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RADIUS1 = 0.5
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RADIUS2 = 1.0
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RADIUS3 = 2.0
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RADIUS4 = 4.0
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with tf.variable_scope('sa1') as scope:
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if reuse:
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scope.reuse_variables()
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# Frame 1, Layer 1
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l1_xyz_f1, l1_points_f1, l1_indices_f1 = pointnet_sa_module(l0_xyz_f1, l0_points_f1, npoint=8192, radius=RADIUS1, nsample=256, mlp=[32,32,64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
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end_points['l1_indices_f1'] = l1_indices_f1
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# Frame 1, Layer 2
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l2_xyz_f1, l2_points_f1, l2_indices_f1 = pointnet_sa_module(l1_xyz_f1, l1_points_f1, npoint=2048, radius=RADIUS2, nsample=256, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
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end_points['l2_indices_f1'] = l2_indices_f1
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if not reuse:
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scope.reuse_variables()
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# Frame 2, Layer 1
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l1_xyz_f2, l1_points_f2, l1_indices_f2 = pointnet_sa_module(l0_xyz_f2, l0_points_f2, npoint=8192, radius=RADIUS1, nsample=256, mlp=[32,32,64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1')
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# Frame 2, Layer 2
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l2_xyz_f2, l2_points_f2, l2_indices_f2 = pointnet_sa_module(l1_xyz_f2, l1_points_f2, npoint=2048, radius=RADIUS2, nsample=256, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2')
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_, l2_points_f1_new = flow_embedding_module(l2_xyz_f1, l2_xyz_f2, l2_points_f1, l2_points_f2, radius=3.0, nsample=256, mlp=[128,128,128], is_training=is_training, bn_decay=bn_decay, scope='flow_embedding', bn=True, pooling='max', knn=True, corr_func='concat')
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# Layer 3
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l3_xyz_f1, l3_points_f1, l3_indices_f1 = pointnet_sa_module(l2_xyz_f1, l2_points_f1_new, npoint=512, radius=RADIUS3, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer3')
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end_points['l3_indices_f1'] = l3_indices_f1
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# Layer 4
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l4_xyz_f1, l4_points_f1, l4_indices_f1 = pointnet_sa_module(l3_xyz_f1, l3_points_f1, npoint=256, radius=RADIUS4, nsample=64, mlp=[256,256,512], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer4')
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end_points['l4_indices_f1'] = l4_indices_f1
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# Feature Propagation
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l3_feat_f1 = set_upconv_module(l3_xyz_f1, l4_xyz_f1, l3_points_f1, l4_points_f1, nsample=4, radius=2.4, mlp=[], mlp2=[256,256], scope='up_sa_layer1', is_training=is_training, bn_decay=bn_decay, knn=True)
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l2_feat_f1 = set_upconv_module(l2_xyz_f1, l3_xyz_f1, tf.concat(axis=-1, values=[l2_points_f1, l2_points_f1_new]), l3_feat_f1, nsample=4, radius=1.2, mlp=[128,128,256], mlp2=[256], scope='up_sa_layer2', is_training=is_training, bn_decay=bn_decay, knn=True)
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l1_feat_f1 = set_upconv_module(l1_xyz_f1, l2_xyz_f1, l1_points_f1, l2_feat_f1, nsample=4, radius=0.6, mlp=[128,128,256], mlp2=[256], scope='up_sa_layer3', is_training=is_training, bn_decay=bn_decay, knn=True)
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l0_feat_f1 = pointnet_fp_module(l0_xyz_f1, l1_xyz_f1, l0_points_f1, l1_feat_f1, [256,256], is_training, bn_decay, scope='fa_layer4')
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# FC layers
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net = tf_util.conv1d(l0_feat_f1, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)
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net = tf_util.conv1d(net, 3, 1, padding='VALID', activation_fn=None, scope='fc2')
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return net, end_points
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def huber_loss(error, delta):
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abs_error = tf.abs(error)
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quadratic = tf.minimum(abs_error, delta)
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linear = (abs_error - quadratic)
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losses = 0.5 * quadratic**2 + delta * linear
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return tf.reduce_mean(losses)
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def get_loss(pred, label, mask, end_points):
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""" pred: BxNx3,
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label: BxNx3,
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mask: BxN
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"""
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batch_size = pred.get_shape()[0].value
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num_point = pred.get_shape()[1].value
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l2_loss = tf.reduce_mean(mask * tf.reduce_sum((pred-label) * (pred-label), axis=2) / 2.0)
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tf.summary.scalar('l2 loss', l2_loss)
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tf.add_to_collection('losses', l2_loss)
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return l2_loss
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if __name__=='__main__':
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with tf.Graph().as_default():
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inputs = tf.zeros((32,1024*2,6))
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outputs = get_model(inputs, tf.constant(True))
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print(outputs)
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# <FILESEP>
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# -*- coding: utf-8 -*-
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#/usr/bin/python2
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import tensorflow as tf
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import numpy as np
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from tensorflow.contrib.rnn import MultiRNNCell
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from tensorflow.contrib.rnn import RNNCell
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from params import Params
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from zoneout import ZoneoutWrapper
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'''
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attention weights from https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf
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W_u^Q.shape: (2 * attn_size, attn_size)
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W_u^P.shape: (2 * attn_size, attn_size)
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W_v^P.shape: (attn_size, attn_size)
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W_g.shape: (4 * attn_size, 4 * attn_size)
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W_h^P.shape: (2 * attn_size, attn_size)
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W_v^Phat.shape: (2 * attn_size, attn_size)
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W_h^a.shape: (2 * attn_size, attn_size)
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W_v^Q.shape: (attn_size, attn_size)
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'''
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def get_attn_params(attn_size,initializer = tf.truncated_normal_initializer):
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'''
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Args:
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attn_size: the size of attention specified in https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf
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