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fakedata_generation.py
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fakedata_generation.py
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import os
import sys
import contextlib
import tarfile
import json
import numpy as np
import PIL
import torch
from common_utils import get_tmp_dir
PYTHON2 = sys.version_info[0] == 2
if PYTHON2:
import cPickle as pickle
else:
import pickle
@contextlib.contextmanager
def mnist_root(num_images, cls_name):
def _encode(v):
return torch.tensor(v, dtype=torch.int32).numpy().tobytes()[::-1]
def _make_image_file(filename, num_images):
img = torch.randint(0, 255, size=(28 * 28 * num_images,), dtype=torch.uint8)
with open(filename, "wb") as f:
f.write(_encode(2051)) # magic header
f.write(_encode(num_images))
f.write(_encode(28))
f.write(_encode(28))
f.write(img.numpy().tobytes())
def _make_label_file(filename, num_images):
labels = torch.zeros((num_images,), dtype=torch.uint8)
with open(filename, "wb") as f:
f.write(_encode(2049)) # magic header
f.write(_encode(num_images))
f.write(labels.numpy().tobytes())
with get_tmp_dir() as tmp_dir:
raw_dir = os.path.join(tmp_dir, cls_name, "raw")
os.makedirs(raw_dir)
_make_image_file(os.path.join(raw_dir, "train-images-idx3-ubyte"), num_images)
_make_label_file(os.path.join(raw_dir, "train-labels-idx1-ubyte"), num_images)
_make_image_file(os.path.join(raw_dir, "t10k-images-idx3-ubyte"), num_images)
_make_label_file(os.path.join(raw_dir, "t10k-labels-idx1-ubyte"), num_images)
yield tmp_dir
@contextlib.contextmanager
def cifar_root(version):
def _get_version_params(version):
if version == 'CIFAR10':
return {
'base_folder': 'cifar-10-batches-py',
'train_files': ['data_batch_{}'.format(batch) for batch in range(1, 6)],
'test_file': 'test_batch',
'target_key': 'labels',
'meta_file': 'batches.meta',
'classes_key': 'label_names',
}
elif version == 'CIFAR100':
return {
'base_folder': 'cifar-100-python',
'train_files': ['train'],
'test_file': 'test',
'target_key': 'fine_labels',
'meta_file': 'meta',
'classes_key': 'fine_label_names',
}
else:
raise ValueError
def _make_pickled_file(obj, file):
with open(file, 'wb') as fh:
pickle.dump(obj, fh, 2)
def _make_data_file(file, target_key):
obj = {
'data': np.zeros((1, 32 * 32 * 3), dtype=np.uint8),
target_key: [0]
}
_make_pickled_file(obj, file)
def _make_meta_file(file, classes_key):
obj = {
classes_key: ['fakedata'],
}
_make_pickled_file(obj, file)
params = _get_version_params(version)
with get_tmp_dir() as root:
base_folder = os.path.join(root, params['base_folder'])
os.mkdir(base_folder)
for file in list(params['train_files']) + [params['test_file']]:
_make_data_file(os.path.join(base_folder, file), params['target_key'])
_make_meta_file(os.path.join(base_folder, params['meta_file']),
params['classes_key'])
yield root
@contextlib.contextmanager
def imagenet_root():
import scipy.io as sio
WNID = 'n01234567'
CLS = 'fakedata'
def _make_image(file):
PIL.Image.fromarray(np.zeros((32, 32, 3), dtype=np.uint8)).save(file)
def _make_tar(archive, content, arcname=None, compress=False):
mode = 'w:gz' if compress else 'w'
if arcname is None:
arcname = os.path.basename(content)
with tarfile.open(archive, mode) as fh:
fh.add(content, arcname=arcname)
def _make_train_archive(root):
with get_tmp_dir() as tmp:
wnid_dir = os.path.join(tmp, WNID)
os.mkdir(wnid_dir)
_make_image(os.path.join(wnid_dir, WNID + '_1.JPEG'))
wnid_archive = wnid_dir + '.tar'
_make_tar(wnid_archive, wnid_dir)
train_archive = os.path.join(root, 'ILSVRC2012_img_train.tar')
