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# Description:
# Demo code from the article:
# Deep learning based pupil model predicts time and wavelength dependent light responses
# Technical University of Darmstadt, Laboratory of Lighting Technology
# Published in Scientific Reports
# Link: www.nature.com/articles/s41598-020-79908-5
# GitHub Link: https://github.com/BZandi/DL-PupilModel
import numpy as np
import pandas as pd
import torch
import torch.utils.data as data_utils
import A02_Networks.FeedForward as FF_Class
import pytorch_lightning as pl
from argparse import Namespace
from pytorch_lightning.callbacks import ModelCheckpoint
from argparse import ArgumentParser
def calcParam_from_NN(Variant, argsValues):
if argsValues.Condition == 'Single':
if Variant == 1:
print('Single - Variant 1')
print("Leuchtdichte: " + str(argsValues.L) +
" Farbort x: " + str(argsValues.Fx) +
" Farbort y: " + str(argsValues.Fy))
Leuchtdichte = argsValues.L
Farbort_x = argsValues.Fx
Farbort_y = argsValues.Fy
PATH = "A03_Models/FF/Intrapersonal_SingleSubject/Variant_1_Lxy/FF_Variant_1_BatchSize_7_epoch=3999.ckpt"
model = FF_Class.FeedForward.load_from_checkpoint(PATH)
model.eval()
Eingangswerte = np.array(
[argsValues.L, argsValues.Fx, argsValues.Fy])
Eingangswerte = torch.from_numpy(Eingangswerte).float()
output = model(Eingangswerte)
output_np = output.data.numpy()
dataset = pd.DataFrame({'f_p': output_np[0],
'f_s': output_np[1],
'P_0': output_np[2],
'tp': output_np[3],
'ts': output_np[4],
'Delta_tp': output_np[5],
'Delta_ts': output_np[6],
'p1': output_np[7],
'p2': output_np[8],
'p3': output_np[9],
'p4': output_np[10],
'p5': output_np[11],
'p6': output_np[12],
'p7': output_np[13],
'p8': output_np[14],
'p9': output_np[15],
'p10': output_np[16]}, index=[0])
dataset.to_csv('output.csv', index=False)
# Eingangswerte:
# 470 nm - Leuchtdichte: 0 Farbort x: 0 Farbort y: 0
# 530 nm - Leuchtdichte: 0.886363636363642 Farbort x: 0.0509930220075148 Farbort y: 1
# 610 nm - Leuchtdichte: 0.977272727272716 Farbort x: 0.932045089 Farbort y: 0.932045089
# 660 nm - Leuchtdichte: 0.545454545 Farbort x: 1 Farbort y: 0.361349796
if Variant == 2:
print('Single - Variant 2')
print("Lcone: " + str(argsValues.Lcone) +
" Mcone: " + str(argsValues.Mcone) +
" Scone: " + str(argsValues.Scone) +
" Mel: " + str(argsValues.Mel))
Lcone = argsValues.Lcone
Mcone = argsValues.Mcone
Scone = argsValues.Scone
Mel = argsValues.Mel
PATH = "A03_Models/FF/Intrapersonal_SingleSubject/Variant_2_LMSMEL/FF_Variant_2_BatchSize_7_epoch=2900.ckpt"
model = FF_Class.FeedForward.load_from_checkpoint(PATH)
model.eval()
Eingangswerte = np.array([argsValues.Scone,
argsValues.Mcone,
argsValues.Lcone,
argsValues.Mel])
Eingangswerte = torch.from_numpy(Eingangswerte).float()
output = model(Eingangswerte)
output_np = output.data.numpy()
dataset = pd.DataFrame({'f_p': output_np[0],
'f_s': output_np[1],
'P_0': output_np[2],
'tp': output_np[3],
'ts': output_np[4],
'Delta_tp': output_np[5],
'Delta_ts': output_np[6],
'p1': output_np[7],
'p2': output_np[8],
'p3': output_np[9],
'p4': output_np[10],
'p5': output_np[11],
