PaddleX/paddlex/ppdet/utils/download.py

#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import os.path as osp
import sys
import yaml
import time
import shutil
import requests
import tqdm
import hashlib
import base64
import binascii
import tarfile
import zipfile

from paddle.utils.download import _get_unique_endpoints
from paddlex.ppdet.core.workspace import BASE_KEY
from .logger import setup_logger
from .voc_utils import create_list

logger = setup_logger(__name__)

__all__ = [
    'get_weights_path', 'get_dataset_path', 'get_config_path',
    'download_dataset', 'create_voc_list'
]

WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/weights")
DATASET_HOME = osp.expanduser("~/.cache/paddle/dataset")
CONFIGS_HOME = osp.expanduser("~/.cache/paddle/configs")

# dict of {dataset_name: (download_info, sub_dirs)}
# download info: [(url, md5sum)]
DATASETS = {
    'coco': ([
        (
            'http://images.cocodataset.org/zips/train2017.zip',
            'cced6f7f71b7629ddf16f17bbcfab6b2', ),
        (
            'http://images.cocodataset.org/zips/val2017.zip',
            '442b8da7639aecaf257c1dceb8ba8c80', ),
        (
            'http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
            'f4bbac642086de4f52a3fdda2de5fa2c', ),
    ], ["annotations", "train2017", "val2017"]),
    'voc': ([
        (
            'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
            '6cd6e144f989b92b3379bac3b3de84fd', ),
        (
            'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
            'c52e279531787c972589f7e41ab4ae64', ),
        (
            'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
            'b6e924de25625d8de591ea690078ad9f', ),
        (
            'https://paddledet.bj.bcebos.com/data/label_list.txt',
            '5ae5d62183cfb6f6d3ac109359d06a1b', ),
    ], ["VOCdevkit/VOC2012", "VOCdevkit/VOC2007"]),
    'wider_face': ([
        (
            'https://dataset.bj.bcebos.com/wider_face/WIDER_train.zip',
            '3fedf70df600953d25982bcd13d91ba2', ),
        (
            'https://dataset.bj.bcebos.com/wider_face/WIDER_val.zip',
            'dfa7d7e790efa35df3788964cf0bbaea', ),
        (
            'https://dataset.bj.bcebos.com/wider_face/wider_face_split.zip',
            'a4a898d6193db4b9ef3260a68bad0dc7', ),
    ], ["WIDER_train", "WIDER_val", "wider_face_split"]),
    'fruit': ([(
        'https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit.tar',
        'baa8806617a54ccf3685fa7153388ae6', ), ],
              ['Annotations', 'JPEGImages']),
    'roadsign_voc': ([(
        'https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.tar',
        '8d629c0f880dd8b48de9aeff44bf1f3e', ), ], ['annotations', 'images']),
    'roadsign_coco': ([(
        'https://paddlemodels.bj.bcebos.com/object_detection/roadsign_coco.tar',
        '49ce5a9b5ad0d6266163cd01de4b018e', ), ], ['annotations', 'images']),
    'spine_coco': ([(
        'https://paddledet.bj.bcebos.com/data/spine_coco.tar',
        '7ed69ae73f842cd2a8cf4f58dc3c5535', ), ], ['annotations', 'images']),
    'mot': (),
    'objects365': ()
}

DOWNLOAD_RETRY_LIMIT = 3

PPDET_WEIGHTS_DOWNLOAD_URL_PREFIX = 'https://paddledet.bj.bcebos.com/'


def parse_url(url):
    url = url.replace("ppdet://", PPDET_WEIGHTS_DOWNLOAD_URL_PREFIX)
    return url


def get_weights_path(url):
    """Get weights path from WEIGHTS_HOME, if not exists,
    download it from url.
    """
    url = parse_url(url)
    path, _ = get_path(url, WEIGHTS_HOME)
    return path


def get_config_path(url):
    """Get weights path from CONFIGS_HOME, if not exists,
    download it from url.
    """
    url = parse_url(url)
    path = map_path(url, CONFIGS_HOME, path_depth=2)
    if os.path.isfile(path):
        return path

    # config file not found, try download
    # 1. clear configs directory
    if osp.isdir(CONFIGS_HOME):
        shutil.rmtree(CONFIGS_HOME)

    # 2. get url
    try:
        from paddlex.ppdet import __version__ as version
    except ImportError:
        version = None

    cfg_url = "ppdet://configs/{}/configs.tar".format(version) \
                if version else "ppdet://configs/configs.tar"
    cfg_url = parse_url(cfg_url)

    # 3. download and decompress
    cfg_fullname = _download_dist(cfg_url, osp.dirname(CONFIGS_HOME))
    _decompress_dist(cfg_fullname)

