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import os
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils, datasets, models
import warnings
warnings.filterwarnings("ignore")
#dir_gainDPM="gain/DPM/",
#dir_gainDPMcars="gain/carsDPM/",
#dir_gainIRT2="gain/IRT2/",
#dir_gainIRT2cars="gain/carsIRT2/",
#dir_buildings="png/",
#dir_antenna= ,
class RadioUNet_c(Dataset):
"""RadioMapSeer Loader for accurate buildings and no measurements (RadioUNet_c)"""
def __init__(self,maps_inds=np.zeros(1), phase="train",
ind1=0,ind2=0,
dir_dataset="RadioMapSeer/",
numTx=80,
thresh=0.2,
simulation="DPM",
carsSimul="no",
carsInput="no",
IRT2maxW=1,
cityMap="complete",
missing=1,
transform= transforms.ToTensor()):
"""
Args:
maps_inds: optional shuffled sequence of the maps. Leave it as maps_inds=0 (default) for the standart split.
phase:"train", "val", "test", "custom". If "train", "val" or "test", uses a standard split.
"custom" means that the loader will read maps ind1 to ind2 from the list maps_inds.
ind1,ind2: First and last indices from maps_inds to define the maps of the loader, in case phase="custom".
dir_dataset: directory of the RadioMapSeer dataset.
numTx: Number of transmitters per map. Default and maximal value of numTx = 80.
thresh: Pathlos threshold between 0 and 1. Defaoult is the noise floor 0.2.
simulation:"DPM", "IRT2", "rand". Default= "DPM"
carsSimul:"no", "yes". Use simulation with or without cars. Default="no".
carsInput:"no", "yes". Take inputs with or without cars channel. Default="no".
IRT2maxW: in case of "rand" simulation, the maximal weight IRT2 can take. Default=1.
cityMap: "complete", "missing", "rand". Use the full city, or input map with missing buildings "rand" means that there is
a random number of missing buildings.
missing: 1 to 4. in case of input map with missing buildings, and not "rand", the number of missing buildings. Default=1.
transform: Transform to apply on the images of the loader. Default= transforms.ToTensor())
Output:
inputs: The RadioUNet inputs.
image_gain
"""
#self.phase=phase
if maps_inds.size==1:
self.maps_inds=np.arange(0,700,1,dtype=np.int16)
#Determenistic "random" shuffle of the maps:
np.random.seed(42)
np.random.shuffle(self.maps_inds)
else:
self.maps_inds=maps_inds
if phase=="train":
self.ind1=0
self.ind2=500
elif phase=="val":
self.ind1=501
self.ind2=600
elif phase=="test":
self.ind1=601
self.ind2=699
else: # custom range
self.ind1=ind1
self.ind2=ind2
self.dir_dataset = dir_dataset
self.numTx= numTx
self.thresh=thresh
self.simulation=simulation
self.carsSimul=carsSimul
self.carsInput=carsInput
if simulation=="DPM" :
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/DPM/"
else:
self.dir_gain=self.dir_dataset+"gain/carsDPM/"
elif simulation=="IRT2":
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/IRT2/"
else:
self.dir_gain=self.dir_dataset+"gain/carsIRT2/"
elif simulation=="rand":
if carsSimul=="no":
self.dir_gainDPM=self.dir_dataset+"gain/DPM/"
self.dir_gainIRT2=self.dir_dataset+"gain/IRT2/"
else:
self.dir_gainDPM=self.dir_dataset+"gain/carsDPM/"
self.dir_gainIRT2=self.dir_dataset+"gain/carsIRT2/"
self.IRT2maxW=IRT2maxW
self.cityMap=cityMap
self.missing=missing
if cityMap=="complete":
self.dir_buildings=self.dir_dataset+"png/buildings_complete/"
else:
self.dir_buildings = self.dir_dataset+"png/buildings_missing" # a random index will be concatenated in the code
#else: #missing==number
# self.dir_buildings = self.dir_dataset+ "png/buildings_missing"+str(missing)+"/"
self.transform= transform
self.dir_Tx = self.dir_dataset+ "png/antennas/"
#later check if reading the JSON file and creating antenna images on the fly is faster
if carsInput!="no":
self.dir_cars = self.dir_dataset+ "png/cars/"
self.height = 256
self.width = 256
def __len__(self):
return (self.ind2-self.ind1+1)*self.numTx
def __getitem__(self, idx):
idxr=np.floor(idx/self.numTx).astype(int)
idxc=idx-idxr*self.numTx
dataset_map_ind=self.maps_inds[idxr+self.ind1]+1
