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three_nn gpu implementation. #96

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three_nn gpu implementation. #96

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13 changes: 12 additions & 1 deletion utils/pointnet_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,6 +195,17 @@ def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_l
new_points_concat = tf.concat(new_points_list, axis=-1)
return new_xyz, new_points_concat

def three_nn_gpu(xyz1, xyz2):
s1=tf.shape(xyz1)
s2=tf.shape(xyz2)
xyz1=tf.reshape(xyz1, (s1[0],s1[1],1,-1))
xyz2=tf.reshape(xyz2, (s2[0],1,s2[1],-1))
dist=xyz2-xyz1
dist=dist*dist
dist=tf.reduce_sum(dist,-1)
if dist.shape[-1]==1:
dist=tf.tile(dist,(1,1,3))
return tf.nn.top_k(-dist, k=3)

def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True):
''' PointNet Feature Propogation (FP) Module
Expand All @@ -208,7 +219,7 @@ def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay,
new_points: (batch_size, ndataset1, mlp[-1]) TF tensor
'''
with tf.variable_scope(scope) as sc:
dist, idx = three_nn(xyz1, xyz2)
dist, idx = three_nn_gpu(xyz1, xyz2)
dist = tf.maximum(dist, 1e-10)
norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True)
norm = tf.tile(norm,[1,1,3])
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