    # 4. check config file existing
    if os.path.isfile(path):
        return path
    else:
        logger.error("Get config {} failed after download, please contact us on " \
            "https://github.com/PaddlePaddle/PaddleDetection/issues".format(path))
        sys.exit(1)


def get_dataset_path(path, annotation, image_dir):
    """
    If path exists, return path.
    Otherwise, get dataset path from DATASET_HOME, if not exists,
    download it.
    """
    if _dataset_exists(path, annotation, image_dir):
        return path

    logger.info(
        "Dataset {} is not valid for reason above, try searching {} or "
        "downloading dataset...".format(osp.realpath(path), DATASET_HOME))

    data_name = os.path.split(path.strip().lower())[-1]
    for name, dataset in DATASETS.items():
        if data_name == name:
            logger.debug("Parse dataset_dir {} as dataset "
                         "{}".format(path, name))
            if name == 'objects365':
                raise NotImplementedError(
                    "Dataset {} is not valid for download automatically. "
                    "Please apply and download the dataset from "
                    "https://www.objects365.org/download.html".format(name))
            data_dir = osp.join(DATASET_HOME, name)

            if name == 'mot':
                if osp.exists(path) or osp.exists(data_dir):
                    return data_dir
                else:
                    raise NotImplementedError(
                        "Dataset {} is not valid for download automatically. "
                        "Please apply and download the dataset following docs/tutorials/PrepareMOTDataSet.md".
                        format(name))

            if name == "spine_coco":
                if _dataset_exists(data_dir, annotation, image_dir):
                    return data_dir

            # For voc, only check dir VOCdevkit/VOC2012, VOCdevkit/VOC2007
            if name in ['voc', 'fruit', 'roadsign_voc']:
                exists = True
                for sub_dir in dataset[1]:
                    check_dir = osp.join(data_dir, sub_dir)
                    if osp.exists(check_dir):
                        logger.info("Found {}".format(check_dir))
                    else:
                        exists = False
                if exists:
                    return data_dir

            # voc exist is checked above, voc is not exist here
            check_exist = name != 'voc' and name != 'fruit' and name != 'roadsign_voc'
            for url, md5sum in dataset[0]:
                get_path(url, data_dir, md5sum, check_exist)

            # voc should create list after download
            if name == 'voc':
                create_voc_list(data_dir)
            return data_dir

    # not match any dataset in DATASETS
    raise ValueError(
        "Dataset {} is not valid and cannot parse dataset type "
        "'{}' for automaticly downloading, which only supports "
        "'voc' , 'coco', 'wider_face', 'fruit', 'roadsign_voc' and 'mot' currently".
        format(path, osp.split(path)[-1]))


def create_voc_list(data_dir, devkit_subdir='VOCdevkit'):
    logger.debug("Create voc file list...")
    devkit_dir = osp.join(data_dir, devkit_subdir)
    years = ['2007', '2012']

    # NOTE: since using auto download VOC
    # dataset, VOC default label list should be used,
    # do not generate label_list.txt here. For default
    # label, see ../data/source/voc.py
    create_list(devkit_dir, years, data_dir)
    logger.debug("Create voc file list finished")


def map_path(url, root_dir, path_depth=1):
    # parse path after download to decompress under root_dir
    assert path_depth > 0, "path_depth should be a positive integer"
    dirname = url
    for _ in range(path_depth):
        dirname = osp.dirname(dirname)
    fpath = osp.relpath(url, dirname)

    zip_formats = ['.zip', '.tar', '.gz']
    for zip_format in zip_formats:
        fpath = fpath.replace(zip_format, '')
    return osp.join(root_dir, fpath)


def get_path(url, root_dir, md5sum=None, check_exist=True):
    """ Download from given url to root_dir.
    if file or directory specified by url is exists under
    root_dir, return the path directly, otherwise download
    from url and decompress it, return the path.

    url (str): download url
    root_dir (str): root dir for downloading, it should be
                    WEIGHTS_HOME or DATASET_HOME
    md5sum (str): md5 sum of download package
    """
    # parse path after download to decompress under root_dir
    fullpath = map_path(url, root_dir)

    # For same zip file, decompressed directory name different
    # from zip file name, rename by following map
    decompress_name_map = {
        "VOCtrainval_11-May-2012": "VOCdevkit/VOC2012",
        "VOCtrainval_06-Nov-2007": "VOCdevkit/VOC2007",
        "VOCtest_06-Nov-2007": "VOCdevkit/VOC2007",
        "annotations_trainval": "annotations"
    }
    for k, v in decompress_name_map.items():
        if fullpath.find(k) >= 0:
            fullpath = osp.join(osp.split(fullpath)[0], v)

    if osp.exists(fullpath) and check_exist:
        if not osp.isfile(fullpath) or \
                _check_exist_file_md5(fullpath, md5sum, url):
            logger.debug("Found {}".format(fullpath))
            return fullpath, True
        else:
            os.remove(fullpath)

    fullname = _download_dist(url, root_dir, md5sum)

