I-m trying to run my python program it seems that it should run smoothly however I encounter an error that I haven't seen before it says:
free(): invalid pointer
Aborted (core dumped)
However I'm not sure how to try and fix error since it doesn't give me too much information about the problem itself.
At first I thought it should be a problem with the sizes of the tensor in my network however they are completely fine. I've google the problem a little and found that I can see that is a problem with allocating memory where I shouldn't, but I don't know how to fix this problem
My code is divided in two different files, and I use two libraries to be able to use Sinkhorn loss function and make sample randomly a mesh.
import argparse
import point_cloud_utils as pcu
import time
import numpy as np
import torch
import torch.nn as nn
from fml.nn import SinkhornLoss
import common
def main():
# x is a tensor of shape [n, 3] containing the positions of the vertices that
x = torch._C.from_numpy(common.loadpointcloud("sphere.txt"))
# t is a tensor of shape [n, 3] containing a set of nicely distributed samples in the unit cube
v, f = common.unit_cube()
t = torch._C.sample_mesh_lloyd(pcu.lloyd(v,f,x.shape[0]).astype(np.float32)) # sample randomly a point cloud (cube for now?)
# The model is a simple fully connected network mapping a 3D parameter point to 3D
phi = common.MLP(in_dim=3, out_dim=3)
# Eps is 1/lambda and max_iters is the maximum number of Sinkhorn iterations to do
emd_loss_fun = SinkhornLoss(eps=1e-3, max_iters=20,
stop_thresh=1e-3, return_transport_matrix=True)
mse_loss_fun = torch.nn.MSELoss()
# Adam optimizer at first
optimizer = torch.optim.Adam(phi.parameters(), lr= 10e-3)
fit_start_time = time.time()
for epoch in range(100):
optimizer.zero_grad()
# Do the forward pass of the neural net, evaluating the function at the parametric points
y = phi(t)
# Compute the Sinkhorn divergence between the reconstruction*(using the francis library) and the target
# NOTE: The Sinkhorn function expects a batch of b point sets (i.e. tensors of shape [b, n, 3])
# since we only have 1, we unsqueeze so x and y have dimension [1, n, 3]
with torch.no_grad():
_, P = emd_loss_fun(phi(t).unsqueeze(0), x.unsqueeze(0))
# Project the transport matrix onto the space of permutation matrices and compute the L-2 loss
# between the permuted points
loss = mse_loss_fun(y[P.squeeze().max(0)[1], :], x)
# loss = mse_loss_fun(P.squeeze() @ y, x) # Use the transport matrix directly
# Take an optimizer step
loss.backward()
optimizer.step()
print("Epoch %d, loss = %f" % (epoch, loss.item()))
fit_end_time = time.time()
print("Total time = %f" % (fit_end_time - fit_start_time))
# Plot the ground truth, reconstructed points, and a mesh representing the fitted function, phi
common.visualitation(x,t,phi)
if __name__ == "__main__":
main()
The error message is: free(): invalid pointer Aborted (core dumped)
That again doesn't help me that much. I'll appreciate it a lot if someone has any idea what is happening or if you know more about this error.