#names of files that depend only on the map:
name1 = str(dataset_map_ind) + ".png"
#names of files that depend on the map and the Tx:
name2 = str(dataset_map_ind) + "_" + str(idxc) + ".png"
#Load buildings:
if self.cityMap == "complete":
img_name_buildings = os.path.join(self.dir_buildings, name1)
else:
if self.cityMap == "rand":
self.missing=np.random.randint(low=1, high=5)
version=np.random.randint(low=1, high=7)
img_name_buildings = os.path.join(self.dir_buildings+str(self.missing)+"/"+str(version)+"/", name1)
str(self.missing)
image_buildings = np.asarray(io.imread(img_name_buildings))
#Load Tx (transmitter):
img_name_Tx = os.path.join(self.dir_Tx, name2)
image_Tx = np.asarray(io.imread(img_name_Tx))
#Load radio map:
if self.simulation!="rand":
img_name_gain = os.path.join(self.dir_gain, name2)
image_gain = np.expand_dims(np.asarray(io.imread(img_name_gain)),axis=2)/255
else: #random weighted average of DPM and IRT2
img_name_gainDPM = os.path.join(self.dir_gainDPM, name2)
img_name_gainIRT2 = os.path.join(self.dir_gainIRT2, name2)
#image_gainDPM = np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/255
#image_gainIRT2 = np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/255
w=np.random.uniform(0,self.IRT2maxW) # IRT2 weight of random average
image_gain= w*np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/256 \
+ (1-w)*np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/256
#pathloss threshold transform
if self.thresh>0:
mask = image_gain < self.thresh
image_gain[mask]=self.thresh
image_gain=image_gain-self.thresh*np.ones(np.shape(image_gain))
image_gain=image_gain/(1-self.thresh)
#inputs to radioUNet
if self.carsInput=="no":
inputs=np.stack([image_buildings, image_Tx], axis=2)
#The fact that the buildings and antenna are normalized 256 and not 1 promotes convergence,
#so we can use the same learning rate as RadioUNets
else: #cars
#Normalization, so all settings can have the same learning rate
image_buildings=image_buildings/256
image_Tx=image_Tx/256
img_name_cars = os.path.join(self.dir_cars, name1)
image_cars = np.asarray(io.imread(img_name_cars))/256
inputs=np.stack([image_buildings, image_Tx, image_cars], axis=2)
#note that ToTensor moves the channel from the last asix to the first!
if self.transform:
inputs = self.transform(inputs).type(torch.float32)
image_gain = self.transform(image_gain).type(torch.float32)
#note that ToTensor moves the channel from the last asix to the first!
return [inputs, image_gain]
class RadioUNet_c_sprseIRT4(Dataset):
"""RadioMapSeer Loader for accurate buildings and no measurements (RadioUNet_c)"""
def __init__(self,maps_inds=np.zeros(1), phase="train",
ind1=0,ind2=0,
dir_dataset="RadioMapSeer/",
numTx=2,
thresh=0.2,
simulation="IRT4",
carsSimul="no",
carsInput="no",
cityMap="complete",
missing=1,
num_samples=300,
transform= transforms.ToTensor()):
"""
Args:
maps_inds: optional shuffled sequence of the maps. Leave it as maps_inds=0 (default) for the standart split.
phase:"train", "val", "test", "custom". If "train", "val" or "test", uses a standard split.
"custom" means that the loader will read maps ind1 to ind2 from the list maps_inds.
ind1,ind2: First and last indices from maps_inds to define the maps of the loader, in case phase="custom".
dir_dataset: directory of the RadioMapSeer dataset.
numTx: Number of transmitters per map. Default = 2. Note that IRT4 works only with numTx<=2.
thresh: Pathlos threshold between 0 and 1. Defaoult is the noise floor 0.2.
simulation: default="IRT4", with an option to "DPM", "IRT2".
carsSimul:"no", "yes". Use simulation with or without cars. Default="no".
carsInput:"no", "yes". Take inputs with or without cars channel. Default="no".
cityMap: "complete", "missing", "rand". Use the full city, or input map with missing buildings "rand" means that there is
a random number of missing buildings.
missing: 1 to 4. in case of input map with missing buildings, and not "rand", the number of missing buildings. Default=1.
