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net.py
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import torch.nn as nn
import torch
import math
from torch.nn import init
# 2D Conv
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes,
kernel_size=1, stride=stride, padding=0,
bias=False)
def conv2x2(in_planes, out_planes, stride=2):
return nn.Conv2d(in_planes, out_planes,
kernel_size=2, stride=stride, padding=0,
bias=False)
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes,
kernel_size=3, stride=stride, padding=1,
bias=False)
def conv4x4(in_planes, out_planes, stride=2):
return nn.Conv2d(in_planes, out_planes,
kernel_size=4, stride=stride, padding=1,
bias=False)
# 3D Conv
def conv1x1x1(in_planes, out_planes, stride=1):
return nn.Conv3d(in_planes, out_planes,
kernel_size=1, stride=stride, padding=0,
bias=False)
def conv3x3x3(in_planes, out_planes, stride=1):
return nn.Conv3d(in_planes, out_planes,
kernel_size=3, stride=stride, padding=1,
bias=False)
def conv4x4x4(in_planes, out_planes, stride=2):
return nn.Conv3d(in_planes, out_planes,
kernel_size=4, stride=stride, padding=1,
bias=False)
# 2D Deconv
def deconv1x1(in_planes, out_planes, stride):
return nn.ConvTranspose2d(in_planes, out_planes,
kernel_size=1, stride=stride, padding=0, output_padding=0,
bias=False)
def deconv2x2(in_planes, out_planes, stride):
return nn.ConvTranspose2d(in_planes, out_planes,
kernel_size=2, stride=stride, padding=0, output_padding=0,
bias=False)
def deconv3x3(in_planes, out_planes, stride):
return nn.ConvTranspose2d(in_planes, out_planes,
kernel_size=3, stride=stride, padding=1, output_padding=0,
bias=False)
def deconv4x4(in_planes, out_planes, stride):
return nn.ConvTranspose2d(in_planes, out_planes,
kernel_size=4, stride=stride, padding=1, output_padding=0,
bias=False)
# 3D Deconv
def deconv1x1x1(in_planes, out_planes, stride):
return nn.ConvTranspose3d(in_planes, out_planes,
kernel_size=1, stride=stride, padding=0, output_padding=0,
bias=False)
def deconv3x3x3(in_planes, out_planes, stride):
return nn.ConvTranspose3d(in_planes, out_planes,
kernel_size=3, stride=stride, padding=1, output_padding=0,
bias=False)
def deconv4x4x4(in_planes, out_planes, stride):
return nn.ConvTranspose3d(in_planes, out_planes,
kernel_size=4, stride=stride, padding=1, output_padding=0,
bias=False)
def _make_layers(in_channels, output_channels, type, batch_norm=False, activation=None):
layers = []
if type == 'conv1_s1':
layers.append(conv1x1(in_channels, output_channels, stride=1))
elif type == 'conv2_s2':
layers.append(conv2x2(in_channels, output_channels, stride=2))
elif type == 'conv3_s1':
layers.append(conv3x3(in_channels, output_channels, stride=1))
elif type == 'conv4_s2':
layers.append(conv4x4(in_channels, output_channels, stride=2))
elif type == 'deconv1_s1':
layers.append(deconv1x1(in_channels, output_channels, stride=1))
elif type == 'deconv2_s2':
layers.append(deconv2x2(in_channels, output_channels, stride=2))
elif type == 'deconv3_s1':
layers.append(deconv3x3(in_channels, output_channels, stride=1))
elif type == 'deconv4_s2':
layers.append(deconv4x4(in_channels, output_channels, stride=2))
elif type == 'conv1x1_s1':
layers.append(conv1x1x1(in_channels, output_channels, stride=1))
elif type == 'deconv1x1_s1':
layers.append(deconv1x1x1(in_channels, output_channels, stride=1))
elif type == 'deconv3x3_s1':
layers.append(deconv3x3x3(in_channels, output_channels, stride=1))
elif type == 'deconv4x4_s2':
layers.append(deconv4x4x4(in_channels, output_channels, stride=2))
else:
raise NotImplementedError('layer type [{}] is not implemented'.format(type))
if batch_norm == '2d':
layers.append(nn.BatchNorm2d(output_channels))
elif batch_norm == '3d':
layers.append(nn.BatchNorm3d(output_channels))
if activation == 'relu':
layers.append(nn.ReLU(inplace=True))
elif activation == 'sigm':
layers.append(nn.Sigmoid())
elif activation == 'leakyrelu':
layers.append(nn.LeakyReLU(0.2, True))
