# ------------------------------------------ # 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.nn.functional as F from model.text_segmenter.unet_parts import * class UNet(nn.Module): def __init__(self, n_channels, n_classes, bilinear=True): super(UNet, self).__init__() self.n_channels = n_channels self.n_classes = n_classes self.bilinear = bilinear self.inc = DoubleConv(n_channels, 64) self.down1 = Down(64, 128) self.down2 = Down(128, 256) self.down3 = Down(256, 512) factor = 2 if bilinear else 1 self.down4 = Down(512, 1024 // factor) self.up1 = Up(1024, 512 // factor, bilinear) self.up2 = Up(512, 256 // factor, bilinear) self.up3 = Up(256, 128 // factor, bilinear) self.up4 = Up(128, 64, bilinear) 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) logits = self.outc(x) # logits = torch.sigmoid(logits) return logits if __name__ == '__main__': net = UNet(39,39,True) net = net.cuda() image = torch.Tensor(32,39,64,64).cuda() result = net(image) print(result.shape)