def loadData(self):
trainset = SimulationDataset("train", transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(self.input_shape),
# utils.RandomResizedCrop(self.input_shape),
# utils.RandomNoise(),
utils.RandomTranslate(10, 10),
# utils.RandomBrightness(),
# utils.RandomContrast(),
# utils.RandomHue(),
utils.RandomHorizontalFlip(),
utils.ToTensor(),
utils.Normalize([0.1, 0.4, 0.4], [0.9, 0.6, 0.5])
]))
# weights = utils.get_weights(trainset)
# sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights), replacement=False)
# self.trainloader = torch.utils.data.DataLoader(trainset, batch_size=self.cfg.batch_size, sampler=sampler, num_workers=0, pin_memory=True)
self.trainloader = torch.utils.data.DataLoader(trainset, shuffle=True, batch_size=self.cfg.batch_size, num_workers=0, pin_memory=True)
testset = SimulationDataset("test", transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(self.input_shape),
utils.ToTensor(),
utils.Normalize([0.1, 0.4, 0.4], [0.9, 0.6, 0.5])
]))
self.testloader = torch.utils.data.DataLoader(testset, batch_size=self.cfg.batch_size, shuffle=False, num_workers=0, pin_memory=True)
def loadMotorData(self, DATA_FILES):
trainset_motor = SimulationDataset(DATA_FILES, type="motor")
# weights = utils.get_weights(trainset)
# sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights), replacement=False)
# self.trainloader = torch.utils.data.DataLoader(trainset, batch_size=self.cfg.batch_size, sampler=sampler, num_workers=0, pin_memory=True)
self.trainloader_motor = torch.utils.data.DataLoader(
trainset_motor,
shuffle=True,
batch_size=self.cfg.batch_size,
num_workers=0,
pin_memory=True)
def loadData(self):
trainset = SimulationDataset(
"train",
transforms=transforms.Compose([
utils.RandomCoose(['centre', 'left', 'right']),
utils.Preprocess(self.input_shape),
utils.RandomTranslate(100, 10),
utils.RandomBrightness(),
utils.RandomContrast(),
utils.RandomHue(),
utils.RandomHorizontalFlip(),
utils.ToTensor(),
utils.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]))
weights = utils.get_weights(trainset)
sampler = torch.utils.data.sampler.WeightedRandomSampler(
weights, len(weights), replacement=True)
# self.trainloader = torch.utils.data.DataLoader(trainset, batch_size=self.cfg.batch_size, sampler=sampler, num_workers=4)
self.trainloader = torch.utils.data.DataLoader(
trainset, batch_size=self.cfg.batch_size, num_workers=4)
testset = SimulationDataset("test",
transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(self.input_shape),
utils.ToTensor(),
utils.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]))
self.testloader = torch.utils.data.DataLoader(
testset,
batch_size=self.cfg.batch_size,
shuffle=False,
num_workers=4)
def loadData(self):
#DATA_FILES = ['C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_0.csv', 'C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_1.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_2.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_3.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_4.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_5.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_6.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_7.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_8.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_9.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_10.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_11.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_12.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_13.csv']
#DATA_FILES = ['C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_0.csv', 'C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_1.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_2.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_3.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_4.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_5.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_6.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_7.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_8.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_9.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_10.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_11.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_12.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_13.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_14.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_15.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_16.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_17.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_18.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_19.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_20.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_21.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_22.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_23.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_24.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_25.csv','C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_26.csv']
DATA_FILES = [
'C:/Users/circle/Desktop/RCDATA_CSV_7_15/output_{}.csv'.format(i)
for i in range(27)
]
trainset = SimulationDataset(DATA_FILES)
# weights = utils.get_weights(trainset)
# sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights), replacement=False)
# self.trainloader = torch.utils.data.DataLoader(trainset, batch_size=self.cfg.batch_size, sampler=sampler, num_workers=0, pin_memory=True)
self.trainloader = torch.utils.data.DataLoader(
trainset,
shuffle=True,
batch_size=self.cfg.batch_size,
num_workers=0,
pin_memory=True)
from dataloader import SimulationDataset
import matplotlib.pyplot as plt
from PIL import Image
import utils as utils
import torch
from torchvision import transforms
import torchvision.transforms.functional as F
input_shape = (utils.IMAGE_HEIGHT, utils.IMAGE_WIDTH)
dataset = SimulationDataset("train",
transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(input_shape),
utils.ToTensor(),
utils.Normalize([0.1, 0.4, 0.4],
[0.9, 0.6, 0.5])
]))
targets = []
for i in range(dataset.__len__()):
image, target = dataset.__getitem__(i)
targets.append(target)
# plt.imshow(F.to_pil_image(image))
# plt.title(str(target))
# plt.show()
plt.hist(targets, 50)
plt.show()