From d12dd6b04a4e7c2b1ee43ab6f36f25d0c35ca364 Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Fri, 19 Feb 2021 22:39:49 +0800
Subject: New branch with auto-encoder only

---
 models/layers.py | 96 --------------------------------------------------------
 1 file changed, 96 deletions(-)

(limited to 'models/layers.py')

diff --git a/models/layers.py b/models/layers.py
index ef53a95..1b4640f 100644
--- a/models/layers.py
+++ b/models/layers.py
@@ -1,6 +1,5 @@
 from typing import Union
 
-import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
@@ -97,98 +96,3 @@ class BasicLinear(nn.Module):
         x = self.fc(x)
         x = self.bn(x)
         return x
-
-
-class FocalConv2d(BasicConv2d):
-    def __init__(
-            self,
-            in_channels: int,
-            out_channels: int,
-            kernel_size: Union[int, tuple[int, int]],
-            halving: int,
-            **kwargs
-    ):
-        super().__init__(in_channels, out_channels, kernel_size, **kwargs)
-        self.halving = halving
-
-    def forward(self, x):
-        h = x.size(2)
-        split_size = h // 2 ** self.halving
-        z = x.split(split_size, dim=2)
-        z = torch.cat([self.conv(_) for _ in z], dim=2)
-        return F.leaky_relu(z, inplace=True)
-
-
-class FocalConv2dBlock(nn.Module):
-    def __init__(
-            self,
-            in_channels: int,
-            out_channels: int,
-            kernel_sizes: tuple[int, int],
-            paddings: tuple[int, int],
-            halving: int,
-            use_pool: bool = True,
-            **kwargs
-    ):
-        super().__init__()
-        self.use_pool = use_pool
-        self.fconv1 = FocalConv2d(in_channels, out_channels, kernel_sizes[0],
-                                  halving, padding=paddings[0], **kwargs)
-        self.fconv2 = FocalConv2d(out_channels, out_channels, kernel_sizes[1],
-                                  halving, padding=paddings[1], **kwargs)
-        self.max_pool = nn.MaxPool2d(2)
-
-    def forward(self, x):
-        x = self.fconv1(x)
-        x = self.fconv2(x)
-        if self.use_pool:
-            x = self.max_pool(x)
-        return x
-
-
-class BasicConv1d(nn.Module):
-    def __init__(
-            self,
-            in_channels: int,
-            out_channels: int,
-            kernel_size: Union[int, tuple[int]],
-            **kwargs
-    ):
-        super().__init__()
-        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
-                              bias=False, **kwargs)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class HorizontalPyramidPooling(nn.Module):
-    def __init__(
-            self,
-            in_channels: int,
-            out_channels: int,
-            use_1x1conv: bool = False,
-            use_avg_pool: bool = True,
-            use_max_pool: bool = False,
-            **kwargs
-    ):
-        super().__init__()
-        self.use_1x1conv = use_1x1conv
-        if use_1x1conv:
-            self.conv = BasicConv2d(in_channels, out_channels, 1, **kwargs)
-        self.use_avg_pool = use_avg_pool
-        self.use_max_pool = use_max_pool
-        assert use_avg_pool or use_max_pool, 'Pooling layer(s) required.'
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.max_pool = nn.AdaptiveMaxPool2d(1)
-
-    def forward(self, x):
-        if self.use_avg_pool and self.use_max_pool:
-            x = self.avg_pool(x) + self.max_pool(x)
-        elif self.use_avg_pool and not self.use_max_pool:
-            x = self.avg_pool(x)
-        elif not self.use_avg_pool and self.use_max_pool:
-            x = self.max_pool(x)
-        if self.use_1x1conv:
-            x = self.conv(x)
-        return x
-- 
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