我之前写了一段代码,用于在Numpy中复制PyTorch的gather
功能。在这种情况下,self
代表你的x
。
def gather(self, dim, index):
"""
Gathers values along an axis specified by ``dim``.
For a 3-D tensor the output is specified by:
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0
out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
Parameters
----------
dim:
The axis along which to index
index:
A tensor of indices of elements to gather
Returns
-------
Output Tensor
"""
idx_xsection_shape = index.shape[:dim] + \
index.shape[dim + 1:]
self_xsection_shape = self.shape[:dim] + self.shape[dim + 1:]
if idx_xsection_shape != self_xsection_shape:
raise ValueError("Except for dimension " + str(dim) +
", all dimensions of index and self should be the same size")
if index.dtype != np.dtype('int_'):
raise TypeError("The values of index must be integers")
data_swaped = np.swapaxes(self, 0, dim)
index_swaped = np.swapaxes(index, 0, dim)
gathered = np.choose(index_swaped, data_swaped)
return np.swapaxes(gathered, 0, dim)
以下是测试用例:
t = np.array([[65, 17], [14, 25], [76, 22]])
idx = np.array([[0], [1], [0]])
dim = 1
result = gather(t, dim=dim, index=idx)
expected = np.array([[65], [25], [76]])
print(np.array_equal(result, expected))
t = np.array([[47, 74, 44], [56, 9, 37]])
idx = np.array([[0, 0, 1], [1, 1, 0], [0, 1, 0]])
dim = 0
result = gather(t, dim=dim, index=idx)
expected = np.array([[47, 74, 37], [56, 9, 44.], [47, 9, 44]])
print(np.array_equal(result, expected))