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model.py
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model.py
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"""
With inspiration from https://github.com/asanakoy/kaggle_carvana_segmentation/blob/master/albu/src/pytorch_zoo/linknet.py
"""
import importlib
import torch
import torch.nn as nn
import torch.nn.functional as F
nonlinearity = nn.ReLU
def class_for_name(module_name, class_name):
# load the module, will raise ImportError if module cannot be loaded
m = importlib.import_module(module_name)
# get the class, will raise AttributeError if class cannot be found
return getattr(m, class_name)
class DecoderBlock(nn.Module):
def __init__(self, in_channels, n_filters):
super().__init__()
# B, C, H, W -> B, C/4, H, W
self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1)
self.norm1 = nn.BatchNorm2d(in_channels // 4)
self.relu1 = nonlinearity(inplace=True)
# B, C/4, H, W -> B, C/4, H, W
self.deconv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4, 3,
stride=2, padding=1, output_padding=1)
self.norm2 = nn.BatchNorm2d(in_channels // 4)
self.relu2 = nonlinearity(inplace=True)
# B, C/4, H, W -> B, C, H, W
self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1)
self.norm3 = nn.BatchNorm2d(n_filters)
self.relu3 = nonlinearity(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
x = self.relu1(x)
x = self.deconv2(x)
x = self.norm2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.norm3(x)
x = self.relu3(x)
return x
class LinkNet(nn.Module):
def __init__(self, num_classes, num_channels=3, encoder='resnet34',
final='softmax'):
super().__init__()
assert num_channels > 0, "Incorrect num channels"
assert encoder in ['resnet18', 'resnet34', 'resnet50',\
'resnet101', 'resnet152'],\
"Incorrect encoder type"
assert final in ['softmax', 'sigmoid'],\
"Incorrect output type"
if encoder in ['resnet18', 'resnet34']:
filters = [64, 128, 256, 512]
else:
filters = [256, 512, 1024, 2048]
# Padding is used to take 800x600 px input
#self.pad = nn.ReflectionPad2d((0, 0, 4, 4))
resnet = class_for_name("torchvision.models", encoder)\
(pretrained=False)
if num_channels != 3: # Number of input channels
self.firstconv = nn.Conv2d(num_channels, 64,
kernel_size=(7, 7), stride=(2, 2),
padding=(3, 3), bias=False)
else:
self.firstconv = resnet.conv1
self.firstbn = resnet.bn1
self.firstrelu = resnet.relu
self.firstmaxpool = resnet.maxpool
self.encoder1 = resnet.layer1
self.encoder2 = resnet.layer2
self.encoder3 = resnet.layer3
self.encoder4 = resnet.layer4
# Decoder
self.decoder4 = DecoderBlock(filters[3], filters[2])
self.decoder3 = DecoderBlock(filters[2], filters[1])
self.decoder2 = DecoderBlock(filters[1], filters[0])
self.decoder1 = DecoderBlock(filters[0], filters[0])
# Final Classifier
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 3,
stride=2)
self.finalrelu1 = nonlinearity(inplace=True)
self.finalconv2 = nn.Conv2d(32, 32, 3)
self.finalrelu2 = nonlinearity(inplace=True)
self.finalconv3 = nn.Conv2d(32, num_classes, 2, padding=1)
if final=='softmax':
self.final = nn.Softmax(dim=1)
else:
self.final = nn.Sigmoid()
def forward(self, x):
#x = self.pad(x)
# Encoder
x = self.firstconv(x)
x = self.firstbn(x)
x = self.firstrelu(x)
x = self.firstmaxpool(x)
e1 = self.encoder1(x)
e2 = self.encoder2(e1)
e3 = self.encoder3(e2)
e4 = self.encoder4(e3)
# Decoder with Skip Connections
d4 = self.decoder4(e4) + e3
d3 = self.decoder3(d4) + e2
d2 = self.decoder2(d3) + e1
d1 = self.decoder1(d2)
# Final Classification
x = self.finaldeconv1(d1)
x = self.finalrelu1(x)
x = self.finalconv2(x)
x = self.finalrelu2(x)
x = self.finalconv3(x)
return self.final(x)