Source code for tensorpack.dataflow.dataset.ilsvrc

# -*- coding: utf-8 -*-
# File:

import os
import tarfile
import numpy as np
import tqdm

from ...utils import logger
from ...utils.loadcaffe import get_caffe_pb
from ...utils.fs import mkdir_p, download, get_dataset_path
from ...utils.timer import timed_operation
from ..base import RNGDataFlow

__all__ = ['ILSVRCMeta', 'ILSVRC12', 'ILSVRC12Files']

CAFFE_ILSVRC12_URL = ("", 17858008)

[docs]class ILSVRCMeta(object): """ Provide methods to access metadata for ILSVRC dataset. """ def __init__(self, dir=None): if dir is None: dir = get_dataset_path('ilsvrc_metadata') self.dir = os.path.expanduser(dir) mkdir_p(self.dir) f = os.path.join(self.dir, 'synsets.txt') if not os.path.isfile(f): self._download_caffe_meta() self.caffepb = None
[docs] def get_synset_words_1000(self): """ Returns: dict: {cls_number: cls_name} """ fname = os.path.join(self.dir, 'synset_words.txt') assert os.path.isfile(fname) lines = [x.strip() for x in open(fname).readlines()] return dict(enumerate(lines))
[docs] def get_synset_1000(self): """ Returns: dict: {cls_number: synset_id} """ fname = os.path.join(self.dir, 'synsets.txt') assert os.path.isfile(fname) lines = [x.strip() for x in open(fname).readlines()] return dict(enumerate(lines))
def _download_caffe_meta(self): fpath = download(CAFFE_ILSVRC12_URL[0], self.dir, expect_size=CAFFE_ILSVRC12_URL[1]), 'r:gz').extractall(self.dir)
[docs] def get_image_list(self, name, dir_structure='original'): """ Args: name (str): 'train' or 'val' or 'test' dir_structure (str): same as in :meth:`ILSVRC12.__init__()`. Returns: list: list of (image filename, label) """ assert name in ['train', 'val', 'test'] assert dir_structure in ['original', 'train'] add_label_to_fname = (name != 'train' and dir_structure != 'original') if add_label_to_fname: synset = self.get_synset_1000() fname = os.path.join(self.dir, name + '.txt') assert os.path.isfile(fname), fname with open(fname) as f: ret = [] for line in f.readlines(): name, cls = line.strip().split() cls = int(cls) if add_label_to_fname: name = os.path.join(synset[cls], name) ret.append((name.strip(), cls)) assert len(ret), fname return ret
[docs] def get_per_pixel_mean(self, size=None): """ Args: size (tuple): image size in (h, w). Defaults to (256, 256). Returns: np.ndarray: per-pixel mean of shape (h, w, 3 (BGR)) in range [0, 255]. """ if self.caffepb is None: self.caffepb = get_caffe_pb() obj = self.caffepb.BlobProto() mean_file = os.path.join(self.dir, 'imagenet_mean.binaryproto') with open(mean_file, 'rb') as f: obj.ParseFromString( arr = np.array(, 256, 256)).astype('float32') arr = np.transpose(arr, [1, 2, 0]) if size is not None: arr = cv2.resize(arr, size[::-1]) return arr
[docs] @staticmethod def guess_dir_structure(dir): """ Return the directory structure of "dir". Args: dir(str): something like '/path/to/imagenet/val' Returns: either 'train' or 'original' """ subdir = os.listdir(dir)[0] # find a subdir starting with 'n' if subdir.startswith('n') and \ os.path.isdir(os.path.join(dir, subdir)): dir_structure = 'train' else: dir_structure = 'original' "[ILSVRC12] Assuming directory {} has '{}' structure.".format( dir, dir_structure)) return dir_structure
[docs]class ILSVRC12Files(RNGDataFlow): """ Same as :class:`ILSVRC12`, but produces filenames of the images instead of nparrays. This could be useful when ``cv2.imread`` is a bottleneck and you want to decode it in smarter ways (e.g. in parallel). """
