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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.einops import rearrange, repeat
class FinePreprocess(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.cat_c_feat = config['fine_concat_coarse_feat']
self.W = self.config['fine_window_size']
d_model_c = self.config['coarse']['d_model']
d_model_f = self.config['fine']['d_model']
self.d_model_f = d_model_f
if self.cat_c_feat:
self.down_proj = nn.Linear(d_model_c, d_model_f, bias=True)
self.merge_feat = nn.Linear(2*d_model_f, d_model_f, bias=True)
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.kaiming_normal_(p, mode="fan_out", nonlinearity="relu")
def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data):
W = self.W
stride = data['hw0_f'][0] // data['hw0_c'][0]
data.update({'W': W})
if data['b_ids'].shape[0] == 0:
feat0 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device)
feat1 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device)
return feat0, feat1
# 1. unfold(crop) all local windows
feat_f0_unfold = F.unfold(feat_f0, kernel_size=(W, W), stride=stride, padding=W//2)
feat_f0_unfold = rearrange(feat_f0_unfold, 'n (c ww) l -> n l ww c', ww=W**2)
feat_f1_unfold = F.unfold(feat_f1, kernel_size=(W, W), stride=stride, padding=W//2)
feat_f1_unfold = rearrange(feat_f1_unfold, 'n (c ww) l -> n l ww c', ww=W**2)
# 2. select only the predicted matches
feat_f0_unfold = feat_f0_unfold[data['b_ids'], data['i_ids']] # [n, ww, cf]
feat_f1_unfold = feat_f1_unfold[data['b_ids'], data['j_ids']]
# option: use coarse-level feature as context: concat and linear
if self.cat_c_feat:
feat_c_win = self.down_proj(torch.cat([feat_c0[data['b_ids'], data['i_ids']],
feat_c1[data['b_ids'], data['j_ids']]], 0)) # [2n, c]
feat_cf_win = self.merge_feat(torch.cat([
torch.cat([feat_f0_unfold, feat_f1_unfold], 0), # [2n, ww, cf]
repeat(feat_c_win, 'n c -> n ww c', ww=W**2), # [2n, ww, cf]
], -1))
feat_f0_unfold, feat_f1_unfold = torch.chunk(feat_cf_win, 2, dim=0)
return feat_f0_unfold, feat_f1_unfold
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