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models.py
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models.py
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from torch import nn
import torch
from torchvision import models
import torchvision
from torch.nn import functional as F
def conv3x3(in_, out):
return nn.Conv2d(in_, out, 3, padding=1)
class ConvRelu(nn.Module):
def __init__(self, in_: int, out: int):
super().__init__()
self.conv = conv3x3(in_, out)
self.activation = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.activation(x)
return x
class DecoderBlock(nn.Module):
def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True):
super(DecoderBlock, self).__init__()
self.in_channels = in_channels
if is_deconv:
self.block = nn.Sequential(
ConvRelu(in_channels, middle_channels),
nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=4, stride=2,
padding=1),
nn.ReLU(inplace=True)
)
else:
self.block = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
ConvRelu(in_channels, middle_channels),
ConvRelu(middle_channels, out_channels),
)
def forward(self, x):
return self.block(x)
class UNet11(nn.Module):
def __init__(self, num_classes=1, num_filters=32, pretrained=False, is_deconv=False):
"""
:param num_classes:
:param num_filters:
:param pretrained:
False - no pre-trained network used
vgg - encoder pre-trained with VGG11
"""
super().__init__()
self.pool = nn.MaxPool2d(2, 2)
self.num_classes = num_classes
self.encoder = models.vgg11(pretrained=pretrained).features
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Sequential(self.encoder[0],
self.relu)
self.conv2 = nn.Sequential(self.encoder[3],
self.relu)
self.conv3 = nn.Sequential(
self.encoder[6],
self.relu,
self.encoder[8],
self.relu,
)
self.conv4 = nn.Sequential(
self.encoder[11],
self.relu,
self.encoder[13],
self.relu,
)
self.conv5 = nn.Sequential(
self.encoder[16],
self.relu,
self.encoder[18],
self.relu,
)
self.center = DecoderBlock(256 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=is_deconv)
self.dec5 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=is_deconv)
self.dec4 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 4, is_deconv=is_deconv)
self.dec3 = DecoderBlock(256 + num_filters * 4, num_filters * 4 * 2, num_filters * 2, is_deconv=is_deconv)
self.dec2 = DecoderBlock(128 + num_filters * 2, num_filters * 2 * 2, num_filters, is_deconv=is_deconv)
self.dec1 = ConvRelu(64 + num_filters, num_filters)
self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)
def forward(self, x):
conv1 = self.conv1(x)
conv2 = self.conv2(self.pool(conv1))
conv3 = self.conv3(self.pool(conv2))
conv4 = self.conv4(self.pool(conv3))
conv5 = self.conv5(self.pool(conv4))
center = self.center(self.pool(conv5))
dec5 = self.dec5(torch.cat([center, conv5], 1))
dec4 = self.dec4(torch.cat([dec5, conv4], 1))
dec3 = self.dec3(torch.cat([dec4, conv3], 1))
dec2 = self.dec2(torch.cat([dec3, conv2], 1))
dec1 = self.dec1(torch.cat([dec2, conv1], 1))
if self.num_classes > 1:
x_out = F.log_softmax(self.final(dec1), dim=1)
else:
x_out = self.final(dec1)
return x_out
class UNet16(nn.Module):
def __init__(self, num_classes=1, num_filters=32, pretrained=False, is_deconv=False):
"""
:param num_classes:
:param num_filters:
:param pretrained: if encoder uses pre-trained weigths from VGG16
"""
super().__init__()
self.num_classes = num_classes
self.pool = nn.MaxPool2d(2, 2)
self.encoder = torchvision.models.vgg16(pretrained=pretrained).features
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Sequential(self.encoder[0],
self.relu,
self.encoder[2],
self.relu)
self.conv2 = nn.Sequential(self.encoder[5],
self.relu,
self.encoder[7],
self.relu)
self.conv3 = nn.Sequential(self.encoder[10],
self.relu,
self.encoder[12],
self.relu,
self.encoder[14],
self.relu)
self.conv4 = nn.Sequential(self.encoder[17],
self.relu,
self.encoder[19],
self.relu,
self.encoder[21],
self.relu)
self.conv5 = nn.Sequential(self.encoder[24],
self.relu,
self.encoder[26],
self.relu,
self.encoder[28],
self.relu)
self.center = DecoderBlock(512, num_filters * 8 * 2, num_filters * 8, is_deconv=is_deconv)
self.dec5 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=is_deconv)
self.dec4 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv=is_deconv)
self.dec3 = DecoderBlock(256 + num_filters * 8, num_filters * 4 * 2, num_filters * 2, is_deconv=is_deconv)
self.dec2 = DecoderBlock(128 + num_filters * 2, num_filters * 2 * 2, num_filters, is_deconv=is_deconv)
self.dec1 = ConvRelu(64 + num_filters, num_filters)
self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)
def forward(self, x):
conv1 = self.conv1(x)
conv2 = self.conv2(self.pool(conv1))
conv3 = self.conv3(self.pool(conv2))
conv4 = self.conv4(self.pool(conv3))
conv5 = self.conv5(self.pool(conv4))
center = self.center(self.pool(conv5))
dec5 = self.dec5(torch.cat([center, conv5], 1))
dec4 = self.dec4(torch.cat([dec5, conv4], 1))
dec3 = self.dec3(torch.cat([dec4, conv3], 1))
dec2 = self.dec2(torch.cat([dec3, conv2], 1))
dec1 = self.dec1(torch.cat([dec2, conv1], 1))
if self.num_classes > 1:
x_out = F.log_softmax(self.final(dec1), dim=1)
else:
x_out = self.final(dec1)
return x_out
class Conv3BN(nn.Module):
def __init__(self, in_: int, out: int, bn=False):
super().__init__()
self.conv = conv3x3(in_, out)
self.bn = nn.BatchNorm2d(out) if bn else None
self.activation = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
x = self.activation(x)
return x
class UNetModule(nn.Module):
def __init__(self, in_: int, out: int):
super().__init__()
self.l1 = Conv3BN(in_, out)
self.l2 = Conv3BN(out, out)
def forward(self, x):
x = self.l1(x)
x = self.l2(x)
return x
class UNet(nn.Module):
"""
Vanilla UNet.
