import torch
[docs]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):
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
[docs]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)