage_detector.lib.efficientdet.utils.Anchors(anchor_scale=4.0, pyramid_levels=None, **kwargs)
Bases:
Moduleadapted and modified from https://github.com/google/automl/blob/master/efficientdet/anchors.py by Zylo117
- forward(image, dtype=torch.float32)
Generates multiscale anchor boxes.
- Args:
- image_size: integer number of input image size. The input image has the
same dimension for width and height. The image_size should be divided by the largest feature stride 2^max_level.
- anchor_scale: float number representing the scale of size of the base
anchor to the feature stride 2^level.
- anchor_configs: a dictionary with keys as the levels of anchors and
values as a list of anchor configuration.
- Returns:
- anchor_boxes: a numpy array with shape [N, 4], which stacks anchors on all
feature levels.
- Raises:
ValueError: input size must be the multiple of largest feature stride.
- class brails.processors.garage_detector.lib.efficientdet.utils.BBoxTransform(*args, **kwargs)
Bases:
Module- forward(anchors, regression)
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
- Args:
anchors: [batchsize, boxes, (y1, x1, y2, x2)] regression: [batchsize, boxes, (dy, dx, dh, dw)]
Returns:
- class brails.processors.garage_detector.lib.efficientdet.utils.ClipBoxes
Bases:
Module- forward(boxes, img)
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.