num_samples: number of samples in the sparse IRT4 radio map. Default=300.
transform: Transform to apply on the images of the loader. Default= transforms.ToTensor())
Output:
"""
if maps_inds.size==1:
self.maps_inds=np.arange(0,700,1,dtype=np.int16)
#Determenistic "random" shuffle of the maps:
np.random.seed(42)
np.random.shuffle(self.maps_inds)
else:
self.maps_inds=maps_inds
if phase=="train":
self.ind1=0
self.ind2=500
elif phase=="val":
self.ind1=501
self.ind2=600
elif phase=="test":
self.ind1=601
self.ind2=699
else: # custom range
self.ind1=ind1
self.ind2=ind2
self.dir_dataset = dir_dataset
self.numTx= numTx
self.thresh=thresh
self.simulation=simulation
self.carsSimul=carsSimul
self.carsInput=carsInput
if simulation=="IRT4":
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/IRT4/"
else:
self.dir_gain=self.dir_dataset+"gain/carsIRT4/"
elif simulation=="DPM" :
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/DPM/"
else:
self.dir_gain=self.dir_dataset+"gain/carsDPM/"
elif simulation=="IRT2":
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/IRT2/"
else:
self.dir_gain=self.dir_dataset+"gain/carsIRT2/"
self.cityMap=cityMap
self.missing=missing
if cityMap=="complete":
self.dir_buildings=self.dir_dataset+"png/buildings_complete/"
else:
self.dir_buildings = self.dir_dataset+"png/buildings_missing" # a random index will be concatenated in the code
#else: #missing==number
# self.dir_buildings = self.dir_dataset+ "png/buildings_missing"+str(missing)+"/"
self.transform= transform
self.num_samples=num_samples
self.dir_Tx = self.dir_dataset+ "png/antennas/"
#later check if reading the JSON file and creating antenna images on the fly is faster
if carsInput!="no":
self.dir_cars = self.dir_dataset+ "png/cars/"
self.height = 256
self.width = 256
def __len__(self):
return (self.ind2-self.ind1+1)*self.numTx
def __getitem__(self, idx):
idxr=np.floor(idx/self.numTx).astype(int)
idxc=idx-idxr*self.numTx
dataset_map_ind=self.maps_inds[idxr+self.ind1]+1
#names of files that depend only on the map:
name1 = str(dataset_map_ind) + ".png"
#names of files that depend on the map and the Tx:
name2 = str(dataset_map_ind) + "_" + str(idxc) + ".png"
#Load buildings:
if self.cityMap == "complete":
img_name_buildings = os.path.join(self.dir_buildings, name1)
else:
if self.cityMap == "rand":
self.missing=np.random.randint(low=1, high=5)
version=np.random.randint(low=1, high=7)
img_name_buildings = os.path.join(self.dir_buildings+str(self.missing)+"/"+str(version)+"/", name1)
str(self.missing)
image_buildings = np.asarray(io.imread(img_name_buildings))
#Load Tx (transmitter):
img_name_Tx = os.path.join(self.dir_Tx, name2)
image_Tx = np.asarray(io.imread(img_name_Tx))
#Load radio map:
if self.simulation!="rand":
img_name_gain = os.path.join(self.dir_gain, name2)
image_gain = np.expand_dims(np.asarray(io.imread(img_name_gain)),axis=2)/256
else: #random weighted average of DPM and IRT2
img_name_gainDPM = os.path.join(self.dir_gainDPM, name2)
img_name_gainIRT2 = os.path.join(self.dir_gainIRT2, name2)
#image_gainDPM = np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/255
#image_gainIRT2 = np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/255
w=np.random.uniform(0,self.IRT2maxW) # IRT2 weight of random average
image_gain= w*np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/256 \
+ (1-w)*np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/256
#pathloss threshold transform
if self.thresh>0:
mask = image_gain < self.thresh
image_gain[mask]=self.thresh
image_gain=image_gain-self.thresh*np.ones(np.shape(image_gain))
image_gain=image_gain/(1-self.thresh)
#Saprse IRT4 samples, determenistic and fixed samples per map
image_samples = np.zeros((self.width,self.height))
seed_map=np.sum(image_buildings) # Each map has its fixed samples, independent of the transmitter location.
