Upload UNetMSS3D
Browse files- UNetConfigs.py +28 -0
- UNets.py +25 -0
- config.json +15 -0
- model.safetensors +3 -0
- unet3d.py +295 -0
UNetConfigs.py
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from transformers import PretrainedConfig
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from typing import List
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class UNet3DConfig(PretrainedConfig):
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model_type = "UNet"
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def __init__(
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self,
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in_ch=1,
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out_ch=1,
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init_features=64,
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**kwargs):
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.init_features = init_features
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super().__init__(**kwargs)
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class UNetMSS3DConfig(PretrainedConfig):
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model_type = "UNetMSS"
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def __init__(
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self,
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in_ch=1,
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out_ch=1,
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init_features=64,
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**kwargs):
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.init_features = init_features
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super().__init__(**kwargs)
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UNets.py
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from transformers import PreTrainedModel
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from .unet3d import U_Net, U_Net_DeepSup
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from .UNetConfigs import UNet3DConfig, UNetMSS3DConfig
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class UNet3D(PreTrainedModel):
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config_class = UNet3DConfig
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def __init__(self, config):
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super().__init__(config)
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self.model = U_Net(
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in_ch=config.in_ch,
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out_ch=config.out_ch,
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init_features=config.init_features)
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def forward(self, x):
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return self.model(x)
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class UNetMSS3D(PreTrainedModel):
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config_class = UNetMSS3DConfig
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def __init__(self, config):
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super().__init__(config)
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self.model = U_Net_DeepSup(
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in_ch=config.in_ch,
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out_ch=config.out_ch,
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init_features=config.init_features)
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def forward(self, x):
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return self.model(x)
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config.json
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{
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"architectures": [
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"UNetMSS3D"
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],
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"auto_map": {
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"AutoConfig": "UNetConfigs.UNetMSS3DConfig",
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"AutoModel": "UNets.UNetMSS3D"
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},
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"in_ch": 1,
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"init_features": 64,
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"model_type": "UNetMSS",
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"out_ch": 1,
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"torch_dtype": "float32",
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"transformers_version": "4.44.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e411015a637b591e642a4bebd38d884ba93dc7fce42541f27ae3260305f94d4b
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size 414260220
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unet3d.py
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#!/usr/bin/env python
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# from __future__ import print_function, division
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'''
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Purpose :
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'''
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import torch
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import torch.nn as nn
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import torch.utils.data
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__author__ = "Kartik Prabhu, Mahantesh Pattadkal, and Soumick Chatterjee"
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__copyright__ = "Copyright 2020, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany"
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__credits__ = ["Kartik Prabhu", "Mahantesh Pattadkal", "Soumick Chatterjee"]
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__license__ = "GPL"
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__version__ = "1.0.0"
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__maintainer__ = "Soumick Chatterjee"
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__email__ = "[email protected]"
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__status__ = "Production"
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class conv_block(nn.Module):
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"""
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Convolution Block
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"""
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def __init__(self, in_channels, out_channels, k_size=3, stride=1, padding=1, bias=True):
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super(conv_block, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=k_size,
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stride=stride, padding=padding, bias=bias),
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nn.BatchNorm3d(num_features=out_channels),
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nn.ReLU(inplace=True),
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nn.Conv3d(in_channels=out_channels, out_channels=out_channels, kernel_size=k_size,
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stride=stride, padding=padding, bias=bias),
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nn.BatchNorm3d(num_features=out_channels),
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nn.ReLU(inplace=True)
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)
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def forward(self, x):
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x = self.conv(x)
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return x
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class up_conv(nn.Module):
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"""
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Up Convolution Block
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"""
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# def __init__(self, in_ch, out_ch):
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def __init__(self, in_channels, out_channels, k_size=3, stride=1, padding=1, bias=True):
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super(up_conv, self).__init__()
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self.up = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=k_size,
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stride=stride, padding=padding, bias=bias),
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nn.BatchNorm3d(num_features=out_channels),
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nn.ReLU(inplace=True))
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def forward(self, x):
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x = self.up(x)
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return x
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class U_Net(nn.Module):
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"""
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UNet - Basic Implementation
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Input _ [batch * channel(# of channels of each image) * depth(# of frames) * height * width].
