text: iou_similarity¶
  paddle.fluid.layers.
  iou_similarity(x, y, box_normalized=True, name=None)[source]
  IOU Similarity Operator
  Computes intersection-over-union (IOU) between two box lists. Box list ‘X’ should be a LoDTensor and ‘Y’ is a common Tensor, boxes in ‘Y’ are shared by all instance of the batched inputs of X. Given two boxes A and B, the calculation of IOU is as follows:
  $$ IOU(A, B) = \frac{area(A\cap B)}{area(A)+area(B)-area(A\cap B)} $$
  - Parameters
  x (Variable) – (LoDTensor, default LoDTensor<float>) Box list X is a 2-D LoDTensor with shape [N, 4] holds N boxes, each box is represented as [xmin, ymin, xmax, ymax], the shape of X is [N, 4]. [xmin, ymin] is the left top coordinate of the box if the input is image feature map, they are close to the origin of the coordinate system. [xmax, ymax] is the right bottom coordinate of the box. This tensor can contain LoD information to represent a batch of inputs. One instance of this batch can contain different numbers of entities.The data type is float32 or float64.
  y (Variable) – (Tensor, default Tensor<float>) Box list Y holds M boxes, each box is represented as [xmin, ymin, xmax, ymax], the shape of X is [N, 4]. [xmin, ymin] is the left top coordinate of the box if the input is image feature map, and [xmax, ymax] is the right bottom coordinate of the box.The data type is float32 or float64.
  box_normalized (bool) – Whether treat the priorbox as a normalized box. Set true by default.
  - Returns
  (LoDTensor, the lod is same as input X) The output of iou_similarity op, a tensor with shape [N, M] representing pairwise iou scores.The data type is same with x.
  - Return type
  Variable
  Examples
  import numpy as np import paddle.fluid as fluid use_gpu = False place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) x = fluid.data(name='x', shape=[None, 4], dtype='float32') y = fluid.data(name='y', shape=[None, 4], dtype='float32') iou = fluid.layers.iou_similarity(x=x, y=y) exe.run(fluid.default_startup_program()) test_program = fluid.default_main_program().clone(for_test=True) [out_iou] = exe.run(test_program, fetch_list=iou, feed={'x': np.array([[0.5, 0.5, 2.0, 2.0], [0., 0., 1.0, 1.0]]).astype('float32'), 'y': np.array([[1.0, 1.0, 2.5, 2.5]]).astype('float32')}) # out_iou is [[0.2857143], # [0. ]] with shape: [2, 1]