else:
if activation is not None:
raise NotImplementedError('activation function [{}] is not implemented'.format(activation))
return nn.Sequential(*layers)
def _initialize_weights(net):
for m in net.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Conv3d) or isinstance(m, nn.ConvTranspose3d):
n = m.kernel_size[0] * m.kernel_size[1] * m.kernel_size[2] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
class ReconNet(nn.Module):
def __init__(self, in_channels, out_channels, gain=0.02, init_type='standard'):
super(ReconNet, self).__init__()
######### representation network - convolution layers
self.conv_layer1 = _make_layers(in_channels, 256, 'conv4_s2', False)
self.conv_layer2 = _make_layers(256, 256, 'conv3_s1', '2d')
self.relu2 = nn.ReLU(inplace=True)
self.conv_layer3 = _make_layers(256, 512, 'conv4_s2', '2d', 'relu')
self.conv_layer4 = _make_layers(512, 512, 'conv3_s1', '2d')
self.relu4 = nn.ReLU(inplace=True)
self.conv_layer5 = _make_layers(512, 1024, 'conv4_s2', '2d', 'relu')
self.conv_layer6 = _make_layers(1024, 1024, 'conv3_s1', '2d')
self.relu6 = nn.ReLU(inplace=True)
self.conv_layer7 = _make_layers(1024, 2048, 'conv4_s2', '2d', 'relu')
self.conv_layer8 = _make_layers(2048, 2048, 'conv3_s1', '2d')
self.relu8 = nn.ReLU(inplace=True)
self.conv_layer9 = _make_layers(2048, 4096, 'conv4_s2', '2d', 'relu')
self.conv_layer10 = _make_layers(4096, 4096, 'conv3_s1', '2d')
self.relu10 = nn.ReLU(inplace=True)
######### transform module
self.trans_layer1 = _make_layers(4096, 4096, 'conv1_s1', False, 'relu')
self.trans_layer2 = _make_layers(2048, 2048, 'deconv1x1_s1', False, 'relu')
######### generation network - deconvolution layers
self.deconv_layer10 = _make_layers(2048, 1024, 'deconv4x4_s2', '3d', 'relu')
self.deconv_layer8 = _make_layers(1024, 512, 'deconv4x4_s2', '3d', 'relu')
self.deconv_layer7 = _make_layers(512, 512, 'deconv3x3_s1', '3d', 'relu')
self.deconv_layer6 = _make_layers(512, 256, 'deconv4x4_s2', '3d', 'relu')
self.deconv_layer5 = _make_layers(256, 256, 'deconv3x3_s1', '3d', 'relu')
self.deconv_layer4 = _make_layers(256, 128, 'deconv4x4_s2', '3d', 'relu')
self.deconv_layer3 = _make_layers(128, 128, 'deconv3x3_s1', '3d', 'relu')
self.deconv_layer2 = _make_layers(128, 64, 'deconv4x4_s2', '3d', 'relu')
self.deconv_layer1 = _make_layers(64, 64, 'deconv3x3_s1', '3d', 'relu')
self.deconv_layer0 = _make_layers(64, 1, 'conv1x1_s1', False, 'relu')
self.output_layer = _make_layers(64, out_channels, 'conv1_s1', False)
# network initialization
_initialize_weights(self)
def forward(self, x, out_feature=False):
### representation network
conv1 = self.conv_layer1(x)
conv2 = self.conv_layer2(conv1)
relu2 = self.relu2(conv1 + conv2)
conv3 = self.conv_layer3(relu2)
conv4 = self.conv_layer4(conv3)
relu4 = self.relu4(conv3 + conv4)
conv5 = self.conv_layer5(relu4)
conv6 = self.conv_layer6(conv5)
relu6 = self.relu6(conv5 + conv6)
conv7 = self.conv_layer7(relu6)
conv8 = self.conv_layer8(conv7)
relu8 = self.relu8(conv7 + conv8)
conv9 = self.conv_layer9(relu8)
conv10 = self.conv_layer10(conv9)
relu10 = self.relu10(conv9 + conv10)
### transform module
features = self.trans_layer1(relu10)
trans_features = features.view(-1, 2048, 2, 4, 4)
trans_features = self.trans_layer2(trans_features)
### generation network
deconv10 = self.deconv_layer10(trans_features)
deconv8 = self.deconv_layer8(deconv10)
deconv7 = self.deconv_layer7(deconv8)
deconv6 = self.deconv_layer6(deconv7)
deconv5 = self.deconv_layer5(deconv6)
deconv4 = self.deconv_layer4(deconv5)
deconv3 = self.deconv_layer3(deconv4)
deconv2 = self.deconv_layer2(deconv3)
deconv1 = self.deconv_layer1(deconv2)
### output
out = self.deconv_layer0(deconv1)
out = torch.squeeze(out, 1)
out = self.output_layer(out)
if out_feature:
return out, features, trans_features
else:
return out
# def reconnet(in_channels, out_channels, **kwargs):
# model = ReconNet(in_channels, out_channels, **kwargs)
# return model