np.random.seed(seed_map)
x_samples=np.random.randint(0, 255, size=self.num_samples)
y_samples=np.random.randint(0, 255, size=self.num_samples)
image_samples[x_samples,y_samples]= 1
#inputs to radioUNet
if self.carsInput=="no":
inputs=np.stack([image_buildings, image_Tx], axis=2)
#The fact that the buildings and antenna are normalized 256 and not 1 promotes convergence,
#so we can use the same learning rate as RadioUNets
else: #cars
#Normalization, so all settings can have the same learning rate
image_buildings=image_buildings/256
image_Tx=image_Tx/256
img_name_cars = os.path.join(self.dir_cars, name1)
image_cars = np.asarray(io.imread(img_name_cars))/256
inputs=np.stack([image_buildings, image_Tx, image_cars], axis=2)
#note that ToTensor moves the channel from the last asix to the first!
if self.transform:
inputs = self.transform(inputs).type(torch.float32)
image_gain = self.transform(image_gain).type(torch.float32)
image_samples = self.transform(image_samples).type(torch.float32)
return [inputs, image_gain, image_samples]
class RadioUNet_s(Dataset):
"""RadioMapSeer Loader for accurate buildings and no measurements (RadioUNet_c)"""
def __init__(self,maps_inds=np.zeros(1), phase="train",
ind1=0,ind2=0,
dir_dataset="RadioMapSeer/",
numTx=80,
thresh=0.2,
simulation="DPM",
carsSimul="no",
carsInput="no",
IRT2maxW=1,
cityMap="complete",
missing=1,
fix_samples=0,
num_samples_low= 10,
num_samples_high= 300,
transform= transforms.ToTensor()):
"""
Args:
maps_inds: optional shuffled sequence of the maps. Leave it as maps_inds=0 (default) for the standart split.
phase:"train", "val", "test", "custom". If "train", "val" or "test", uses a standard split.
"custom" means that the loader will read maps ind1 to ind2 from the list maps_inds.
ind1,ind2: First and last indices from maps_inds to define the maps of the loader, in case phase="custom".
dir_dataset: directory of the RadioMapSeer dataset.
numTx: Number of transmitters per map. Default and maximal value of numTx = 80.
thresh: Pathlos threshold between 0 and 1. Defaoult is the noise floor 0.2.
simulation:"DPM", "IRT2", "rand". Default= "DPM"
carsSimul:"no", "yes". Use simulation with or without cars. Default="no".
carsInput:"no", "yes". Take inputs with or without cars channel. Default="no".
IRT2maxW: in case of "rand" simulation, the maximal weight IRT2 can take. Default=1.
cityMap: "complete", "missing", "rand". Use the full city, or input map with missing buildings "rand" means that there is
a random number of missing buildings.
missing: 1 to 4. in case of input map with missing buildings, and not "rand", the number of missing buildings. Default=1.
fix_samples: fixed or a random number of samples. If zero, fixed, else, fix_samples is the number of samples. Default = 0.
num_samples_low: if random number of samples, this is the minimum number of samples. Default = 10.
num_samples_high: if random number of samples, this is the maximal number of samples. Default = 300.
transform: Transform to apply on the images of the loader. Default= transforms.ToTensor())
Output:
inputs: The RadioUNet inputs.
