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helpers.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Feb 12 12:41:28 2020
@author: MATK0001
"""
import os
import numpy as np
import random
import torch
import shutil
import torch.nn as nn
from torchvision import models
def listFilesfromDB(root_dir,dbName,listSet):
setFiles = [os.path.join(dbName,i,j) for i in listSet for j in os.listdir(os.path.join(root_dir,dbName, i))]
return setFiles
def listFilesfromDB2(root_dir,dbName,listSet):
setFiles = [os.path.join(dbName,i) for i in listSet]
return setFiles
def getListOfDisjointIDs(root_dir,dbName,train_size,val_fraction,seedR):
random.seed(seedR)
listOfFile = os.listdir(os.path.join(root_dir,dbName))
# shuffle IDs
print('---shuffling list of IDs in->'+dbName)
random.shuffle(listOfFile)
train_size = int(train_size * len(listOfFile))
listTrain = listOfFile[0:train_size]
listTestTmp = listOfFile[train_size:]
test_size = int(val_fraction * len(listTestTmp))
listVal = listTestTmp[0:test_size]
listTest = listTestTmp[test_size:]
trainFiles = list()
valFiles = list()
testFiles = list()
print('---listing train files')
trainFiles=listFilesfromDB(root_dir,dbName,listTrain)
print('---listing val files')
valFiles=listFilesfromDB(root_dir,dbName,listVal)
print('---listing test files')
testFiles=listFilesfromDB(root_dir,dbName,listTest)
return trainFiles, valFiles, testFiles
def getPathToCSV(params,set_p):
root_dir=params.root_dir
female=params.female
male=params.male
seedR=params.seedR
b = params.b
val_set_size = (1-params.train_size)*params.val_fraction*100
test_set_size = (1-params.train_size-val_set_size/100)*100
csv_fold_name = os.path.join('sets'
+str(int(params.train_size*100))+'_'
+str(int(val_set_size))+'_'
+str(int(test_set_size))+'_'
+str(b).replace(".",""),
"Class1-"+female+"_"+"Class0-"+male
,str(seedR))
if seedR == 'all':
pass
else:
if not os.path.exists(os.path.join(root_dir,csv_fold_name)):
os.makedirs(os.path.join(root_dir,csv_fold_name))
if set_p is not None:
csv_file_name = set_p+"_set.csv"
return os.path.join(root_dir,csv_fold_name,csv_file_name)
else:
return os.path.join(root_dir,csv_fold_name)
def checkIfCSVexist(params,train_set,val_set,test_set):
root_dir=params.root_dir
female=params.female
male=params.male
seedR=params.seedR
b = params.b
train_set_exist = False
test_set_exist = False
val_set_exist = False
sets_exist = False
val_set_size = (1-params.train_size)*params.val_fraction*100
test_set_size = (1-params.train_size-val_set_size/100)*100
csv_fold_name = os.path.join('sets'
+str(int(params.train_size*100))+'_'
+str(int(val_set_size))+'_'
+str(int(test_set_size))+'_'
+str(b).replace(".",""),
"class1-"+female+"_"+"class2-"+male
,str(seedR))
csv_file_name = train_set+"_set.csv"
if os.path.exists(os.path.join(root_dir,csv_fold_name,csv_file_name)):
print('already exists->'+os.path.join(root_dir,csv_fold_name,csv_file_name))
train_set_exist = True
else:
print('do not exists->'+os.path.join(root_dir,csv_fold_name,csv_file_name))
csv_file_name = val_set+"_set.csv"
if os.path.exists(os.path.join(root_dir,csv_fold_name,csv_file_name)):
print('already exists->'+os.path.join(root_dir,csv_fold_name,csv_file_name))
val_set_exist = True
else:
print('do not exists->'+os.path.join(root_dir,csv_fold_name,csv_file_name))
csv_file_name = test_set+"_set.csv"
if os.path.exists(os.path.join(root_dir,csv_fold_name,csv_file_name)):
print('already exists->'+os.path.join(root_dir,csv_fold_name,csv_file_name))
test_set_exist = True
else:
print('do not exists->'+os.path.join(root_dir,csv_fold_name,csv_file_name))
if (train_set_exist == True and val_set_exist == True and test_set_exist == True):
sets_exist = True
return sets_exist
def buildTainValTestSets(params):
root_dir=params.root_dir
female=params.female
male=params.male
train_size=params.train_size
val_fraction=params.val_fraction
seedR=params.seedR
b = params.b
random.seed(seedR)
# set
print('--building male classes')
train0,val0,test0 = getListOfDisjointIDs(root_dir,male,train_size,
val_fraction,seedR)
