def create_valid_awgn_dataloaders(patchsize, batchsize):
transform_valid = Compose([
sar_dataset.CenterCropPil(patchsize),
sar_dataset.PilToGrayTensor(bayes=0.0,scale=scale_img),
])
validset = sar_dataset.PlainImageFolder(dirs=folders_data.valid68_dir, transform=transform_valid, cache=True)
validloader = torch.utils.data.DataLoader(validset, batch_size=batchsize, shuffle=False, num_workers=1)
return validloader
def create_train_awgn_dataloaders(patchsize, batchsize, trainsetiters):
transform_train = Compose([
sar_dataset.RandomCropPil(patchsize),
sar_dataset.Random8OrientationPil(),
sar_dataset.PilToGrayTensor(bayes=0.0, scale=scale_img),
])
trainset = sar_dataset.PlainImageFolder(dirs=folders_data.train400_dir, transform=transform_train, cache=True)
trainset = torch.utils.data.ConcatDataset([trainset]*trainsetiters)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batchsize, shuffle=True, num_workers=20)
return trainloader
def create_train_syncsar_dataloaders_old(patchsize, batchsize, trainsetiters):
import torchvision.transforms as transforms
transform_train = Compose([
transforms.RandomCrop(patchsize),
sar_dataset.RandomOrientation90Pil(),
#sar_dataset.Random8OrientationPil(),
transforms.RandomVerticalFlip(),
sar_dataset.ToGrayscale(),
transforms.ToTensor(),
sar_dataset.AddBayes(),
])
train_folders = folders_data.train400_dir
trainset = sar_dataset.PlainImageFolder(dirs=train_folders, transform=transform_train, cache=True)
trainset = torch.utils.data.ConcatDataset([trainset] * trainsetiters)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batchsize, shuffle=True, num_workers=20)
return trainloader