image_gain
"""
#self.phase=phase
if maps_inds.size==1:
self.maps_inds=np.arange(0,700,1,dtype=np.int16)
#Determenistic "random" shuffle of the maps:
np.random.seed(42)
np.random.shuffle(self.maps_inds)
else:
self.maps_inds=maps_inds
if phase=="train":
self.ind1=0
self.ind2=500
elif phase=="val":
self.ind1=501
self.ind2=600
elif phase=="test":
self.ind1=601
self.ind2=699
else: # custom range
self.ind1=ind1
self.ind2=ind2
self.dir_dataset = dir_dataset
self.numTx= numTx
self.thresh=thresh
self.simulation=simulation
self.carsSimul=carsSimul
self.carsInput=carsInput
if simulation=="DPM" :
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/DPM/"
else:
self.dir_gain=self.dir_dataset+"gain/carsDPM/"
elif simulation=="IRT2":
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/IRT2/"
else:
self.dir_gain=self.dir_dataset+"gain/carsIRT2/"
elif simulation=="rand":
if carsSimul=="no":
self.dir_gainDPM=self.dir_dataset+"gain/DPM/"
self.dir_gainIRT2=self.dir_dataset+"gain/IRT2/"
else:
self.dir_gainDPM=self.dir_dataset+"gain/carsDPM/"
self.dir_gainIRT2=self.dir_dataset+"gain/carsIRT2/"
self.IRT2maxW=IRT2maxW
self.cityMap=cityMap
self.missing=missing
if cityMap=="complete":
self.dir_buildings=self.dir_dataset+"png/buildings_complete/"
else:
self.dir_buildings = self.dir_dataset+"png/buildings_missing" # a random index will be concatenated in the code
#else: #missing==number
# self.dir_buildings = self.dir_dataset+ "png/buildings_missing"+str(missing)+"/"
self.fix_samples= fix_samples
self.num_samples_low= num_samples_low
self.num_samples_high= num_samples_high
self.transform= transform
self.dir_Tx = self.dir_dataset+ "png/antennas/"
#later check if reading the JSON file and creating antenna images on the fly is faster
if carsInput!="no":
self.dir_cars = self.dir_dataset+ "png/cars/"
self.height = 256
self.width = 256
def __len__(self):
return (self.ind2-self.ind1+1)*self.numTx
def __getitem__(self, idx):
idxr=np.floor(idx/self.numTx).astype(int)
idxc=idx-idxr*self.numTx
dataset_map_ind=self.maps_inds[idxr+self.ind1]+1
#names of files that depend only on the map:
name1 = str(dataset_map_ind) + ".png"
#names of files that depend on the map and the Tx:
name2 = str(dataset_map_ind) + "_" + str(idxc) + ".png"
#Load buildings:
if self.cityMap == "complete":
img_name_buildings = os.path.join(self.dir_buildings, name1)
else:
if self.cityMap == "rand":
self.missing=np.random.randint(low=1, high=5)
version=np.random.randint(low=1, high=7)
img_name_buildings = os.path.join(self.dir_buildings+str(self.missing)+"/"+str(version)+"/", name1)
str(self.missing)
image_buildings = np.asarray(io.imread(img_name_buildings))/256
#Load Tx (transmitter):
img_name_Tx = os.path.join(self.dir_Tx, name2)
image_Tx = np.asarray(io.imread(img_name_Tx))/256
#Load radio map:
if self.simulation!="rand":
img_name_gain = os.path.join(self.dir_gain, name2)
image_gain = np.expand_dims(np.asarray(io.imread(img_name_gain)),axis=2)/256
else: #random weighted average of DPM and IRT2
img_name_gainDPM = os.path.join(self.dir_gainDPM, name2)
img_name_gainIRT2 = os.path.join(self.dir_gainIRT2, name2)
#image_gainDPM = np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/255
#image_gainIRT2 = np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/255
w=np.random.uniform(0,self.IRT2maxW) # IRT2 weight of random average
image_gain= w*np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/256 \
+ (1-w)*np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/256
#pathloss threshold transform
if self.thresh>0:
mask = image_gain < self.thresh
image_gain[mask]=self.thresh
image_gain=image_gain-self.thresh*np.ones(np.shape(image_gain))
image_gain=image_gain/(1-self.thresh)
image_gain=image_gain*256 # we use this normalization so all RadioUNet methods can have the same learning rate.
# Namely, the loss of RadioUNet_s is 256 the loss of RadioUNet_c
# Important: when evaluating the accuracy, remember to devide the errors by 256!
#input measurements
image_samples = np.zeros((256,256))
if self.fix_samples==0:
num_samples=np.random.randint(self.num_samples_low, self.num_samples_high, size=1)
else:
num_samples=np.floor(self.fix_samples).astype(int)
x_samples=np.random.randint(0, 255, size=num_samples)
y_samples=np.random.randint(0, 255, size=num_samples)
image_samples[x_samples,y_samples]= image_gain[x_samples,y_samples,0]
#inputs to radioUNet
if self.carsInput=="no":
inputs=np.stack([image_buildings, image_Tx, image_samples], axis=2)
#The fact that the buildings and antenna are normalized 256 and not 1 promotes convergence,
#so we can use the same learning rate as RadioUNets
else: #cars
#Normalization, so all settings can have the same learning rate
img_name_cars = os.path.join(self.dir_cars, name1)
image_cars = np.asarray(io.imread(img_name_cars))/256
inputs=np.stack([image_buildings, image_Tx, image_samples, image_cars], axis=2)
#note that ToTensor moves the channel from the last asix to the first!
