File size: 2,244 Bytes
29d330e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | from easyfsl.samplers import TaskSampler
from torch.utils.data import DataLoader
from torch.utils.data import DataLoader
from utils.Dataset import Jammer_Dataset
from torch.utils.data import DataLoader, random_split
from pathlib import Path
from utils.Dataset import augment_sample,augment_data,balance_data
from statistics import mean
import warnings
import torchvision.transforms as transforms
from utils.utils import *
warnings.filterwarnings("ignore")
warnings.filterwarnings("default")
transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
])
def create_datasets_and_loaders(
root_dir,
train_test_split_dir,
permutations,
n_way,
n_shot,
n_query,
n_tasks_per_epoch,
n_validation_tasks,
n_workers,
env_no,
transforms=None,
):
# train_loaders = []
# valid_loaders = []
permutations_str = "_".join(map(str, permutations))
env_str = "_".join(map(str, env_no))
train_data = load_data_from_csv(f'train_data_{permutations_str}_env{env_str}.csv')
valid_data = load_data_from_csv(f'valid_data_{permutations_str}_env{env_str}.csv')
train_dataset = Jammer_Dataset(root_dir, train_data, transform=transforms)
valid_dataset = Jammer_Dataset(root_dir, valid_data, transform=transforms)
# Create TaskSamplers for training and validation
train_sampler = TaskSampler(
train_dataset, n_way=n_way, n_shot=n_shot, n_query=n_query, n_tasks=n_tasks_per_epoch
)
val_sampler = TaskSampler(
valid_dataset, n_way=n_way, n_shot=n_shot, n_query=n_query, n_tasks=n_validation_tasks
)
# Create DataLoader for training
train_loader = DataLoader(
train_dataset,
batch_sampler=train_sampler,
num_workers=n_workers,
pin_memory=True,
collate_fn=train_sampler.episodic_collate_fn,
)
# Create DataLoader for validation
val_loader = DataLoader(
valid_dataset,
batch_sampler=val_sampler,
num_workers=n_workers,
pin_memory=True,
collate_fn=val_sampler.episodic_collate_fn,
)
return train_loader, val_loader
|