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MCGINetworkRTEval.py
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MCGINetworkRTEval.py
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'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
\file MCGINetworkRTEval.py
\copyright Copyright (c) 2019 Visual Computing group of Ulm University,
Germany. See the LICENSE file at the top-level directory of
this distribution.
\author pedro hermosilla ([email protected])
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
import sys
import os
import math
import tensorflow as tf
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(ROOT_DIR, 'MCCNN/tf_ops'))
sys.path.append(os.path.join(ROOT_DIR, 'MCCNN/utils'))
from MCConvBuilder import PointHierarchy, ConvolutionBuilder
from MCNetworkUtils import conv_1x1
def compute_initial_pts(points, batchIds, features, batchSize):
mPointHierarchy = PointHierarchy(points, features, batchIds, [0.01], "MCGIInit_PH1", batchSize, relativeRadius=False)
return mPointHierarchy.sampledIndexs_[0], mPointHierarchy.points_[1]
def create_network(points, batchIds, features,
points2, batchIds2, features2, outputs,
batchSize, k, isTraining,
bnMomentum, brnMomentum, brnClipping,
keepProbConv, keepProbFull,
useConvDropOut = False, useDropOutFull = True,
dataset = 0):
def BN_NL_DP_Conv(layerName, inFeatures):
inFeatures = tf.layers.batch_normalization(inputs = inFeatures, momentum=bnMomentum,
trainable = True, training = isTraining, name = layerName+"_BN", renorm=True,
renorm_clipping=brnClipping, renorm_momentum=brnMomentum)
inFeatures = tf.nn.leaky_relu(inFeatures)
if useConvDropOut:
inFeatures = tf.nn.dropout(inFeatures, keepProbConv)
return inFeatures
def BN_NL_DP_F(layerName, inFeatures):
inFeatures = tf.layers.batch_normalization(inputs = inFeatures, momentum=bnMomentum,
trainable = True, training = isTraining, name = layerName+"_BN", renorm=True,
renorm_clipping=brnClipping, renorm_momentum=brnMomentum)
inFeatures = tf.nn.leaky_relu(inFeatures)
if useDropOutFull:
inFeatures = tf.nn.dropout(inFeatures, keepProbFull)
return inFeatures
############################################ Compute point hierarchy
mPointHierarchy1 = PointHierarchy(points, features, batchIds, [0.05, 0.1, 0.2], "MCGI_PH1", batchSize, relativeRadius=False)
mPointHierarchy2 = PointHierarchy(points2, features2, batchIds2, [], "MCGI_PH2", batchSize, relativeRadius=False)
############################################ Convolutions
mConvBuilder = ConvolutionBuilder(KDEWindow=0.25, relativeRadius=False)
resultColor = []
with tf.variable_scope("feature_channel_scope", reuse=tf.AUTO_REUSE):
for i in range(3):
if dataset == 1:
numInFeatures = 5
inputFeatures = features[:,0:3]
inputFeatures = tf.concat([inputFeatures, features[:,3+i:3+i+1]], axis=1)
inputFeatures = tf.concat([inputFeatures, features[:,6+i:6+i+1]], axis=1)
inputFeatures2 = features2[:,0:3]
elif dataset == 2:
numInFeatures = 7
