File size: 3,136 Bytes
e3814d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeepHiC: https://github.com/omegahh/DeepHiC
# --------------------------------------------------------
import torch
import torch.nn as nn
def swish(x):
return x * torch.sigmoid(x)
# DeepHiC Residual Block
class residualBlock(nn.Module):
def __init__(self, channels, k=3, s=1):
super(residualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, k, stride=s, padding=1)
self.bn1 = nn.BatchNorm2d(channels)
# a swish layer here
self.conv2 = nn.Conv2d(channels, channels, k, stride=s, padding=1)
self.bn2 = nn.BatchNorm2d(channels)
def forward(self, x):
residual = swish(self.bn1(self.conv1(x)))
residual = self.bn2(self.conv2(residual))
return x + residual
# DeepHiC Generator
class Generator(nn.Module):
def __init__(self, num_channels=64, resblock_num=5):
super(Generator, self).__init__()
self.conv1 = nn.Conv2d(1, num_channels, kernel_size=9, stride=1, padding=4)
# have a swish here in forward
resblocks = [residualBlock(num_channels) for _ in range(resblock_num)]
self.resblocks = nn.Sequential(*resblocks)
self.conv2 = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(num_channels)
# have a swish here in forward
self.conv3 = nn.Conv2d(num_channels, 1, kernel_size=9, stride=1, padding=4)
def forward(self, x):
emb = swish(self.conv1(x))
x = self.resblocks(emb)
x = swish(self.bn2(self.conv2(x)))
x = self.conv3(x + emb)
return (torch.tanh(x) + 1) / 2
# DeepHiC Discriminator
class Discriminator(nn.Module):
def __init__(self, in_channel=3):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(in_channel, 64, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.conv4 = nn.Conv2d(128, 128, 3, stride=2, padding=1)
self.bn4 = nn.BatchNorm2d(128)
self.conv5 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
self.bn5 = nn.BatchNorm2d(256)
self.conv6 = nn.Conv2d(256, 256, 3, stride=2, padding=1)
self.bn6 = nn.BatchNorm2d(256)
# Replaced original paper FC layers with FCN
self.conv7 = nn.Conv2d(256, 1, 1, stride=1, padding=0)
self.avgpool = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
batch_size = x.size(0)
x = swish(self.conv1(x))
x = swish(self.bn2(self.conv2(x)))
x = swish(self.bn3(self.conv3(x)))
x = swish(self.bn4(self.conv4(x)))
x = swish(self.bn5(self.conv5(x)))
x = swish(self.bn6(self.conv6(x)))
x = self.conv7(x)
x = self.avgpool(x)
return torch.sigmoid(x.view(batch_size))
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