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multiresunet.py
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multiresunet.py
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from typing import Tuple, Dict
import torch.nn as nn
import torch.nn.functional as F
import torch
class Multiresblock(nn.Module):
def __init__(self,input_features : int, corresponding_unet_filters : int ,alpha : float =1.67)->None:
"""
MultiResblock
Arguments:
x - input layer
corresponding_unet_filters - Unet filters for the same stage
alpha - 1.67 - factor used in the paper to dervie number of filters for multiresunet filters from Unet filters
Returns - None
"""
super().__init__()
self.corresponding_unet_filters = corresponding_unet_filters
self.alpha = alpha
self.W = corresponding_unet_filters * alpha
self.conv2d_bn_1x1 = Conv2d_batchnorm(input_features=input_features,num_of_filters = int(self.W*0.167)+int(self.W*0.333)+int(self.W*0.5),
kernel_size = (1,1),activation='None',padding = 0)
self.conv2d_bn_3x3 = Conv2d_batchnorm(input_features=input_features,num_of_filters = int(self.W*0.167),
kernel_size = (3,3),activation='relu',padding = 1)
self.conv2d_bn_5x5 = Conv2d_batchnorm(input_features=int(self.W*0.167),num_of_filters = int(self.W*0.333),
kernel_size = (3,3),activation='relu',padding = 1)
self.conv2d_bn_7x7 = Conv2d_batchnorm(input_features=int(self.W*0.333),num_of_filters = int(self.W*0.5),
kernel_size = (3,3),activation='relu',padding = 1)
self.batch_norm1 = nn.BatchNorm2d(int(self.W*0.5)+int(self.W*0.167)+int(self.W*0.333) ,affine=False)
def forward(self,x: torch.Tensor)->torch.Tensor:
temp = self.conv2d_bn_1x1(x)
a = self.conv2d_bn_3x3(x)
b = self.conv2d_bn_5x5(a)
c = self.conv2d_bn_7x7(b)
x = torch.cat([a,b,c],axis=1)
x = self.batch_norm1(x)
x += temp
x = self.batch_norm1(x)
return x
class Conv2d_batchnorm(nn.Module):
def __init__(self,input_features : int,num_of_filters : int ,kernel_size : Tuple = (2,2),stride : Tuple = (1,1), activation : str = 'relu',padding : int= 0)->None:
"""
Arguments:
x - input layer
num_of_filters - no. of filter outputs
filters - shape of the filters to be used
stride - stride dimension
activation -activation function to be used
Returns - None
"""
super().__init__()
self.activation = activation
self.conv1 = nn.Conv2d(in_channels=input_features,out_channels=num_of_filters,kernel_size=kernel_size,stride=stride,padding = padding)
self.batchnorm = nn.BatchNorm2d(num_of_filters,affine=False)
def forward(self,x : torch.Tensor)->torch.Tensor:
x = self.conv1(x)
x = self.batchnorm(x)
if self.activation == 'relu':
return F.relu(x)
else:
return x
class Respath(nn.Module):
def __init__(self,input_features : int,filters : int,respath_length : int)->None:
"""
Arguments:
input_features - input layer filters
filters - output channels
respath_length - length of the Respath
Returns - None
"""
super().__init__()
self.filters = filters
self.respath_length = respath_length
self.conv2d_bn_1x1 = Conv2d_batchnorm(input_features=input_features,num_of_filters = self.filters,
kernel_size = (1,1),activation='None',padding = 0)
self.conv2d_bn_3x3 = Conv2d_batchnorm(input_features=input_features,num_of_filters = self.filters,
kernel_size = (3,3),activation='relu',padding = 1)
self.conv2d_bn_1x1_common = Conv2d_batchnorm(input_features=self.filters,num_of_filters = self.filters,
kernel_size = (1,1),activation='None',padding = 0)
self.conv2d_bn_3x3_common = Conv2d_batchnorm(input_features=self.filters,num_of_filters = self.filters,
kernel_size = (3,3),activation='relu',padding = 1)
self.batch_norm1 = nn.BatchNorm2d(filters,affine=False)
def forward(self,x : torch.Tensor)->torch.Tensor:
shortcut = self.conv2d_bn_1x1(x)
x = self.conv2d_bn_3x3(x)
x += shortcut
x = F.relu(x)
x = self.batch_norm1(x)
if self.respath_length>1:
for i in range(self.respath_length):
shortcut = self.conv2d_bn_1x1_common(x)
x = self.conv2d_bn_3x3_common(x)
x += shortcut
x = F.relu(x)
x = self.batch_norm1(x)
return x
else:
return x
class MultiResUnet(nn.Module):
def __init__(self,channels : int,filters : int =32,nclasses : int =1)->None:
"""
Arguments:
channels - input image channels
filters - filters to begin with (Unet)
nclasses - number of classes
Returns - None
"""
super().__init__()
self.alpha = 1.67
self.filters = filters
self.nclasses = nclasses
self.multiresblock1 = Multiresblock(input_features=channels,corresponding_unet_filters=self.filters)
self.pool1 = nn.MaxPool2d(2,stride= 2)
self.in_filters1 = int(self.filters*self.alpha* 0.5)+int(self.filters*self.alpha*0.167)+int(self.filters*self.alpha*0.333)
