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models.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
from PIL import Image
from os.path import join
import imageio
from torch import nn
from torch.nn.modules.linear import Linear
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm import tqdm
import numpy as np
import os,sys,os.path
import pandas as pd
import pickle
import pydicom
import glob
import collections
import pprint
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms.functional as TF
import warnings
import tarfile
import zipfile
import random
from skimage.io import imread, imsave
from skimage.transform import resize
import hashlib
import pickle
from tqdm import tqdm
import time
import math
from torch.utils.data import Dataset
import argparse
from sklearn.cluster import KMeans
import scipy
import torch.nn as nn
import torch.optim as optim
import copy
from scipy.optimize import linear_sum_assignment as linear_assignment
class CIFAR_ConvEncoder(nn.Module):
def __init__(self,
in_channels=3,
input_shape=[32,32],
code_length=128,
classes = 10,
drop_rate=0.2,
):
super(CIFAR_ConvEncoder,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
# Convolutonal Layer 1
self.convlayer1 = nn.Sequential(
nn.Conv2d(in_channels,32, kernel_size = 3, padding = 1, bias=False),
nn.PReLU(),
nn.BatchNorm2d(32),
)
# Convolutonal Layer 2
self.convlayer2 = nn.Sequential(
nn.Conv2d(32,32, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
# Convolutonal Layer 3
self.convlayer3 = nn.Sequential(
nn.Conv2d(32,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
)
# Convolutonal Layer 4
self.convlayer4 = nn.Sequential(
nn.Conv2d(64,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size =2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
# Dense Layer 1
self.denselayer1 = nn.Sequential(
nn.Linear(64*8*8, self.code_length),
)
def forward(self,x): #x = [batch,time,freq]
x = self.convlayer1(x)
x = self.convlayer2(x)
x = self.convlayer3(x)
x = self.convlayer4(x)
x = torch.flatten(x,start_dim=1)
# code = self.denselayer1(x)
return x
class CIFAR_CNN(nn.Module):
def __init__(self,
in_channels=3,
input_shape=[32,32],
code_length=128,
classes = 10,
drop_rate=0.2,
):
super(CIFAR_CNN,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
self.Encoder = CIFAR_ConvEncoder(in_channels=in_channels,input_shape=input_shape,code_length=code_length, classes = classes,drop_rate=drop_rate)
# self.Classifier = nn.Linear(self.code_length,self.classes)
self.Classifier = nn.Sequential(
nn.Linear(64*8*8, self.code_length),
nn.Linear(self.code_length,classes),
)
def forward(self,x): #x = [batch,time,freq]
x = self.Encoder(x)
p = self.Classifier(x)
return p
class CIFAR_VAE_Encoder(nn.Module):
def __init__(self,
in_channels=3,
input_shape=[32,32],
code_length=10,
classes = 10,
drop_rate=0.2,
):
super(CIFAR_VAE_Encoder,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
# Convolutonal Layer 1
self.convlayer1 = nn.Sequential(
nn.Conv2d(in_channels,32, kernel_size = 3, padding = 1, bias=False),
nn.PReLU(),
nn.BatchNorm2d(32),
)
# Convolutonal Layer 2
self.convlayer2 = nn.Sequential(
nn.Conv2d(32,32, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
# Convolutonal Layer 3
self.convlayer3 = nn.Sequential(
nn.Conv2d(32,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
)
# Convolutonal Layer 4
self.convlayer4 = nn.Sequential(
nn.Conv2d(64,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size =2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
def forward(self,x): #x = [batch,time,freq]
x = self.convlayer1(x)
x = self.convlayer2(x)
x = self.convlayer3(x)
x = self.convlayer4(x)
x = torch.flatten(x,start_dim=1)
return x
class CIFAR_VAE_Decoder(nn.Module):
def __init__(self,