_make_tar(train_archive, wnid_archive)
def _make_val_archive(root):
with get_tmp_dir() as tmp:
val_image = os.path.join(tmp, 'ILSVRC2012_val_00000001.JPEG')
_make_image(val_image)
val_archive = os.path.join(root, 'ILSVRC2012_img_val.tar')
_make_tar(val_archive, val_image)
def _make_devkit_archive(root):
with get_tmp_dir() as tmp:
data_dir = os.path.join(tmp, 'data')
os.mkdir(data_dir)
meta_file = os.path.join(data_dir, 'meta.mat')
synsets = np.core.records.fromarrays([
(0.0, 1.0),
(WNID, ''),
(CLS, ''),
('fakedata for the torchvision testsuite', ''),
(0.0, 1.0),
], names=['ILSVRC2012_ID', 'WNID', 'words', 'gloss', 'num_children'])
sio.savemat(meta_file, {'synsets': synsets})
groundtruth_file = os.path.join(data_dir,
'ILSVRC2012_validation_ground_truth.txt')
with open(groundtruth_file, 'w') as fh:
fh.write('0\n')
devkit_name = 'ILSVRC2012_devkit_t12'
devkit_archive = os.path.join(root, devkit_name + '.tar.gz')
_make_tar(devkit_archive, tmp, arcname=devkit_name, compress=True)
with get_tmp_dir() as root:
_make_train_archive(root)
_make_val_archive(root)
_make_devkit_archive(root)
yield root
@contextlib.contextmanager
def cityscapes_root():
def _make_image(file):
PIL.Image.fromarray(np.zeros((1024, 2048, 3), dtype=np.uint8)).save(file)
def _make_regular_target(file):
PIL.Image.fromarray(np.zeros((1024, 2048), dtype=np.uint8)).save(file)
def _make_color_target(file):
PIL.Image.fromarray(np.zeros((1024, 2048, 4), dtype=np.uint8)).save(file)
def _make_polygon_target(file):
polygon_example = {
'imgHeight': 1024,
'imgWidth': 2048,
'objects': [{'label': 'sky',
'polygon': [[1241, 0], [1234, 156],
[1478, 197], [1611, 172],
[1606, 0]]},
{'label': 'road',
'polygon': [[0, 448], [1331, 274],
[1473, 265], [2047, 605],
[2047, 1023], [0, 1023]]}]}
with open(file, 'w') as outfile:
json.dump(polygon_example, outfile)
with get_tmp_dir() as tmp_dir:
for mode in ['Coarse', 'Fine']:
gt_dir = os.path.join(tmp_dir, 'gt%s' % mode)
os.makedirs(gt_dir)
if mode == 'Coarse':
splits = ['train', 'train_extra', 'val']
else:
splits = ['train', 'test', 'val']
for split in splits:
split_dir = os.path.join(gt_dir, split)
os.makedirs(split_dir)
for city in ['bochum', 'bremen']:
city_dir = os.path.join(split_dir, city)
os.makedirs(city_dir)
_make_color_target(os.path.join(city_dir,
'{city}_000000_000000_gt{mode}_color.png'.format(
city=city, mode=mode)))
_make_regular_target(os.path.join(city_dir,
'{city}_000000_000000_gt{mode}_instanceIds.png'.format(
city=city, mode=mode)))
_make_regular_target(os.path.join(city_dir,
'{city}_000000_000000_gt{mode}_labelIds.png'.format(
city=city, mode=mode)))
_make_polygon_target(os.path.join(city_dir,
'{city}_000000_000000_gt{mode}_polygons.json'.format(
city=city, mode=mode)))
# leftImg8bit dataset
leftimg_dir = os.path.join(tmp_dir, 'leftImg8bit')
os.makedirs(leftimg_dir)
for split in ['test', 'train_extra', 'train', 'val']:
split_dir = os.path.join(leftimg_dir, split)
os.makedirs(split_dir)
for city in ['bochum', 'bremen']:
city_dir = os.path.join(split_dir, city)
os.makedirs(city_dir)
_make_image(os.path.join(city_dir,
'{city}_000000_000000_leftImg8bit.png'.format(city=city)))
yield tmp_dir
@contextlib.contextmanager
def svhn_root():
import scipy.io as sio
def _make_mat(file):
images = np.zeros((32, 32, 3, 2), dtype=np.uint8)
targets = np.zeros((2,), dtype=np.uint8)
sio.savemat(file, {'X': images, 'y': targets})
with get_tmp_dir() as root:
_make_mat(os.path.join(root, "train_32x32.mat"))
_make_mat(os.path.join(root, "test_32x32.mat"))
_make_mat(os.path.join(root, "extra_32x32.mat"))
yield root