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dataset.py
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dataset.py
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################
#
# Deep Flow Prediction - N. Thuerey, K. Weissenov, H. Mehrotra, N. Mainali, L. Prantl, X. Hu (TUM)
#
# Dataset handling
#
################
from torch.utils.data import Dataset
import numpy as np
from os import listdir
import random
# global switch, use fixed max values for dim-less airfoil data?
fixedAirfoilNormalization = True
# global switch, make data dimensionless?
makeDimLess = True
# global switch, remove constant offsets from pressure channel?
removePOffset = True
## helper - compute absolute of inputs or targets
def find_absmax(data, use_targets, x):
maxval = 0
for i in range(data.totalLength):
if use_targets == 0:
temp_tensor = data.inputs[i]
else:
temp_tensor = data.targets[i]
temp_max = np.max(np.abs(temp_tensor[x]))
if temp_max > maxval:
maxval = temp_max
return maxval
######################################## DATA LOADER #########################################
# also normalizes data with max , and optionally makes it dimensionless #
def LoaderNormalizer(data, isTest = False, shuffle = 0, dataProp = None):
"""
# data: pass TurbDataset object with initialized dataDir / dataDirTest paths
# train: when off, process as test data (first load regular for normalization if needed, then replace by test data)
# dataProp: proportions for loading & mixing 3 different data directories "reg", "shear", "sup"
# should be array with [total-length, fraction-regular, fraction-superimposed, fraction-sheared],
# passing None means off, then loads from single directory
"""
if dataProp is None:
# load single directory
files = listdir(data.dataDir)
files.sort()
for i in range(shuffle):
random.shuffle(files)
if isTest:
print("Reducing data to load for tests")
files = files[0:min(10, len(files))]
data.totalLength = len(files)
data.inputs = np.empty((len(files), 3, 128, 128))
data.targets = np.empty((len(files), 3, 128, 128))
for i, file in enumerate(files):
npfile = np.load(data.dataDir + file)
d = npfile['a']
data.inputs[i] = d[0:3]
data.targets[i] = d[3:6]
print("Number of data loaded:", len(data.inputs) )
else:
# load from folders reg, sup, and shear under the folder dataDir
data.totalLength = int(dataProp[0])
data.inputs = np.empty((data.totalLength, 3, 128, 128))
data.targets = np.empty((data.totalLength, 3, 128, 128))
files1 = listdir(data.dataDir + "reg/")
files1.sort()
files2 = listdir(data.dataDir + "sup/")
files2.sort()
files3 = listdir(data.dataDir + "shear/" )
files3.sort()
for i in range(shuffle):
random.shuffle(files1)
random.shuffle(files2)
random.shuffle(files3)
temp_1, temp_2 = 0, 0
for i in range(data.totalLength):
if i >= (1-dataProp[3])*dataProp[0]:
npfile = np.load(data.dataDir + "shear/" + files3[i-temp_2])
d = npfile['a']
data.inputs[i] = d[0:3]
data.targets[i] = d[3:6]
elif i >= (dataProp[1])*dataProp[0]:
npfile = np.load(data.dataDir + "sup/" + files2[i-temp_1])
d = npfile['a']
data.inputs[i] = d[0:3]
data.targets[i] = d[3:6]
temp_2 = i + 1
else:
npfile = np.load(data.dataDir + "reg/" + files1[i])
d = npfile['a']
data.inputs[i] = d[0:3]
data.targets[i] = d[3:6]
temp_1 = i + 1
temp_2 = i + 1
print("Number of data loaded (reg, sup, shear):", temp_1, temp_2 - temp_1, i+1 - temp_2)
################################## NORMALIZATION OF TRAINING DATA ##########################################
if removePOffset:
for i in range(data.totalLength):
data.targets[i,0,:,:] -= np.mean(data.targets[i,0,:,:]) # remove offset
data.targets[i,0,:,:] -= data.targets[i,0,:,:] * data.inputs[i,2,:,:] # pressure * mask
# make dimensionless based on current data set
if makeDimLess:
for i in range(data.totalLength):
# only scale outputs, inputs are scaled by max only
v_norm = ( np.max(np.abs(data.inputs[i,0,:,:]))**2 + np.max(np.abs(data.inputs[i,1,:,:]))**2 )**0.5
data.targets[i,0,:,:] /= v_norm**2
data.targets[i,1,:,:] /= v_norm
data.targets[i,2,:,:] /= v_norm
# normalize to -1..1 range, from min/max of predefined
if fixedAirfoilNormalization:
# hard coded maxima , inputs dont change
data.max_inputs_0 = 100.
data.max_inputs_1 = 38.12
data.max_inputs_2 = 1.0
# targets depend on normalization
if makeDimLess:
data.max_targets_0 = 4.65
data.max_targets_1 = 2.04
data.max_targets_2 = 2.37
print("Using fixed maxima "+format( [data.max_targets_0,data.max_targets_1,data.max_targets_2] ))
else: # full range
data.max_targets_0 = 40000.
data.max_targets_1 = 200.
data.max_targets_2 = 216.