in_channels=64,
input_shape=[8,8],
code_length=10,
classes = 10,
drop_rate=0.2,
):
super(CIFAR_VAE_Decoder,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
# Dense Layer 2
self.denselayer = nn.Sequential(
nn.Linear(code_length,self.in_channels*self.input_shape[0]*self.input_shape[1]),
)
# Transpose Convolutonal Layer 1
self.deconvlayer1 = nn.Sequential(
nn.ConvTranspose2d(64,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
)
# Transpose Convolutonal Layer 2
self.deconvlayer2 = nn.Sequential(
nn.ConvTranspose2d(64,32, kernel_size = 3,stride=2,padding = 1,output_padding = 1),
nn.PReLU(),
nn.BatchNorm2d(32),
)
# Transpose Convolutonal Layer 3
self.deconvlayer3 = nn.Sequential(
nn.ConvTranspose2d(32,32, kernel_size = 3,padding = 1),
nn.BatchNorm2d(32),
nn.PReLU(),
)
# Transpose Convolutonal Layer 4
self.deconvlayer4 = nn.Sequential(
nn.ConvTranspose2d(32,3, kernel_size = 3,stride=2,padding = 1,output_padding = 1),
nn.Sigmoid(),
)
def forward(self,z): #x = [batch,time,freq]
x = self.denselayer(z)
batch = x.shape[0]
x = torch.reshape(x,(batch,self.in_channels,self.input_shape[0],self.input_shape[1]))
x = self.deconvlayer1(x)
x = self.deconvlayer2(x)
x = self.deconvlayer3(x)
x = self.deconvlayer4(x)
return x
class CIFAR_VAE(nn.Module):
def __init__(self,
in_channels=3,
input_shape=[32,32],
code_length=128,
classes = 10,
drop_rate=0.2,
decoder = False,
):
super(CIFAR_VAE,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
self.size = input_shape
self.decoder = decoder
self.Encoder = CIFAR_VAE_Encoder(in_channels=in_channels,input_shape=input_shape,code_length=code_length, classes = classes,drop_rate=drop_rate)
self.Decoder = CIFAR_VAE_Decoder(in_channels=64,input_shape=[8,8],code_length=code_length, classes = classes,drop_rate=drop_rate)
self.Mu = nn.Sequential(
nn.Linear(64*8*8, self.code_length),
)
# FC Layer 2
self.LogVar = nn.Sequential(
nn.Linear(64*8*8, self.code_length),
)
# FC Layer 3
self.Classifier = nn.Sequential(
nn.Linear(64*8*8, self.code_length),
nn.Linear(self.code_length, self.classes),
)
def reparameterize(self, mu, logVar):
#Reparameterization takes in the input mu and logVar and sample the mu + std * eps
std = torch.exp(logVar/2)
eps = torch.randn_like(std)
return mu + std*eps
def setDecoder(self, boolean):
self.decoder = boolean
return
def forward(self,x): #x = [batch,time,freq]
x = self.Encoder(x)
categorical = self.Classifier(x)
if self.decoder == False:
return categorical
else:
mu = self.Mu(x)
logVar = self.LogVar(x)
z = self.reparameterize(mu,logVar)
_x = self.Decoder(z)
return mu,logVar,categorical,_x
class FMNIST_ConvEncoder(nn.Module):
def __init__(self,
in_channels=1,
input_shape=[28,28],
code_length=32,
classes = 10,
drop_rate=0.2,
):
super(FMNIST_ConvEncoder,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
# Convolutonal Layer 1
self.convlayer1 = nn.Sequential(
nn.Conv2d(in_channels,32, kernel_size = 3, padding = 1, bias=False),
nn.PReLU(),
nn.BatchNorm2d(32),
)
# Convolutonal Layer 2
self.convlayer2 = nn.Sequential(
nn.Conv2d(32,32, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
# Convolutonal Layer 3
self.convlayer3 = nn.Sequential(
nn.Conv2d(32,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
# Dense Layer 1
self.denselayer1 = nn.Sequential(
nn.Linear(64*7*7, self.code_length),
)
def forward(self,x): #x = [batch,time,freq]
x = self.convlayer1(x)
x = self.convlayer2(x)
x = self.convlayer3(x)
x = torch.flatten(x,start_dim=1)
code = self.denselayer1(x)
return code
class FMNIST_CNN(nn.Module):
def __init__(self,
in_channels=1,
input_shape=[28,28],
code_length=32,
classes = 10,
drop_rate=0.2,
):
super(FMNIST_CNN,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
