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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.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) | |