if self.transform:
inputs = self.transform(inputs).type(torch.float32)
image_gain = self.transform(image_gain).type(torch.float32)
#note that ToTensor moves the channel from the last asix to the first!
return [inputs, image_gain]
class RadioUNet_s_sprseIRT4(Dataset):
"""RadioMapSeer Loader for accurate buildings and no measurements (RadioUNet_c)"""
def __init__(self,maps_inds=np.zeros(1), phase="train",
ind1=0,ind2=0,
dir_dataset="RadioMapSeer/",
numTx=2,
thresh=0.2,
simulation="IRT4",
carsSimul="no",
carsInput="no",
cityMap="complete",
missing=1,
data_samples=300,
fix_samples=0,
num_samples_low= 10,
num_samples_high= 299,
transform= transforms.ToTensor()):
"""
Args:
maps_inds: optional shuffled sequence of the maps. Leave it as maps_inds=0 (default) for the standart split.
phase:"train", "val", "test", "custom". If "train", "val" or "test", uses a standard split.
"custom" means that the loader will read maps ind1 to ind2 from the list maps_inds.
ind1,ind2: First and last indices from maps_inds to define the maps of the loader, in case phase="custom".
dir_dataset: directory of the RadioMapSeer dataset.
numTx: Number of transmitters per map. Default = 2. Note that IRT4 works only with numTx<=2.
thresh: Pathlos threshold between 0 and 1. Defaoult is the noise floor 0.2.
simulation: default="IRT4", with an option to "DPM", "IRT2".
carsSimul:"no", "yes". Use simulation with or without cars. Default="no".
carsInput:"no", "yes". Take inputs with or without cars channel. Default="no".
cityMap: "complete", "missing", "rand". Use the full city, or input map with missing buildings "rand" means that there is
a random number of missing buildings.
missing: 1 to 4. in case of input map with missing buildings, and not "rand", the number of missing buildings. Default=1.
data_samples: number of samples in the sparse IRT4 radio map. Default=300. All input samples are taken from the data_samples
fix_samples: fixed or a random number of samples. If zero, fixed, else, fix_samples is the number of samples. Default = 0.
num_samples_low: if random number of samples, this is the minimum number of samples. Default = 10.
num_samples_high: if random number of samples, this is the maximal number of samples. Default = 300.
transform: Transform to apply on the images of the loader. Default= transforms.ToTensor())
Output:
"""
if maps_inds.size==1:
self.maps_inds=np.arange(0,700,1,dtype=np.int16)
#Determenistic "random" shuffle of the maps:
np.random.seed(42)
np.random.shuffle(self.maps_inds)
else:
self.maps_inds=maps_inds
if phase=="train":
self.ind1=0
self.ind2=500
elif phase=="val":
self.ind1=501
self.ind2=600
elif phase=="test":
self.ind1=601
self.ind2=699
else: # custom range
self.ind1=ind1
self.ind2=ind2
self.dir_dataset = dir_dataset
self.numTx= numTx
self.thresh=thresh
self.simulation=simulation
self.carsSimul=carsSimul
self.carsInput=carsInput
if simulation=="IRT4":
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/IRT4/"
else:
self.dir_gain=self.dir_dataset+"gain/carsIRT4/"
elif simulation=="DPM" :
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/DPM/"
else:
self.dir_gain=self.dir_dataset+"gain/carsDPM/"
elif simulation=="IRT2":
if carsSimul=="no":
self.dir_gain=self.dir_dataset+"gain/IRT2/"
else:
self.dir_gain=self.dir_dataset+"gain/carsIRT2/"
self.cityMap=cityMap
self.missing=missing
if cityMap=="complete":
self.dir_buildings=self.dir_dataset+"png/buildings_complete/"
else:
self.dir_buildings = self.dir_dataset+"png/buildings_missing" # a random index will be concatenated in the code
#else: #missing==number
# self.dir_buildings = self.dir_dataset+ "png/buildings_missing"+str(missing)+"/"
self.data_samples=data_samples
self.fix_samples= fix_samples
self.num_samples_low= num_samples_low
self.num_samples_high= num_samples_high
self.transform= transform
self.dir_Tx = self.dir_dataset+ "png/antennas/"
#later check if reading the JSON file and creating antenna images on the fly is faster
if carsInput!="no":
self.dir_cars = self.dir_dataset+ "png/cars/"
self.height = 256
self.width = 256
def __len__(self):
