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#!/usr/bin/env python
# from __future__ import print_function, division
'''
This script is from the DS6 (https://github.com/soumickmj/DS6/blob/main/Models/unet3d.py),
and then the SPOCKMIP repository (https://github.com/soumickmj/SPOCKMIP/blob/master/Models/unet3d.py)
Part of the DS6 paper:
"DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data"
(https://doi.org/10.3390/jimaging8100259)
and the SPOCKMIP paper:
"SPOCKMIP: Segmentation of Vessels in MRAs with Enhanced Continuity using Maximum Intensity Projection as Loss"
(https://doi.org/10.48550/arXiv.2407.08655)
'''
import torch
import torch.nn as nn
import torch.utils.data
import os
__author__ = "Kartik Prabhu, Mahantesh Pattadkal, and Soumick Chatterjee"
__copyright__ = "Copyright 2020, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany"
__credits__ = ["Kartik Prabhu", "Mahantesh Pattadkal", "Soumick Chatterjee"]
__license__ = "GPL"
__version__ = "1.0.0"
__maintainer__ = "Soumick Chatterjee"
__email__ = "soumick.chatterjee@ovgu.de"
__status__ = "Production"
class conv_block(nn.Module):
"""
Convolution Block
"""
def __init__(self, in_channels, out_channels, k_size=3, stride=1, padding=1, bias=True):
super(conv_block, self).__init__()
self.conv = nn.Sequential(
nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=k_size,
stride=stride, padding=padding, bias=bias),
nn.BatchNorm3d(num_features=out_channels),
nn.LeakyReLU(inplace=True),
nn.Conv3d(in_channels=out_channels, out_channels=out_channels, kernel_size=k_size,
stride=stride, padding=padding, bias=bias),
nn.BatchNorm3d(num_features=out_channels),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class up_conv(nn.Module):
"""
Up Convolution Block
"""
# def __init__(self, in_ch, out_ch):
def __init__(self, in_channels, out_channels, k_size=3, stride=1, padding=1, bias=True):
super(up_conv, self).__init__()
self.up = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=k_size,
stride=stride, padding=padding, bias=bias),
nn.BatchNorm3d(num_features=out_channels),
nn.LeakyReLU(inplace=True))
def forward(self, x):
x = self.up(x)
return x
class U_Net(nn.Module):
"""
UNet - Basic Implementation
Input _ [batch * channel(# of channels of each image) * depth(# of frames) * height * width].
Paper : https://arxiv.org/abs/1505.04597
"""
def __init__(self, in_ch=1, out_ch=1, init_features=64):
super(U_Net, self).__init__()
n1 = init_features
filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] # 64,128,256,512,1024
self.Maxpool1 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Maxpool2 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Maxpool3 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Maxpool4 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Conv1 = conv_block(in_ch, filters[0])
self.Conv2 = conv_block(filters[0], filters[1])
self.Conv3 = conv_block(filters[1], filters[2])
self.Conv4 = conv_block(filters[2], filters[3])
self.Conv5 = conv_block(filters[3], filters[4])
self.Up5 = up_conv(filters[4], filters[3])
self.Up_conv5 = conv_block(filters[4], filters[3])
self.Up4 = up_conv(filters[3], filters[2])
self.Up_conv4 = conv_block(filters[3], filters[2])
self.Up3 = up_conv(filters[2], filters[1])
self.Up_conv3 = conv_block(filters[2], filters[1])
self.Up2 = up_conv(filters[1], filters[0])
self.Up_conv2 = conv_block(filters[1], filters[0])
self.Conv = nn.Conv3d(filters[0], out_ch, kernel_size=1, stride=1, padding=0)
# self.active = torch.nn.Sigmoid()
def forward(self, x):
# print("unet")
# print(x.shape)
# print(padded.shape)
e1 = self.Conv1(x)
# print("conv1:")
# print(e1.shape)
e2 = self.Maxpool1(e1)
e2 = self.Conv2(e2)
# print("conv2:")
# print(e2.shape)
e3 = self.Maxpool2(e2)
e3 = self.Conv3(e3)
# print("conv3:")
# print(e3.shape)
e4 = self.Maxpool3(e3)
e4 = self.Conv4(e4)
# print("conv4:")
# print(e4.shape)
e5 = self.Maxpool4(e4)
e5 = self.Conv5(e5)
# print("conv5:")
# print(e5.shape)
d5 = self.Up5(e5)
# print("d5:")
# print(d5.shape)
# print("e4:")
# print(e4.shape)
d5 = torch.cat((e4, d5), dim=1)
d5 = self.Up_conv5(d5)
# print("upconv5:")
# print(d5.size)
d4 = self.Up4(d5)
# print("d4:")
# print(d4.shape)
d4 = torch.cat((e3, d4), dim=1)
d4 = self.Up_conv4(d4)
# print("upconv4:")
# print(d4.shape)
d3 = self.Up3(d4)
d3 = torch.cat((e2, d3), dim=1)
d3 = self.Up_conv3(d3)
# print("upconv3:")
# print(d3.shape)
d2 = self.Up2(d3)
d2 = torch.cat((e1, d2), dim=1)
d2 = self.Up_conv2(d2)
# print("upconv2:")
# print(d2.shape)
out = self.Conv(d2)
# print("out:")
# print(out.shape)
# d1 = self.active(out)
return [out]
class U_Net_DeepSup(nn.Module):
"""
UNet - Basic Implementation
Input _ [batch * channel(# of channels of each image) * depth(# of frames) * height * width].
