Created
May 5, 2019 14:00
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random_direction1 = [] | |
random_direction2 = [] | |
for w in copy_of_the_weights: | |
if w.dim() == 1: | |
random_direction1.append(torch.zeros_like(w)) | |
random_direction2.append(torch.zeros_like(w)) | |
else: | |
random_vector = w.clone().cpu().numpy() | |
random_vector1 = random_vector - random_vector.mean((2,3),keepdims=True) | |
random_vector2 = random_vector - random_vector.mean((0,1),keepdims=True) | |
random_vector2 = np.transpose(random_vector2,(2,3,0,1)) | |
sigma1 = tf.matmul(tf.transpose(random_vector1,(0,1,3,2)),random_vector1) / random_vector1.shape[3] | |
sigma2 = tf.matmul(tf.transpose(random_vector2,(0,1,3,2)),random_vector2) / random_vector2.shape[3] | |
s1,u1,v1 = tf.linalg.svd(sigma1,False) | |
s2,u2,v2 = tf.linalg.svd(sigma2,False) | |
tmp1 = tf.matmul(u1,1/(tf.sqrt(tf.linalg.diag(s1))+1e-5)) | |
tmp1 = tmp1 @ tf.transpose(u1,(0,1,3,2)) | |
tmp2 = tf.matmul(u2,1/(tf.sqrt(tf.linalg.diag(s2))+1e-5)) | |
tmp2 = tmp2 @ tf.transpose(u2,(0,1,3,2)) | |
random_vector1 = random_vector1 @ tf.transpose(tmp1,(0,1,3,2)) | |
random_vector2 = random_vector2 @ tf.transpose(tmp2,(0,1,3,2)) | |
random_vector2 = tf.transpose(random_vector2,(2,3,0,1)) | |
random_vector1 = torch.from_numpy(random_vector1.eval()).cuda() | |
random_vector2 = torch.from_numpy(random_vector2.eval()).cuda() | |
w_norm = w.view((w.shape[0],-1)) .norm(dim=(1),keepdim=True)[:,:,None,None] | |
d_norm1 = random_vector1.view((random_vector1.shape[0],-1)).norm(dim=(1),keepdim=True)[:,:,None,None] | |
d_norm2 = random_vector2.view((random_vector2.shape[0],-1)).norm(dim=(1),keepdim=True)[:,:,None,None] | |
random_vector1 = random_vector1 * (w_norm/(d_norm1.cuda()+1e-10)) | |
random_vector2 = random_vector2 * (w_norm/(d_norm2.cuda()+1e-10)) | |
print(random_vector1.shape) | |
print(random_vector2.shape) | |
random_direction1.append(random_vector1) | |
random_direction2.append(random_vector2) |
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