Remove pyramid model

map2map/models/__init__.py

@@ -1,6 +1,5 @@
 from .unet import UNet
 from .vnet import VNet, VNetFat
-from .pyramid import PyramidNet
 from .patchgan import PatchGAN, PatchGAN42
 
 from .narrow import narrow_by, narrow_cast, narrow_like

map2map/models/pyramid.py

@@ -1,45 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .conv import ConvBlock, ResBlock, narrow_like
-
-
-class PyramidNet(nn.Module):
-    def __init__(self, in_chan, out_chan, **kwargs):
-        super().__init__()
-
-        self.down = nn.AvgPool3d(2, stride=2)
-        self.up = nn.Upsample(scale_factor=2, mode='nearest')
-
-        self.conv_l0 = ResBlock(in_chan, 64, seq='CAC')
-        self.conv_l1 = ResBlock(64, seq='CBAC')
-
-        self.conv_c = ResBlock(64, seq='CBAC')
-
-        self.conv_r1 = ResBlock(128, 64, seq='CBAC')
-        self.conv_r0 = ResBlock(128, out_chan, seq='CAC')
-
-    def forward(self, x):
-        y0 = self.conv_l0(x)
-        x = self.down(y0)
-        y0 = y0 - self.up(x)
-
-        y1 = self.conv_l1(x)
-        x = self.down(y1)
-        y1 = y1 - self.up(x)
-
-        x = self.conv_c(x)
-
-        x = self.up(x)
-        y1 = narrow_like(y1, x)
-        x = torch.cat([y1, x], dim=1)
-        del y1
-        x = self.conv_r1(x)
-
-        x = self.up(x)
-        y0 = narrow_like(y0, x)
-        x = torch.cat([y0, x], dim=1)
-        del y0
-        x = self.conv_r0(x)
-
-        return x