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- """
- PointNet++ Model for point clouds classification
- """
-
- import os
- import sys
- BASE_DIR = os.path.dirname(__file__)
- sys.path.append(BASE_DIR)
- sys.path.append(os.path.join(BASE_DIR, '../utils'))
- import tensorflow as tf
- import numpy as np
- import tf_util
- from pointnet_util import *
-
- def placeholder_inputs(batch_size, num_point):
- pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3))
- labels_pl = tf.placeholder(tf.int32, shape=(batch_size))
- return pointclouds_pl, labels_pl
-
- def get_model(point_cloud, is_training, bn_decay=None):
- """ Classification PointNet, input is BxNx3, output Bx40 """
- batch_size = point_cloud.get_shape()[0].value
- num_point = point_cloud.get_shape()[1].value
- end_points = {}
- l0_xyz = point_cloud
- l0_points = None
- end_points['l0_xyz'] = l0_xyz
-
- # Set abstraction layers
- # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4).
- # So we only use NCHW for layer 1 until this issue can be resolved.
- l1_xyz, l1_points, l1_indices, xyz1_feature = LSA_layer(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=[64,64],
- group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
- l2_xyz, l2_points, l2_indices, xyz2_feature = LSA_layer(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=[64,64],
- group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2', xyz_feature=xyz1_feature)
- l3_xyz, l3_points, l3_indices, xyz3_feature = LSA_layer(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=[64,64],
- group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3', xyz_feature=xyz2_feature,end=True)
-
- # Fully connected layers
- net = tf.reshape(l3_points, [batch_size, -1])
- net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
- net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')
- net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay)
- net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2')
- net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')
-
- return net, end_points
-
-
- def get_loss(pred, label, end_points):
- """ pred: B*NUM_CLASSES,
- label: B, """
- labels = tf.one_hot(indices=label, depth=40)
- loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=pred, label_smoothing=0.2)
- #loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=pred)
- classify_loss = tf.reduce_mean(loss)
- tf.summary.scalar('classify loss', classify_loss)
- tf.add_to_collection('losses', classify_loss)
- return classify_loss
-
-
- if __name__=='__main__':
- with tf.Graph().as_default():
- inputs = tf.zeros((32,1024,3))
- output, _ = get_model(inputs, tf.constant(True))
- print(output)
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