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Paper : https://arxiv.org/abs/1505.04597
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"""
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def __init__(self, in_ch=1, out_ch=1, init_features=64):
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super(U_Net, self).__init__()
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n1 = init_features
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filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] # 64,128,256,512,1024
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self.Maxpool1 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Maxpool2 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Maxpool3 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Maxpool4 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Conv1 = conv_block(in_ch, filters[0])
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self.Conv2 = conv_block(filters[0], filters[1])
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self.Conv3 = conv_block(filters[1], filters[2])
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self.Conv4 = conv_block(filters[2], filters[3])
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self.Conv5 = conv_block(filters[3], filters[4])
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self.Up5 = up_conv(filters[4], filters[3])
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self.Up_conv5 = conv_block(filters[4], filters[3])
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self.Up4 = up_conv(filters[3], filters[2])
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self.Up_conv4 = conv_block(filters[3], filters[2])
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self.Up3 = up_conv(filters[2], filters[1])
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self.Up_conv3 = conv_block(filters[2], filters[1])
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self.Up2 = up_conv(filters[1], filters[0])
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self.Up_conv2 = conv_block(filters[1], filters[0])
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self.Conv = nn.Conv3d(filters[0], out_ch, kernel_size=1, stride=1, padding=0)
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# self.active = torch.nn.Sigmoid()
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def forward(self, x):
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# print("unet")
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# print(x.shape)
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# print(padded.shape)
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e1 = self.Conv1(x)
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# print("conv1:")
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# print(e1.shape)
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e2 = self.Maxpool1(e1)
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e2 = self.Conv2(e2)
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# print("conv2:")
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# print(e2.shape)
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e3 = self.Maxpool2(e2)
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e3 = self.Conv3(e3)
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# print("conv3:")
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# print(e3.shape)
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e4 = self.Maxpool3(e3)
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e4 = self.Conv4(e4)
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# print("conv4:")
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# print(e4.shape)
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e5 = self.Maxpool4(e4)
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e5 = self.Conv5(e5)
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# print("conv5:")
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# print(e5.shape)
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d5 = self.Up5(e5)
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# print("d5:")
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# print(d5.shape)
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# print("e4:")
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# print(e4.shape)
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d5 = torch.cat((e4, d5), dim=1)
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d5 = self.Up_conv5(d5)
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# print("upconv5:")
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# print(d5.size)
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d4 = self.Up4(d5)
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# print("d4:")
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# print(d4.shape)
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d4 = torch.cat((e3, d4), dim=1)
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d4 = self.Up_conv4(d4)
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# print("upconv4:")
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152 |
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# print(d4.shape)
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d3 = self.Up3(d4)
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154 |
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d3 = torch.cat((e2, d3), dim=1)
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d3 = self.Up_conv3(d3)
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# print("upconv3:")
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# print(d3.shape)
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d2 = self.Up2(d3)
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d2 = torch.cat((e1, d2), dim=1)
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d2 = self.Up_conv2(d2)
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# print("upconv2:")
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# print(d2.shape)
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out = self.Conv(d2)
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# print("out:")
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# print(out.shape)
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# d1 = self.active(out)
