gen_unet.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 22 11:19:45 2019

@author: Macrobull
"""

from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F

from onnx2fluid.torch_export_helper import export_onnx_with_validation


# from https://github.com/milesial/Pytorch-UNet
class double_conv(nn.Module):
    '''(conv => BN => ReLU) * 2'''

    def __init__(self, in_ch, out_ch):
        super(double_conv, self).__init__()
        self.conv = nn.Sequential(nn.Conv2d(in_ch, out_ch, 3, padding=1),
                                  nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True),
                                  nn.Conv2d(out_ch, out_ch, 3, padding=1),
                                  nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True))

    def forward(self, x):
        x = self.conv(x)
        return x


class inconv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(inconv, self).__init__()
        self.conv = double_conv(in_ch, out_ch)

    def forward(self, x):
        x = self.conv(x)
        return x


class down(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(down, self).__init__()
        self.mpconv = nn.Sequential(nn.MaxPool2d(2), double_conv(in_ch, out_ch))

    def forward(self, x):
        x = self.mpconv(x)
        return x


class up(nn.Module):
    def __init__(self, in_ch, out_ch, bilinear=True):
        super(up, self).__init__()

        #  would be a nice idea if the upsampling could be learned too,
        #  but my machine do not have enough memory to handle all those weights
        if bilinear:
            self.up = nn.Upsample(scale_factor=2,
                                  mode='bilinear')  #, align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_ch // 2, in_ch // 2, 2, stride=2)

        self.conv = double_conv(in_ch, out_ch)

    def forward(self, x1, x2):
        x1 = self.up(x1)

        # input is CHW
        if hasattr(self, 'diffY'):
            diffY = self.diffY
            diffX = self.diffX
        else:
            diffY = self.diffY = x2.size()[2] - x1.size()[2]
            diffX = self.diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(
            x1,
            (diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2))

        # for padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd

        x = torch.cat([x2, x1], dim=1)
        x = self.conv(x)
        return x


class outconv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(outconv, self).__init__()
        self.conv = nn.Conv2d(in_ch, out_ch, 1)

    def forward(self, x):
        x = self.conv(x)
        return x


class UNet(nn.Module):
    def __init__(self, n_channels, n_classes):
        super(UNet, self).__init__()
        self.inc = inconv(n_channels, 64)
        self.down1 = down(64, 128)
        self.down2 = down(128, 256)
        self.down3 = down(256, 512)
        self.down4 = down(512, 512)
        self.up1 = up(1024, 256)
        self.up2 = up(512, 128)
        self.up3 = up(256, 64)
        self.up4 = up(128, 64)
        self.outc = outconv(64, n_classes)

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        x = self.outc(x)
        return F.sigmoid(x)


model = UNet(3, 80)
model.eval()
xb = torch.rand((1, 3, 512, 512))
yp = model(xb)
export_onnx_with_validation(model, [xb],
                            'sample_unet', ['image'], ['pred'],
                            verbose=True,
                            training=False)