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Update segmentator_3d_asymm_spconv.py #113

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94 changes: 51 additions & 43 deletions network/segmentator_3d_asymm_spconv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,8 @@
# @file: segmentator_3d_asymm_spconv.py

import numpy as np
import spconv
#import spconv
import spconv.pytorch as spconv
import torch
from torch import nn

Expand Down Expand Up @@ -49,16 +50,19 @@ def __init__(self, in_filters, out_filters, kernel_size=(3, 3, 3), stride=1, ind
self.conv1 = conv1x3(in_filters, out_filters, indice_key=indice_key + "bef")
self.bn0 = nn.BatchNorm1d(out_filters)
self.act1 = nn.LeakyReLU()

#elf.conv1_2 = conv3x1(out_filters, out_filters, indice_key=indice_key + "bef")
self.conv1_2 = conv1x3(out_filters, out_filters, indice_key=indice_key + "bef")

self.conv1_2 = conv3x1(out_filters, out_filters, indice_key=indice_key + "bef")
self.bn0_2 = nn.BatchNorm1d(out_filters)
self.act1_2 = nn.LeakyReLU()

self.conv2 = conv3x1(in_filters, out_filters, indice_key=indice_key + "bef")
self.act2 = nn.LeakyReLU()
self.bn1 = nn.BatchNorm1d(out_filters)

self.conv3 = conv1x3(out_filters, out_filters, indice_key=indice_key + "bef")
#self.conv3 = conv1x3(out_filters, out_filters, indice_key=indice_key + "bef")
self.conv3 = conv3x1(out_filters, out_filters, indice_key=indice_key + "bef")
self.act3 = nn.LeakyReLU()
self.bn2 = nn.BatchNorm1d(out_filters)

Expand All @@ -72,21 +76,21 @@ def weight_initialization(self):

def forward(self, x):
shortcut = self.conv1(x)
shortcut.features = self.act1(shortcut.features)
shortcut.features = self.bn0(shortcut.features)
shortcut = shortcut.replace_feature(self.act1(shortcut.features))
shortcut = shortcut.replace_feature(self.bn0(shortcut.features))

shortcut = self.conv1_2(shortcut)
shortcut.features = self.act1_2(shortcut.features)
shortcut.features = self.bn0_2(shortcut.features)
shortcut = shortcut.replace_feature(self.act1_2(shortcut.features))
shortcut = shortcut.replace_feature(self.bn0_2(shortcut.features))

resA = self.conv2(x)
resA.features = self.act2(resA.features)
resA.features = self.bn1(resA.features)
resA = resA.replace_feature(self.act2(resA.features))
reaA = resA.replace_feature(self.bn1(resA.features))

resA = self.conv3(resA)
resA.features = self.act3(resA.features)
resA.features = self.bn2(resA.features)
resA.features = resA.features + shortcut.features
resA = resA.replace_feature(self.act3(resA.features))
resA = resA.replace_feature(self.bn2(resA.features))
resA = resA.replace_feature(resA.features + shortcut.features)

return resA

Expand All @@ -102,15 +106,17 @@ def __init__(self, in_filters, out_filters, dropout_rate, kernel_size=(3, 3, 3),
self.act1 = nn.LeakyReLU()
self.bn0 = nn.BatchNorm1d(out_filters)

self.conv1_2 = conv1x3(out_filters, out_filters, indice_key=indice_key + "bef")
#self.conv1_2 = conv1x3(out_filters, out_filters, indice_key=indice_key + "bef")
self.conv1_2 = conv3x1(out_filters, out_filters, indice_key=indice_key + "bef")
self.act1_2 = nn.LeakyReLU()
self.bn0_2 = nn.BatchNorm1d(out_filters)

self.conv2 = conv1x3(in_filters, out_filters, indice_key=indice_key + "bef")
self.act2 = nn.LeakyReLU()
self.bn1 = nn.BatchNorm1d(out_filters)

self.conv3 = conv3x1(out_filters, out_filters, indice_key=indice_key + "bef")
#self.conv3 = conv3x1(out_filters, out_filters, indice_key=indice_key + "bef")
self.conv3 = conv1x3(out_filters, out_filters, indice_key=indice_key + "bef")
self.act3 = nn.LeakyReLU()
self.bn2 = nn.BatchNorm1d(out_filters)

