Python mmcv.mkdir_or_exist方法代碼示例

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def main():
    args = parse_args()
    cityscapes_path = args.cityscapes_path
    out_dir = args.out_dir if args.out_dir else cityscapes_path
    mmcv.mkdir_or_exist(out_dir)

    img_dir = osp.join(cityscapes_path, args.img_dir)
    gt_dir = osp.join(cityscapes_path, args.gt_dir)

    set_name = dict(
        train='instancesonly_filtered_gtFine_train.json',
        val='instancesonly_filtered_gtFine_val.json',
        test='instancesonly_filtered_gtFine_test.json')

    for split, json_name in set_name.items():
        print(f'Converting {split} into {json_name}')
        with mmcv.Timer(
                print_tmpl='It tooks {}s to convert Cityscapes annotation'):
            files = collect_files(
                osp.join(img_dir, split), osp.join(gt_dir, split))
            image_infos = collect_annotations(files, nproc=args.nproc)
            cvt_annotations(image_infos, osp.join(out_dir, json_name)) 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def collect_results_cpu(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full((MAX_LEN, ),
                                32,
                                dtype=torch.uint8,
                                device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, f'part_{i}.pkl')
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_results 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def collect_results(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full((MAX_LEN, ),
                                32,
                                dtype=torch.uint8,
                                device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, f'part_{i}.pkl')
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_results 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def main():
    args = parse_args()
    devkit_path = args.devkit_path
    out_dir = args.out_dir if args.out_dir else devkit_path
    mmcv.mkdir_or_exist(out_dir)

    years = []
    if osp.isdir(osp.join(devkit_path, 'VOC2007')):
        years.append('2007')
    if osp.isdir(osp.join(devkit_path, 'VOC2012')):
        years.append('2012')
    if '2007' in years and '2012' in years:
        years.append(['2007', '2012'])
    if not years:
        raise IOError(f'The devkit path {devkit_path} contains neither '
                      '"VOC2007" nor "VOC2012" subfolder')
    for year in years:
        if year == '2007':
            prefix = 'voc07'
        elif year == '2012':
            prefix = 'voc12'
        elif year == ['2007', '2012']:
            prefix = 'voc0712'
        for split in ['train', 'val', 'trainval']:
            dataset_name = prefix + '_' + split
            print(f'processing {dataset_name} ...')
            cvt_annotations(devkit_path, year, split,
                            osp.join(out_dir, dataset_name + '.pkl'))
        if not isinstance(year, list):
            dataset_name = prefix + '_test'
            print(f'processing {dataset_name} ...')
            cvt_annotations(devkit_path, year, 'test',
                            osp.join(out_dir, dataset_name + '.pkl'))
    print('Done!') 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def collect_results(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full((MAX_LEN, ),
                                32,
                                dtype=torch.uint8,
                                device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_results 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def main():
    args = parse_args()
    devkit_path = args.devkit_path
    out_dir = args.out_dir if args.out_dir else devkit_path
    mmcv.mkdir_or_exist(out_dir)

    years = []
    if osp.isdir(osp.join(devkit_path, 'VOC2007')):
        years.append('2007')
    if osp.isdir(osp.join(devkit_path, 'VOC2012')):
        years.append('2012')
    if '2007' in years and '2012' in years:
        years.append(['2007', '2012'])
    if not years:
        raise IOError('The devkit path {} contains neither "VOC2007" nor '
                      '"VOC2012" subfolder'.format(devkit_path))
    for year in years:
        if year == '2007':
            prefix = 'voc07'
        elif year == '2012':
            prefix = 'voc12'
        elif year == ['2007', '2012']:
            prefix = 'voc0712'
        for split in ['train', 'val', 'trainval']:
            dataset_name = prefix + '_' + split
            print('processing {} ...'.format(dataset_name))
            cvt_annotations(devkit_path, year, split,
                            osp.join(out_dir, dataset_name + '.pkl'))
        if not isinstance(year, list):
            dataset_name = prefix + '_test'
            print('processing {} ...'.format(dataset_name))
            cvt_annotations(devkit_path, year, 'test',
                            osp.join(out_dir, dataset_name + '.pkl'))
    print('Done!') 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def collect_results(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full((MAX_LEN,), 32, dtype=torch.uint8, device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.Tensor(bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)

    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_results 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def _get_mmcv_home():
    mmcv_home = os.path.expanduser(
        os.getenv(
            ENV_MMCV_HOME,
            os.path.join(
                os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))

    mkdir_or_exist(mmcv_home)
    return mmcv_home 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def save_checkpoint(model, filename, optimizer=None, meta=None):
    """Save checkpoint to file.

    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
    ``optimizer``. By default ``meta`` will contain version and time info.

    Args:
        model (Module): Module whose params are to be saved.
        filename (str): Checkpoint filename.
        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
        meta (dict, optional): Metadata to be saved in checkpoint.
    """
    if meta is None:
        meta = {}
    elif not isinstance(meta, dict):
        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())

    mmcv.mkdir_or_exist(osp.dirname(filename))
    if is_module_wrapper(model):
        model = model.module

    checkpoint = {
        'meta': meta,
        'state_dict': weights_to_cpu(model.state_dict())
    }
    # save optimizer state dict in the checkpoint
    if isinstance(optimizer, Optimizer):
        checkpoint['optimizer'] = optimizer.state_dict()
    elif isinstance(optimizer, dict):
        checkpoint['optimizer'] = {}
        for name, optim in optimizer.items():
            checkpoint['optimizer'][name] = optim.state_dict()
    # immediately flush buffer
    with open(filename, 'wb') as f:
        torch.save(checkpoint, f)
        f.flush() 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def collect_results(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full(
            (MAX_LEN, ), 32, dtype=torch.uint8, device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_results 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def collect_results_cpu(result_part, size, tmpdir=None):
    rank, world_size = get_dist_info()
    # create a tmp dir if it is not specified
    if tmpdir is None:
        MAX_LEN = 512
        # 32 is whitespace
        dir_tensor = torch.full((MAX_LEN, ),
                                32,
                                dtype=torch.uint8,
                                device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]
        # remove tmp dir
        shutil.rmtree(tmpdir)
        return ordered_results 

# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import mkdir_or_exist [as 別名]
def before_run(self, runner):
        self.lock_dir = osp.join(runner.work_dir, '.lock_map_hook')
        if runner.rank == 0:
            if osp.exists(self.lock_dir):
                shutil.rmtree(self.lock_dir)
            mmcv.mkdir_or_exist(self.lock_dir)