'p6': output_np[12],
'p7': output_np[13],
'p8': output_np[14],
'p9': output_np[15],
'p10': output_np[16]}, index=[0])
dataset.to_csv('output.csv', index=False)
if Variant == 3:
print('Single - Variant 3')
print("Leuchtdichte: " + str(argsValues.L) +
" Farbort x: " + str(argsValues.Fx) +
" Farbort y: " + str(argsValues.Fy) +
" Mel: " + str(argsValues.Mel))
Leuchtdichte = argsValues.L
Farbort_x = argsValues.Fx
Farbort_y = argsValues.Fy
Mel = argsValues.Mel
PATH = "A03_Models/FF/Intrapersonal_SingleSubject/Variant_3_LxyMel/FF_Variant_3_BatchSize_7_epoch=3999.ckpt"
model = FF_Class.FeedForward.load_from_checkpoint(PATH)
model.eval()
Eingangswerte = np.array([argsValues.L,
argsValues.Fx,
argsValues.Fy,
argsValues.Mel])
Eingangswerte = torch.from_numpy(Eingangswerte).float()
output = model(Eingangswerte)
output_np = output.data.numpy()
dataset = pd.DataFrame({'f_p': output_np[0],
'f_s': output_np[1],
'P_0': output_np[2],
'tp': output_np[3],
'ts': output_np[4],
'Delta_tp': output_np[5],
'Delta_ts': output_np[6],
'p1': output_np[7],
'p2': output_np[8],
'p3': output_np[9],
'p4': output_np[10],
'p5': output_np[11],
'p6': output_np[12],
'p7': output_np[13],
'p8': output_np[14],
'p9': output_np[15],
'p10': output_np[16]}, index=[0])
dataset.to_csv('output.csv', index=False)
if argsValues.Condition == 'Multi':
if Variant == 1:
print('Many - Variant 1')
print("Leuchtdichte: " + str(argsValues.L) +
" Farbort x: " + str(argsValues.Fx) +
" Farbort y: " + str(argsValues.Fy))
Leuchtdichte = argsValues.L
Farbort_x = argsValues.Fx
Farbort_y = argsValues.Fy
PATH = "A03_Models/FF/Interpersonal_ManySubject/Variant_1_Lxy/FF_Variant_1_BatchSize_7_epoch=800.ckpt"
model = FF_Class.FeedForward.load_from_checkpoint(PATH)
model.eval()
Eingangswerte = np.array(
[argsValues.L, argsValues.Fx, argsValues.Fy])
Eingangswerte = torch.from_numpy(Eingangswerte).float()
output = model(Eingangswerte)
output_np = output.data.numpy()
dataset = pd.DataFrame({'f_p': output_np[0],
'f_s': output_np[1],
'P_0': output_np[2],
'tp': output_np[3],
'ts': output_np[4],
'Delta_tp': output_np[5],
'Delta_ts': output_np[6],
'p1': output_np[7],
'p2': output_np[8],
'p3': output_np[9],
'p4': output_np[10],
'p5': output_np[11],
'p6': output_np[12],
'p7': output_np[13],
'p8': output_np[14],
'p9': output_np[15],
'p10': output_np[16]}, index=[0])
dataset.to_csv('output.csv', index=False)
# Eingangswerte:
# 470 nm - Leuchtdichte: 0 Farbort x: 0 Farbort y: 0
# 530 nm - Leuchtdichte: 0.886363636363642 Farbort x: 0.0509930220075148 Farbort y: 1
# 610 nm - Leuchtdichte: 0.977272727272716 Farbort x: 0.932045089 Farbort y: 0.932045089
# 660 nm - Leuchtdichte: 0.545454545 Farbort x: 1 Farbort y: 0.361349796
if Variant == 2:
print('Many - Variant 2')
print("Lcone: " + str(argsValues.Lcone) +
" Mcone: " + str(argsValues.Mcone) +
" Scone: " + str(argsValues.Scone) +
" Mel: " + str(argsValues.Mel))
Lcone = argsValues.Lcone
Mcone = argsValues.Mcone
Scone = argsValues.Scone
Mel = argsValues.Mel
PATH = "A03_Models/FF/Interpersonal_ManySubject/Variant_2_LMSMEL/FF_Variant_2_BatchSize_7_epoch=3800.ckpt"
model = FF_Class.FeedForward.load_from_checkpoint(PATH)
