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from torch.utils import data
from PIL import Image
import os
class Dataset(data.Dataset):
'Characterizes a dataset for PyTorch'
def __init__(self, path, transform=None):
'Initialization'
self.file_names = self.get_filenames(path)
self.transform = transform
def __len__(self):
'Denotes the total number of samples'
return len(self.file_names)
def __getitem__(self, index):
'Generates one sample of data'
img = Image.open(self.file_names[index]).convert('RGB')
# Convert image and label to torch tensors
if self.transform is not None:
img = self.transform(img)
return img
def get_filenames(self, data_path):
images = []
for path, subdirs, files in os.walk(data_path):
for name in files:
if name.rfind('jpg') != -1 or name.rfind('png') != -1:
filename = os.path.join(path, name)
if os.path.isfile(filename):
images.append(filename)
return images
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