Paper : https://arxiv.org/abs/1505.04597
"""
def __init__(self, in_ch=1, out_ch=1, output_dir=None, init_features=64):
super(U_Net_DeepSup, self).__init__()
self.output_dir = output_dir
n1 = init_features
filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] # 64,128,256,512,1024
self.Maxpool1 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Maxpool2 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Maxpool3 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Maxpool4 = nn.MaxPool3d(kernel_size=2, stride=2)
self.Conv1 = conv_block(in_ch, filters[0])
self.Conv2 = conv_block(filters[0], filters[1])
self.Conv3 = conv_block(filters[1], filters[2])
self.Conv4 = conv_block(filters[2], filters[3])
self.Conv5 = conv_block(filters[3], filters[4])
#1x1x1 Convolution for Deep Supervision
self.Conv_d3 = conv_block(filters[1], 1)
self.Conv_d4 = conv_block(filters[2], 1)
self.Up5 = up_conv(filters[4], filters[3])
self.Up_conv5 = conv_block(filters[4], filters[3])
self.Up4 = up_conv(filters[3], filters[2])
self.Up_conv4 = conv_block(filters[3], filters[2])
self.Up3 = up_conv(filters[2], filters[1])
self.Up_conv3 = conv_block(filters[2], filters[1])
self.Up2 = up_conv(filters[1], filters[0])
self.Up_conv2 = conv_block(filters[1], filters[0])
self.Conv = nn.Conv3d(filters[0], out_ch, kernel_size=1, stride=1, padding=0)
for submodule in self.modules():
submodule.register_forward_hook(self.nan_hook)
# self.active = torch.nn.Sigmoid()
def nan_hook(self, module, inp, output):
for i, out in enumerate(output):
nan_mask = torch.isnan(out)
if nan_mask.any():
print("In", self.__class__.__name__)
torch.save(inp, os.path.join(self.output_dir, 'nan_values_ip.pt'))
module_params = module.named_parameters()
for name, param in module_params:
torch.save(param, os.path.join(self.output_dir, 'nan_{}_param.pt'.format(name)))
torch.save(self.input_to_net, os.path.join(self.output_dir, 'nan_ip_batch.pt'))
raise RuntimeError(" classname "+self.__class__.__name__+"i "+str(i)+f" module: {module} classname {self.__class__.__name__} Found NAN in output {i} at indices: ", nan_mask.nonzero(), "where:", out[nan_mask.nonzero()[:, 0].unique(sorted=True)])
def forward(self, x):
# print("unet")
# print(x.shape)
# print(padded.shape)
self.input_to_net = x
e1 = self.Conv1(x)
# print("conv1:")
# print(e1.shape)
e2 = self.Maxpool1(e1)
e2 = self.Conv2(e2)
# print("conv2:")
# print(e2.shape)
e3 = self.Maxpool2(e2)
e3 = self.Conv3(e3)
# print("conv3:")
# print(e3.shape)
e4 = self.Maxpool3(e3)
e4 = self.Conv4(e4)
# print("conv4:")
# print(e4.shape)
e5 = self.Maxpool4(e4)
e5 = self.Conv5(e5)
# print("conv5:")
# print(e5.shape)
d5 = self.Up5(e5)
# print("d5:")
# print(d5.shape)
# print("e4:")
# print(e4.shape)
d5 = torch.cat((e4, d5), dim=1)
d5 = self.Up_conv5(d5)
# print("upconv5:")
# print(d5.size)
d4 = self.Up4(d5)
# print("d4:")
# print(d4.shape)
d4 = torch.cat((e3, d4), dim=1)
d4 = self.Up_conv4(d4)
d4_out = self.Conv_d4(d4)
# print("upconv4:")
# print(d4.shape)
d3 = self.Up3(d4)
d3 = torch.cat((e2, d3), dim=1)
d3 = self.Up_conv3(d3)
d3_out = self.Conv_d3(d3)
# print("upconv3:")
# print(d3.shape)
d2 = self.Up2(d3)
d2 = torch.cat((e1, d2), dim=1)
d2 = self.Up_conv2(d2)
# print("upconv2:")
# print(d2.shape)
out = self.Conv(d2)
# print("out:")
# print(out.shape)
# d1 = self.active(out)
return [out, d3_out , d4_out]
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