Combine FPFE and TFA to GaitPart

1 files changed, 0 insertions, 77 deletions

diff --git a/models/tfa.py b/models/tfa.py
deleted file mode 100644
index b80328a..0000000
--- a/models/tfa.py
+++ /dev/null
@@ -1,77 +0,0 @@
-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