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- # Copyright (c) OpenMMLab. All rights reserved.
- import torch
-
-
- def images_to_levels(target, num_levels):
- """Convert targets by image to targets by feature level.
-
- [target_img0, target_img1] -> [target_level0, target_level1, ...]
- """
- target = torch.stack(target, 0)
- level_targets = []
- start = 0
- for n in num_levels:
- end = start + n
- # level_targets.append(target[:, start:end].squeeze(0))
- level_targets.append(target[:, start:end])
- start = end
- return level_targets
-
-
- def anchor_inside_flags(flat_anchors,
- valid_flags,
- img_shape,
- allowed_border=0):
- """Check whether the anchors are inside the border.
-
- Args:
- flat_anchors (torch.Tensor): Flatten anchors, shape (n, 4).
- valid_flags (torch.Tensor): An existing valid flags of anchors.
- img_shape (tuple(int)): Shape of current image.
- allowed_border (int, optional): The border to allow the valid anchor.
- Defaults to 0.
-
- Returns:
- torch.Tensor: Flags indicating whether the anchors are inside a \
- valid range.
- """
- img_h, img_w = img_shape[:2]
- if allowed_border >= 0:
- inside_flags = valid_flags & \
- (flat_anchors[:, 0] >= -allowed_border) & \
- (flat_anchors[:, 1] >= -allowed_border) & \
- (flat_anchors[:, 2] < img_w + allowed_border) & \
- (flat_anchors[:, 3] < img_h + allowed_border)
- else:
- inside_flags = valid_flags
- return inside_flags
-
-
- def calc_region(bbox, ratio, featmap_size=None):
- """Calculate a proportional bbox region.
-
- The bbox center are fixed and the new h' and w' is h * ratio and w * ratio.
-
- Args:
- bbox (Tensor): Bboxes to calculate regions, shape (n, 4).
- ratio (float): Ratio of the output region.
- featmap_size (tuple): Feature map size used for clipping the boundary.
-
- Returns:
- tuple: x1, y1, x2, y2
- """
- x1 = torch.round((1 - ratio) * bbox[0] + ratio * bbox[2]).long()
- y1 = torch.round((1 - ratio) * bbox[1] + ratio * bbox[3]).long()
- x2 = torch.round(ratio * bbox[0] + (1 - ratio) * bbox[2]).long()
- y2 = torch.round(ratio * bbox[1] + (1 - ratio) * bbox[3]).long()
- if featmap_size is not None:
- x1 = x1.clamp(min=0, max=featmap_size[1])
- y1 = y1.clamp(min=0, max=featmap_size[0])
- x2 = x2.clamp(min=0, max=featmap_size[1])
- y2 = y2.clamp(min=0, max=featmap_size[0])
- return (x1, y1, x2, y2)
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