#!/usr/bin/env python """ Purpose : """ import torch import torch.nn as nn import torch.utils.data __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 ConvBlock(nn.Module): """ Convolution Block """ def __init__(self, in_channels, out_channels, k_size=3, stride=1, padding=1, bias=True, dropout_rate=None): super(ConvBlock, self).__init__() if bool(dropout_rate): 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.Dropout3d(p=dropout_rate), #This changes the order in the sequential model 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) ) else: 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 UpConv(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(UpConv, 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 UNet(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, dropout_rate=None): super(UNet, 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 = ConvBlock(in_ch, filters[0], dropout_rate=dropout_rate) self.Conv2 = ConvBlock(filters[0], filters[1], dropout_rate=dropout_rate) self.Conv3 = ConvBlock(filters[1], filters[2], dropout_rate=dropout_rate) self.Conv4 = ConvBlock(filters[2], filters[3], dropout_rate=dropout_rate) self.Conv5 = ConvBlock(filters[3], filters[4], dropout_rate=dropout_rate) self.Up5 = UpConv(filters[4], filters[3]) self.Up_conv5 = ConvBlock(filters[4], filters[3], dropout_rate=dropout_rate) self.Up4 = UpConv(filters[3], filters[2]) self.Up_conv4 = ConvBlock(filters[3], filters[2], dropout_rate=dropout_rate) self.Up3 = UpConv(filters[2], filters[1]) self.Up_conv3 = ConvBlock(filters[2], filters[1], dropout_rate=dropout_rate) self.Up2 = UpConv(filters[1], filters[0]) self.Up_conv2 = ConvBlock(filters[1], filters[0], dropout_rate=dropout_rate) 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 UNetDeepSup(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, dropout_rate=None): super(UNetDeepSup, 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 = ConvBlock(in_ch, filters[0], dropout_rate=dropout_rate) self.Conv2 = ConvBlock(filters[0], filters[1], dropout_rate=dropout_rate) self.Conv3 = ConvBlock(filters[1], filters[2], dropout_rate=dropout_rate) self.Conv4 = ConvBlock(filters[2], filters[3], dropout_rate=dropout_rate) self.Conv5 = ConvBlock(filters[3], filters[4], dropout_rate=dropout_rate) # 1x1x1 Convolution for Deep Supervision self.Conv_d3 = ConvBlock(filters[1], 1, dropout_rate=None) self.Conv_d4 = ConvBlock(filters[2], 1, dropout_rate=None) self.Up5 = UpConv(filters[4], filters[3]) self.Up_conv5 = ConvBlock(filters[4], filters[3], dropout_rate=dropout_rate) self.Up4 = UpConv(filters[3], filters[2]) self.Up_conv4 = ConvBlock(filters[3], filters[2], dropout_rate=dropout_rate) self.Up3 = UpConv(filters[2], filters[1]) self.Up_conv3 = ConvBlock(filters[2], filters[1], dropout_rate=dropout_rate) self.Up2 = UpConv(filters[1], filters[0]) self.Up_conv2 = ConvBlock(filters[1], filters[0], dropout_rate=dropout_rate) 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, '/nfs1/sutrave/outputs/nan_values_input/inp_2_Nov.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) 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]