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import torch
import math
def compute_delta(bboxes_init, gt_segments, wc=2.0, wl=2.0):
assert bboxes_init.size(0) == gt_segments.size(0)
assert bboxes_init.size(-1) == gt_segments.size(-1) == 2
# wc, wl = (10.0, 5.0)
# wc, wl = (1.0, 1.0)
init_c = (bboxes_init[..., 0] + bboxes_init[..., 1]) * 0.5
init_w = bboxes_init[..., 1] - bboxes_init[..., 0]
gt_c = (gt_segments[..., 0] + gt_segments[..., 1]) * 0.5
gt_w = gt_segments[..., 1] - gt_segments[..., 0]
dc = (gt_c - init_c) / init_w * wc
dw = torch.log(gt_w / init_w) * wl
deltas = torch.stack([dc, dw], dim=-1)
return deltas
def delta_to_pred(bboxes_init, pred_delta, wc=2.0, wl=2.0):
assert pred_delta.shape[-2] == bboxes_init.shape[-2]
# wc, wl = (10.0, 5.0)
# wc, wl = (1.0, 1.0)
dc = pred_delta[..., 0] / wc
# Prevent sending too large values into torch.exp()
dw = torch.clamp(pred_delta[..., 1] / wl, max=math.log(1000.0 / 16))
init_c = (bboxes_init[..., 0] + bboxes_init[..., 1]) * 0.5
init_w = bboxes_init[..., 1] - bboxes_init[..., 0]
pred_c = dc * init_w + init_c
pred_w = torch.exp(dw) * init_w
pred_bboxes = torch.stack([pred_c - 0.5 * pred_w, pred_c + 0.5 * pred_w], dim=-1)
pred_bboxes = pred_bboxes.clamp(min=0)
return pred_bboxes
def proposal_cw_to_se(x):
c, w = x.unbind(-1)
s = c - 0.5 * w
e = c + 0.5 * w
return torch.stack([s, e], dim=-1)
def proposal_se_to_cw(x):
s, e = x.unbind(-1)
c = (s + e) * 0.5
w = e - s
return torch.stack([c, w], dim=-1)
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