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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 = False,
):
super().__init__()
self.scales = scales
self.num_parts = sum(scales)
self.use_avg_pool = use_avg_pool
self.use_max_pool = use_max_pool
self.pyramids = nn.ModuleList([
self._make_pyramid(scale) for scale in scales
])
self.fc_mat = nn.Parameter(
torch.empty(self.num_parts, in_channels, out_channels)
)
def _make_pyramid(self, scale: int):
pyramid = nn.ModuleList([
HorizontalPyramidPooling(self.use_avg_pool, self.use_max_pool)
for _ in range(scale)
])
return pyramid
def _horizontal_pyramid_pool(self, x):
n, t, c, h, w = x.size()
x = x.view(n * t, c, h, w)
feature = []
for scale, pyramid in zip(self.scales, self.pyramids):
h_per_hpp = h // scale
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(n, t, c)
feature.append(x_slice)
x = torch.stack(feature)
return x
def forward(self, f_c1_t2, f_c1_t1=None, f_c2_t2=None):
# n, t, c, h, w
f_c1_t2_ = self._horizontal_pyramid_pool(f_c1_t2)
# p, n, t, c
x = f_c1_t2_.mean(2)
# p, n, c
x = x @ self.fc_mat
# p, n, d
if self.training:
f_c1_t1_ = self._horizontal_pyramid_pool(f_c1_t1)
f_c2_t2_ = self._horizontal_pyramid_pool(f_c2_t2)
return x, (f_c1_t2_, f_c1_t1_, f_c2_t2_)
else:
return x
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