model.eval()
Eingangswerte = np.array([argsValues.Scone,
argsValues.Mcone,
argsValues.Lcone,
argsValues.Mel])
Eingangswerte = torch.from_numpy(Eingangswerte).float()
output = model(Eingangswerte)
output_np = output.data.numpy()
dataset = pd.DataFrame({'f_p': output_np[0],
'f_s': output_np[1],
'P_0': output_np[2],
'tp': output_np[3],
'ts': output_np[4],
'Delta_tp': output_np[5],
'Delta_ts': output_np[6],
'p1': output_np[7],
'p2': output_np[8],
'p3': output_np[9],
'p4': output_np[10],
'p5': output_np[11],
'p6': output_np[12],
'p7': output_np[13],
'p8': output_np[14],
'p9': output_np[15],
'p10': output_np[16]}, index=[0])
dataset.to_csv('output.csv', index=False)
if Variant == 3:
print('Many - Variant 3')
print("Leuchtdichte: " + str(argsValues.L) +
" Farbort x: " + str(argsValues.Fx) +
" Farbort y: " + str(argsValues.Fy) +
" Mel: " + str(argsValues.Mel))
Leuchtdichte = argsValues.L
Farbort_x = argsValues.Fx
Farbort_y = argsValues.Fy
Mel = argsValues.Mel
PATH = "A03_Models/FF/Interpersonal_ManySubject/Variant_3_LxyMel/FF_Variant_3_BatchSize_7_epoch=3800.ckpt"
model = FF_Class.FeedForward.load_from_checkpoint(PATH)
model.eval()
Eingangswerte = np.array([argsValues.L,
argsValues.Fx,
argsValues.Fy,
argsValues.Mel])
Eingangswerte = torch.from_numpy(Eingangswerte).float()
output = model(Eingangswerte)
output_np = output.data.numpy()
dataset = pd.DataFrame({'f_p': output_np[0],
'f_s': output_np[1],
'P_0': output_np[2],
'tp': output_np[3],
'ts': output_np[4],
'Delta_tp': output_np[5],
'Delta_ts': output_np[6],
'p1': output_np[7],
'p2': output_np[8],
'p3': output_np[9],
'p4': output_np[10],
'p5': output_np[11],
'p6': output_np[12],
'p7': output_np[13],
'p8': output_np[14],
'p9': output_np[15],
'p10': output_np[16]}, index=[0])
dataset.to_csv('output.csv', index=False)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--Condition', type=str, default='Single')
parser.add_argument('--Variant', type=int, default=1)
parser.add_argument('--L', type=float, default=0)
parser.add_argument('--Fx', type=float, default=0)
parser.add_argument('--Fy', type=float, default=0)
parser.add_argument('--Lcone', type=float, default=0)
parser.add_argument('--Mcone', type=float, default=0)
parser.add_argument('--Scone', type=float, default=0)
parser.add_argument('--Mel', type=float, default=0)
args = parser.parse_args()
calcParam_from_NN(Variant=args.Variant, argsValues=args)
print("Values calculated and exported to csv")
# Zum ausführen der verschiedenen Varianten müssen folgende Befehle eingegeben werden
# Variante 1: python CalcParam.py --Condition Single --Variant 1 --L 0 --Fx 0 --Fy 0
# Variante 2: python CalcParam.py --Condition Single --Variant 2 --Lcone 0 --Mcone 0 --Scone 0 --Mel 0
# Variante 3: python CalcParam.py --Condition Single --Variant 3 --L 0 --Fx 0 --Fy 0 --Mel 0
# Variante 1: python CalcParam.py --Condition Multi --Variant 1 --L 0 --Fx 0 --Fy 0
# Variante 2: python CalcParam.py --Condition Multi --Variant 2 --Lcone 0 --Mcone 0 --Scone 0 --Mel 0
# Variante 3: python CalcParam.py --Condition Multi --Variant 1 --L 0 --Fx 0 --Fy 0 --Mel 0