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blocks.py
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blocks.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jan 2 11:29:06 2020
@author: T_ESTIENNE
"""
import torch
import torch.nn as nn
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x):
return x
def get_activation(activation_type):
activation_type = activation_type.lower()
if activation_type == 'prelu':
return nn.PReLU()
elif activation_type == 'leaky':
return nn.LeakyReLU(inplace=True)
elif activation_type == 'tanh':
return nn.Tanh()
elif activation_type == 'softmax':
return nn.Softmax()
elif activation_type == 'sigmoid':
return nn.Sigmoid()
elif activation_type == 'relu':
return nn.ReLU(inplace=True)
else:
return Identity()
class UpPoolingConvolution(nn.Module):
def __init__(self, in_channels, out_channels, activation_type='prelu',
instance_norm=False, batch_norm=False):
super(UpPoolingConvolution, self).__init__()
self.instance_norm = instance_norm or batch_norm
self.conv = nn.ConvTranspose3d(in_channels, out_channels,
kernel_size=2, padding=0, stride=2)
self.activation = get_activation(activation_type)
if instance_norm:
self.norm = nn.InstanceNorm3d(out_channels)
elif batch_norm:
self.norm = nn.BatchNorm3d(out_channels)
def forward(self, x):
x = self.conv(x)
if self.instance_norm:
x = self.norm(x)
out = self.activation(x)
return out
class DownPoolingConvolution(nn.Module):
def __init__(self, in_channels, out_channels, activation_type='prelu',
instance_norm=False, batch_norm=False):
super(DownPoolingConvolution, self).__init__()
self.instance_norm = instance_norm or batch_norm
self.conv = nn.Conv3d(in_channels, out_channels,
kernel_size=2, padding=0, stride=2)
self.activation = get_activation(activation_type)
if instance_norm:
self.norm = nn.InstanceNorm3d(out_channels)
elif batch_norm:
self.norm = nn.BatchNorm3d(out_channels)
def forward(self, x):
x = self.conv(x)
if self.instance_norm:
x = self.norm(x)
out = self.activation(x)
return out
class Convolution(nn.Module):
def __init__(self, in_channels, out_channels, activation_type='prelu',
instance_norm=False, batch_norm=False):
super(Convolution, self).__init__()
self.instance_norm = instance_norm or batch_norm
self.conv = nn.Conv3d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
padding=1,
stride=1)
self.activation = get_activation(activation_type)
if instance_norm:
self.norm = nn.InstanceNorm3d(out_channels)
elif batch_norm:
self.norm = nn.BatchNorm3d(out_channels)
def forward(self, x):
x = self.conv(x)
if self.instance_norm:
x = self.norm(x)
out = self.activation(x)
return out
def _make_nConv(in_channels, out_channels, activation, instance_norm,
batch_norm, nb_Conv):
layers = []
layers.append(Convolution(in_channels, out_channels, activation,
instance_norm, batch_norm))
for _ in range(nb_Conv-1):
layers.append(Convolution(out_channels, out_channels,
activation, instance_norm=instance_norm,
batch_norm=batch_norm))
return nn.Sequential(*layers)
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels,
activation_type='leaky', instance_norm=False, batch_norm=False,
nb_Conv=1
):
super(ConvBlock, self).__init__()
self.conv_pool = DownPoolingConvolution(in_channels, out_channels,
activation_type,
instance_norm, batch_norm
)
self.nConvs = _make_nConv(out_channels, out_channels, activation_type,
instance_norm, batch_norm, nb_Conv)
def forward(self, x):
down = self.conv_pool(x)
out = self.nConvs(down)
return out
class DeconvBlock(nn.Module):
def __init__(self, in_channels, out_channels,
activation_type='leaky',
instance_norm=False, batch_norm=False,
nb_Conv=1):
