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autoencoder_pos.py
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import torch
import MinkowskiEngine as ME
from transformer_pos import Pct
from data_utils import isin, istopk
class InceptionResNet(torch.nn.Module):
"""Inception Residual Network
"""
def __init__(self, channels):
super().__init__()
self.conv0_0 = ME.MinkowskiConvolution(
in_channels=channels,
out_channels=channels//4,
kernel_size= 3,
stride=1,
bias=True,
dimension=3)
self.conv0_1 = ME.MinkowskiConvolution(
in_channels=channels//4,
out_channels=channels//2,
kernel_size= 3,
stride=1,
bias=True,
dimension=3)
self.conv1_0 = ME.MinkowskiConvolution(
in_channels=channels,
out_channels=channels//4,
kernel_size= 1,
stride=1,
bias=True,
dimension=3)
self.conv1_1 = ME.MinkowskiConvolution(
in_channels=channels//4,
out_channels=channels//4,
kernel_size= 3,
stride=1,
bias=True,
dimension=3)
self.conv1_2 = ME.MinkowskiConvolution(
in_channels=channels//4,
out_channels=channels//2,
kernel_size= 1,
stride=1,
bias=True,
dimension=3)
self.relu = ME.MinkowskiReLU(inplace=True)
def forward(self, x):
out0 = self.conv0_1(self.relu(self.conv0_0(x)))
out1 = self.conv1_2(self.relu(self.conv1_1(self.relu(self.conv1_0(x)))))
out = ME.cat(out0, out1) + x
return out
def make_layer(block, block_layers, channels):
"""make stacked InceptionResNet layers.
"""
layers = []
for i in range(block_layers):
layers.append(block(channels=channels))
return torch.nn.Sequential(*layers)
class Encoder(torch.nn.Module):
def __init__(self, channels=[1,16,32,64,32,8]):
super().__init__()
self.conv0 = ME.MinkowskiConvolution(
in_channels=channels[0],
out_channels=channels[1],
kernel_size=3,
stride=1,
bias=True,
dimension=3)
self.down_0 = ME.MinkowskiConvolution(
in_channels=channels[1],
out_channels=channels[2],
kernel_size=2,
stride=2,
bias=True,
dimension=3)
self.block0 = make_layer(
block=InceptionResNet,
block_layers=3,
channels=channels[2])
self.conv1 = ME.MinkowskiConvolution(
in_channels=channels[2],
out_channels=channels[2],
kernel_size=3,
stride=1,
bias=True,
dimension=3)
self.down_1 = ME.MinkowskiConvolution(
in_channels=channels[2],
out_channels=channels[3],
kernel_size=2,
stride=2,
bias=True,
dimension=3)
self.block1 = make_layer(
block=InceptionResNet,
block_layers=3,
channels=channels[3])
self.conv2 = ME.MinkowskiConvolution(
in_channels=channels[3],
out_channels=channels[3],
kernel_size=3,
stride=1,
bias=True,
dimension=3)
self.down_2 = ME.MinkowskiConvolution(
in_channels=channels[3],
out_channels=channels[4],
kernel_size=2,
stride=2,
bias=True,
dimension=3)
self.block2 = make_layer(
block=InceptionResNet,
block_layers=3,
channels=channels[4])
self.conv3 = ME.MinkowskiConvolution(
in_channels=channels[4],
out_channels=channels[5],
kernel_size=3,
stride=1,
bias=True,
dimension=3)
self.Pct_0=Pct(channels[2])
self.Pct_1=Pct(channels[3])
self.Pct_2=Pct(channels[4])
self.relu = ME.MinkowskiReLU(inplace=True)
def forward(self, x):
out0 = self.relu(self.conv0(x))
out0 = self.relu(self.down_0(out0))
out0 = self.block0(out0)
out0 = self.Pct_0(out0)
out1=self.relu(self.conv1(out0))
out1=self.relu(self.down_1(out1))
out1 = self.block1(out1)
out1 = self.Pct_1(out1)
out2=self.relu(self.conv2(out1))
out2 = self.relu(self.down_2(out2))
out2 = self.block2(out2)
out2 = self.Pct_2(out2)
out2 = self.conv3(out2)
return [out2, out1, out0]
class Decoder(torch.nn.Module):
"""the decoding network with upsampling.
