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 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)
# plt.imshow(F.to_pil_image(sample['image']))
# plt.title(str(sample['target']))
# plt.show()
return sample['image'], sample['target']
def __len__(self):
return len(self.image_paths)
if __name__ == '__main__':
input_shape = (utils.IMAGE_HEIGHT, utils.IMAGE_WIDTH)
dataset = SimulationDataset("train",
transforms=transforms.Compose([
utils.RandomCoose(['center']),
utils.Preprocess(input_shape),
utils.RandomHorizontalFlip(),
utils.ToTensor(),
utils.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
]))
print(dataset.__len__())
print(dataset.__getitem__(0)[0].size())
for c in range(3):
for i in range(dataset.__len__()):
print(dataset.__getitem__(i)[c].mean())
print(dataset.__getitem__(i)[c].std())
# print(dataset.__getitem__(0))
# print(len(dataset.__get_annotations__()))