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| # ------------------------------------------ | |
| # TextDiffuser: Diffusion Models as Text Painters | |
| # Paper Link: https://arxiv.org/abs/2305.10855 | |
| # Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser | |
| # Copyright (c) Microsoft Corporation. | |
| # This file define the architecture of unet. | |
| # ------------------------------------------ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class DoubleConv(nn.Module): | |
| """(convolution => [BN] => ReLU) * 2""" | |
| def __init__(self, in_channels, out_channels, mid_channels=None): | |
| super().__init__() | |
| if not mid_channels: | |
| mid_channels = out_channels | |
| self.double_conv = nn.Sequential( | |
| nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(mid_channels), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(out_channels), | |
| nn.ReLU(inplace=True) | |
| ) | |
| def forward(self, x): | |
| return self.double_conv(x) | |
| class Down(nn.Module): | |
| """Downscaling with maxpool then double conv""" | |
| def __init__(self, in_channels, out_channels): | |
| super().__init__() | |
| self.maxpool_conv = nn.Sequential( | |
| nn.MaxPool2d(2), | |
| DoubleConv(in_channels, out_channels) | |
| ) | |
| def forward(self, x): | |
| return self.maxpool_conv(x) | |
| class Up(nn.Module): | |
| """Upscaling then double conv""" | |
| def __init__(self, in_channels, out_channels, bilinear=True): | |
| super().__init__() | |
| # if bilinear, use the normal convolutions to reduce the number of channels | |
| if bilinear: | |
| self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) | |
| else: | |
| self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2) | |
| self.conv = DoubleConv(in_channels, out_channels) | |
| def forward(self, x1, x2): | |
| x1 = self.up(x1) | |
| # input is CHW | |
| diffY = x2.size()[2] - x1.size()[2] | |
| diffX = x2.size()[3] - x1.size()[3] | |
| x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, | |
| diffY // 2, diffY - diffY // 2]) | |
| x = torch.cat([x2, x1], dim=1) | |
| return self.conv(x) | |
| class OutConv(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(OutConv, self).__init__() | |
| self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) | |
| def forward(self, x): | |
| return self.conv(x) | |