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import copy
import torch
import torch.nn as nn
from models.layers import BasicConv1d
class TemporalFeatureAggregator(nn.Module):
def __init__(
self,
in_channels: int,
squeeze: int = 4,
num_part: int = 16
):
super(TemporalFeatureAggregator, self).__init__()
hidden_dim = in_channels // squeeze
self.num_part = num_part
# MTB1
conv3x1 = nn.Sequential(
BasicConv1d(in_channels, hidden_dim, kernel_size=3, padding=1),
nn.LeakyReLU(inplace=True),
BasicConv1d(hidden_dim, in_channels, kernel_size=1, padding=0)
)
self.conv1d3x1 = self._parted(conv3x1)
self.avg_pool3x1 = nn.AvgPool1d(kernel_size=3, stride=1, padding=1)
self.max_pool3x1 = nn.MaxPool1d(kernel_size=3, stride=1, padding=1)
# MTB2
conv3x3 = nn.Sequential(
BasicConv1d(in_channels, hidden_dim, kernel_size=3, padding=1),
nn.LeakyReLU(inplace=True),
BasicConv1d(hidden_dim, in_channels, kernel_size=3, padding=1)
)
self.conv1d3x3 = self._parted(conv3x3)
self.avg_pool3x3 = nn.AvgPool1d(kernel_size=5, stride=1, padding=2)
self.max_pool3x3 = nn.MaxPool1d(kernel_size=5, stride=1, padding=2)
def _parted(self, module: nn.Module):
"""Duplicate module `part_num` times."""
return nn.ModuleList([copy.deepcopy(module)
for _ in range(self.num_part)])
def forward(self, x):
"""
Input: x, [p, n, c, s]
"""
p, n, c, s = x.size()
feature = x.split(1, 0)
x = x.view(-1, c, s)
# MTB1: ConvNet1d & Sigmoid
logits3x1 = torch.cat(
[conv(_.squeeze(0)).unsqueeze(0)
for conv, _ in zip(self.conv1d3x1, feature)], dim=0
)
scores3x1 = torch.sigmoid(logits3x1)
# MTB1: Template Function
feature3x1 = self.avg_pool3x1(x) + self.max_pool3x1(x)
feature3x1 = feature3x1.view(p, n, c, s)
feature3x1 = feature3x1 * scores3x1
# MTB2: ConvNet1d & Sigmoid
logits3x3 = torch.cat(
[conv(_.squeeze(0)).unsqueeze(0)
for conv, _ in zip(self.conv1d3x3, feature)], dim=0
)
scores3x3 = torch.sigmoid(logits3x3)
# MTB2: Template Function
feature3x3 = self.avg_pool3x3(x) + self.max_pool3x3(x)
feature3x3 = feature3x3.view(p, n, c, s)
feature3x3 = feature3x3 * scores3x3
# Temporal Pooling
ret = (feature3x1 + feature3x3).max(-1)[0]
return ret
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