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import os | |
import os.path | |
import json | |
from PIL import Image | |
import torch.utils.data as data | |
def pil_loader(path): | |
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) | |
with open(path, 'rb') as f: | |
img = Image.open(f) | |
return img.convert('RGB') | |
class ImageNet(data.Dataset): | |
""" ImageNet | |
Args: | |
root (string): Root directory where images are downloaded to. | |
annFile (string): Path to json annotation file. | |
transform (callable, optional): A function/transform that takes in an PIL image | |
and returns a transformed version. E.g, ``transforms.ToTensor`` | |
""" | |
def __init__(self, ann_file, root, remove_images_without_annotations=None, transforms=None): | |
self.root = root | |
self.transform = transforms | |
meta_file = os.path.join(root, ann_file) | |
assert os.path.exists(meta_file), 'meta file %s under root %s not found' % (os.path.basename(meta_file), root) | |
with open(meta_file, 'r') as f: | |
meta = json.load(f) | |
self.classes = meta['classes'] | |
self.class_to_idx = meta['class_to_idx'] | |
self.samples = meta['samples'] | |
self.num_sample = len(self.samples) | |
self.allsamples = self.samples | |
def select_class(self, cls): | |
new_samples = [sample for sample in self.allsamples if sample[-1] in cls] | |
self.samples = new_samples | |
self.num_sample = len(self.samples) | |
def __getitem__(self, index): | |
""" | |
Args: | |
index (int): Index | |
Returns: | |
tuple: (sample, target) where target is class_index of the target class. | |
""" | |
img_path, target = self.samples[index] | |
sample = pil_loader(self.root + '/' + img_path) | |
if self.transform is not None: | |
sample = self.transform(sample) | |
return sample, target, index | |
def __len__(self): | |
return len(self.samples) |