return (self.ind2-self.ind1+1)*self.numTx
def __getitem__(self, idx):
idxr=np.floor(idx/self.numTx).astype(int)
idxc=idx-idxr*self.numTx
dataset_map_ind=self.maps_inds[idxr+self.ind1]+1
#names of files that depend only on the map:
name1 = str(dataset_map_ind) + ".png"
#names of files that depend on the map and the Tx:
name2 = str(dataset_map_ind) + "_" + str(idxc) + ".png"
#Load buildings:
if self.cityMap == "complete":
img_name_buildings = os.path.join(self.dir_buildings, name1)
else:
if self.cityMap == "rand":
self.missing=np.random.randint(low=1, high=5)
version=np.random.randint(low=1, high=7)
img_name_buildings = os.path.join(self.dir_buildings+str(self.missing)+"/"+str(version)+"/", name1)
str(self.missing)
image_buildings = np.asarray(io.imread(img_name_buildings)) #Will be normalized later, after random seed is computed from it
#Load Tx (transmitter):
img_name_Tx = os.path.join(self.dir_Tx, name2)
image_Tx = np.asarray(io.imread(img_name_Tx))/256
#Load radio map:
if self.simulation!="rand":
img_name_gain = os.path.join(self.dir_gain, name2)
image_gain = np.expand_dims(np.asarray(io.imread(img_name_gain)),axis=2)/256
else: #random weighted average of DPM and IRT2
img_name_gainDPM = os.path.join(self.dir_gainDPM, name2)
img_name_gainIRT2 = os.path.join(self.dir_gainIRT2, name2)
#image_gainDPM = np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/255
#image_gainIRT2 = np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/255
w=np.random.uniform(0,self.IRT2maxW) # IRT2 weight of random average
image_gain= w*np.expand_dims(np.asarray(io.imread(img_name_gainIRT2)),axis=2)/256 \
+ (1-w)*np.expand_dims(np.asarray(io.imread(img_name_gainDPM)),axis=2)/256
#pathloss threshold transform
if self.thresh>0:
mask = image_gain < self.thresh
image_gain[mask]=self.thresh
image_gain=image_gain-self.thresh*np.ones(np.shape(image_gain))
image_gain=image_gain/(1-self.thresh)
image_gain=image_gain*256 # we use this normalization so all RadioUNet methods can have the same learning rate.
# Namely, the loss of RadioUNet_s is 256 the loss of RadioUNet_c
# Important: when evaluating the accuracy, remember to devide the errors by 256!
#Saprse IRT4 samples, determenistic and fixed samples per map
sparse_samples = np.zeros((self.width,self.height))
seed_map=np.sum(image_buildings) # Each map has its fixed samples, independent of the transmitter location.
np.random.seed(seed_map)
x_samples=np.random.randint(0, 255, size=self.data_samples)
y_samples=np.random.randint(0, 255, size=self.data_samples)
sparse_samples[x_samples,y_samples]= 1
#input samples from the sparse gain samples
input_samples = np.zeros((256,256))
if self.fix_samples==0:
num_in_samples=np.random.randint(self.num_samples_low, self.num_samples_high, size=1)
else:
num_in_samples=np.floor(self.fix_samples).astype(int)
data_inds=range(self.data_samples)
input_inds=np.random.permutation(data_inds)[0:num_in_samples[0]]
x_samples_in=x_samples[input_inds]
y_samples_in=y_samples[input_inds]
input_samples[x_samples_in,y_samples_in]= image_gain[x_samples_in,y_samples_in,0]
#normalize image_buildings, after random seed computed from it as an int
image_buildings=image_buildings/256
#inputs to radioUNet
if self.carsInput=="no":
inputs=np.stack([image_buildings, image_Tx, input_samples], axis=2)
#The fact that the buildings and antenna are normalized 256 and not 1 promotes convergence,
#so we can use the same learning rate as RadioUNets
else: #cars
#Normalization, so all settings can have the same learning rate
img_name_cars = os.path.join(self.dir_cars, name1)
image_cars = np.asarray(io.imread(img_name_cars))/256
inputs=np.stack([image_buildings, image_Tx, input_samples, image_cars], axis=2)
#note that ToTensor moves the channel from the last asix to the first!
if self.transform:
inputs = self.transform(inputs).type(torch.float32)
image_gain = self.transform(image_gain).type(torch.float32)
sparse_samples = self.transform(sparse_samples).type(torch.float32)
return [inputs, image_gain, sparse_samples]
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