print("Using fixed maxima "+format( [data.max_targets_0,data.max_targets_1,data.max_targets_2] ))
else: # use current max values from loaded data
data.max_inputs_0 = find_absmax(data, 0, 0)
data.max_inputs_1 = find_absmax(data, 0, 1)
data.max_inputs_2 = find_absmax(data, 0, 2) # mask, not really necessary
print("Maxima inputs "+format( [data.max_inputs_0,data.max_inputs_1,data.max_inputs_2] ))
data.max_targets_0 = find_absmax(data, 1, 0)
data.max_targets_1 = find_absmax(data, 1, 1)
data.max_targets_2 = find_absmax(data, 1, 2)
print("Maxima targets "+format( [data.max_targets_0,data.max_targets_1,data.max_targets_2] ))
data.inputs[:,0,:,:] *= (1.0/data.max_inputs_0)
data.inputs[:,1,:,:] *= (1.0/data.max_inputs_1)
data.targets[:,0,:,:] *= (1.0/data.max_targets_0)
data.targets[:,1,:,:] *= (1.0/data.max_targets_1)
data.targets[:,2,:,:] *= (1.0/data.max_targets_2)
###################################### NORMALIZATION OF TEST DATA #############################################
if isTest:
files = listdir(data.dataDirTest)
files.sort()
data.totalLength = len(files)
data.inputs = np.empty((len(files), 3, 128, 128))
data.targets = np.empty((len(files), 3, 128, 128))
for i, file in enumerate(files):
npfile = np.load(data.dataDirTest + file)
d = npfile['a']
data.inputs[i] = d[0:3]
data.targets[i] = d[3:6]
if removePOffset:
for i in range(data.totalLength):
data.targets[i,0,:,:] -= np.mean(data.targets[i,0,:,:]) # remove offset
data.targets[i,0,:,:] -= data.targets[i,0,:,:] * data.inputs[i,2,:,:] # pressure * mask
if makeDimLess:
for i in range(len(files)):
v_norm = ( np.max(np.abs(data.inputs[i,0,:,:]))**2 + np.max(np.abs(data.inputs[i,1,:,:]))**2 )**0.5
data.targets[i,0,:,:] /= v_norm**2
data.targets[i,1,:,:] /= v_norm
data.targets[i,2,:,:] /= v_norm
data.inputs[:,0,:,:] *= (1.0/data.max_inputs_0)
data.inputs[:,1,:,:] *= (1.0/data.max_inputs_1)
data.targets[:,0,:,:] *= (1.0/data.max_targets_0)
data.targets[:,1,:,:] *= (1.0/data.max_targets_1)
data.targets[:,2,:,:] *= (1.0/data.max_targets_2)
print("Data stats, input mean %f, max %f; targets mean %f , max %f " % (
np.mean(np.abs(data.targets), keepdims=False), np.max(np.abs(data.targets), keepdims=False) ,
np.mean(np.abs(data.inputs), keepdims=False) , np.max(np.abs(data.inputs), keepdims=False) ) )
return data
######################################## DATA SET CLASS #########################################
class TurbDataset(Dataset):
# mode "enum" , pass to mode param of TurbDataset (note, validation mode is not necessary anymore)
TRAIN = 0
TEST = 2
def __init__(self, dataProp=None, mode=TRAIN, dataDir="../data/train/", dataDirTest="../data/test/", shuffle=0, normMode=0):
global makeDimLess, removePOffset
"""
:param dataProp: for split&mix from multiple dirs, see LoaderNormalizer; None means off
:param mode: TRAIN|TEST , toggle regular 80/20 split for training & validation data, or load test data
:param dataDir: directory containing training data
:param dataDirTest: second directory containing test data , needs training dir for normalization
:param normMode: toggle normalization
"""
if not (mode==self.TRAIN or mode==self.TEST):
print("Error - TurbDataset invalid mode "+format(mode) ); exit(1)
if normMode==1:
print("Warning - poff off!!")
removePOffset = False
if normMode==2:
print("Warning - poff and dimless off!!!")
makeDimLess = False
removePOffset = False
self.mode = mode
self.dataDir = dataDir
self.dataDirTest = dataDirTest # only for mode==self.TEST
# load & normalize data
self = LoaderNormalizer(self, isTest=(mode==self.TEST), dataProp=dataProp, shuffle=shuffle)
if not self.mode==self.TEST:
# split for train/validation sets (80/20) , max 400
targetLength = self.totalLength - min( int(self.totalLength*0.2) , 400)
self.valiInputs = self.inputs[targetLength:]
self.valiTargets = self.targets[targetLength:]
self.valiLength = self.totalLength - targetLength
self.inputs = self.inputs[:targetLength]
self.targets = self.targets[:targetLength]
self.totalLength = self.inputs.shape[0]
def __len__(self):
return self.totalLength
def __getitem__(self, idx):
return self.inputs[idx], self.targets[idx]
# reverts normalization
def denormalize(self, data, v_norm):
a = data.copy()
a[0,:,:] /= (1.0/self.max_targets_0)
a[1,:,:] /= (1.0/self.max_targets_1)
a[2,:,:] /= (1.0/self.max_targets_2)
if makeDimLess:
a[0,:,:] *= v_norm**2
a[1,:,:] *= v_norm
a[2,:,:] *= v_norm
return a
# simplified validation data set (main one is TurbDataset above)
class ValiDataset(TurbDataset):
def __init__(self, dataset):
self.inputs = dataset.valiInputs
self.targets = dataset.valiTargets
self.totalLength = dataset.valiLength
def __len__(self):
return self.totalLength
def __getitem__(self, idx):
return self.inputs[idx], self.targets[idx]