crop_tensor

根据偏移量（offsets）和形状（shape），裁剪输入（x）Tensor。

示例：

参数:

• x (Variable): 1-D到6-D Tensor，数据类型为float32、float64、int32或者int64。

• shape (list|tuple|Variable) - 输出Tensor的形状，数据类型为int32。如果是列表或元组，则其长度必须与x的维度大小相同，如果是Variable，则其应该是1-D Tensor。当它是列表时，每一个元素可以是整数或者形状为[1]的Tensor。含有Variable的方式适用于每次迭代时需要改变输出形状的情况。

• name (str，可选) - 具体用法请参见 ，一般无需设置，默认值为None。

返回: 裁剪后的Tensor，数据类型与输入（x）相同。

返回类型: Variable

抛出异常：

• TypeError - x 的数据类型应该是float32、float64、int32或者int64。

• TypeError - shape 的数据类型应该是int32。

• TypeError - offsets 应该是列表、元组、Variable或None。

• TypeError - offsets 的数据类型应该是int32。

• TypeError - offsets 的元素应该大于等于0。

代码示例:

import paddle.fluid as fluid
x = fluid.data(name="x", shape=[None, 3, 5], dtype="float32")
crop_shape = fluid.data(name="crop_shape", shape=[3], dtype="int32")
crop0 = fluid.layers.crop_tensor(x, shape=crop_shape)
# crop0.shape = [-1, -1, -1], it means crop0.shape[0] = x.shape[0] in runtime.
# or shape is a list in which each element is a constant
crop1 = fluid.layers.crop_tensor(x, shape=[-1, -1, 3], offsets=[0, 1, 0])
# crop1.shape = [-1, 2, 3]
# or shape is a list in which each element is a constant or Tensor
y = fluid.data(name="y", shape=[3, 8, 8], dtype="float32")
crop2 = fluid.layers.crop_tensor(y, shape=[3, dim1, 4])
# crop2.shape = [3, -1, 4]
# offsets is a 1-D Tensor
crop_offsets = fluid.data(name="crop_offsets", shape=[3], dtype="int32")
crop3 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3], offsets=crop_offsets)
# crop3.shape = [-1, 2, 3]
# offsets is a list in which each element is a constant or Tensor
offsets_var =  fluid.data(name="offset", shape=[1], dtype="int32")
# crop4.shape = [-1, 2, 3]