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import io |
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import os |
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import time |
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import torch.distributed as dist |
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import torch.utils.data as data |
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from PIL import Image |
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from .zipreader import is_zip_path, ZipReader |
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def has_file_allowed_extension(filename, extensions): |
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"""Checks if a file is an allowed extension. |
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Args: |
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filename (string): path to a file |
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Returns: |
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bool: True if the filename ends with a known image extension |
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""" |
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filename_lower = filename.lower() |
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return any(filename_lower.endswith(ext) for ext in extensions) |
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def find_classes(dir): |
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classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))] |
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classes.sort() |
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class_to_idx = {classes[i]: i for i in range(len(classes))} |
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return classes, class_to_idx |
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def make_dataset(dir, class_to_idx, extensions): |
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images = [] |
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dir = os.path.expanduser(dir) |
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for target in sorted(os.listdir(dir)): |
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d = os.path.join(dir, target) |
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if not os.path.isdir(d): |
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continue |
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for root, _, fnames in sorted(os.walk(d)): |
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for fname in sorted(fnames): |
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if has_file_allowed_extension(fname, extensions): |
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path = os.path.join(root, fname) |
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item = (path, class_to_idx[target]) |
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images.append(item) |
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return images |
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def make_dataset_with_ann(ann_file, img_prefix, extensions): |
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images = [] |
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with open(ann_file, "r") as f: |
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contents = f.readlines() |
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for line_str in contents: |
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path_contents = [c for c in line_str.split('\t')] |
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im_file_name = path_contents[0] |
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class_index = int(path_contents[1]) |
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assert str.lower(os.path.splitext(im_file_name)[-1]) in extensions |
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item = (os.path.join(img_prefix, im_file_name), class_index) |
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images.append(item) |
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return images |
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class DatasetFolder(data.Dataset): |
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"""A generic data loader where the samples are arranged in this way: :: |
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root/class_x/xxx.ext |
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root/class_x/xxy.ext |
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root/class_x/xxz.ext |
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root/class_y/123.ext |
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root/class_y/nsdf3.ext |
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root/class_y/asd932_.ext |
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Args: |
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root (string): Root directory path. |
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loader (callable): A function to load a sample given its path. |
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extensions (list[string]): A list of allowed extensions. |
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transform (callable, optional): A function/transform that takes in |
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a sample and returns a transformed version. |
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E.g, ``transforms.RandomCrop`` for images. |
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target_transform (callable, optional): A function/transform that takes |
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in the target and transforms it. |
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Attributes: |
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samples (list): List of (sample path, class_index) tuples |
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""" |
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def __init__(self, root, loader, extensions, ann_file='', img_prefix='', transform=None, target_transform=None, |
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cache_mode="no"): |
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if ann_file == '': |
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_, class_to_idx = find_classes(root) |
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samples = make_dataset(root, class_to_idx, extensions) |
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else: |
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samples = make_dataset_with_ann(os.path.join(root, ann_file), |
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os.path.join(root, img_prefix), |
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extensions) |
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if len(samples) == 0: |
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raise (RuntimeError("Found 0 files in subfolders of: " + root + "\n" + |
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"Supported extensions are: " + ",".join(extensions))) |
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self.root = root |
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self.loader = loader |
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self.extensions = extensions |
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self.samples = samples |
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self.labels = [y_1k for _, y_1k in samples] |
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self.classes = list(set(self.labels)) |
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self.transform = transform |
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self.target_transform = target_transform |
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self.cache_mode = cache_mode |
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if self.cache_mode != "no": |
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self.init_cache() |
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def init_cache(self): |
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assert self.cache_mode in ["part", "full"] |
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n_sample = len(self.samples) |
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global_rank = dist.get_rank() |
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world_size = dist.get_world_size() |
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samples_bytes = [None for _ in range(n_sample)] |
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start_time = time.time() |
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for index in range(n_sample): |
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if index % (n_sample // 10) == 0: |
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t = time.time() - start_time |
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print(f'global_rank {dist.get_rank()} cached {index}/{n_sample} takes {t:.2f}s per block') |
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start_time = time.time() |
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path, target = self.samples[index] |
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if self.cache_mode == "full": |
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samples_bytes[index] = (ZipReader.read(path), target) |
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elif self.cache_mode == "part" and index % world_size == global_rank: |
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samples_bytes[index] = (ZipReader.read(path), target) |
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else: |
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samples_bytes[index] = (path, target) |
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self.samples = samples_bytes |
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def __getitem__(self, index): |
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""" |
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Args: |
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index (int): Index |
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Returns: |
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tuple: (sample, target) where target is class_index of the target class. |
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""" |
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path, target = self.samples[index] |
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sample = self.loader(path) |
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if self.transform is not None: |
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sample = self.transform(sample) |
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if self.target_transform is not None: |
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target = self.target_transform(target) |
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return sample, target |
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def __len__(self): |
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return len(self.samples) |
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def __repr__(self): |
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fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' |
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fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) |
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fmt_str += ' Root Location: {}\n'.format(self.root) |
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tmp = ' Transforms (if any): ' |
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fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) |
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tmp = ' Target Transforms (if any): ' |
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fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) |
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return fmt_str |
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IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif'] |
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def pil_loader(path): |
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if isinstance(path, bytes): |
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img = Image.open(io.BytesIO(path)) |
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elif is_zip_path(path): |
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data = ZipReader.read(path) |
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img = Image.open(io.BytesIO(data)) |
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else: |
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with open(path, 'rb') as f: |
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img = Image.open(f) |
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return img.convert('RGB') |
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def accimage_loader(path): |
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import accimage |
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try: |
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return accimage.Image(path) |
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except IOError: |
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return pil_loader(path) |
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def default_img_loader(path): |
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from torchvision import get_image_backend |
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if get_image_backend() == 'accimage': |
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return accimage_loader(path) |
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else: |
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return pil_loader(path) |
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class CachedImageFolder(DatasetFolder): |
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"""A generic data loader where the images are arranged in this way: :: |
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root/dog/xxx.png |
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root/dog/xxy.png |
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root/dog/xxz.png |
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root/cat/123.png |
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root/cat/nsdf3.png |
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root/cat/asd932_.png |
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Args: |
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root (string): Root directory path. |
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transform (callable, optional): A function/transform that takes in an PIL image |
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and returns a transformed version. E.g, ``transforms.RandomCrop`` |
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target_transform (callable, optional): A function/transform that takes in the |
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target and transforms it. |
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loader (callable, optional): A function to load an image given its path. |
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Attributes: |
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imgs (list): List of (image path, class_index) tuples |
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""" |
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def __init__(self, root, ann_file='', img_prefix='', transform=None, target_transform=None, |
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loader=default_img_loader, cache_mode="no"): |
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super(CachedImageFolder, self).__init__(root, loader, IMG_EXTENSIONS, |
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ann_file=ann_file, img_prefix=img_prefix, |
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transform=transform, target_transform=target_transform, |
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cache_mode=cache_mode) |
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self.imgs = self.samples |
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def __getitem__(self, index): |
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""" |
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Args: |
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index (int): Index |
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Returns: |
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tuple: (image, target) where target is class_index of the target class. |
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""" |
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path, target = self.samples[index] |
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image = self.loader(path) |
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if self.transform is not None: |
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img = self.transform(image) |
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else: |
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img = image |
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if self.target_transform is not None: |
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target = self.target_transform(target) |
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return img, target |
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