File size: 6,869 Bytes
d26715f |
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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
import torch.nn as nn
import torch.nn.functional as F
import torch
from huggingface_hub import PyTorchModelHubMixin
class conv3d(nn.Module, PyTorchModelHubMixin):
def __init__(self, in_channels, out_channels):
"""
+ Instantiate modules: conv-relu-norm
+ Assign them as member variables
"""
super(conv3d, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=5, padding=2)
self.relu = nn.LeakyReLU(0.2)
# with learnable parameters
self.norm = nn.InstanceNorm3d(out_channels, affine=True)
def forward(self, x):
return self.relu(self.norm(self.conv(x)))
class conv3d_x3(nn.Module, PyTorchModelHubMixin):
"""Three serial convs with a residual connection.
Structure:
inputs --> â‘ --> â‘¡ --> â‘¢ --> outputs
↓ --> add--> ↑
"""
def __init__(self, in_channels, out_channels):
super(conv3d_x3, self).__init__()
self.conv_1 = conv3d(in_channels, out_channels)
self.conv_2 = conv3d(out_channels, out_channels)
self.conv_3 = conv3d(out_channels, out_channels)
self.skip_connection=nn.Conv3d(in_channels,out_channels,1)
def forward(self, x):
z_1 = self.conv_1(x)
z_3 = self.conv_3(self.conv_2(z_1))
return z_3 + self.skip_connection(x)
class conv3d_x2(nn.Module, PyTorchModelHubMixin):
"""Three serial convs with a residual connection.
Structure:
inputs --> â‘ --> â‘¡ --> â‘¢ --> outputs
↓ --> add--> ↑
"""
def __init__(self, in_channels, out_channels):
super(conv3d_x2, self).__init__()
self.conv_1 = conv3d(in_channels, out_channels)
self.conv_2 = conv3d(out_channels, out_channels)
self.skip_connection=nn.Conv3d(in_channels,out_channels,1)
def forward(self, x):
z_1 = self.conv_1(x)
z_2 = self.conv_2(z_1)
return z_2 + self.skip_connection(x)
class conv3d_x1(nn.Module, PyTorchModelHubMixin):
"""Three serial convs with a residual connection.
Structure:
inputs --> â‘ --> â‘¡ --> â‘¢ --> outputs
↓ --> add--> ↑
"""
def __init__(self, in_channels, out_channels):
super(conv3d_x1, self).__init__()
self.conv_1 = conv3d(in_channels, out_channels)
self.skip_connection=nn.Conv3d(in_channels,out_channels,1)
def forward(self, x):
z_1 = self.conv_1(x)
return z_1 + self.skip_connection(x)
class deconv3d_x3(nn.Module, PyTorchModelHubMixin):
def __init__(self, in_channels, out_channels):
super(deconv3d_x3, self).__init__()
self.up = deconv3d_as_up(in_channels, out_channels, 2, 2)
self.lhs_conv = conv3d(out_channels // 2, out_channels)
self.conv_x3 = nn.Sequential(
nn.Conv3d(2*out_channels, out_channels,5,1,2),
nn.LeakyReLU(0.1),
nn.Conv3d(out_channels, out_channels,5,1,2),
nn.LeakyReLU(0.1),
nn.Conv3d(out_channels, out_channels,5,1,2),
nn.LeakyReLU(0.1),
)
def forward(self, lhs, rhs):
rhs_up = self.up(rhs)
lhs_conv = self.lhs_conv(lhs)
rhs_add = torch.cat((rhs_up, lhs_conv),dim=1)
return self.conv_x3(rhs_add)+ rhs_up
class deconv3d_x2(nn.Module, PyTorchModelHubMixin):
def __init__(self, in_channels, out_channels):
super(deconv3d_x2, self).__init__()
self.up = deconv3d_as_up(in_channels, out_channels, 2, 2)
self.lhs_conv = conv3d(out_channels // 2, out_channels)
self.conv_x2= nn.Sequential(
nn.Conv3d(2*out_channels, out_channels,5,1,2),
nn.LeakyReLU(0.1),
nn.Conv3d(out_channels, out_channels,5,1,2),
nn.LeakyReLU(0.1),
)
def forward(self, lhs, rhs):
rhs_up = self.up(rhs)
lhs_conv = self.lhs_conv(lhs)
rhs_add = torch.cat((rhs_up, lhs_conv),dim=1)
return self.conv_x2(rhs_add)+ rhs_up
class deconv3d_x1(nn.Module, PyTorchModelHubMixin):
def __init__(self, in_channels, out_channels):
super(deconv3d_x1, self).__init__()
self.up = deconv3d_as_up(in_channels, out_channels, 2, 2)
self.lhs_conv = conv3d(out_channels // 2, out_channels)
self.conv_x1 = nn.Sequential(
nn.Conv3d(2*out_channels, out_channels,5,1,2),
nn.LeakyReLU(0.2),
)
def forward(self, lhs, rhs):
rhs_up = self.up(rhs)
lhs_conv = self.lhs_conv(lhs)
rhs_add = torch.cat((rhs_up, lhs_conv),dim=1)
return self.conv_x1(rhs_add)+ rhs_up
def conv3d_as_pool(in_channels, out_channels, kernel_size=2, stride=2):
return nn.Sequential(
nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding=0),
nn.LeakyReLU(0.2))
def deconv3d_as_up(in_channels, out_channels, kernel_size=2, stride=2):
return nn.Sequential(
nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride),
nn.PReLU()
)
class softmax_out(nn.Module, PyTorchModelHubMixin):
def __init__(self, in_channels, out_channels):
super(softmax_out, self).__init__()
self.conv_1 = nn.Conv3d(in_channels, out_channels, kernel_size=5, padding=2)
self.conv_2 = nn.Conv3d(out_channels, out_channels, kernel_size=1, padding=0)
def forward(self, x):
"""Output with shape [batch_size, 1, depth, height, width]."""
# Do NOT add normalize layer, or its values vanish.
y_conv = self.conv_2(self.conv_1(x))
return y_conv
class VNet(nn.Module, PyTorchModelHubMixin):
def __init__(self):
super(VNet, self).__init__()
self.conv_1 = conv3d_x1(1, 16)
self.pool_1 = conv3d_as_pool(16, 32)
self.conv_2 = conv3d_x2(32, 32)
self.pool_2 = conv3d_as_pool(32, 64)
self.conv_3 = conv3d_x3(64, 64)
self.pool_3 = conv3d_as_pool(64, 128)
self.conv_4 = conv3d_x3(128, 128)
self.pool_4 = conv3d_as_pool(128, 256)
self.bottom = conv3d_x3(256, 256)
self.deconv_4 = deconv3d_x3(256, 256)
self.deconv_3 = deconv3d_x3(256, 128)
self.deconv_2 = deconv3d_x2(128, 64)
self.deconv_1 = deconv3d_x1(64, 32)
self.out = softmax_out(32, 1)
def forward(self, x):
conv_1 = self.conv_1(x)
pool = self.pool_1(conv_1)
conv_2 = self.conv_2(pool)
pool = self.pool_2(conv_2)
conv_3 = self.conv_3(pool)
pool = self.pool_3(conv_3)
conv_4 = self.conv_4(pool)
pool = self.pool_4(conv_4)
bottom = self.bottom(pool)
deconv = self.deconv_4(conv_4, bottom)
deconv = self.deconv_3(conv_3, deconv)
deconv = self.deconv_2(conv_2, deconv)
deconv = self.deconv_1(conv_1, deconv)
return self.out(deconv)
|