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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import csv
from enum import Enum
import logging
import os
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
from .extended import ExtendedVisionDataset
logger = logging.getLogger("dinov2")
_Target = int
class _Split(Enum):
TRAIN = "train"
VAL = "val"
TEST = "test" # NOTE: torchvision does not support the test split
@property
def length(self) -> int:
split_lengths = {
_Split.TRAIN: 1_281_167,
_Split.VAL: 50_000,
_Split.TEST: 100_000,
}
return split_lengths[self]
def get_dirname(self, class_id: Optional[str] = None) -> str:
return self.value if class_id is None else os.path.join(self.value, class_id)
def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str:
dirname = self.get_dirname(class_id)
if self == _Split.TRAIN:
basename = f"{class_id}_{actual_index}"
else: # self in (_Split.VAL, _Split.TEST):
basename = f"ILSVRC2012_{self.value}_{actual_index:08d}"
return os.path.join(dirname, basename + ".JPEG")
def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]:
assert self != _Split.TEST
dirname, filename = os.path.split(image_relpath)
class_id = os.path.split(dirname)[-1]
basename, _ = os.path.splitext(filename)
actual_index = int(basename.split("_")[-1])
return class_id, actual_index
class ImageNet(ExtendedVisionDataset):
Target = Union[_Target]
Split = Union[_Split]
def __init__(
self,
*,
split: "ImageNet.Split",
root: str,
extra: str,
transforms: Optional[Callable] = None,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
) -> None:
super().__init__(root, transforms, transform, target_transform)
self._extra_root = extra
self._split = split
self._entries = None
self._class_ids = None
self._class_names = None
@property
def split(self) -> "ImageNet.Split":
return self._split
def _get_extra_full_path(self, extra_path: str) -> str:
return os.path.join(self._extra_root, extra_path)
def _load_extra(self, extra_path: str) -> np.ndarray:
extra_full_path = self._get_extra_full_path(extra_path)
return np.load(extra_full_path, mmap_mode="r")
def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
extra_full_path = self._get_extra_full_path(extra_path)
os.makedirs(self._extra_root, exist_ok=True)
np.save(extra_full_path, extra_array)
@property
def _entries_path(self) -> str:
return f"entries-{self._split.value.upper()}.npy"
@property
def _class_ids_path(self) -> str:
return f"class-ids-{self._split.value.upper()}.npy"
@property
def _class_names_path(self) -> str:
return f"class-names-{self._split.value.upper()}.npy"
def _get_entries(self) -> np.ndarray:
if self._entries is None:
self._entries = self._load_extra(self._entries_path)
assert self._entries is not None
return self._entries
def _get_class_ids(self) -> np.ndarray:
if self._split == _Split.TEST:
assert False, "Class IDs are not available in TEST split"
if self._class_ids is None:
self._class_ids = self._load_extra(self._class_ids_path)
assert self._class_ids is not None
return self._class_ids
def _get_class_names(self) -> np.ndarray:
if self._split == _Split.TEST:
assert False, "Class names are not available in TEST split"
if self._class_names is None:
self._class_names = self._load_extra(self._class_names_path)
assert self._class_names is not None
return self._class_names
def find_class_id(self, class_index: int) -> str:
class_ids = self._get_class_ids()
return str(class_ids[class_index])
def find_class_name(self, class_index: int) -> str:
class_names = self._get_class_names()
return str(class_names[class_index])
def get_image_data(self, index: int) -> bytes:
entries = self._get_entries()
actual_index = entries[index]["actual_index"]
class_id = self.get_class_id(index)
image_relpath = self.split.get_image_relpath(actual_index, class_id)
image_full_path = os.path.join(self.root, image_relpath)
with open(image_full_path, mode="rb") as f:
image_data = f.read()
return image_data
def get_target(self, index: int) -> Optional[Target]:
entries = self._get_entries()
class_index = entries[index]["class_index"]
return None if self.split == _Split.TEST else int(class_index)
def get_targets(self) -> Optional[np.ndarray]:
entries = self._get_entries()
return None if self.split == _Split.TEST else entries["class_index"]
