reader.py

# Copyright (c) 2020 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.

import os
import copy
import traceback
import six
import sys
import multiprocessing as mp
if sys.version_info >= (3, 0):
    import queue as Queue
else:
    import Queue
import numpy as np

from paddle.io import DataLoader
from paddle.io import DistributedBatchSampler

from ppdet.core.workspace import register, serializable, create
from . import transform
from .shm_utils import _get_shared_memory_size_in_M

from ppdet.utils.logger import setup_logger
logger = setup_logger('reader')

MAIN_PID = os.getpid()


class Compose(object):
    def __init__(self, transforms, num_classes=80):
        self.transforms = transforms
        self.transforms_cls = []
        for t in self.transforms:
            for k, v in t.items():
                op_cls = getattr(transform, k)
                f = op_cls(**v)
                if hasattr(f, 'num_classes'):
                    f.num_classes = num_classes

                self.transforms_cls.append(f)

    def __call__(self, data):
        for f in self.transforms_cls:
            try:
                data = f(data)
            except Exception as e:
                stack_info = traceback.format_exc()
                logger.warn("fail to map op [{}] with error: {} and stack:\n{}".
                            format(f, e, str(stack_info)))
                raise e

        return data


class BatchCompose(Compose):
    def __init__(self, transforms, num_classes=80, collate_batch=True):
        super(BatchCompose, self).__init__(transforms, num_classes)
        self.output_fields = mp.Manager().list([])
        self.lock = mp.Lock()
        self.collate_batch = collate_batch

    def __call__(self, data):
        for f in self.transforms_cls:
            try:
                data = f(data)
            except Exception as e:
                stack_info = traceback.format_exc()
                logger.warn("fail to map op [{}] with error: {} and stack:\n{}".
                            format(f, e, str(stack_info)))
                raise e

        # accessing ListProxy in main process (no worker subprocess)
        # may incur errors in some enviroments, ListProxy back to
        # list if no worker process start, while this `__call__`
        # will be called in main process
        global MAIN_PID
        if os.getpid() == MAIN_PID and \
            isinstance(self.output_fields, mp.managers.ListProxy):
            self.output_fields = []

        # parse output fields by first sample
        # **this shoule be fixed if paddle.io.DataLoader support**
        # For paddle.io.DataLoader not support dict currently,
        # we need to parse the key from the first sample,
        # BatchCompose.__call__ will be called in each worker
        # process, so lock is need here.
        if len(self.output_fields) == 0:
            self.lock.acquire()
            if len(self.output_fields) == 0:
                for k, v in data[0].items():
                    # FIXME(dkp): for more elegent coding
                    if k not in ['flipped', 'h', 'w']:
                        self.output_fields.append(k)
            self.lock.release()

        batch_data = []
        # If set collate_batch=True, all data will collate a batch
        # and it will transfor to paddle.tensor.
        # If set collate_batch=False, `image`, `im_shape` and
        # `scale_factor` will collate a batch, but `gt` data(such as:
        # gt_bbox, gt_class, gt_poly.etc.) will not collate a batch
        # and it will transfor to list[Tensor] or list[list].
        if self.collate_batch:
            data = [[data[i][k] for k in self.output_fields]
                    for i in range(len(data))]
            data = list(zip(*data))
            batch_data = [np.stack(d, axis=0) for d in data]
        else:
            for k in self.output_fields:
                tmp_data = []
                for i in range(len(data)):
                    tmp_data.append(data[i][k])
                if not 'gt_' in k and not 'is_crowd' in k:
                    tmp_data = np.stack(tmp_data, axis=0)
                batch_data.append(tmp_data)

        return batch_data


class BaseDataLoader(object):
    """
    Base DataLoader implementation for detection models