# set
print('--building female classes')
train1,val1,test1 = getListOfDisjointIDs(root_dir,female,train_size,
val_fraction,seedR)
if(len(train0)*b > len(train1)):
print('---taking random train subset from male classes')
train0 = random.sample(train0, round(len(train1)/b))
if(len(train1)*b > len(train0)):
print('---taking random train subset from female classes')
train1 = random.sample(train1, round(len(train0)/b))
label0=[0]*len(train0)
label1=[1]*len(train1)
ratioS2U = len(train1)/len(train0)
print('[female to male] ratio->',ratioS2U)
np.savetxt(os.path.join(getPathToCSV(params,None),'ratio_train.txt'),[ratioS2U], fmt='%s')
labelTrain=label0 + label1
#random.shuffle(labelTrain) #only to test overfit
set_p = 'train'
path_save = getPathToCSV(params,set_p)
np.savetxt(path_save, np.column_stack((train0 + train1, labelTrain)),
delimiter=",", fmt='%s')
print('--train set saved in:' + path_save)
print('---train set number of samples:',str(len(train0)+len(train1)))
print('---train set number of male samples:',str(len(train0)))
print('---train set number of female samples:',str(len(train1)))
if(len(val0)*b > len(val1)):
print('---taking random val subset from male classes')
val0 = random.sample(val0, len(val1))
if(len(val1)*b > len(val0)):
print('---taking random val subset from female classes')
val1 = random.sample(val1, len(val0))
label0=[0]*len(val0)
label1=[1]*len(val1)
set_p = 'val'
path_save = getPathToCSV(params,set_p)
np.savetxt(path_save, np.column_stack((val0 + val1, label0 + label1)),
delimiter=",", fmt='%s')
print('--val set saved in:' + path_save)
print('---val set number of samples:',str(len(val0)+len(val1)))
print('---val set number of male samples:',str(len(val0)))
print('---val set number of female samples:',str(len(val1)))
if(len(test0)*b > len(test1)):
print('---taking random test subset from male classes')
test0 = random.sample(test0, len(test1))
if(len(test1)*b > len(test0)):
print('---taking random test subset from female classes')
test1 = random.sample(test1, len(test0))
label0=[0]*len(test0)
label1=[1]*len(test1)
set_p = 'test'
path_save = getPathToCSV(params,set_p)
np.savetxt(path_save, np.column_stack((test0 + test1, label0 + label1)),
delimiter=",", fmt='%s')
print('--test set saved in:' + path_save)
print('---test set number of samples:',str(len(test0)+len(test1)))
print('---test set number of male samples:',str(len(test0)))
print('---test set number of female samples:',str(len(test1)))
def path_to_saved_model(params,modelType):
if not os.path.exists(os.path.join(getPathToCSV(params,None),modelType)):
os.mkdir(os.path.join(getPathToCSV(params,None),modelType))
return os.path.join(getPathToCSV(params,None),modelType)
def path_to_saved_model_results(params,modelType):
if not os.path.exists(os.path.join(getPathToCSV(params,None),modelType,'results')):
os.mkdir(os.path.join(getPathToCSV(params,None),modelType,'results'))
return os.path.join(getPathToCSV(params,None),modelType,'results')
def save_checkpoint(state, is_best, params, modelType):
filename=os.path.join(path_to_saved_model(params,modelType),'checkpoint.pth.tar')
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, os.path.join(path_to_saved_model(params,modelType),'model_best.pth.tar'))
print('saving best model')
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
model_ft = None
if model_name == "resnet50":
""" Resnet50
"""
model_ft = models.resnet50(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
elif model_name == "alexnet":
""" Alexnet
"""
model_ft = models.alexnet(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
elif model_name == "vgg16":
""" VGG11_bn
"""
model_ft = models.vgg16(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
elif model_name == "squeezenet10":
""" Squeezenet
"""
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
model_ft.num_classes = num_classes
elif model_name == "densenet121":
""" Densenet
"""
model_ft = models.densenet121(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
else:
print("Invalid model name, exiting...")
# exit()
return model_ft