fakedata_generation.py

import os
import contextlib
import tarfile
import json
import numpy as np
import PIL
import torch
from common_utils import get_tmp_dir
import pickle
import random
from itertools import cycle
from torchvision.io.video import write_video
import unittest.mock
import hashlib


@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


@contextlib.contextmanager
def voc_root():
    with get_tmp_dir() as tmp_dir:
        voc_dir = os.path.join(tmp_dir, 'VOCdevkit',
                               'VOC2012', 'ImageSets', 'Main')
        os.makedirs(voc_dir)
        train_file = os.path.join(voc_dir, 'train.txt')
        with open(train_file, 'w') as f:
            f.write('test')

        yield tmp_dir


@contextlib.contextmanager
def ucf101_root():
    with get_tmp_dir() as tmp_dir:
        ucf_dir = os.path.join(tmp_dir, 'UCF-101')
        video_dir = os.path.join(ucf_dir, 'video')
        annotations = os.path.join(ucf_dir, 'annotations')

        os.makedirs(ucf_dir)
        os.makedirs(video_dir)
        os.makedirs(annotations)

        fold_files = []
        for split in {'train', 'test'}:
            for fold in range(1, 4):
                fold_file = '{:s}list{:02d}.txt'.format(split, fold)
                fold_files.append(os.path.join(annotations, fold_file))

        file_handles = [open(x, 'w') for x in fold_files]
        file_iter = cycle(file_handles)

        for i in range(0, 2):
            current_class = 'class_{0}'.format(i + 1)
            class_dir = os.path.join(video_dir, current_class)
            os.makedirs(class_dir)
            for group in range(0, 3):
                for clip in range(0, 4):
                    # Save sample file
                    clip_name = 'v_{0}_g{1}_c{2}.avi'.format(
                        current_class, group, clip)
                    clip_path = os.path.join(class_dir, clip_name)
                    length = random.randrange(10, 21)
                    this_clip = torch.randint(
                        0, 256, (length * 25, 320, 240, 3), dtype=torch.uint8)
                    write_video(clip_path, this_clip, 25)
                    # Add to annotations
                    ann_file = next(file_iter)
                    ann_file.write('{0}\n'.format(
                        os.path.join(current_class, clip_name)))
        # Close all file descriptors
        for f in file_handles:
            f.close()
        yield (video_dir, annotations)


@contextlib.contextmanager
def places365_root(split="train-standard", small=False, extract_images=True):
    CATEGORIES = (("/a/airfield", 0), ("/a/apartment_building/outdoor", 8), ("/b/badlands", 30))
    FILE_LIST = [(f"{idx}.png", idx) for idx in tuple(zip(*CATEGORIES))[1]]

    def compute_md5(file):
        with open(file, "rb") as fh:
            return hashlib.md5(fh.read()).hexdigest()

    def make_txt(root, name, cls_or_image_seq):
        file = os.path.join(root, name)
        with open(file, "w") as fh:
            for cls_or_image, idx in cls_or_image_seq:
                fh.write(f"{cls_or_image} {idx}\n")
        return name, compute_md5(file)

    def make_categories_txt(root, name):
        return make_txt(root, name, CATEGORIES)

    def make_file_list_txt(root, name):
        return make_txt(root, name, FILE_LIST)

    def make_image(root, name, size):
        PIL.Image.fromarray(np.zeros((*size, 3), dtype=np.uint8)).save(os.path.join(root, name))

    def make_tar(root, name, files, remove_files=True):
        archive = os.path.join(root, name)
        files = [os.path.join(root, file) for file in files]

        with tarfile.open(archive, "w") as fh:
            for file in files:
                fh.add(file, os.path.basename(file))

        if remove_files:
            for file in files:
                os.remove(file)

        return name, compute_md5(archive)

    def mock_target(attr, partial="torchvision.datasets.places365.Places365"):
        return f"{partial}.{attr}"

    def mock_class_attribute(stack, attr, new):
        mock = unittest.mock.patch(mock_target(attr), new_callable=unittest.mock.PropertyMock, return_value=new)
        stack.enter_context(mock)
        return mock

    def split_to_variant(split):
        return "challenge" if split == "train-challenge" else "standard"

    def make_devkit_archive(stack, root, split):
        variant = split_to_variant(split)
        archive = f"filelist_places365-{variant}.tar"
        files = []

        meta = make_categories_txt(root, "categories_places365.txt")
        mock_class_attribute(stack, "_CATEGORIES_META", meta)
        files.append(meta[0])

        meta = {
            split: make_file_list_txt(root, f"places365_{split.replace('-', '_')}.txt")
            for split in (f"train-{variant}", "val", "test")
        }
        mock_class_attribute(stack, "_FILE_LIST_META", meta)
        files.extend([item[0] for item in meta.values()])

        meta = {variant: make_tar(root, archive, files)}
        mock_class_attribute(stack, "_DEVKIT_META", meta)

    def make_images_archive(stack, root, split, small):
        if split.startswith("train"):
            images_dir = f"train_{'256' if small else 'large'}_places365{split_to_variant(split)}"
        else:
            images_dir = f"{split}_{'256' if small else 'large'}"
        archive = f"{images_dir}.tar"

        size = (256, 256) if small else (512, random.randint(512, 1024))
        imgs = [item[0] for item in FILE_LIST]
        for img in imgs:
            make_image(root, img, size)

        meta = {(split, small): make_tar(root, archive, imgs)}
        mock_class_attribute(stack, "_IMAGES_META", meta)

        return images_dir

    with contextlib.ExitStack() as stack:
        with get_tmp_dir() as root:
            make_devkit_archive(stack, root, split)
            class_to_idx = dict(CATEGORIES)
            classes = list(class_to_idx.keys())
            data = {"class_to_idx": class_to_idx, "classes": classes}

            if extract_images:
                images_dir = make_images_archive(stack, root, split, small)
                data["imgs"] = [(os.path.join(root, images_dir, file), idx) for file, idx in FILE_LIST]
            else:
                stack.enter_context(unittest.mock.patch(mock_target("download_images")))

            yield root, data