    Args:
        sample_transforms (list): a list of transforms to perform
                                  on each sample
        batch_transforms (list): a list of transforms to perform
                                 on batch
        batch_size (int): batch size for batch collating, default 1.
        shuffle (bool): whether to shuffle samples
        drop_last (bool): whether to drop the last incomplete,
                          default False
        drop_empty (bool): whether to drop samples with no ground
                           truth labels, default True
        num_classes (int): class number of dataset, default 80
        use_shared_memory (bool): whether to use shared memory to
                accelerate data loading, enable this only if you
                are sure that the shared memory size of your OS
                is larger than memory cost of input datas of model.
                Note that shared memory will be automatically
                disabled if the shared memory of OS is less than
                1G, which is not enough for detection models.
                Default False.
    """

    def __init__(self,
                 sample_transforms=[],
                 batch_transforms=[],
                 batch_size=1,
                 shuffle=False,
                 drop_last=False,
                 drop_empty=True,
                 num_classes=80,
                 collate_batch=True,
                 use_shared_memory=False,
                 **kwargs):
        # sample transform
        self._sample_transforms = Compose(
            sample_transforms, num_classes=num_classes)

        # batch transfrom 
        self._batch_transforms = BatchCompose(batch_transforms, num_classes,
                                              collate_batch)
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.drop_last = drop_last
        self.use_shared_memory = use_shared_memory
        self.kwargs = kwargs

    def __call__(self,
                 dataset,
                 worker_num,
                 batch_sampler=None,
                 return_list=False):
        self.dataset = dataset
        self.dataset.check_or_download_dataset()
        self.dataset.parse_dataset()
        # get data
        self.dataset.set_transform(self._sample_transforms)
        # set kwargs
        self.dataset.set_kwargs(**self.kwargs)
        # batch sampler
        if batch_sampler is None:
            self._batch_sampler = DistributedBatchSampler(
                self.dataset,
                batch_size=self.batch_size,
                shuffle=self.shuffle,
                drop_last=self.drop_last)
        else:
            self._batch_sampler = batch_sampler

        use_shared_memory = self.use_shared_memory
        # check whether shared memory size is bigger than 1G(1024M)
        if use_shared_memory:
            shm_size = _get_shared_memory_size_in_M()
            if shm_size is not None and shm_size < 1024.:
                logger.warn("Shared memory size is less than 1G, "
                            "disable shared_memory in DataLoader")
                use_shared_memory = False

        self.dataloader = DataLoader(
            dataset=self.dataset,
            batch_sampler=self._batch_sampler,
            collate_fn=self._batch_transforms,
            num_workers=worker_num,
            return_list=return_list,
            use_shared_memory=use_shared_memory)
        self.loader = iter(self.dataloader)

        return self

    def __len__(self):
        return len(self._batch_sampler)

    def __iter__(self):
        return self

    def __next__(self):
        # pack {filed_name: field_data} here
        # looking forward to support dictionary
        # data structure in paddle.io.DataLoader
        try:
            data = next(self.loader)
            return {
                k: v
                for k, v in zip(self._batch_transforms.output_fields, data)
            }
        except StopIteration:
            self.loader = iter(self.dataloader)
            six.reraise(*sys.exc_info())

    def next(self):
        # python2 compatibility
        return self.__next__()


@register
class TrainReader(BaseDataLoader):
    __shared__ = ['num_classes']

    def __init__(self,
                 sample_transforms=[],
                 batch_transforms=[],
                 batch_size=1,
                 shuffle=True,
                 drop_last=True,
                 drop_empty=True,
                 num_classes=80,
                 collate_batch=True,
                 **kwargs):
        super(TrainReader, self).__init__(
            sample_transforms, batch_transforms, batch_size, shuffle, drop_last,
            drop_empty, num_classes, collate_batch, **kwargs)


@register
class EvalReader(BaseDataLoader):
    __shared__ = ['num_classes']

    def __init__(self,
                 sample_transforms=[],
                 batch_transforms=[],
                 batch_size=1,
                 shuffle=False,
                 drop_last=True,
                 drop_empty=True,
                 num_classes=80,
                 **kwargs):
        super(EvalReader, self).__init__(sample_transforms, batch_transforms,
                                         batch_size, shuffle, drop_last,
                                         drop_empty, num_classes, **kwargs)


@register
class TestReader(BaseDataLoader):
    __shared__ = ['num_classes']

    def __init__(self,
                 sample_transforms=[],
                 batch_transforms=[],
                 batch_size=1,
                 shuffle=False,
                 drop_last=False,
                 drop_empty=True,
                 num_classes=80,
                 **kwargs):
        super(TestReader, self).__init__(sample_transforms, batch_transforms,
                                         batch_size, shuffle, drop_last,
                                         drop_empty, num_classes, **kwargs)