inputFeatures = features[:,0:3]
inputFeatures = tf.concat([inputFeatures, features[:,3+i:3+i+1]], axis=1)
inputFeatures = tf.concat([inputFeatures, features[:,6+i:6+i+1]], axis=1)
inputFeatures = tf.concat([inputFeatures, features[:,9+i:9+i+1]], axis=1)
inputFeatures = tf.concat([inputFeatures, features[:,12:13]], axis=1)
inputFeatures2 = features2[:,0:3]
inputFeatures2 = tf.concat([inputFeatures2, features2[:,3+i:3+i+1]], axis=1)
inputFeatures2 = tf.concat([inputFeatures2, features2[:,6+i:6+i+1]], axis=1)
inputFeatures2 = tf.concat([inputFeatures2, features2[:,9+i:9+i+1]], axis=1)
inputFeatures2 = tf.concat([inputFeatures2, features2[:,12:13]], axis=1)
else:
numInFeatures = 3
inputFeatures = features
inputFeatures2 = features2
############################################ Encoder
# First convolution
convFeatures1 = mConvBuilder.create_convolution(
convName="Conv_1",
inPointHierarchy=mPointHierarchy1,
inPointLevel=0,
inFeatures=inputFeatures,
inNumFeatures=numInFeatures,
outNumFeatures=k,
convRadius=0.025,
multiFeatureConv=True)
# First pooling
bnConvFeatures1 = BN_NL_DP_Conv("Reduce_Pool_1_In_BN", convFeatures1)
bnConvFeatures1 = conv_1x1("Reduce_Pool_1", bnConvFeatures1, k, k*2)
bnConvFeatures1 = BN_NL_DP_Conv("Reduce_Pool_1_Out_BN", bnConvFeatures1)
poolFeatures1 = mConvBuilder.create_convolution(
convName="Pool_1",
inPointHierarchy=mPointHierarchy1,
inPointLevel=0,
outPointLevel=1,
inFeatures=bnConvFeatures1,
inNumFeatures=k*2,
convRadius=0.05,
KDEWindow= 0.2)
# Second convolution
bnPoolFeatures1 = BN_NL_DP_Conv("Reduce_Conv_2_In_BN", poolFeatures1)
bnPoolFeatures1 = conv_1x1("Reduce_Conv_2", bnPoolFeatures1, k*2, k*2)
bnPoolFeatures1 = BN_NL_DP_Conv("Reduce_Conv_2_Out_BN", bnPoolFeatures1)
convFeatures2 = mConvBuilder.create_convolution(
convName="Conv_2",
inPointHierarchy=mPointHierarchy1,
inPointLevel=1,
inFeatures=bnPoolFeatures1,
inNumFeatures=k*2,
convRadius=0.1)
convFeatures2 = tf.concat([poolFeatures1, convFeatures2], 1)
# Second pooling
bnConvFeatures2 = BN_NL_DP_Conv("Reduce_Pool_2_In_BN", convFeatures2)
bnConvFeatures2 = conv_1x1("Reduce_Pool_2", bnConvFeatures2, k*4, k*4)
bnConvFeatures2 = BN_NL_DP_Conv("Reduce_Pool_2_Out_BN", bnConvFeatures2)
poolFeatures2 = mConvBuilder.create_convolution(
convName="Pool_2",
inPointHierarchy=mPointHierarchy1,
inPointLevel=1,
outPointLevel=2,
inFeatures=bnConvFeatures2,
inNumFeatures=k*4,
convRadius=0.2,
KDEWindow= 0.2)
# Third convolution
bnPoolFeatures2 = BN_NL_DP_Conv("Reduce_Conv_3_In_BN", poolFeatures2)
bnPoolFeatures2 = conv_1x1("Reduce_Conv_3", bnPoolFeatures2, k*4, k*4)
bnPoolFeatures2 = BN_NL_DP_Conv("Reduce_Conv_3_Out_BN", bnPoolFeatures2)
convFeatures3 = mConvBuilder.create_convolution(
convName="Conv_3",
inPointHierarchy=mPointHierarchy1,
inPointLevel=2,
inFeatures=bnPoolFeatures2,
inNumFeatures=k*4,
convRadius=0.2)
convFeatures3 = tf.concat([poolFeatures2, convFeatures3], 1)
# Third pooling
bnConvFeatures3 = BN_NL_DP_Conv("Reduce_Pool_3_In_BN", convFeatures3)
