Spaces:
Sleeping
Sleeping
File size: 1,789 Bytes
34d43ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
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
|