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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-11-23 19:23:31 -10:00
parent b027c66048
commit 4aff400777
1 changed files with 25 additions and 8 deletions
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utils/utils.py
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@ -11,6 +11,7 @@ import numpy as np
import torch import torch
import torch.nn as nn import torch.nn as nn
from tqdm import tqdm from tqdm import tqdm
import math
from . import torch_utils # , google_utils from . import torch_utils # , google_utils
@ -234,7 +235,7 @@ def compute_ap(recall, precision):
return ap return ap
def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False): def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
box2 = box2.t() box2 = box2.t()
@ -255,15 +256,31 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False):
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
# Union Area # Union Area
union_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1) + 1e-16) + \ w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1
(b2_x2 - b2_x1) * (b2_y2 - b2_y1) - inter_area w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
iou = inter_area / union_area # iou iou = inter_area / union_area # iou
if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf if GIoU or DIoU or CIoU:
c_x1, c_x2 = torch.min(b1_x1, b2_x1), torch.max(b1_x2, b2_x2) cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
c_y1, c_y2 = torch.min(b1_y1, b2_y1), torch.max(b1_y2, b2_y2) ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
c_area = (c_x2 - c_x1) * (c_y2 - c_y1) + 1e-16 # convex area if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
return iou - (c_area - union_area) / c_area # GIoU c_area = cw * ch + 1e-16 # convex area
return iou - (c_area - union_area) / c_area # GIoU
if DIoU or CIoU: # Distance IoU https://arxiv.org/abs/1911.08287v1
c2 = cw ** 2 + ch ** 2 + 1e-16 # convex diagonal squared
# b1_xc, b1_yc = (b1_x1 + b1_x2) / 2, (b1_y1 + b1_y2) / 2
# b2_xc, b2_yc = (b2_x1 + b2_x2) / 2, (b2_y1 + b2_y2) / 2
# rho2 = (b2_xc - b1_xc) ** 2 + (b2_yc - b1_yc) ** 2 # centerpoint distance squared
rho2 = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2)) ** 2 / 4 + ((b2_y1 + b2_y2) - (b1_y1 + b1_y2)) ** 2 / 4
if DIoU:
return iou - rho2 / c2 # DIoU
elif CIoU:
atan = torch.atan(w2 / h2) - torch.atan(w1 / h1)
v = (4 / math.pi ** 2) * torch.pow(atan, 2)
alpha = v / (1 - iou + v)
# ar = - (8 / (math.pi ** 2)) * atan * (w1 * h1)
return iou - (rho2 / c2 + alpha * v) # CIoU
return iou return iou