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| import torch | |
| import torch.nn as nn | |
| from ldm.modules.attention import BasicTransformerBlock | |
| from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder | |
| import torch.nn.functional as F | |
| class GroundingDownsampler(nn.Module): | |
| def __init__(self, resize_input=256, out_dim=8): | |
| super().__init__() | |
| self.resize_input = resize_input | |
| self.out_dim = out_dim | |
| self.layers = nn.Sequential( | |
| nn.Conv2d(1,4,4,2,1), | |
| nn.SiLU(), | |
| nn.Conv2d(4,self.out_dim,4,2,1) | |
| ) | |
| def forward(self, grounding_extra_input): | |
| # this is actually gary scale, but converted to rgb in dataset, information redudant | |
| grounding_extra_input = grounding_extra_input[:,0].unsqueeze(1) | |
| out = torch.nn.functional.interpolate(grounding_extra_input, (self.resize_input,self.resize_input), mode='bicubic') | |
| out = self.layers(out) | |
| assert out.shape[1] == self.out_dim | |
| return out | |