Expand All @@ -131,22 +137,22 @@ def weight_initialization(self):

def forward(self, x):
shortcut = self.conv1(x)
shortcut.features = self.act1(shortcut.features)
shortcut.features = self.bn0(shortcut.features)
shortcut = shortcut.replace_feature(self.act1(shortcut.features))
shortcut = shortcut.replace_feature(self.bn0(shortcut.features))

shortcut = self.conv1_2(shortcut)
shortcut.features = self.act1_2(shortcut.features)
shortcut.features = self.bn0_2(shortcut.features)
shortcut = shortcut.replace_feature(self.act1_2(shortcut.features))
shortcut = shortcut.replace_feature(self.bn0_2(shortcut.features))

resA = self.conv2(x)
resA.features = self.act2(resA.features)
resA.features = self.bn1(resA.features)
resA = resA.replace_feature(self.act2(resA.features))
resA = resA.replace_feature(self.bn1(resA.features))

resA = self.conv3(resA)
resA.features = self.act3(resA.features)
resA.features = self.bn2(resA.features)
resA = resA.replace_feature(self.act3(resA.features))
resA = resA.replace_feature(self.bn2(resA.features))

resA.features = resA.features + shortcut.features
resA = resA.replace_feature(resA.features + shortcut.features)

if self.pooling:
resB = self.pool(resA)
Expand All @@ -167,11 +173,13 @@ def __init__(self, in_filters, out_filters, kernel_size=(3, 3, 3), indice_key=No
self.act1 = nn.LeakyReLU()
self.bn1 = nn.BatchNorm1d(out_filters)

self.conv2 = conv3x1(out_filters, out_filters, indice_key=indice_key)
#self.conv3 = conv3x1(out_filters, out_filters, indice_key=indice_key + "bef")
self.conv2 = conv1x3(out_filters, out_filters, indice_key=indice_key)
self.act2 = nn.LeakyReLU()
self.bn2 = nn.BatchNorm1d(out_filters)

self.conv3 = conv3x3(out_filters, out_filters, indice_key=indice_key)
#self.conv3 = conv3x3(out_filters, out_filters, indice_key=indice_key)
self.conv3 = conv1x3(out_filters, out_filters, indice_key=indice_key)
self.act3 = nn.LeakyReLU()
self.bn3 = nn.BatchNorm1d(out_filters)
# self.dropout3 = nn.Dropout3d(p=dropout_rate)
Expand All @@ -189,25 +197,25 @@ def weight_initialization(self):

def forward(self, x, skip):
upA = self.trans_dilao(x)
upA.features = self.trans_act(upA.features)
upA.features = self.trans_bn(upA.features)
upA = upA.replace_feature(self.trans_act(upA.features))
upA = upA.replace_feature(self.trans_bn(upA.features))

## upsample
upA = self.up_subm(upA)

upA.features = upA.features + skip.features
upA = upA.replace_feature(upA.features + skip.features)

upE = self.conv1(upA)
upE.features = self.act1(upE.features)
upE.features = self.bn1(upE.features)
upE = upE.replace_feature(self.act1(upE.features))
upE = upE.replace_feature(self.bn1(upE.features))

upE = self.conv2(upE)
upE.features = self.act2(upE.features)
upE.features = self.bn2(upE.features)
upE = upE.replace_feature(self.act2(upE.features))
upE = upE.replace_feature(self.bn2(upE.features))

upE = self.conv3(upE)
upE.features = self.act3(upE.features)
upE.features = self.bn3(upE.features)
upE = upE.replace_feature(self.act3(upE.features))
upE = upE.replace_feature(self.bn3(upE.features))

return upE

Expand All @@ -229,19 +237,19 @@ def __init__(self, in_filters, out_filters, kernel_size=(3, 3, 3), stride=1, ind

def forward(self, x):
shortcut = self.conv1(x)
shortcut.features = self.bn0(shortcut.features)
shortcut.features = self.act1(shortcut.features)
shortcut = shortcut.replace_feature(self.bn0(shortcut.features))
shortcut = shortcut.replace_feature(self.act1(shortcut.features))

shortcut2 = self.conv1_2(x)
shortcut2.features = self.bn0_2(shortcut2.features)
shortcut2.features = self.act1_2(shortcut2.features)
shortcut2 = shortcut2.replace_feature(self.bn0_2(shortcut2.features))
shortcut2 = shortcut2.replace_feature(self.act1_2(shortcut2.features))

shortcut3 = self.conv1_3(x)
shortcut3.features = self.bn0_3(shortcut3.features)
shortcut3.features = self.act1_3(shortcut3.features)
shortcut.features = shortcut.features + shortcut2.features + shortcut3.features
shortcut3 = shortcut.replace_feature(self.bn0_3(shortcut3.features))
shortcut3 = shortcut3.replace_feature(self.act1_3(shortcut3.features))
shortcut = shortcut.replace_feature(shortcut.features + shortcut2.features + shortcut3.features)

shortcut.features = shortcut.features * x.features
shortcut = shortcut.replace_feature(shortcut.features * x.features)

return shortcut

Expand Down Expand Up @@ -300,7 +308,7 @@ def forward(self, voxel_features, coors, batch_size):

up0e = self.ReconNet(up1e)

up0e.features = torch.cat((up0e.features, up1e.features), 1)
up0e = up0e.replace_feature(torch.cat((up0e.features, up1e.features), 1))

logits = self.logits(up0e)
y = logits.dense()
Expand Down