Fixed Spherical_Chol not accepting batches
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@ -518,7 +518,6 @@ class CholNet(nn.Module):
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return chol
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def _chol_from_sphe_chol(self, sphe_chol):
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# TODO: Test with batched data
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# TODO: Make efficient more
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# Note:
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# We must should ensure:
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@ -527,16 +526,21 @@ class CholNet(nn.Module):
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# We already ensure S > 0 in _chol_from_flat_sphe_chol
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# We ensure < pi by applying tanh*pi to all applicable elements
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batch = (len(sphe_chol.shape) == 3)
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batch_size = sphe_chol.shape[0]
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S = sphe_chol
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n = sphe_chol.shape[-1]
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L = th.zeros_like(sphe_chol)
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for i in range(n):
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#t = 1
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t = th.Tensor([1])[0]
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if batch:
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t = t.expand((batch_size, 1))
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#s = ''
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for j in range(i+1):
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#maybe_cos = 1
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maybe_cos = th.Tensor([1])[0]
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if batch:
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maybe_cos = maybe_cos.expand((batch_size, 1))
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#s_maybe_cos = ''
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if i != j and j < n-1 and i < n:
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if batch:
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@ -545,14 +549,19 @@ class CholNet(nn.Module):
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maybe_cos = th.cos(th.tanh(S[i, j+1])*pi)
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#s_maybe_cos = 'cos([l_'+str(i+1)+']_'+str(j+2)+')'
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if batch:
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L[:, i, j] = S[:, i, 0] * t * maybe_cos
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# try:
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L[:, i, j] = (S[:, i, 0] * t.T) * maybe_cos.T
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# except:
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# import pdb
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# pdb.set_trace()
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else:
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L[i, j] = S[i, 0] * t * maybe_cos
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# print('[L_'+str(i+1)+']_'+str(j+1) +
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# '=[l_'+str(i+1)+']_1'+s+s_maybe_cos)
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if j <= i and j < n-1 and i < n:
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if batch:
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t *= th.sin(th.tanh(S[:, i, j+1])*pi)
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tc = t.clone()
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t = (tc.T * th.sin(th.tanh(S[:, i, j+1])*pi)).T
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else:
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t *= th.sin(th.tanh(S[i, j+1])*pi)
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#s += 'sin([l_'+str(i+1)+']_'+str(j+2)+')'
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