super(DeconvBlock, self).__init__()
self.conv_tr = UpPoolingConvolution(in_channels, out_channels,
activation_type=activation_type,
instance_norm=instance_norm,
batch_norm=batch_norm)
self.nConvs = _make_nConv(in_channels, out_channels, activation_type,
instance_norm, batch_norm, nb_Conv)
def forward(self, x, skip_x=None):
up = self.conv_tr(x)
cat = torch.cat((up, skip_x), 1)
out = self.nConvs(cat)
return out
class InputBlock(nn.Module):
def __init__(self, n_channels, input_channels,
activation_type='leaky'):
super(InputBlock, self).__init__()
self.add_conv = nn.Conv3d(in_channels=input_channels,
out_channels=n_channels,
kernel_size=3, padding=1, stride=1)
self.conv = nn.Conv3d(in_channels=n_channels,
out_channels=n_channels,
kernel_size=3,
padding=1,
stride=1)
self.activation = get_activation(activation_type)
def forward(self, x):
x = self.activation(self.add_conv(x))
out = self.activation(self.conv(x))
out = torch.add(out, x)
return out
class Decoder(nn.Module):
def __init__(self, pool_blocks, channels, out_channels, last_activation,
activation_type='leaky',
instance_norm=False, batch_norm=False,
nb_Convs=[1, 1, 1, 1, 1],
freeze_registration=False, zeros_init=False,
deep_supervision=False):
super(Decoder, self).__init__()
self.conv_list = nn.ModuleList()
self.deep_supervision = deep_supervision
for i in range(0, pool_blocks):
self.conv_list.append(DeconvBlock(channels[-i-1], channels[-i-2],
activation_type, instance_norm,
batch_norm, nb_Convs[-i-1]))
self.last_conv = nn.Conv3d(in_channels=channels[-pool_blocks-1],
out_channels=out_channels,
kernel_size=3,
padding=1,
stride=1)
self.last_activation = get_activation(last_activation)
if freeze_registration:
for param in self.last_conv.parameters():
param.requires_grad = False
if zeros_init:
torch.nn.init.zeros_(self.last_conv.weight)
torch.nn.init.zeros_(self.last_conv.bias)
def forward(self, skip_x):
if self.deep_supervision:
pred_list = []
out = skip_x[-1]
for i, conv in enumerate(self.conv_list):
if self.deep_supervision:
pred_list.append(out)
out = conv(out, skip_x[-i-2])
out = self.last_conv(out)
out = self.last_activation(out)
if self.deep_supervision:
pred_list.append(out) # Last one is the final prediction
return pred_list
else:
return out
class Encoder(nn.Module):
def __init__(self, pool_blocks, channels,
activation_type, input_channel=4,
instance_norm=False, batch_norm=False,
nb_Convs=[1, 1, 1, 1, 1]):
super(Encoder, self).__init__()
self.input = InputBlock(channels[0], input_channel,
activation_type)
self.conv_blocks = nn.ModuleList()
for i in range(pool_blocks):
self.conv_blocks.append(ConvBlock(channels[i], channels[i+1],
activation_type,
instance_norm, batch_norm,
nb_Convs[i]
)
)
def forward(self, x):
skip_x = []
skip_x.append(self.input(x))
for conv in self.conv_blocks:
skip_x.append(conv(skip_x[-1]))
return skip_x
class DeepSupervisionBlock(nn.Module):
def __init__(self, pool_blocks, channels, out_channels,
last_activation):
super(DeepSupervisionBlock, self).__init__()
self.convs_list = nn.ModuleList()
self.upsample_list = nn.ModuleList()
for i in range(pool_blocks):
self.convs_list.append(nn.Conv3d(in_channels=channels[-i-1],
out_channels=out_channels,
kernel_size=3, padding=1,
stride=1))
self.upsample_list.append(nn.Upsample(scale_factor=2**(pool_blocks-i)))
self.last_activation = get_activation(last_activation)
def forward(self, x_list):
pred_list = []
for i, x in enumerate(x_list[:-1]):
out = self.convs_list[i](x) # Change number of filter
up = self.last_activation(self.upsample_list[i](out)) # Upsample
pred_list.append(up)
pred_list.append(x_list[-1])
return pred_list