Pytorch之扩充tensor的操作

我就废话不多说了，大家还是直接看代码吧~

b = torch.zeros((3, 2, 6, 6))
a = torch.zeros((3, 2, 1, 1))
a.expand_as(b).size()
Out[32]: torch.Size([3, 2, 6, 6])
a = torch.zeros((3, 2, 2, 1))
a.expand_as(b).size()
Traceback (most recent call last):
 File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3267, in run_code
  exec(code_obj, self.user_global_ns, self.user_ns)
 File "<ipython-input-34-972575f79e92>", line 1, in <module>
  a.expand_as(b).size()
RuntimeError: The expanded size of the tensor (6) must match the existing size (2) at non-singleton dimension 2. Target sizes: [3, 2, 6, 6]. Tensor sizes: [3, 2, 2, 1]
a = torch.zeros((3, 2, 1, 2))
a.expand_as(b).size()
Traceback (most recent call last):
 File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3267, in run_code
  exec(code_obj, self.user_global_ns, self.user_ns)
 File "<ipython-input-36-972575f79e92>", line 1, in <module>
  a.expand_as(b).size()
RuntimeError: The expanded size of the tensor (6) must match the existing size (2) at non-singleton dimension 3. Target sizes: [3, 2, 6, 6]. Tensor sizes: [3, 2, 1, 2]
a = torch.zeros((3, 2, 2, 2))
a.expand_as(b).size()
Traceback (most recent call last):
 File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3267, in run_code
  exec(code_obj, self.user_global_ns, self.user_ns)
 File "<ipython-input-38-972575f79e92>", line 1, in <module>
  a.expand_as(b).size()
RuntimeError: The expanded size of the tensor (6) must match the existing size (2) at non-singleton dimension 3. Target sizes: [3, 2, 6, 6]. Tensor sizes: [3, 2, 2, 2]
a = torch.zeros((3, 2, 6, 2))
a.expand_as(b).size()
Traceback (most recent call last):
 File "/home/lart/.conda/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3267, in run_code
  exec(code_obj, self.user_global_ns, self.user_ns)
 File "<ipython-input-40-972575f79e92>", line 1, in <module>
  a.expand_as(b).size()
RuntimeError: The expanded size of the tensor (6) must match the existing size (2) at non-singleton dimension 3. Target sizes: [3, 2, 6, 6]. Tensor sizes: [3, 2, 6, 2]
a = torch.zeros((3, 2, 6, 1))
a.expand_as(b).size()
Out[44]: torch.Size([3, 2, 6, 6])
a = torch.zeros((3, 2, 1, 6))
a.expand_as(b).size()
Out[46]: torch.Size([3, 2, 6, 6])

tensor.expand_as在这里用于扩展tensor到目标形状，常用的多是在H和W方向上的扩展。

假设目标形状为N, C, H, W，则要求tensor.size()=n, c, h, w(这里假设N，C不变)：

1、h=w=1

2、h=1, w!=1

3、h!=1, w=1

补充：tensorflow 利用expand_dims和squeeze扩展和压缩tensor维度

在利用tensorflow进行文本挖掘工作的时候，经常涉及到维度扩展和压缩工作。

比如对文本进行embedding操作完成之后，若要进行卷积操作，就需要对embedded的向量扩展维度，将[batch_size, embedding_dims]扩展成为[batch_size, embedding_dims, 1]，利用tf.expand_dims(input, -1)就可实现，反过来用squeeze(input, -1)或者tf.squeeze（input）也可以把最第三维去掉。

tf.expand_dims()

tf.squeeze()

tf.expand_dims()

tf.expand_dims(input, axis=None, name=None, dim=None)

在第axis位置增加一个维度.

给定张量输入，此操作在输入形状的维度索引轴处插入1的尺寸。 尺寸索引轴从零开始; 如果您指定轴的负数，则从最后向后计数。

如果要将批量维度添加到单个元素，则此操作非常有用。 例如，如果您有一个单一的形状[height，width，channels]，您可以使用expand_dims（image，0）使其成为1个图像，这将使形状[1，高度，宽度，通道]。

例子

# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]

tf.squeeze()

tf.squeeze(input, axis=None, name=None, squeeze_dims=None)

直接上例子

# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
 shape(squeeze(t)) ==> [2, 3]
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
 shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]

以上为个人经验，希望能给大家一个参考，也希望大家多多支持我们。如有错误或未考虑完全的地方，望不吝赐教。