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function.py
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import os
import numpy as np
from PIL import Image
from torch.utils import data
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import json
from tqdm import tqdm
import glob
from torchvision import transforms
import h5py
import time
def EDAanalysis(EDAdata):
featurelist=[]
featurelist.append(np.mean(EDAdata))
featurelist.append(np.amin(EDAdata))
featurelist.append(np.amax(EDAdata))
featurelist.append(np.median(EDAdata))
featurelist.append(np.var(EDAdata))
featurelist.append(np.std(EDAdata))
featurelist=np.stack(featurelist)
return featurelist
## ---------------------- Dataloaders ---------------------- ##
class H5Dataset(data.Dataset):
def __init__(self, path,task,user):
self.file_path = path
self.dataset = None
self.task=task
self.distance={}
with h5py.File(path, 'r') as f:
self.dataset_len = (len(f["thermal"])-10)//15
if self.task=='stress':
self.dataset_len=self.dataset_len//4
if self.task=='count' or self.task=='jelly' or self.task=='relax' :
self.labels=torch.zeros(self.dataset_len)
else:
self.labels=torch.ones(self.dataset_len)
#print(path)
self.dataset = None
mapping={'brian': (6.12170518972332, 2.738155744105745), 'yuxuan': (2.0315562459903767, 1.5920591764902547), 'jingyu': (2.46085564398223, 2.6931856119445623), 'kirat': (0.7343852279005525, 0.3855071339384172), 'ruipeng': (6.327232446714744, 1.5537809487986232), 'sawin': (0.4034144615692554, 0.36409854590922397), 'huaiyu': (12.993516515114377, 2.2831871745269106), 'avery': (3.029731816350711, 0.9548403458802), 'coung': (4.185422265297203, 2.2880900195114644), 'yue': (3.671172082326892, 2.062053762316798),'vicki': (2.647892785887097, 1.969948917103175), 'moria': (7.3366887202380955, 1.8039903986669337), 'missy': (10.627276104098362, 3.302517839261801), 'jordan': (0.22407646935483871, 0.2712477562927061), 'gabrielle': (2.5076784529761906, 1.0701750774829135), 'shocky': (1.834749351428571, 0.8893038932418217), 'emily': (0.37484980344129554, 0.1071017964909624), 'carolyn': (5.16409214063745, 0.9667285651523427), 'joshua': (18.791469480708663, 5.734413946086138), 'lorn': (1.1227593221518988, 1.4207486203136652), 'timothy': (0.5828537324108818, 0.6048582465276751), 'yuhsun': (2.383287602291326, 0.6819557269910703), 'colleen': (1.3359034143243245, 1.3222589681695731), 'georgie': (0.417138202360515, 0.2145258540252646), 'hannah': (5.529630988802084, 0.8766097280926766), 'kaite': (13.0041588308, 2.8773194679990675),'kara': (0.15257421383363473, 0.10102582678159579), 'lohith': (0.1491865224022879, 0.07598298815996307), 'lydia': (0.166922578407225, 0.02978501354612667), 'shaily': (1.3505397171900162, 0.9783480415001867), 'susan': (0.49087909404315205, 0.3126269549023632), 'tabitha': (0.444800847970174, 0.07149893286787508),'christine': (6.680975991561182, 2.1038563665621)}
user = ''.join([i for i in user if not i.isdigit()])
self.user=user
self.mean,self.std=mapping[user]
far=['avery','brian','yuxuan','coung','ruipeng','yue','sawin']
self.distance=0
if user in far:
self.distance=1
def __getitem__(self, index):
if self.dataset is None:
self.dataset = h5py.File(self.file_path, 'r', libver="latest", swmr=True)
label = (self.labels[index])
label=label.type(torch.FloatTensor)
#thermal=self.dataset["thermal"][index]
#mask=self.dataset["mask"][index]
if self.task=='stress':
index+=self.dataset_len
thermal=(self.dataset["thermal"][15*index:15*index+25].astype(np.float32))
mask=(self.dataset["mask"][15*index:15*index+25].astype(np.float32))
box=(self.dataset["z_box"][15*index:15*index+25].astype(np.float32))
mask[mask>0]=1
thermal=thermal*mask
idxes=np.nonzero(mask)
value=thermal[idxes]
std=np.std(value, axis=0)
mean=np.mean(value, axis=0)
raw=thermal
thermal=(thermal-mean)/std
ppg=torch.from_numpy(self.dataset["ppg"][3*index:3*index+5])
eda=(self.dataset["eda"][3*index:3*index+5])
eda=(eda-self.mean)/(self.std)
eda=EDAanalysis(eda)
eda=torch.from_numpy(eda)
#ppg=ppg.view(-1)
std, mean=torch.std_mean(ppg,1)
ppg=mean
ppg=ppg.view(-1)
return thermal ,label,eda,ppg,self.user#,self.distance
def __len__(self):
return self.dataset_len
def get_train_dataloader( batch_size,idx):
all_train_set = []
all_test_set = []
all_val_set = []
#task_names = ['jelly', 'animal', 'stress', 'count', 'arithmetic', 'good', 'bad','prepareSong']
task_names = ['arithmetic', 'stress', 'jelly','count']#
#all_test=[['gabrielle','lohith7','moria','huaiyu','jingyu'],
#['shocky','susan8','emily','yuxuan','vicki'],
# ['lorn','carolyn','missy''kirat','shaily5'],
#]
all_val=[['georgie','kara8','hannah','yue','coung'],
['ruipeng','avery','lydia7','carolyn','jordan'],
['christine','yuhsun','tabitha5','brian','sawin'],
['shocky','susan8','emily','yuxuan','vicki'],
['lorn','carolyn','missy','kirat','shaily5'],
]
val_name=all_val[idx]
all_label=[]
all_user=[]
path = '../dessa_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if task not in task_names:
continue
if user[-1]=='3':
continue
