Python 构造矩阵中单个变量的自动加载失败
我想创建一个Python 构造矩阵中单个变量的自动加载失败,python,torch,autograd,Python,Torch,Autograd,我想创建一个4x4矩阵来变形我的4xn张量图像，并将其优化为损失函数。损失函数是有效的，但我初始化变形的方式有点错误，尽管我使用了纯向量演算来确保它应该是可微的 def affine_transform( points1, points2, stepSize=0.01, iterations=2000): def zero_grads(elements): for elem in elements:
4x44xndef affine_transform(
points1,
points2,
stepSize=0.01,
iterations=2000):
def zero_grads(elements):
for elem in elements:
elem.grad.data.zero_()
# Constructing the affine transformation matrix
## translation in x and y which should be optimised
tx = tc.zeros((1, 1), requires_grad=True).float()
ty = tc.zeros((1, 1), requires_grad=True).float()
# Construct vectors from the single variables size 4x1
v1 = tc.from_numpy(np.array([[0, 0, 0, 1]]).T).float()
vx = v1.mm(tx)
v2 = tc.from_numpy(np.array([[0, 0, 1, 0]]).T).float()
vy = v2.mm(ty)
## use outer product to increase dimentionality to 4x4
u = tc.from_numpy(np.array([[1, 0, 0, 0]]).T).float()
T = u.mm(vx.T) + u.mm(vy.T).float()
I = tc.from_numpy(np.eye(4)).float()
R = I + T
# Add ones to data
n = points1.shape[1]
one = np.ones(n)
p1 = tc.from_numpy(np.vstack((points1, one))).float()
p2 = tc.from_numpy(np.vstack((points2, one))).float()
# Setup optimizer to tx and ty only
optimizer = optim.Adam([tx, ty], stepSize)
count = 0
first = True
while True:
# Constructing the affine transformation matrix
R = I + T # + P
# Calculate transition image (works as expected)
transImg = bilinear_interpolate_torch(
p1, tc.mm(R, p1))
# Calculate loss
loss = tc_ssd(p2, transImg)
if first:
loss.backward(retain_graph=True)
else:
loss.backward()
# take a step
optimizer.step()
# Zero the gradients
zero_grads([tx, ty])
if count >= iterations and iterations != -1:
print("Max iterations reached!")
break
count += 1
R = I + T
H3 = bilinear_interpolate_torch(p1.float(), tc.mm(R, p1.float()))
return (H3, R)
[W python_anomaly_mode.cpp:60] Warning: Error detected in MmBackward. Traceback of forward call that caused the error:
File "<stdin>", line 1, in <module>
File "/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py", line 314, in <module>
b1mesh.T, b2mesh.T)
File "/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py", line 225, in affine_transform
vy = v2.mm(ty)
(function print_stack)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py", line 314, in <module>
b1mesh.T, b2mesh.T)
File "/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py", line 257, in affine_transform
# Stepping
File "/home/fuzie/.pyenv/versions/mia/lib/python3.8/site-packages/torch/tensor.py", line 185, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/fuzie/.pyenv/versions/mia/lib/python3.8/site-packages/torch/autograd/__init__.py", line 125, in backward
Variable._execution_engine.run_backward(
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1, 1]] is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
[W python\u normal\u mode.cpp:60]警告：在MmBackward中检测到错误。对导致错误的转发呼叫的回溯：
文件“”，第1行，在
文件“/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py”，第314行，在
b1mesh.T，b2mesh.T）
文件“/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py”，第225行，仿射变换
vy=v2.mm（ty）
（功能打印_堆栈）
回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
文件“/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py”，第314行，在
b1mesh.T，b2mesh.T）
文件“/home/fuzie/Dropbox/KU/7.aar/MIA/Handins/hw4/code/affine_im.py”，第257行，仿射变换
#步进
文件“/home/fuzie/.pyenv/versions/mia/lib/python3.8/site packages/torch/tensor.py”，第185行，向后
火炬。自动标记。向后（自我、梯度、保留图、创建图）
文件“/home/fuzie/.pyenv/versions/mia/lib/python3.8/site packages/torch/autograd/_init__.py”，第125行，向后
变量。\u执行\u引擎。向后运行\u(
RuntimeError:梯度计算所需的一个变量已被一个就地操作修改：[torch.FloatTensor[1，1]]为版本1；应为版本0。提示：上面的回溯显示无法计算其梯度的操作。相关变量已在那里或以后的任何地方更改。祝您好运！
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