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return [out]
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170 |
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class U_Net_DeepSup(nn.Module):
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171 |
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"""
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172 |
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UNet - Basic Implementation
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173 |
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Input _ [batch * channel(# of channels of each image) * depth(# of frames) * height * width].
|
174 |
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Paper : https://arxiv.org/abs/1505.04597
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175 |
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"""
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176 |
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177 |
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def __init__(self, in_ch=1, out_ch=1, init_features=64):
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178 |
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super(U_Net_DeepSup, self).__init__()
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180 |
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n1 = init_features
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filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] # 64,128,256,512,1024
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182 |
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self.Maxpool1 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Maxpool2 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Maxpool3 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Maxpool4 = nn.MaxPool3d(kernel_size=2, stride=2)
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self.Conv1 = conv_block(in_ch, filters[0])
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self.Conv2 = conv_block(filters[0], filters[1])
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self.Conv3 = conv_block(filters[1], filters[2])
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self.Conv4 = conv_block(filters[2], filters[3])
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self.Conv5 = conv_block(filters[3], filters[4])
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193 |
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194 |
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#1x1x1 Convolution for Deep Supervision
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self.Conv_d3 = conv_block(filters[1], 1)
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196 |
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self.Conv_d4 = conv_block(filters[2], 1)
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197 |
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198 |
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199 |
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self.Up5 = up_conv(filters[4], filters[3])
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201 |
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self.Up_conv5 = conv_block(filters[4], filters[3])
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202 |
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self.Up4 = up_conv(filters[3], filters[2])
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204 |
+
self.Up_conv4 = conv_block(filters[3], filters[2])
|
205 |
+
|
206 |
+
self.Up3 = up_conv(filters[2], filters[1])
|
207 |
+
self.Up_conv3 = conv_block(filters[2], filters[1])
|
208 |
+
|
209 |
+
self.Up2 = up_conv(filters[1], filters[0])
|
210 |
+
self.Up_conv2 = conv_block(filters[1], filters[0])
|
211 |
+
|
212 |
+
self.Conv = nn.Conv3d(filters[0], out_ch, kernel_size=1, stride=1, padding=0)
|
213 |
+
|
214 |
+
for submodule in self.modules():
|
215 |
+
submodule.register_forward_hook(self.nan_hook)
|
216 |
+
|
217 |
+
# self.active = torch.nn.Sigmoid()
|
218 |
+
|
219 |
+
def nan_hook(self, module, inp, output):
|
220 |
+
for i, out in enumerate(output):
|
221 |
+
nan_mask = torch.isnan(out)
|
222 |
+
if nan_mask.any():
|
223 |
+
print("In", self.__class__.__name__)
|
224 |
+
print(module)
|
225 |
+
raise RuntimeError(f"Found NAN in output {i} at indices: ", nan_mask.nonzero(), "where:", out[nan_mask.nonzero()[:, 0].unique(sorted=True)])
|
226 |
+
|
227 |
+
def forward(self, x):
|
228 |
+
# print("unet")
|
229 |
+
# print(x.shape)
|
230 |
+
# print(padded.shape)
|
231 |
+
|
232 |
+
e1 = self.Conv1(x)
|
233 |
+
# print("conv1:")
|
234 |
+
# print(e1.shape)
|
235 |
+
|
236 |
+
e2 = self.Maxpool1(e1)
|
237 |
+
e2 = self.Conv2(e2)
|
238 |
+
# print("conv2:")
|
239 |
+
# print(e2.shape)
|
240 |
+
|
241 |
+
e3 = self.Maxpool2(e2)
|
242 |
+
e3 = self.Conv3(e3)
|
243 |
+
# print("conv3:")
|
244 |
+
# print(e3.shape)
|
245 |
+
|
246 |
+
e4 = self.Maxpool3(e3)
|
247 |
+
e4 = self.Conv4(e4)
|
248 |
+
# print("conv4:")
|
249 |
+
# print(e4.shape)
|
250 |
+
|
251 |
+
e5 = self.Maxpool4(e4)
|
252 |
+
e5 = self.Conv5(e5)
|
253 |
+
# print("conv5:")
|
254 |
+
# print(e5.shape)
|
255 |
+
|
256 |
+
d5 = self.Up5(e5)
|
257 |
+
# print("d5:")
|
258 |
+
# print(d5.shape)
|
259 |
+
# print("e4:")
|
260 |
+
# print(e4.shape)
|
261 |
+
d5 = torch.cat((e4, d5), dim=1)
|
262 |
+
d5 = self.Up_conv5(d5)
|
263 |
+
# print("upconv5:")
|
264 |
+
# print(d5.size)
|
265 |
+
|
266 |
+
d4 = self.Up4(d5)
|
267 |
+
# print("d4:")
|
268 |
+
# print(d4.shape)
|
269 |
+
d4 = torch.cat((e3, d4), dim=1)
|
270 |
+
d4 = self.Up_conv4(d4)
|
271 |
+
d4_out = self.Conv_d4(d4)
|
272 |
+
|
273 |
+
|
274 |
+
# print("upconv4:")
|
275 |
+
# print(d4.shape)
|
276 |
+
d3 = self.Up3(d4)
|
277 |
+
d3 = torch.cat((e2, d3), dim=1)
|
278 |
+
d3 = self.Up_conv3(d3)
|
279 |
+
d3_out = self.Conv_d3(d3)
|
280 |
+
|
281 |
+
# print("upconv3:")
|
282 |
+
# print(d3.shape)
|
283 |
+
d2 = self.Up2(d3)
|
284 |
+
d2 = torch.cat((e1, d2), dim=1)
|
285 |
+
d2 = self.Up_conv2(d2)
|
286 |
+
# print("upconv2:")
|
287 |
+
# print(d2.shape)
|
288 |
+
out = self.Conv(d2)
|
289 |
+
# print("out:")
|
290 |
+
# print(out.shape)
|
291 |
+
# d1 = self.active(out)
|
292 |
+
|
293 |
+
return [out, d3_out , d4_out]
|
294 |
+
|
295 |
+
|