我正在研究如何对图像进行语义分割，但是我想为这些类分配权重。我正在使用此Semantic Segmentation PyTorch，在实际的实现中，无法将权重应用于类。 我试图更改util / functional.py，如下面报告的代码一样，但是培训阶段并未提高性能。特别是IOU_Score在0.26的值上振荡，而没有任何良好的性能。
我正在使用example文件夹中报告的笔记本电脑进行多类配置。
任何人都可以帮助我了解如何更改指标或如何使用分配了课程权重的其他指标。
谢谢您的帮助。
def iou(pr, gt, eps=1e-7, threshold=None, ignore_channels=None, weights=None):
"""Calculate Intersection over Union between ground truth and prediction
Args:
pr (torch.Tensor): predicted tensor
gt (torch.Tensor): ground truth tensor
eps (float): epsilon to avoid zero division
threshold: threshold for outputs binarization
Returns:
float: IoU (Jaccard) score
"""
pr = _threshold(pr, threshold=threshold)
pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
#print('PR', pr.shape, type(pr))
#print('GT', gt.shape, type(gt))
pr1 = torch.zeros(pr.shape[0], pr.shape[1], pr.shape[2], pr.shape[3])
gt1 = torch.zeros(gt.shape[0], gt.shape[1], gt.shape[2], gt.shape[3])
pr1[:, 0, :, :] = pr[:, 0, :, :] / 100
pr1[:, 1, :, :] = pr[:, 1, :, :] / 2
pr1[:, 2, :, :] = pr[:, 2, :, :] / 2
pr1[:, 3, :, :] = pr[:, 3, :, :] / 100
pr1[:, 4, :, :] = pr[:, 4, :, :] / 100
gt1[:, 0, :, :] = gt[:, 0, :, :] / 100
gt1[:, 1, :, :] = gt[:, 1, :, :] / 2
gt1[:, 2, :, :] = gt[:, 2, :, :] / 2
gt1[:, 3, :, :] = gt[:, 3, :, :] / 100
gt1[:, 4, :, :] = gt[:, 4, :, :] / 100
intersection = torch.sum(gt1 * pr1)
union = torch.sum(gt1) + torch.sum(pr1) - intersection + eps
return (intersection + eps) / union
jaccard = iou
def f_score(pr, gt, beta=1, eps=1e-7, threshold=None, ignore_channels=None, weights=None):
"""Calculate F-score between ground truth and prediction
Args:
pr (torch.Tensor): predicted tensor
gt (torch.Tensor): ground truth tensor
beta (float): positive constant
eps (float): epsilon to avoid zero division
threshold: threshold for outputs binarization
Returns:
float: F score
"""
pr = _threshold(pr, threshold=threshold)
pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
#print('PR', pr.shape, type(pr))
#print('GT', gt.shape, type(gt))
pr1 = torch.zeros(pr.shape[0], pr.shape[1], pr.shape[2], pr.shape[3])
gt1 = torch.zeros(gt.shape[0], gt.shape[1], gt.shape[2], gt.shape[3])
pr1[:, 0, :, :] = pr[:, 0, :, :] / 100
pr1[:, 1, :, :] = pr[:, 1, :, :] / 2
pr1[:, 2, :, :] = pr[:, 2, :, :] / 2
pr1[:, 3, :, :] = pr[:, 3, :, :] / 100
pr1[:, 4, :, :] = pr[:, 4, :, :] / 100
gt1[:, 0, :, :] = gt[:, 0, :, :] / 100
gt1[:, 1, :, :] = gt[:, 1, :, :] / 2
gt1[:, 2, :, :] = gt[:, 2, :, :] / 2
gt1[:, 3, :, :] = gt[:, 3, :, :] / 100
gt1[:, 4, :, :] = gt[:, 4, :, :] / 100
tp = torch.sum(gt1 * pr1)
fp = torch.sum(pr1) - tp
fn = torch.sum(gt1) - tp
score = ((1 + beta ** 2) * tp + eps) \
/ ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + eps)
return score