    # new weights format which postfix is 'pdparams' not
    # need to decompress
    if osp.splitext(fullname)[-1] not in ['.pdparams', '.yml']:
        _decompress_dist(fullname)

    return fullpath, False


def download_dataset(path, dataset=None):
    if dataset not in DATASETS.keys():
        logger.error("Unknown dataset {}, it should be "
                     "{}".format(dataset, DATASETS.keys()))
        return
    dataset_info = DATASETS[dataset][0]
    for info in dataset_info:
        get_path(info[0], path, info[1], False)
    logger.debug("Download dataset {} finished.".format(dataset))


def _dataset_exists(path, annotation, image_dir):
    """
    Check if user define dataset exists
    """
    if not osp.exists(path):
        logger.warning("Config dataset_dir {} is not exits, "
                       "dataset config is not valid".format(path))
        return False

    if annotation:
        annotation_path = osp.join(path, annotation)
        if not osp.isfile(annotation_path):
            logger.warning("Config annotation {} is not a "
                           "file, dataset config is not "
                           "valid".format(annotation_path))
            return False
    if image_dir:
        image_path = osp.join(path, image_dir)
        if not osp.isdir(image_path):
            logger.warning("Config image_dir {} is not a "
                           "directory, dataset config is not "
                           "valid".format(image_path))
            return False
    return True


def _download(url, path, md5sum=None):
    """
    Download from url, save to path.

    url (str): download url
    path (str): download to given path
    """
    if not osp.exists(path):
        os.makedirs(path)

    fname = osp.split(url)[-1]
    fullname = osp.join(path, fname)
    retry_cnt = 0

    while not (osp.exists(fullname) and _check_exist_file_md5(fullname, md5sum,
                                                              url)):
        if retry_cnt < DOWNLOAD_RETRY_LIMIT:
            retry_cnt += 1
        else:
            raise RuntimeError("Download from {} failed. "
                               "Retry limit reached".format(url))

        logger.info("Downloading {} from {}".format(fname, url))

        # NOTE: windows path join may incur \, which is invalid in url
        if sys.platform == "win32":
            url = url.replace('\\', '/')

        req = requests.get(url, stream=True)
        if req.status_code != 200:
            raise RuntimeError("Downloading from {} failed with code "
                               "{}!".format(url, req.status_code))

        # For protecting download interupted, download to
        # tmp_fullname firstly, move tmp_fullname to fullname
        # after download finished
        tmp_fullname = fullname + "_tmp"
        total_size = req.headers.get('content-length')
        with open(tmp_fullname, 'wb') as f:
            if total_size:
                for chunk in tqdm.tqdm(
                        req.iter_content(chunk_size=1024),
                        total=(int(total_size) + 1023) // 1024,
                        unit='KB'):
                    f.write(chunk)
            else:
                for chunk in req.iter_content(chunk_size=1024):
                    if chunk:
                        f.write(chunk)
        shutil.move(tmp_fullname, fullname)
        return fullname


def _download_dist(url, path, md5sum=None):
    env = os.environ
    if 'PADDLE_TRAINERS_NUM' in env and 'PADDLE_TRAINER_ID' in env:
        trainer_id = int(env['PADDLE_TRAINER_ID'])
        num_trainers = int(env['PADDLE_TRAINERS_NUM'])
        if num_trainers <= 1:
            return _download(url, path, md5sum)
        else:
            fname = osp.split(url)[-1]
            fullname = osp.join(path, fname)
            lock_path = fullname + '.download.lock'

            if not osp.isdir(path):
                os.makedirs(path)

            if not osp.exists(fullname):
                from paddle.distributed import ParallelEnv
                unique_endpoints = _get_unique_endpoints(ParallelEnv()
                                                         .trainer_endpoints[:])
                with open(lock_path, 'w'):  # touch
                    os.utime(lock_path, None)
                if ParallelEnv().current_endpoint in unique_endpoints:
                    _download(url, path, md5sum)
                    os.remove(lock_path)
                else:
                    while os.path.exists(lock_path):
                        time.sleep(0.5)
            return fullname
    else:
        return _download(url, path, md5sum)