if user in val_name:
continue
all_user.append(user)
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
all_train_set.append(t)
all_label.append(t.labels)
path = '../new_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if task not in task_names:
continue
if user[-1]=='3':
continue
if user in val_name:
continue
all_user.append(user)
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
all_train_set.append(t)
all_label.append(t.labels)
path = '../oct_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if task not in task_names:
continue
if user[-1]=='3':
continue
if user in val_name:
continue
all_user.append(user)
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
all_train_set.append(t)
all_label.append(t.labels)
all_label=torch.cat(all_label,0)
weight_per_class=[0]*2
weight_per_class[0] = len(all_label)/float(len(all_label)-all_label.sum())
weight_per_class[1] = len(all_label)/float(all_label.sum())
weight = [0] * len(all_label)
for idx, val in enumerate(all_label):
weight[idx] = weight_per_class[int(val.item())]
sampler = torch.utils.data.sampler.WeightedRandomSampler(weight, len(weight))
val_labels=[]
path = '../dessa_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if task not in task_names:
continue
if user[-1]=='3':
continue
if user not in val_name:
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../new_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name:
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../oct_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name :
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
val_set = torch.utils.data.ConcatDataset(all_val_set)
train_set = torch.utils.data.ConcatDataset(all_train_set)
train_data_loader = data.DataLoader(dataset=train_set,
num_workers=4,
batch_size=batch_size,
sampler=sampler,
drop_last=True,
)
valid_data_loader = data.DataLoader(dataset=val_set,
num_workers=4,
batch_size=batch_size,
drop_last=True,
shuffle=False,
)
# convert labels -> category
return train_data_loader, valid_data_loader
def get_test_dataloader( batch_size,idx):
all_test_set = []
task_names = ['bad', 'prepareSong', 'jelly','count']#
all_val=[['georgie','kara8','hannah','yue','coung'],
['ruipeng','avery','lydia7','carolyn','jordan'],
['christine','yuhsun','tabitha5','brian','sawin'],
['shocky','susan8','emily','yuxuan','vicki'],
['lorn','carolyn','missy','kirat','shaily5'],
]
val_name=all_val[idx]
all_val_set=[]
val_labels=[]
path = '../dessa_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if task not in task_names:
continue
if user[-1]=='3':
continue
if user not in val_name:
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../new_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name:
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../oct_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name :
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
val_set = torch.utils.data.ConcatDataset(all_val_set)
valid_data_loader = data.DataLoader(dataset=val_set,
num_workers=4,
batch_size=batch_size,
drop_last=True,
)
# convert labels -> category
return valid_data_loader
def get_task_dataloader( batch_size,idx,user,task):
all_val_set = []
all_label=[]
all_user=[]
task_names = [task]#
val_name = [user]
val_labels=[]
path = '../dessa_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if task not in task_names:
continue
if user[-1]=='3':
continue
if user not in val_name:
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../new_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name:
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../oct_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name :
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
test_set = torch.utils.data.ConcatDataset(all_val_set)
data_loader = data.DataLoader(dataset=test_set,
num_workers=4,
batch_size=batch_size,
drop_last=True,
)
# convert labels -> category
return data_loader
def get_user_dataloader( batch_size,idx,user):
all_val_set = []
all_label=[]
all_user=[]
task_names = ['arithmetic', 'stress', 'jelly','count']#
val_name = [user]
val_labels=[]
path = '../dessa_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if task not in task_names:
continue
if user[-1]=='3':
continue
if user not in val_name:
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../new_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name:
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
path='../oct_mask/'
for f in os.listdir(path):
if f[-1] != 'p':
continue
f_split = f.split("_")
user = f_split[-1][:-2] # remove the '.p' suffix
task = f_split[-2]
if user not in val_name :
continue
if task not in task_names:
continue
if user[-1]=='3':
continue
t = H5Dataset(path + f, task,user.replace('3','').replace('2','').replace('1',''))
if user in val_name:
all_val_set.append(t)
test_set = torch.utils.data.ConcatDataset(all_val_set)
data_loader = data.DataLoader(dataset=test_set,
num_workers=4,
batch_size=batch_size,
drop_last=True,
)
# convert labels -> category
return data_loader