Spaces:
Sleeping
Sleeping
| # borrow from https://github.com/Zzh-tju/CIoU/blob/master/layers/modules/multibox_loss.py | |
| import torch, math | |
| def ciou(bboxes1, bboxes2): | |
| bboxes1 = torch.sigmoid(bboxes1) | |
| bboxes2 = torch.sigmoid(bboxes2) | |
| rows = bboxes1.shape[0] | |
| cols = bboxes2.shape[0] | |
| cious = torch.zeros((rows, cols)) | |
| if rows * cols == 0: | |
| return cious | |
| exchange = False | |
| if bboxes1.shape[0] > bboxes2.shape[0]: | |
| bboxes1, bboxes2 = bboxes2, bboxes1 | |
| cious = torch.zeros((cols, rows)) | |
| exchange = True | |
| w1 = torch.exp(bboxes1[:, 2]) | |
| h1 = torch.exp(bboxes1[:, 3]) | |
| w2 = torch.exp(bboxes2[:, 2]) | |
| h2 = torch.exp(bboxes2[:, 3]) | |
| area1 = w1 * h1 | |
| area2 = w2 * h2 | |
| center_x1 = bboxes1[:, 0] | |
| center_y1 = bboxes1[:, 1] | |
| center_x2 = bboxes2[:, 0] | |
| center_y2 = bboxes2[:, 1] | |
| inter_l = torch.max(center_x1 - w1 / 2,center_x2 - w2 / 2) | |
| inter_r = torch.min(center_x1 + w1 / 2,center_x2 + w2 / 2) | |
| inter_t = torch.max(center_y1 - h1 / 2,center_y2 - h2 / 2) | |
| inter_b = torch.min(center_y1 + h1 / 2,center_y2 + h2 / 2) | |
| inter_area = torch.clamp((inter_r - inter_l),min=0) * torch.clamp((inter_b - inter_t),min=0) | |
| c_l = torch.min(center_x1 - w1 / 2,center_x2 - w2 / 2) | |
| c_r = torch.max(center_x1 + w1 / 2,center_x2 + w2 / 2) | |
| c_t = torch.min(center_y1 - h1 / 2,center_y2 - h2 / 2) | |
| c_b = torch.max(center_y1 + h1 / 2,center_y2 + h2 / 2) | |
| inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2 | |
| c_diag = torch.clamp((c_r - c_l),min=0)**2 + torch.clamp((c_b - c_t),min=0)**2 | |
| union = area1+area2-inter_area | |
| u = (inter_diag) / c_diag | |
| iou = inter_area / union | |
| v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w2 / h2) - torch.atan(w1 / h1)), 2) | |
| with torch.no_grad(): | |
| S = (iou>0.5).float() | |
| alpha= S*v/(1-iou+v) | |
| cious = iou - u - alpha * v | |
| cious = torch.clamp(cious,min=-1.0,max = 1.0) | |
| if exchange: | |
| cious = cious.T | |
| return 1-cious | |
| def diou(bboxes1, bboxes2): | |
| bboxes1 = torch.sigmoid(bboxes1) | |
| bboxes2 = torch.sigmoid(bboxes2) | |
| rows = bboxes1.shape[0] | |
| cols = bboxes2.shape[0] | |
| cious = torch.zeros((rows, cols)) | |
| if rows * cols == 0: | |
| return cious | |
| exchange = False | |
| if bboxes1.shape[0] > bboxes2.shape[0]: | |
| bboxes1, bboxes2 = bboxes2, bboxes1 | |
| cious = torch.zeros((cols, rows)) | |
| exchange = True | |
| w1 = torch.exp(bboxes1[:, 2]) | |
| h1 = torch.exp(bboxes1[:, 3]) | |
| w2 = torch.exp(bboxes2[:, 2]) | |
| h2 = torch.exp(bboxes2[:, 3]) | |
| area1 = w1 * h1 | |
| area2 = w2 * h2 | |
| center_x1 = bboxes1[:, 0] | |
| center_y1 = bboxes1[:, 1] | |
| center_x2 = bboxes2[:, 0] | |
| center_y2 = bboxes2[:, 1] | |
| inter_l = torch.max(center_x1 - w1 / 2,center_x2 - w2 / 2) | |
| inter_r = torch.min(center_x1 + w1 / 2,center_x2 + w2 / 2) | |
| inter_t = torch.max(center_y1 - h1 / 2,center_y2 - h2 / 2) | |
| inter_b = torch.min(center_y1 + h1 / 2,center_y2 + h2 / 2) | |
| inter_area = torch.clamp((inter_r - inter_l),min=0) * torch.clamp((inter_b - inter_t),min=0) | |
| c_l = torch.min(center_x1 - w1 / 2,center_x2 - w2 / 2) | |
| c_r = torch.max(center_x1 + w1 / 2,center_x2 + w2 / 2) | |
| c_t = torch.min(center_y1 - h1 / 2,center_y2 - h2 / 2) | |
| c_b = torch.max(center_y1 + h1 / 2,center_y2 + h2 / 2) | |
| inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2 | |
| c_diag = torch.clamp((c_r - c_l),min=0)**2 + torch.clamp((c_b - c_t),min=0)**2 | |
| union = area1+area2-inter_area | |
| u = (inter_diag) / c_diag | |
| iou = inter_area / union | |
| dious = iou - u | |
| dious = torch.clamp(dious,min=-1.0,max = 1.0) | |
| if exchange: | |
| dious = dious.T | |
| return 1-dious | |
| if __name__ == "__main__": | |
| x = torch.rand(10, 4) | |
| y = torch.rand(10,4) | |
| import ipdb;ipdb.set_trace() | |
| cxy = ciou(x, y) | |
| dxy = diou(x, y) | |
| print(cxy.shape, dxy.shape) | |