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data.py
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from torch.utils.data import DataLoader
import os
import os.path
import numpy as np
import sys
import csv
from sklearn.preprocessing import MinMaxScaler
import pickle
import torch
import torch.utils.data as data
from scipy.optimize import leastsq
from itertools import permutations
def set_seed(seed):
torch.manual_seed(seed)
np.random.seed(seed)
def custom_minmax_scaler(data, us):
scaler = MinMaxScaler(feature_range=(0, 1))
if us:
data = scaler.fit_transform(data)
return data.reshape(-1, 1, 12, 51)
else:
return data
def get_dataloader(args):
x = []
dataset_path = args.root,
pr = args.pr,
sa = args.sa,
train_trainData = os.path.join(dataset_path[0], 'train/csi_cdy_am_train_6ac.csv')
filedata = open(train_trainData)
readerdata = csv.reader(filedata)
for ind, k in enumerate(readerdata):
k = list(map(float, k))
k = np.array(k)
k = k.reshape(1, 1, 12, 51)
x.append(k)
trainData = np.array(x).reshape(-1, 1, 1, 12, 51)
"load testdata"
m = []
pathDir = os.path.join(dataset_path[0], 'test/csi_cdy_am_test_6ac.csv')
filedata = open(pathDir)
readerdata = csv.reader(filedata)
for ind, k in enumerate(readerdata):
k = list(map(float, k))
k = np.array(k)
k = k.reshape(1, 1, 12, 51)
m.append(k)
valData = np.array(m).reshape(-1, 1, 1, 12, 51)
data_pro = np.concatenate([trainData, valData], axis=0)
# normalization
batch_size = data_pro.shape[0]
normalized_data = np.zeros_like(data_pro)
for i in range(batch_size):
batch_data = data_pro[i].reshape(-1, 1)
normalized_data[i] = custom_minmax_scaler(batch_data, pr)
trainData1 = np.copy(trainData)
train_trainData = np.concatenate([trainData, trainData1], axis=1)
valData1 = np.copy(valData)
val_trainData = np.concatenate([valData, valData1], axis=1)
train_trainData = torch.tensor(train_trainData)
print("the train_dataset shape is:", train_trainData.size())
val_trainData = torch.tensor(val_trainData)
print("the shape of val_trainData:", val_trainData.size())
l = []
pathDir = os.path.join(dataset_path[0], 'train/kinect_xy_train_cdy_6ac.csv')
filetarget = open(pathDir)
readerdata = csv.reader(filetarget)
for ind, k in enumerate(readerdata):
k = list(map(float, k))
l.append(k)
train_trainTarget = np.array(l)
n = []
pathDir = os.path.join(dataset_path[0], 'test/kinect_xy_test_cdy_6ac.csv')
filetarget = open(pathDir)
readerdata = csv.reader(filetarget)
for ind, k in enumerate(readerdata):
k = list(map(float, k))
n.append(k)
val_trainTarget = np.array(n)
train_trainTarget = torch.tensor(train_trainTarget)
train_trainTarget = train_trainTarget.float()
print("the train_dataset_target shape is:", type(train_trainTarget), train_trainTarget.size())
train_dataset = torch.utils.data.TensorDataset(train_trainData, train_trainTarget)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
drop_last=False
)
val_trainTarget = torch.tensor(val_trainTarget)
val_trainTarget = val_trainTarget.float()
print("the val_dataset_target shape is:", type(val_trainTarget), val_trainTarget.size())
val_dataset = torch.utils.data.TensorDataset(val_trainData, val_trainTarget)
val_loader = torch.utils.data.DataLoader(
dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
drop_last=False
)
return train_loader, val_loader
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--root', type=str, default='dataset')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--num_workers', type=int, default=1)
parser.add_argument('--model_names', type=str, nargs='+', default=['conti', 'conti'])
args = parser.parse_args()
train_loader, val_loader = get_dataloader(args)
for img, label in train_loader:
break
for img, label in val_loader:
print(img.shape, label.shape)
break