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from typing import Tuple
import torch
import torch.nn as nn
from models.layers import HorizontalPyramidPooling
class HorizontalPyramidMatching(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int = 128,
scales: Tuple[int, ...] = (1, 2, 4),
use_avg_pool: bool = True,
use_max_pool: bool = True,
**kwargs
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.scales = scales
self.use_avg_pool = use_avg_pool
self.use_max_pool = use_max_pool
self.pyramids = nn.ModuleList([
self._make_pyramid(scale, **kwargs) for scale in self.scales
])
def _make_pyramid(self, scale: int, **kwargs):
pyramid = nn.ModuleList([
HorizontalPyramidPooling(self.in_channels,
self.out_channels,
use_avg_pool=self.use_avg_pool,
use_max_pool=self.use_max_pool,
**kwargs)
for _ in range(scale)
])
return pyramid
def forward(self, x):
# Flatten canonical features in all batches
t, n, c, h, w = x.size()
x = x.view(t * n, c, h, w)
feature = []
for pyramid_index, pyramid in enumerate(self.pyramids):
h_per_hpp = h // self.scales[pyramid_index]
for hpp_index, hpp in enumerate(pyramid):
h_filter = torch.arange(hpp_index * h_per_hpp,
(hpp_index + 1) * h_per_hpp)
x_slice = x[:, :, h_filter, :]
x_slice = hpp(x_slice)
x_slice = x_slice.view(t * n, -1)
feature.append(x_slice)
x = torch.stack(feature)
# Unfold frames to original batch
p, _, c = x.size()
x = x.view(p, t, n, c)
return x
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