File size: 1,936 Bytes
2d5faf0 | 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 | import os
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
class CustomDataset(Dataset):
def __init__(self, file_paths, transform=None):
self.file_paths = file_paths
self.transform = transform
self.file_names = [os.path.basename(path) for path in file_paths]
def __len__(self):
return len(self.file_paths)
def __getitem__(self, idx):
data = torch.load(self.file_paths[idx], weights_only=True)
images = data[:6]
labels = data[6]
images = images.float()
labels = labels.long()
if self.transform:
images = self.transform(images)
labels = self.transform(labels)
return images, labels, self.file_names[idx]
def generate_file_paths(base_path):
file_paths = []
for frame in os.listdir(base_path):
frame_path = os.path.join(base_path, frame)
if frame_path.endswith('.mat.pt'):
file_paths.append(frame_path)
return [path for path in file_paths if os.path.exists(path)]
def load_data(base_path, batch_size=4, num_workers=2, test_size=0.2):
file_paths = generate_file_paths(base_path)
train_paths, test_paths = train_test_split(file_paths, test_size=test_size, random_state=42)
train_dataset = CustomDataset(file_paths=train_paths)
test_dataset = CustomDataset(file_paths=test_paths)
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True
)
test_loader = DataLoader(
test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
drop_last=True
)
return train_loader, test_loader
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