[docs] def __init__(self, dir, name, meta_dir=None, shuffle=None, dir_structure=None): """ Same as in :class:`ILSVRC12`. """ assert name in ['train', 'test', 'val'], name dir = os.path.expanduser(dir) assert os.path.isdir(dir), dir self.full_dir = os.path.join(dir, name) = name assert os.path.isdir(self.full_dir), self.full_dir assert meta_dir is None or os.path.isdir(meta_dir), meta_dir if shuffle is None: shuffle = name == 'train' self.shuffle = shuffle if name == 'train': dir_structure = 'train' if dir_structure is None: dir_structure = ILSVRCMeta.guess_dir_structure(self.full_dir) meta = ILSVRCMeta(meta_dir) self.imglist = meta.get_image_list(name, dir_structure) for fname, _ in self.imglist[:10]: fname = os.path.join(self.full_dir, fname) assert os.path.isfile(fname), fname
def __len__(self): return len(self.imglist) def __iter__(self): idxs = np.arange(len(self.imglist)) if self.shuffle: self.rng.shuffle(idxs) for k in idxs: fname, label = self.imglist[k] fname = os.path.join(self.full_dir, fname) yield [fname, label]
[docs]class ILSVRC12(ILSVRC12Files): """ Produces uint8 ILSVRC12 images of shape [h, w, 3(BGR)], and a label between [0, 999]. """
[docs] def __init__(self, dir, name, meta_dir=None, shuffle=None, dir_structure=None): """ Args: dir (str): A directory containing a subdir named ``name``, containing the images in a structure described below. name (str): One of 'train' or 'val' or 'test'. shuffle (bool): shuffle the dataset. Defaults to True if name=='train'. dir_structure (str): One of 'original' or 'train'. The directory structure for the 'val' directory. 'original' means the original decompressed directory, which only has list of image files (as below). If set to 'train', it expects the same two-level directory structure similar to 'dir/train/'. By default, it tries to automatically detect the structure. You probably do not need to care about this option because 'original' is what people usually have. Example: When `dir_structure=='original'`, `dir` should have the following structure: .. code-block:: none dir/ train/ n02134418/ n02134418_198.JPEG ... ... val/ ILSVRC2012_val_00000001.JPEG ... test/ ILSVRC2012_test_00000001.JPEG ... With the downloaded ILSVRC12_img_*.tar, you can use the following command to build the above structure: .. code-block:: none mkdir val && tar xvf ILSVRC12_img_val.tar -C val mkdir test && tar xvf ILSVRC12_img_test.tar -C test mkdir train && tar xvf ILSVRC12_img_train.tar -C train && cd train find -type f -name '*.tar' | parallel -P 10 'echo {} && mkdir -p {/.} && tar xf {} -C {/.}' When `dir_structure=='train'`, `dir` should have the following structure: .. code-block:: none dir/ train/ n02134418/ n02134418_198.JPEG ... ... val/ n01440764/ ILSVRC2012_val_00000293.JPEG ... ... test/ ILSVRC2012_test_00000001.JPEG ... """ super(ILSVRC12, self).__init__( dir, name, meta_dir, shuffle, dir_structure)
""" There are some CMYK / png images, but cv2 seems robust to them. """ def __iter__(self): for fname, label in super(ILSVRC12, self).__iter__(): im = cv2.imread(fname, cv2.IMREAD_COLOR) assert im is not None, fname yield [im, label]
[docs] @staticmethod def get_training_bbox(bbox_dir, imglist): import xml.etree.ElementTree as ET ret = [] def parse_bbox(fname): root = ET.parse(fname).getroot() size = root.find('size').getchildren() size = map(int, [size[0].text, size[1].text]) box = root.find('object').find('bndbox').getchildren() box = map(lambda x: float(x.text), box) return np.asarray(box, dtype='float32') with timed_operation('Loading Bounding Boxes ...'): cnt = 0 for k in tqdm.trange(len(imglist)): fname = imglist[k][0] fname = fname[:-4] + 'xml' fname = os.path.join(bbox_dir, fname) try: ret.append(parse_bbox(fname)) cnt += 1 except Exception: ret.append(None)"{}/{} images have bounding box.".format(cnt, len(imglist))) return ret
try: import cv2 except ImportError: from ...utils.develop import create_dummy_class ILSVRC12 = create_dummy_class('ILSVRC12', 'cv2') # noqa if __name__ == '__main__': meta = ILSVRCMeta() # print(meta.get_synset_words_1000()) ds = ILSVRC12('/home/wyx/data/fake_ilsvrc/', 'train', shuffle=False) ds.reset_state() for k in ds: from IPython import embed embed() break