crop_tensor¶
根据偏移量（offsets）和形状（shape），裁剪输入（x）Tensor。
示例：
* 示例1（输入为2-D Tensor）：
输入：
X.shape = [3, 5]
X.data = [[0, 1, 2, 0, 0],
[0, 3, 4, 0, 0],
[0, 0, 0, 0, 0]]
参数：
shape = [2, 2]
offsets = [0, 1]
输出：
Out.shape = [2, 2]
Out.data = [[1, 2],
[3, 4]]
* 示例2（输入为3-D Tensor）：
输入：
X.shape = [2, 3, 4]
X.data = [[[0, 1, 2, 3],
[0, 5, 6, 7],
[0, 0, 0, 0]],
[[0, 3, 4, 5],
[0, 6, 7, 8],
[0, 0, 0, 0]]]
参数：
shape = [2, 2, -1]
offsets = [0, 0, 1]
输出：
Out.shape = [2, 2, 3]
Out.data = [[[1, 2, 3],
[5, 6, 7]],
[[3, 4, 5],
[6, 7, 8]]]
- 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。
offsets (list|tuple|Variable，可选) - 每个维度上裁剪的偏移量，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Variable。含有Variable的方式适用于每次迭代的偏移量（offset）都可能改变的情况。默认值：None，每个维度的偏移量为0。
name (str，可选) - 具体用法请参见 Name ，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 应该是列表、元组或Variable。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")
# x.shape = [-1, 3, 5], where -1 indicates batch size, and it will get the exact value in runtime.
# shape is a 1-D Tensor
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")
dim1 = fluid.layers.data(name="dim1", shape=[1], dtype="int32")
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 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3], offsets=[0, 1, offsets_var])
# crop4.shape = [-1, 2, 3]