self.respath1 = Respath(input_features=self.in_filters1 ,filters=self.filters,respath_length=4)
self.multiresblock2 = Multiresblock(input_features= self.in_filters1,corresponding_unet_filters=self.filters*2)
self.pool2 = nn.MaxPool2d(2, 2)
self.in_filters2 = int(self.filters*2*self.alpha* 0.5)+int(self.filters*2*self.alpha*0.167)+int(self.filters*2*self.alpha*0.333)
self.respath2 = Respath(input_features=self.in_filters2,filters=self.filters*2,respath_length=3)
self.multiresblock3 = Multiresblock(input_features= self.in_filters2,corresponding_unet_filters=self.filters*4)
self.pool3 = nn.MaxPool2d(2, 2)
self.in_filters3 = int(self.filters*4*self.alpha* 0.5)+int(self.filters*4*self.alpha*0.167)+int(self.filters*4*self.alpha*0.333)
self.respath3 = Respath(input_features=self.in_filters3,filters=self.filters*4,respath_length=2)
self.multiresblock4 = Multiresblock(input_features= self.in_filters3,corresponding_unet_filters=self.filters*8)
self.pool4 = nn.MaxPool2d(2, 2)
self.in_filters4 = int(self.filters*8*self.alpha* 0.5)+int(self.filters*8*self.alpha*0.167)+int(self.filters*8*self.alpha*0.333)
self.respath4 = Respath(input_features=self.in_filters4,filters=self.filters*8,respath_length=1)
self.multiresblock5 = Multiresblock(input_features= self.in_filters4,corresponding_unet_filters=self.filters*16)
self.in_filters5 = int(self.filters*16*self.alpha* 0.5)+int(self.filters*16*self.alpha*0.167)+int(self.filters*16*self.alpha*0.333)
#Decoder path
self.upsample6 = nn.ConvTranspose2d(in_channels=self.in_filters5,out_channels=self.filters*8,kernel_size=(2,2),stride=(2,2),padding = 0)
self.concat_filters1 = self.filters*8+self.filters*8
self.multiresblock6 = Multiresblock(input_features=self.concat_filters1,corresponding_unet_filters=self.filters*8)
self.in_filters6 = int(self.filters*8*self.alpha* 0.5)+int(self.filters*8*self.alpha*0.167)+int(self.filters*8*self.alpha*0.333)
self.upsample7 = nn.ConvTranspose2d(in_channels=self.in_filters6,out_channels=self.filters*4,kernel_size=(2,2),stride=(2,2),padding = 0)
self.concat_filters2 = self.filters*4+self.filters*4
self.multiresblock7 = Multiresblock(input_features=self.concat_filters2,corresponding_unet_filters=self.filters*4)
self.in_filters7 = int(self.filters*4*self.alpha* 0.5)+int(self.filters*4*self.alpha*0.167)+int(self.filters*4*self.alpha*0.333)
self.upsample8 = nn.ConvTranspose2d(in_channels=self.in_filters7,out_channels=self.filters*2,kernel_size=(2,2),stride=(2,2),padding = 0)
self.concat_filters3 = self.filters*2+self.filters*2
self.multiresblock8 = Multiresblock(input_features=self.concat_filters3,corresponding_unet_filters=self.filters*2)
self.in_filters8 = int(self.filters*2*self.alpha* 0.5)+int(self.filters*2*self.alpha*0.167)+int(self.filters*2*self.alpha*0.333)
self.upsample9 = nn.ConvTranspose2d(in_channels=self.in_filters8,out_channels=self.filters,kernel_size=(2,2),stride=(2,2),padding = 0)
self.concat_filters4 = self.filters+self.filters
self.multiresblock9 = Multiresblock(input_features=self.concat_filters4,corresponding_unet_filters=self.filters)
self.in_filters9 = int(self.filters*self.alpha* 0.5)+int(self.filters*self.alpha*0.167)+int(self.filters*self.alpha*0.333)
self.conv_final = Conv2d_batchnorm(input_features=self.in_filters9,num_of_filters = self.nclasses,
kernel_size = (1,1),activation='None')
def forward(self,x : torch.Tensor)->torch.Tensor:
x_multires1 = self.multiresblock1(x)
x_pool1 = self.pool1(x_multires1)
x_multires1 = self.respath1(x_multires1)
x_multires2 = self.multiresblock2(x_pool1)
x_pool2 = self.pool2(x_multires2)
x_multires2 = self.respath2(x_multires2)
x_multires3 = self.multiresblock3(x_pool2)
x_pool3 = self.pool3(x_multires3)
x_multires3 = self.respath3(x_multires3)
x_multires4 = self.multiresblock4(x_pool3)
x_pool4 = self.pool4(x_multires4)
x_multires4 = self.respath4(x_multires4)
x_multires5 = self.multiresblock5(x_pool4)
up6 = torch.cat([self.upsample6(x_multires5),x_multires4],axis=1)
x_multires6 = self.multiresblock6(up6)
up7 = torch.cat([self.upsample7(x_multires6),x_multires3],axis=1)
x_multires7 = self.multiresblock7(up7)
up8 = torch.cat([self.upsample8(x_multires7),x_multires2],axis=1)
x_multires8 = self.multiresblock8(up8)
up9 = torch.cat([self.upsample9(x_multires8),x_multires1],axis=1)
x_multires9 = self.multiresblock9(up9)
if self.nclasses > 1:
conv_final_layer = self.conv_final(x_multires9)
else:
conv_final_layer = torch.sigmoid(self.conv_final(x_multires9))
return conv_final_layer