def get_class_id(self, index: int) -> Optional[str]:
entries = self._get_entries()
class_id = entries[index]["class_id"]
return None if self.split == _Split.TEST else str(class_id)
def get_class_name(self, index: int) -> Optional[str]:
entries = self._get_entries()
class_name = entries[index]["class_name"]
return None if self.split == _Split.TEST else str(class_name)
def __len__(self) -> int:
entries = self._get_entries()
assert len(entries) == self.split.length
return len(entries)
def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]:
labels_full_path = os.path.join(self.root, labels_path)
labels = []
try:
with open(labels_full_path, "r") as f:
reader = csv.reader(f)
for row in reader:
class_id, class_name = row
labels.append((class_id, class_name))
except OSError as e:
raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e
return labels
def _dump_entries(self) -> None:
split = self.split
if split == ImageNet.Split.TEST:
dataset = None
sample_count = split.length
max_class_id_length, max_class_name_length = 0, 0
else:
labels_path = "labels.txt"
logger.info(f'loading labels from "{labels_path}"')
labels = self._load_labels(labels_path)
# NOTE: Using torchvision ImageFolder for consistency
from torchvision.datasets import ImageFolder
dataset_root = os.path.join(self.root, split.get_dirname())
dataset = ImageFolder(dataset_root)
sample_count = len(dataset)
max_class_id_length, max_class_name_length = -1, -1
for sample in dataset.samples:
_, class_index = sample
class_id, class_name = labels[class_index]
max_class_id_length = max(len(class_id), max_class_id_length)
max_class_name_length = max(len(class_name), max_class_name_length)
dtype = np.dtype(
[
("actual_index", "<u4"),
("class_index", "<u4"),
("class_id", f"U{max_class_id_length}"),
("class_name", f"U{max_class_name_length}"),
]
)
entries_array = np.empty(sample_count, dtype=dtype)
if split == ImageNet.Split.TEST:
old_percent = -1
for index in range(sample_count):
percent = 100 * (index + 1) // sample_count
if percent > old_percent:
logger.info(f"creating entries: {percent}%")
old_percent = percent
actual_index = index + 1
class_index = np.uint32(-1)
class_id, class_name = "", ""
entries_array[index] = (actual_index, class_index, class_id, class_name)
else:
class_names = {class_id: class_name for class_id, class_name in labels}
assert dataset
old_percent = -1
for index in range(sample_count):
percent = 100 * (index + 1) // sample_count
if percent > old_percent:
logger.info(f"creating entries: {percent}%")
old_percent = percent
image_full_path, class_index = dataset.samples[index]
image_relpath = os.path.relpath(image_full_path, self.root)
class_id, actual_index = split.parse_image_relpath(image_relpath)
class_name = class_names[class_id]
entries_array[index] = (actual_index, class_index, class_id, class_name)
logger.info(f'saving entries to "{self._entries_path}"')
self._save_extra(entries_array, self._entries_path)
def _dump_class_ids_and_names(self) -> None:
split = self.split
if split == ImageNet.Split.TEST:
return
entries_array = self._load_extra(self._entries_path)
max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1
for entry in entries_array:
class_index, class_id, class_name = (
entry["class_index"],
entry["class_id"],
entry["class_name"],
)
max_class_index = max(int(class_index), max_class_index)
max_class_id_length = max(len(str(class_id)), max_class_id_length)
max_class_name_length = max(len(str(class_name)), max_class_name_length)
class_count = max_class_index + 1
class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}")
class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}")
for entry in entries_array:
class_index, class_id, class_name = (
entry["class_index"],
entry["class_id"],
entry["class_name"],
)
class_ids_array[class_index] = class_id
class_names_array[class_index] = class_name
logger.info(f'saving class IDs to "{self._class_ids_path}"')
self._save_extra(class_ids_array, self._class_ids_path)
logger.info(f'saving class names to "{self._class_names_path}"')
self._save_extra(class_names_array, self._class_names_path)
def dump_extra(self) -> None:
self._dump_entries()
self._dump_class_ids_and_names()
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