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Zero
File size: 1,629 Bytes
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import torch
import torch.nn.functional as F
from torch import nn
class HyperColumnFeatures(nn.Module):
"""
Interpolate 3D tensor given N sparse 2D positions
Input
x: list([C, H, W]) list of feature tensors at different scales (e.g. from a U-Net) -> extract hypercolumn features
pos: [N, 2] tensor of positions
H: int, height of the OUTPUT map
W: int, width of the OUTPUT map
Returns
[N, C] sampled features at 2d positions
"""
def __init__(self, mode="bilinear"):
super().__init__()
self.mode = mode
self.name = "HyperColumnFeatures"
def normgrid(self, x, H, W):
return 2.0 * (x / (torch.tensor([W - 1, H - 1], device=x.device, dtype=x.dtype))) - 1.0
def extract_values_at_poses(self, x, pos, H, W):
"""Extract values from tensor x at the positions given by pos.
Args:
- x (Tensor): Tensor of size (C, H, W).
- pos (Tensor): Tensor of size (N, 2) containing the x, y positions.
Returns:
- values (Tensor): Tensor of size (N, C) with the values from f at the positions given by p.
"""
# check if grid is float32
if x.dtype != torch.float32:
x = x.to(torch.float32)
grid = self.normgrid(pos, H, W).unsqueeze(-2)
x = F.grid_sample(x, grid, mode=self.mode, align_corners=True)
return x.permute(0, 2, 3, 1).squeeze(-2)
def forward(self, x, pos, H, W):
descs = []
for layer in x:
desc = self.extract_values_at_poses(layer, pos, H, W)
descs.append(desc)
return descs
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