RuntimeError：张量 a (10) 的大小必须与非单维 0 处的张量 b (3) 的大小相匹配

乘风 2年前 pytorch 261

原文标题 ：RuntimeError: The size of tensor a (10) must match the size of tensor b (3) at non-singleton dimension 0

我在联合代码上使用这个交集来从我的预测和目标中确定 IOU：

def intersection_over_union(boxes_preds, boxes_labels):
    """
    Calculates intersection over union
    Parameters:
        boxes_preds (tensor): Predictions of Bounding Boxes (BATCH_SIZE, 4)
        boxes_labels (tensor): Correct labels of Bounding Boxes (BATCH_SIZE, 4)
        box_format (str): midpoint/corners, if boxes (x,y,w,h) or (x1,y1,x2,y2)
    Returns:
        tensor: Intersection over union for all examples
    """


    box1_x1 = boxes_preds[..., 0:1]
    box1_y1 = boxes_preds[..., 1:2]
    box1_x2 = boxes_preds[..., 2:3]
    box1_y2 = boxes_preds[..., 3:4]  # (N, 1)
    box2_x1 = boxes_labels[..., 0:1]
    box2_y1 = boxes_labels[..., 1:2]
    box2_x2 = boxes_labels[..., 2:3]
    box2_y2 = boxes_labels[..., 3:4]

    x1 = torch.max(box1_x1, box2_x1)
    y1 = torch.max(box1_y1, box2_y1)
    x2 = torch.min(box1_x2, box2_x2)
    y2 = torch.min(box1_y2, box2_y2)

    # .clamp(0) is for the case when they do not intersect
    intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)

    box1_area = abs((box1_x2 - box1_x1) * (box1_y2 - box1_y1))
    box2_area = abs((box2_x2 - box2_x1) * (box2_y2 - box2_y1))

    return intersection / (box1_area + box2_area - intersection + 1e-6)

我的输入如下所示：

我的目标边界框如下所示：print(targets[0]['boxes'])

tensor([[217., 481., 249., 511.],
        [435., 191., 467., 223.],
        [471.,  86., 503., 118.]])

我的预测边界框看起来像这样：predictions['boxes']

tensor([[ 29.7859, 354.9666,  63.0900, 387.6363],
        [469.1072,  85.6840, 503.1974, 119.7137],
        [ 89.3957, 314.1584, 123.9789, 347.1621],
        [432.2971, 188.4454, 468.4712, 227.3808],
        [214.5407, 482.0136, 248.7030, 512.0000],
        [329.1979, 340.8802, 366.3720, 375.8683],
        [298.5089,  99.0098, 334.4280, 129.4205],
        [  0.0000, 347.7724,  17.3409, 384.5709],
        [485.4312, 181.3882, 512.0000, 213.2009],
        [144.5959, 356.5197, 183.4489, 387.4958]])

但是，当我应用 IOU 功能时：

iou = intersection_over_union(predictions['boxes'], targets[0]['boxes'])

我收到此错误：

RuntimeError: The size of tensor a (10) must match the size of tensor b (3) at non-singleton dimension 0

我不确定如何修复该功能，因为我猜这意味着我的预测比目标多…

原文链接：https://stackoverflow.com//questions/71417786/runtimeerror-the-size-of-tensor-a-10-must-match-the-size-of-tensor-b-3-at-n

我来回复

Alexey Birukov 评论

IoU不能这样表示，通常是用标量代码写的。这里你需要将目标集的每一帧与预测的每一帧进行比较，“torch.max()”不能这样工作。试试 express (或者只是从某个地方复制）IoU 的标量python代码，当它可以工作时，如果强烈需要，你可以尝试通过一些张量操作来优化它。

2年前 0条评论

TSRAI 评论

该回答已被采纳！

我选择了 torchvisions IOU 示例：

iou = torchvision.ops.box_iou(predictions['boxes'], targets[0]['boxes'])

这里没有错误。

我也找到了这个实现（来自https://github.com/amdegroot/ssd.pytorch/blob/master/layers/box_utils.py#L48）：


def intersect(box_a, box_b):
    """ We resize both tensors to [A,B,2] without new malloc:
    [A,2] -> [A,1,2] -> [A,B,2]
    [B,2] -> [1,B,2] -> [A,B,2]
    Then we compute the area of intersect between box_a and box_b.
    Args:
      box_a: (tensor) bounding boxes, Shape: [A,4].
      box_b: (tensor) bounding boxes, Shape: [B,4].
    Return:
      (tensor) intersection area, Shape: [A,B].
    """
    A = box_a.size(0)
    B = box_b.size(0)
    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
                       box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
                       box_b[:, :2].unsqueeze(0).expand(A, B, 2))
    inter = torch.clamp((max_xy - min_xy), min=0)
    return inter[:, :, 0] * inter[:, :, 1]


def jaccard(box_a, box_b):
    """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
    is simply the intersection over union of two boxes.  Here we operate on
    ground truth boxes and default boxes.
    E.g.:
        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
    Args:
        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
    Return:
        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
    """
    inter = intersect(box_a, box_b)
    area_a = ((box_a[:, 2]-box_a[:, 0]) *
              (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
    area_b = ((box_b[:, 2]-box_b[:, 0]) *
              (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
    union = area_a + area_b - inter
    return inter / union  # [A,B]

应用 jaccard 产生与 torchvisions 自己的函数相同的输出：

iou = jaccard(predictions['boxes'], targets[0]['boxes'])

打印 IOU 时的示例张量：

tensor([[0.0000, 0.0000],
        [0.0000, 0.0000],
        [0.0000, 0.0000],
        [0.0000, 0.0000],
        [0.0000, 0.9322],
        [0.8021, 0.0000],
        [0.0000, 0.0000],
        [0.0000, 0.0000]])

2年前 0条评论

RuntimeError：张量 a (10) 的大小必须与非单维 0 处的张量 b (3) 的大小相匹配

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