"""
def __init__(self, channels=[8,64,32,16]):
super().__init__()
self.up0 = ME.MinkowskiGenerativeConvolutionTranspose(
in_channels=channels[0],
out_channels=channels[1],
kernel_size=2,
stride=2,
bias=True,
dimension=3)
self.conv0 = ME.MinkowskiConvolution(
in_channels=channels[1],
out_channels=channels[1],
kernel_size= 3,
stride=1,
bias=True,
dimension=3)
self.block0 = make_layer(
block=InceptionResNet,
block_layers=3,
channels=channels[1])
self.conv0_cls = ME.MinkowskiConvolution(
in_channels=channels[1],
out_channels=1,
kernel_size=3,
stride=1,
bias=True,
dimension=3)
self.up1 = ME.MinkowskiGenerativeConvolutionTranspose(
in_channels=channels[1],
out_channels=channels[1],
kernel_size=2,
stride=2,
bias=True,
dimension=3)
self.conv1 = ME.MinkowskiConvolution(
in_channels=channels[1],
out_channels=channels[2],
kernel_size=3,
stride=1,
bias=True,
dimension=3)
self.block1 = make_layer(
block=InceptionResNet,
block_layers=3,
channels=channels[2])
self.conv1_cls = ME.MinkowskiConvolution(
in_channels=channels[2],
out_channels=1,
kernel_size=3,
stride=1,
bias=True,
dimension=3)
self.up2 = ME.MinkowskiGenerativeConvolutionTranspose(
in_channels=channels[2],
out_channels=channels[3],
kernel_size= 2,
stride=2,
bias=True,
dimension=3)
self.conv2 = ME.MinkowskiConvolution(
in_channels=channels[3],
out_channels=channels[3],
kernel_size= 3,
stride=1,
bias=True,
dimension=3)
self.block2 = make_layer(
block=InceptionResNet,
block_layers=3,
channels=channels[3])
self.conv2_cls = ME.MinkowskiConvolution(
in_channels=channels[3],
out_channels=1,
kernel_size= 3,
stride=1,
bias=True,
dimension=3)
self.relu = ME.MinkowskiReLU(inplace=True)
self.pruning = ME.MinkowskiPruning()
self.Pct0_0=Pct(channels[0])
self.Pct1_0=Pct(channels[1])
self.Pct2_0=Pct(channels[2])
def prune_voxel(self, data, data_cls, nums, ground_truth, training):
mask_topk = istopk(data_cls, nums)
if training:
assert not ground_truth is None
mask_true = isin(data_cls.C, ground_truth.C)
mask = mask_topk + mask_true
else:
mask = mask_topk
data_pruned = self.pruning(data, mask.to(data.device))
return data_pruned
def forward(self, x, nums_list, ground_truth_list, training=True):
out = self.relu(self.up0(self.Pct0_0(x)))
out = self.block0(self.relu(self.conv0(out)))
out_cls_0 = self.conv0_cls(out)
out = self.prune_voxel(out, out_cls_0,
nums_list[0], ground_truth_list[0], training)
out_1=self.Pct1_0(out)
out = self.relu(self.conv1(self.relu(self.up1(out_1))))
out = self.block1(out)
out_cls_1 = self.conv1_cls(out)
out = self.prune_voxel(out, out_cls_1,
nums_list[1], ground_truth_list[1], training)
out_2 = self.Pct2_0(out)
out = self.relu(self.conv2(self.relu(self.up2(out_2))))
out = self.block2(out)
out_cls_2 = self.conv2_cls(out)
out = self.prune_voxel(out, out_cls_2,
nums_list[2], ground_truth_list[2], training)
out_cls_list = [out_cls_0, out_cls_1, out_cls_2]
return out_cls_list, out