def _check_exist_file_md5(filename, md5sum, url):
    # if md5sum is None, and file to check is weights file,
    # read md5um from url and check, else check md5sum directly
    return _md5check_from_url(filename, url) if md5sum is None \
            and filename.endswith('pdparams') \
            else _md5check(filename, md5sum)


def _md5check_from_url(filename, url):
    # For weights in bcebos URLs, MD5 value is contained
    # in request header as 'content_md5'
    req = requests.get(url, stream=True)
    content_md5 = req.headers.get('content-md5')
    req.close()
    if not content_md5 or _md5check(
            filename,
            binascii.hexlify(base64.b64decode(content_md5.strip('"'))).decode(
            )):
        return True
    else:
        return False


def _md5check(fullname, md5sum=None):
    if md5sum is None:
        return True

    logger.debug("File {} md5 checking...".format(fullname))
    md5 = hashlib.md5()
    with open(fullname, 'rb') as f:
        for chunk in iter(lambda: f.read(4096), b""):
            md5.update(chunk)
    calc_md5sum = md5.hexdigest()

    if calc_md5sum != md5sum:
        logger.warning("File {} md5 check failed, {}(calc) != "
                       "{}(base)".format(fullname, calc_md5sum, md5sum))
        return False
    return True


def _decompress(fname):
    """
    Decompress for zip and tar file
    """
    logger.info("Decompressing {}...".format(fname))

    # For protecting decompressing interupted,
    # decompress to fpath_tmp directory firstly, if decompress
    # successed, move decompress files to fpath and delete
    # fpath_tmp and remove download compress file.
    fpath = osp.split(fname)[0]
    fpath_tmp = osp.join(fpath, 'tmp')
    if osp.isdir(fpath_tmp):
        shutil.rmtree(fpath_tmp)
        os.makedirs(fpath_tmp)

    if fname.find('tar') >= 0:
        with tarfile.open(fname) as tf:
            tf.extractall(path=fpath_tmp)
    elif fname.find('zip') >= 0:
        with zipfile.ZipFile(fname) as zf:
            zf.extractall(path=fpath_tmp)
    elif fname.find('.txt') >= 0:
        return
    else:
        raise TypeError("Unsupport compress file type {}".format(fname))

    for f in os.listdir(fpath_tmp):
        src_dir = osp.join(fpath_tmp, f)
        dst_dir = osp.join(fpath, f)
        _move_and_merge_tree(src_dir, dst_dir)

    shutil.rmtree(fpath_tmp)
    os.remove(fname)


def _decompress_dist(fname):
    env = os.environ
    if 'PADDLE_TRAINERS_NUM' in env and 'PADDLE_TRAINER_ID' in env:
        trainer_id = int(env['PADDLE_TRAINER_ID'])
        num_trainers = int(env['PADDLE_TRAINERS_NUM'])
        if num_trainers <= 1:
            _decompress(fname)
        else:
            lock_path = fname + '.decompress.lock'
            from paddle.distributed import ParallelEnv
            unique_endpoints = _get_unique_endpoints(ParallelEnv()
                                                     .trainer_endpoints[:])
            # NOTE(dkp): _decompress_dist always performed after
            # _download_dist, in _download_dist sub-trainers is waiting
            # for download lock file release with sleeping, if decompress
            # prograss is very fast and finished with in the sleeping gap
            # time, e.g in tiny dataset such as coco_ce, spine_coco, main
            # trainer may finish decompress and release lock file, so we
            # only craete lock file in main trainer and all sub-trainer
            # wait 1s for main trainer to create lock file, for 1s is
            # twice as sleeping gap, this waiting time can keep all
            # trainer pipeline in order
            # **change this if you have more elegent methods**
            if ParallelEnv().current_endpoint in unique_endpoints:
                with open(lock_path, 'w'):  # touch
                    os.utime(lock_path, None)
                _decompress(fname)
                os.remove(lock_path)
            else:
                time.sleep(1)
                while os.path.exists(lock_path):
                    time.sleep(0.5)
    else:
        _decompress(fname)


def _move_and_merge_tree(src, dst):
    """
    Move src directory to dst, if dst is already exists,
    merge src to dst
    """
    if not osp.exists(dst):
        shutil.move(src, dst)
    elif osp.isfile(src):
        shutil.move(src, dst)
    else:
        for fp in os.listdir(src):
            src_fp = osp.join(src, fp)
            dst_fp = osp.join(dst, fp)
            if osp.isdir(src_fp):
                if osp.isdir(dst_fp):
                    _move_and_merge_tree(src_fp, dst_fp)
                else:
                    shutil.move(src_fp, dst_fp)
            elif osp.isfile(src_fp) and \
                    not osp.isfile(dst_fp):
                shutil.move(src_fp, dst_fp)