本文整理汇总了Python中torch.autograd.Variable.bmm方法的典型用法代码示例。如果您正苦于以下问题：Python Variable.bmm方法的具体用法？Python Variable.bmm怎么用？Python Variable.bmm使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.autograd.Variable的用法示例。
在下文中一共展示了Variable.bmm方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: F_affine2d
# 需要导入模块: from torch.autograd import Variable [as 别名]
# 或者: from torch.autograd.Variable import bmm [as 别名]
def F_affine2d(x, matrix, center=True):
"""
2D Affine image transform on torch.autograd.Variable
"""
if matrix.dim() == 2:
matrix = matrix.view(-1,2,3)
A_batch = matrix[:,:,:2]
if A_batch.size(0) != x.size(0):
A_batch = A_batch.repeat(x.size(0),1,1)
b_batch = matrix[:,:,2].unsqueeze(1)
# make a meshgrid of normal coordinates
_coords = th_iterproduct(x.size(1),x.size(2))
coords = Variable(_coords.unsqueeze(0).repeat(x.size(0),1,1).float(),
requires_grad=False)
if center:
# shift the coordinates so center is the origin
coords[:,:,0] = coords[:,:,0] - (x.size(1) / 2. + 0.5)
coords[:,:,1] = coords[:,:,1] - (x.size(2) / 2. + 0.5)
# apply the coordinate transformation
new_coords = coords.bmm(A_batch.transpose(1,2)) + b_batch.expand_as(coords)
if center:
# shift the coordinates back so origin is origin
new_coords[:,:,0] = new_coords[:,:,0] + (x.size(1) / 2. + 0.5)
new_coords[:,:,1] = new_coords[:,:,1] + (x.size(2) / 2. + 0.5)
# map new coordinates using bilinear interpolation
x_transformed = F_bilinear_interp2d(x, new_coords)
return x_transformed示例2: F_batch_affine3d
# 需要导入模块: from torch.autograd import Variable [as 别名]
# 或者: from torch.autograd.Variable import bmm [as 别名]
def F_batch_affine3d(x, matrix, center=True):
"""
x : torch.Tensor
shape = (Samples, C, H, W)
NOTE: Assume C is always equal to 1!
matrix : torch.Tensor
shape = (Samples, 6) or (Samples, 2, 3)
Example
-------
>>> x = Variable(torch.zeros(3,1,10,10,10))
>>> x[:,:,3:7,3:7,3:7] = 1
>>> m1 = torch.FloatTensor([[1.2,0,0,0],[0,1.2,0,0],[0,0,1.2,0]])
>>> m2 = torch.FloatTensor([[0.8,0,0,0],[0,0.8,0,0],[0,0,0.8,0]])
>>> m3 = torch.FloatTensor([[1.0,0,0,3],[0,1.0,0,3],[0,0,1.0,3]])
>>> matrix = Variable(torch.stack([m1,m2,m3]))
>>> xx = F_batch_affine3d(x,matrix)
"""
if matrix.dim() == 2:
matrix = matrix.view(-1,3,4)
A_batch = matrix[:,:3,:3]
b_batch = matrix[:,:3,3].unsqueeze(1)
# make a meshgrid of normal coordinates
_coords = th_iterproduct(x.size(2),x.size(3),x.size(4))
coords = Variable(_coords.unsqueeze(0).repeat(x.size(0),1,1).float(),
requires_grad=False)
if center:
# shift the coordinates so center is the origin
coords[:,:,0] = coords[:,:,0] - (x.size(2) / 2. + 0.5)
coords[:,:,1] = coords[:,:,1] - (x.size(3) / 2. + 0.5)
coords[:,:,2] = coords[:,:,2] - (x.size(4) / 2. + 0.5)
# apply the coordinate transformation
new_coords = coords.bmm(A_batch.transpose(1,2)) + b_batch.expand_as(coords)
if center:
# shift the coordinates back so origin is origin
new_coords[:,:,0] = new_coords[:,:,0] + (x.size(2) / 2. + 0.5)
new_coords[:,:,1] = new_coords[:,:,1] + (x.size(3) / 2. + 0.5)
new_coords[:,:,2] = new_coords[:,:,2] + (x.size(4) / 2. + 0.5)
# map new coordinates using bilinear interpolation
x_transformed = F_batch_trilinear_interp3d(x, new_coords)
return x_transformed