Spaces:
Runtime error
Runtime error
File size: 3,565 Bytes
8bb8404 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
import torch.nn as nn
class ConvBnReLU3D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, pad=1, norm_act=nn.BatchNorm3d):
super(ConvBnReLU3D, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=pad, bias=False)
self.bn = norm_act(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.relu(self.bn(self.conv(x)))
class CostRegNet(nn.Module):
def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
super(CostRegNet, self).__init__()
self.conv0 = ConvBnReLU3D(in_channels, 8, norm_act=norm_act)
self.conv1 = ConvBnReLU3D(8, 16, stride=2, norm_act=norm_act)
self.conv2 = ConvBnReLU3D(16, 16, norm_act=norm_act)
self.conv3 = ConvBnReLU3D(16, 32, stride=2, norm_act=norm_act)
self.conv4 = ConvBnReLU3D(32, 32, norm_act=norm_act)
self.conv5 = ConvBnReLU3D(32, 64, stride=2, norm_act=norm_act)
self.conv6 = ConvBnReLU3D(64, 64, norm_act=norm_act)
self.conv7 = nn.Sequential(
nn.ConvTranspose3d(64, 32, 3, padding=1, output_padding=1, stride=2, bias=False),
norm_act(32)
)
self.conv9 = nn.Sequential(
nn.ConvTranspose3d(32, 16, 3, padding=1, output_padding=1, stride=2, bias=False),
norm_act(16)
)
self.conv11 = nn.Sequential(
nn.ConvTranspose3d(16, 8, 3, padding=1, output_padding=1,stride=2, bias=False),
norm_act(8)
)
self.depth_conv = nn.Sequential(nn.Conv3d(8, 1, 3, padding=1, bias=False))
self.feat_conv = nn.Sequential(nn.Conv3d(8, 8, 3, padding=1, bias=False))
def forward(self, x):
conv0 = self.conv0(x)
conv2 = self.conv2(self.conv1(conv0))
conv4 = self.conv4(self.conv3(conv2))
x = self.conv6(self.conv5(conv4))
x = conv4 + self.conv7(x)
del conv4
x = conv2 + self.conv9(x)
del conv2
x = conv0 + self.conv11(x)
del conv0
feat = self.feat_conv(x)
depth = self.depth_conv(x)
return feat, depth
class MinCostRegNet(nn.Module):
def __init__(self, in_channels, norm_act=nn.BatchNorm3d):
super(MinCostRegNet, self).__init__()
self.conv0 = ConvBnReLU3D(in_channels, 8, norm_act=norm_act)
self.conv1 = ConvBnReLU3D(8, 16, stride=2, norm_act=norm_act)
self.conv2 = ConvBnReLU3D(16, 16, norm_act=norm_act)
self.conv3 = ConvBnReLU3D(16, 32, stride=2, norm_act=norm_act)
self.conv4 = ConvBnReLU3D(32, 32, norm_act=norm_act)
self.conv9 = nn.Sequential(
nn.ConvTranspose3d(32, 16, 3, padding=1, output_padding=1,
stride=2, bias=False),
norm_act(16))
self.conv11 = nn.Sequential(
nn.ConvTranspose3d(16, 8, 3, padding=1, output_padding=1,
stride=2, bias=False),
norm_act(8))
self.depth_conv = nn.Sequential(nn.Conv3d(8, 1, 3, padding=1, bias=False))
self.feat_conv = nn.Sequential(nn.Conv3d(8, 8, 3, padding=1, bias=False))
def forward(self, x):
conv0 = self.conv0(x)
conv2 = self.conv2(self.conv1(conv0))
conv4 = self.conv4(self.conv3(conv2))
x = conv4
x = conv2 + self.conv9(x)
del conv2
x = conv0 + self.conv11(x)
del conv0
feat = self.feat_conv(x)
depth = self.depth_conv(x)
return feat, depth
|