Implementation from https://github.com/lopuhin/mapillary-vistas-2017/blob/master/unet_models.py
"""
output_downscaled = 1
module = UNetModule
def __init__(self,
input_channels: int = 3,
filters_base: int = 32,
down_filter_factors=(1, 2, 4, 8, 16),
up_filter_factors=(1, 2, 4, 8, 16),
bottom_s=4,
num_classes=1,
add_output=True):
super().__init__()
self.num_classes = num_classes
assert len(down_filter_factors) == len(up_filter_factors)
assert down_filter_factors[-1] == up_filter_factors[-1]
down_filter_sizes = [filters_base * s for s in down_filter_factors]
up_filter_sizes = [filters_base * s for s in up_filter_factors]
self.down, self.up = nn.ModuleList(), nn.ModuleList()
self.down.append(self.module(input_channels, down_filter_sizes[0]))
for prev_i, nf in enumerate(down_filter_sizes[1:]):
self.down.append(self.module(down_filter_sizes[prev_i], nf))
for prev_i, nf in enumerate(up_filter_sizes[1:]):
self.up.append(self.module(
down_filter_sizes[prev_i] + nf, up_filter_sizes[prev_i]))
pool = nn.MaxPool2d(2, 2)
pool_bottom = nn.MaxPool2d(bottom_s, bottom_s)
upsample = nn.Upsample(scale_factor=2)
upsample_bottom = nn.Upsample(scale_factor=bottom_s)
self.downsamplers = [None] + [pool] * (len(self.down) - 1)
self.downsamplers[-1] = pool_bottom
self.upsamplers = [upsample] * len(self.up)
self.upsamplers[-1] = upsample_bottom
self.add_output = add_output
if add_output:
self.conv_final = nn.Conv2d(up_filter_sizes[0], num_classes, 1)
def forward(self, x):
xs = []
for downsample, down in zip(self.downsamplers, self.down):
x_in = x if downsample is None else downsample(xs[-1])
x_out = down(x_in)
xs.append(x_out)
x_out = xs[-1]
for x_skip, upsample, up in reversed(
list(zip(xs[:-1], self.upsamplers, self.up))):
x_out = upsample(x_out)
x_out = up(torch.cat([x_out, x_skip], 1))
if self.add_output:
x_out = self.conv_final(x_out)
if self.num_classes > 1:
x_out = F.log_softmax(x_out, dim=1)
return x_out
class AlbuNet34(nn.Module):
"""
UNet (https://arxiv.org/abs/1505.04597) with Resnet34(https://arxiv.org/abs/1512.03385) encoder
Proposed by Alexander Buslaev: https://www.linkedin.com/in/al-buslaev/
"""
def __init__(self, num_classes=1, num_filters=32, pretrained=False, is_deconv=False):
"""
:param num_classes:
:param num_filters:
:param pretrained:
False - no pre-trained network is used
True - encoder is pre-trained with resnet34
:is_deconv:
False: bilinear interpolation is used in decoder
True: deconvolution is used in decoder
"""
super().__init__()
self.num_classes = num_classes
self.pool = nn.MaxPool2d(2, 2)
self.encoder = torchvision.models.resnet34(pretrained=pretrained)
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Sequential(self.encoder.conv1,
self.encoder.bn1,
self.encoder.relu,
self.pool)
self.conv2 = self.encoder.layer1
self.conv3 = self.encoder.layer2
self.conv4 = self.encoder.layer3
self.conv5 = self.encoder.layer4
self.center = DecoderBlock(512, num_filters * 8 * 2, num_filters * 8, is_deconv)
self.dec5 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
self.dec4 = DecoderBlock(256 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
self.dec3 = DecoderBlock(128 + num_filters * 8, num_filters * 4 * 2, num_filters * 2, is_deconv)
self.dec2 = DecoderBlock(64 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2, is_deconv)
self.dec1 = DecoderBlock(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv)
self.dec0 = ConvRelu(num_filters, num_filters)
self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)
def forward(self, x):
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
conv3 = self.conv3(conv2)
conv4 = self.conv4(conv3)
conv5 = self.conv5(conv4)
center = self.center(self.pool(conv5))
dec5 = self.dec5(torch.cat([center, conv5], 1))
dec4 = self.dec4(torch.cat([dec5, conv4], 1))
dec3 = self.dec3(torch.cat([dec4, conv3], 1))
dec2 = self.dec2(torch.cat([dec3, conv2], 1))
dec1 = self.dec1(dec2)
dec0 = self.dec0(dec1)
if self.num_classes > 1:
x_out = F.log_softmax(self.final(dec0), dim=1)
else:
x_out = self.final(dec0)
return x_out