I converted this function from tensorflow to pytorch
import tensorflow as tf
def gaussian_log_density(samples, means, chol_covars):
covar_logdet = 2 * tf.reduce_sum(tf.math.log(tf.linalg.diag_part(chol_covars)), -1)
diff = tf.expand_dims(samples - means, -1)
exp_term = tf.reduce_sum(tf.square(tf.linalg.triangular_solve(chol_covars, diff)), axis=[-2, -1])
return - 0.5 * (tf.cast(tf.shape(samples)[-1], exp_term.dtype) * np.log(2 * np.pi) + covar_logdet + exp_term)
and I am wondering whether I correctly converted tf.linalg.diag_part in tf.reduce_sum(..., axis=[-2, -1]) in my implementation?
def gaussian_log_density(samples, means, chol_covars):
covar_logdet = 2 *torch.sum(torch.log(torch.diag_embed(chol_covars, offset=0, dim1=-2, dim2=-1)+1e-10),dim=-1)
diff = torch.unsqueeze(samples - means, -1)
exp_term = torch.sum(torch.square(torch.triangular_solve(chol_covars, diff)), (-2, -1))
return - 0.5 * (samples.size()[-1].type( exp_term.dtype) * torch.log(2 * torch.tensor(np.pi)) + covar_logdet + exp_term)
if __name__=="__main__":
a = torch.tensor([2.0, 1.1, 1.2])
b =torch.tensor([1, 2.4, 5.35])
s= torch.tensor([1.02, 0.9, 1.1])
print(gaussian_log_density(s, a, b))
since I am getting this error just with testing my code:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-44-ab80d557c449> in <module>()
10 b =torch.tensor([1, 2.4, 5.35])
11 s= torch.tensor([1.02, 0.9, 1.1])
---> 12 print(gaussian_log_density(s, a, b))
<ipython-input-44-ab80d557c449> in gaussian_log_density(samples, means, chol_covars)
3 print(covar_logdet)
4 diff = torch.unsqueeze(samples - means, -1)
----> 5 exp_term = torch.sum(torch.square(torch.triangular_solve(chol_covars, diff)), (-2, -1))
6 return - 0.5 * (samples.size()[-1].type( exp_term.dtype) * torch.log(2 * torch.tensor(np.pi)) + covar_logdet + exp_term)
7
RuntimeError: torch.triangular_solve: Expected b to have at least 2 dimensions, but it has 1 dimensions instead
Thanks.