New branch with auto-encoder only

1 files changed, 0 insertions, 55 deletions

diff --git a/models/hpm.py b/models/hpm.py
deleted file mode 100644
index 9879cfb..0000000
--- a/models/hpm.py
+++ /dev/null
@@ -1,55 +0,0 @@
-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,
-            use_1x1conv: bool = False,
-            scales: tuple[int, ...] = (1, 2, 4),
-            use_avg_pool: bool = True,
-            use_max_pool: bool = False,
-            **kwargs
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.use_1x1conv = use_1x1conv
-        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_1x1conv=self.use_1x1conv,
-                                     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):
-        n, c, h, w = x.size()
-        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, -1)
-                feature.append(x_slice)
-        x = torch.stack(feature)
-        return x