bnConvFeatures3 = conv_1x1("Reduce_Pool_3", bnConvFeatures3, k*8, k*8)
bnConvFeatures3 = BN_NL_DP_Conv("Reduce_Pool_3_Out_BN", bnConvFeatures3)
poolFeatures3 = mConvBuilder.create_convolution(
convName="Pool_3",
inPointHierarchy=mPointHierarchy1,
inPointLevel=2,
outPointLevel=3,
inFeatures=bnConvFeatures3,
inNumFeatures=k*8,
convRadius=0.4,
KDEWindow= 0.2)
# Fourth convolution
bnPoolFeatures3 = BN_NL_DP_Conv("Reduce_Conv_4_In_BN", poolFeatures3)
bnPoolFeatures3 = conv_1x1("Reduce_Conv_4", bnPoolFeatures3, k*8, k*8)
bnPoolFeatures3 = BN_NL_DP_Conv("Reduce_Conv_4_Out_BN", bnPoolFeatures3)
convFeatures4 = mConvBuilder.create_convolution(
convName="Conv_4",
inPointHierarchy=mPointHierarchy1,
inPointLevel=3,
inFeatures=bnPoolFeatures3,
inNumFeatures=k*8,
convRadius=1.0,
KDEWindow= 0.2)
############################################ Decoder
# Third upsampling
bnConvFeatures4 = BN_NL_DP_Conv("Up_3_BN", convFeatures4)
upFeatures3 = mConvBuilder.create_convolution(
convName="Up_3",
inPointHierarchy=mPointHierarchy1,
inPointLevel=3,
outPointLevel=2,
inFeatures=bnConvFeatures4,
inNumFeatures=k*8,
convRadius=0.4,
KDEWindow= 0.2)
upFeatures3 = tf.concat([convFeatures3, upFeatures3], 1)
upFeatures3 = BN_NL_DP_Conv("Up_3_Reduce_BN", upFeatures3)
upFeatures3 = conv_1x1("Up_3_Reduce", upFeatures3, k*16, k*4)
# Second upsampling
bnUpFeatures3 = BN_NL_DP_Conv("Up_2_BN", upFeatures3)
upFeatures2 = mConvBuilder.create_convolution(
convName="Up_2",
inPointHierarchy=mPointHierarchy1,
inPointLevel=2,
outPointLevel=1,
inFeatures=bnUpFeatures3,
inNumFeatures=k*4,
convRadius=0.2,
KDEWindow= 0.2)
upFeatures2 = tf.concat([convFeatures2, upFeatures2], 1)
upFeatures2 = BN_NL_DP_Conv("Up_2_Reduce_BN", upFeatures2)
upFeatures2 = conv_1x1("Up_2_Reduce", upFeatures2, k*8, k*2)
# First upsampling
bnUpFeatures2 = BN_NL_DP_Conv("Up_1_2_BN", upFeatures2)
upFeatures1 = mConvBuilder.create_convolution(
convName="Up_1_2",
inPointHierarchy=mPointHierarchy1,
inPointLevel=1,
outPointLevel=0,
inFeatures=bnUpFeatures2,
inNumFeatures=k*2,
convRadius=0.1,
KDEWindow= 0.2)
upFeatures1 = tf.concat([convFeatures1, upFeatures1], 1)
upFeatures1 = BN_NL_DP_Conv("Up_1_Reduce_BN", upFeatures1)
upFeatures1 = conv_1x1("Up_1_Reduce", upFeatures1, k*3, k)
bnUpFeatures1 = BN_NL_DP_Conv("Up_1_BN", upFeatures1)
finalFeatures = mConvBuilder.create_convolution(
convName="Final_Conv",
inPointHierarchy=mPointHierarchy1,
outPointHierarchy=mPointHierarchy2,
inPointLevel=0,
outPointLevel=0,
inFeatures=bnUpFeatures1,
inNumFeatures=k,
convRadius=0.05,
KDEWindow= 0.2)
finalFeatures = BN_NL_DP_F("Final_MLP1_BN", finalFeatures)
finalFeatures = tf.concat([finalFeatures, inputFeatures2], 1)
finalFeatures = conv_1x1("Final_MLP1", finalFeatures, k + numInFeatures, k)
finalFeatures = BN_NL_DP_F("Final_MLP2_BN", finalFeatures)
predVals = conv_1x1("Final_MLP2", finalFeatures, k, 1)
resultColor.append(predVals)
return tf.concat(resultColor, axis=1)