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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 PositionNet(nn.Module): | |
| def __init__(self, positive_len, out_dim, fourier_freqs=8): | |
| super().__init__() | |
| self.positive_len = positive_len | |
| self.out_dim = out_dim | |
| self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) | |
| self.position_dim = fourier_freqs*2*4 # 2 is sin&cos, 4 is xyxy | |
| self.linears = nn.Sequential( | |
| nn.Linear( self.positive_len + self.position_dim, 512), | |
| nn.SiLU(), | |
| nn.Linear( 512, 512), | |
| nn.SiLU(), | |
| nn.Linear(512, out_dim), | |
| ) | |
| self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len])) | |
| self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim])) | |
| def forward(self, boxes, masks, positive_embeddings): | |
| B, N, _ = boxes.shape | |
| masks = masks.unsqueeze(-1) | |
| # embedding position (it may includes padding as placeholder) | |
| xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C | |
| # learnable null embedding | |
| positive_null = self.null_positive_feature.view(1,1,-1) | |
| xyxy_null = self.null_position_feature.view(1,1,-1) | |
| # replace padding with learnable null embedding | |
| positive_embeddings = positive_embeddings*masks + (1-masks)*positive_null | |
| xyxy_embedding = xyxy_embedding*masks + (1-masks)*xyxy_null | |
| objs = self.linears( torch.cat([positive_embeddings, xyxy_embedding], dim=-1) ) | |
| assert objs.shape == torch.Size([B,N,self.out_dim]) | |
| return objs | |