self.Encoder = FMNIST_ConvEncoder(in_channels=in_channels,input_shape=input_shape,code_length=code_length, classes = classes,drop_rate=drop_rate)
self.Classifier = nn.Linear(self.code_length,self.classes)
def forward(self,x): #x = [batch,time,freq]
x = self.Encoder(x)
p = self.Classifier(x)
return p
class FMNIST_VAE_Encoder(nn.Module):
def __init__(self,
in_channels=1,
input_shape=[28,28],
code_length=32,
classes = 10,
drop_rate=0.2,
):
super(FMNIST_VAE_Encoder,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
# Convolutonal Layer 1
self.convlayer1 = nn.Sequential(
nn.Conv2d(in_channels,32, kernel_size = 3, padding = 1, bias=False),
nn.PReLU(),
nn.BatchNorm2d(32),
)
# Convolutonal Layer 2
self.convlayer2 = nn.Sequential(
nn.Conv2d(32,32, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
# Convolutonal Layer 3
self.convlayer3 = nn.Sequential(
nn.Conv2d(32,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(self.drop_rate, inplace=True)
)
# Dense Layer 1
self.denselayer1 = nn.Sequential(
nn.Linear(64*7*7, self.code_length),
)
def forward(self,x): #x = [batch,time,freq]
x = self.convlayer1(x)
x = self.convlayer2(x)
x = self.convlayer3(x)
x = torch.flatten(x,start_dim=1)
return x
class FMNIST_VAE_Decoder(nn.Module):
def __init__(self,
in_channels=64,
input_shape=[7,7],
code_length=32,
classes = 10,
drop_rate=0.2,
):
super(FMNIST_VAE_Decoder,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
# Dense Layer 2
self.denselayer = nn.Sequential(
nn.Linear(code_length,self.in_channels*self.input_shape[0]*self.input_shape[1]),
)
# Transpose Convolutonal Layer 1
self.deconvlayer1 = nn.Sequential(
nn.ConvTranspose2d(64,64, kernel_size = 3,padding = 1),
nn.PReLU(),
nn.BatchNorm2d(64),
)
# Transpose Convolutonal Layer 2
self.deconvlayer2 = nn.Sequential(
nn.ConvTranspose2d(64,32, kernel_size = 3,stride=2,padding = 1,output_padding = 1),
nn.PReLU(),
nn.BatchNorm2d(32),
)
# Transpose Convolutonal Layer 3
self.deconvlayer3 = nn.Sequential(
nn.ConvTranspose2d(32,1, kernel_size = 3,stride=2,padding = 1,output_padding = 1),
nn.Sigmoid(),
)
def forward(self,z): #x = [batch,time,freq]
x = self.denselayer(z)
batch = x.shape[0]
x = torch.reshape(x,(batch,self.in_channels,self.input_shape[0],self.input_shape[1]))
x = self.deconvlayer1(x)
x = self.deconvlayer2(x)
x = self.deconvlayer3(x)
return x
class FMNIST_VAE(nn.Module):
def __init__(self,
in_channels=1,
input_shape=[28,28],
code_length=32,
classes = 10,
drop_rate=0.2,
decoder = False,
):
super(FMNIST_VAE,self).__init__()
self.in_channels = in_channels
self.input_shape = input_shape
self.code_length = code_length
self.classes = classes
self.drop_rate = drop_rate
self.decoder = decoder
self.Encoder = FMNIST_VAE_Encoder(in_channels=in_channels,input_shape=input_shape,code_length=code_length, classes = classes,drop_rate=drop_rate)
self.Decoder = FMNIST_VAE_Decoder(in_channels=64,input_shape=[7,7],code_length=code_length, classes = classes,drop_rate=drop_rate)
self.Mu = nn.Sequential(
nn.Linear(64*7*7, self.code_length),
)
# FC Layer 2
self.LogVar = nn.Sequential(
nn.Linear(64*7*7, self.code_length),
)
# FC Layer 3
self.Classifier = nn.Sequential(
nn.Linear(64*7*7, self.code_length),
nn.Linear(self.code_length,self.classes),
)
def reparameterize(self, mu, logVar):
#Reparameterization takes in the input mu and logVar and sample the mu + std * eps
std = torch.exp(logVar/2)
eps = torch.randn_like(std)
return mu + std*eps
def setDecoder(self, boolean):
self.decoder = boolean
return
def forward(self,x): #x = [batch,time,freq]
x = self.Encoder(x)
categorical = self.Classifier(x)
if self.decoder == False:
return categorical
else:
mu = self.Mu(x)
logVar = self.LogVar(x)
z = self.reparameterize(mu,logVar)
_x = self.Decoder(z)
return mu,logVar,categorical,_x