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Delete nets.py

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Anjok07 2020-11-09 04:27:59 -06:00 committed by GitHub
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lib/nets.py
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import torch
from torch import nn
from lib import layers
class BaseASPPNet(nn.Module):
def __init__(self, nin, ch, dilations=(4, 8, 16)):
super(BaseASPPNet, self).__init__()
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
def __call__(self, x):
h, e1 = self.enc1(x)
h, e2 = self.enc2(h)
h, e3 = self.enc3(h)
h, e4 = self.enc4(h)
h = self.aspp(h)
h = self.dec4(h, e4)
h = self.dec3(h, e3)
h = self.dec2(h, e2)
h = self.dec1(h, e1)
return h
class CascadedASPPNet(nn.Module):
def __init__(self):
super(CascadedASPPNet, self).__init__()
self.low_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
self.high_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
self.bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
self.full_band_net = BaseASPPNet(16, 32)
self.out = nn.Sequential(
layers.Conv2DBNActiv(32, 16, 3, 1, 1),
nn.Conv2d(16, 2, 1, bias=False))
self.aux_out = nn.Conv2d(32, 2, 1, bias=False)
self.offset = 128
def __call__(self, x):
bandw = x.size()[2] // 2
aux = torch.cat([
self.low_band_net(x[:, :, :bandw]),
self.high_band_net(x[:, :, bandw:])
], dim=2)
h = torch.cat([x, aux], dim=1)
h = self.full_band_net(self.bridge(h))
h = torch.sigmoid(self.out(h))
aux = torch.sigmoid(self.aux_out(aux))
return h, aux
def predict(self, x):
bandw = x.size()[2] // 2
aux = torch.cat([
self.low_band_net(x[:, :, :bandw]),
self.high_band_net(x[:, :, bandw:])
], dim=2)
h = torch.cat([x, aux], dim=1)
h = self.full_band_net(self.bridge(h))
h = torch.sigmoid(self.out(h))
if self.offset > 0:
h = h[:, :, :, self.offset:-self.offset]
assert h.size()[3] > 0
return h