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| | |
| | import torch.nn as nn |
| | import torch.nn.functional as F |
| | from .postprocess import postprocess |
| |
|
| |
|
| | class LinearPts3d (nn.Module): |
| | """ |
| | Linear head for dust3r |
| | Each token outputs: - 16x16 3D points (+ confidence) |
| | """ |
| |
|
| | def __init__(self, net, has_conf=False): |
| | super().__init__() |
| | self.patch_size = net.patch_embed.patch_size[0] |
| | self.depth_mode = net.depth_mode |
| | self.conf_mode = net.conf_mode |
| | self.has_conf = has_conf |
| |
|
| | self.proj = nn.Linear(net.dec_embed_dim, (3 + has_conf)*self.patch_size**2) |
| |
|
| | def setup(self, croconet): |
| | pass |
| |
|
| | def forward(self, decout, img_shape): |
| | H, W = img_shape |
| | tokens = decout[-1] |
| | B, S, D = tokens.shape |
| |
|
| | |
| | feat = self.proj(tokens) |
| | feat = feat.transpose(-1, -2).view(B, -1, H//self.patch_size, W//self.patch_size) |
| | feat = F.pixel_shuffle(feat, self.patch_size) |
| |
|
| | |
| | return postprocess(feat, self.depth_mode, self.conf_mode) |
| |
|