forked from PulseFocusPlatform/PulseFocusPlatform
304 lines
10 KiB
Python
304 lines
10 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import traceback
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import six
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import sys
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if sys.version_info >= (3, 0):
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pass
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else:
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pass
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import numpy as np
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from paddle.io import DataLoader, DistributedBatchSampler
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from paddle.fluid.dataloader.collate import default_collate_fn
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from ppdet.core.workspace import register
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from . import transform
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from .shm_utils import _get_shared_memory_size_in_M
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from ppdet.utils.logger import setup_logger
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logger = setup_logger('reader')
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MAIN_PID = os.getpid()
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class Compose(object):
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def __init__(self, transforms, num_classes=80):
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self.transforms = transforms
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self.transforms_cls = []
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for t in self.transforms:
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for k, v in t.items():
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op_cls = getattr(transform, k)
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f = op_cls(**v)
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if hasattr(f, 'num_classes'):
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f.num_classes = num_classes
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self.transforms_cls.append(f)
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def __call__(self, data):
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for f in self.transforms_cls:
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try:
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data = f(data)
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except Exception as e:
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stack_info = traceback.format_exc()
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logger.warning("fail to map sample transform [{}] "
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"with error: {} and stack:\n{}".format(
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f, e, str(stack_info)))
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raise e
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return data
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class BatchCompose(Compose):
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def __init__(self, transforms, num_classes=80, collate_batch=True):
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super(BatchCompose, self).__init__(transforms, num_classes)
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self.collate_batch = collate_batch
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def __call__(self, data):
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for f in self.transforms_cls:
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try:
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data = f(data)
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except Exception as e:
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stack_info = traceback.format_exc()
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logger.warning("fail to map batch transform [{}] "
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"with error: {} and stack:\n{}".format(
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f, e, str(stack_info)))
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raise e
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# remove keys which is not needed by model
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extra_key = ['h', 'w', 'flipped']
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for k in extra_key:
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for sample in data:
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if k in sample:
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sample.pop(k)
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# batch data, if user-define batch function needed
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# use user-defined here
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if self.collate_batch:
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batch_data = default_collate_fn(data)
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else:
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batch_data = {}
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for k in data[0].keys():
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tmp_data = []
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for i in range(len(data)):
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tmp_data.append(data[i][k])
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if not 'gt_' in k and not 'is_crowd' in k and not 'difficult' in k:
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tmp_data = np.stack(tmp_data, axis=0)
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batch_data[k] = tmp_data
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return batch_data
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class BaseDataLoader(object):
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"""
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Base DataLoader implementation for detection models
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Args:
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sample_transforms (list): a list of transforms to perform
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on each sample
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batch_transforms (list): a list of transforms to perform
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on batch
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batch_size (int): batch size for batch collating, default 1.
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shuffle (bool): whether to shuffle samples
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drop_last (bool): whether to drop the last incomplete,
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default False
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num_classes (int): class number of dataset, default 80
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collate_batch (bool): whether to collate batch in dataloader.
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If set to True, the samples will collate into batch according
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to the batch size. Otherwise, the ground-truth will not collate,
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which is used when the number of ground-truch is different in
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samples.
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use_shared_memory (bool): whether to use shared memory to
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accelerate data loading, enable this only if you
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are sure that the shared memory size of your OS
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is larger than memory cost of input datas of model.
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Note that shared memory will be automatically
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disabled if the shared memory of OS is less than
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1G, which is not enough for detection models.
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Default False.
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"""
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def __init__(self,
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sample_transforms=[],
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batch_transforms=[],
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batch_size=1,
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shuffle=False,
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drop_last=False,
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num_classes=80,
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collate_batch=True,
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use_shared_memory=False,
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**kwargs):
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# sample transform
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self._sample_transforms = Compose(
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sample_transforms, num_classes=num_classes)
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# batch transfrom
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self._batch_transforms = BatchCompose(batch_transforms, num_classes,
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collate_batch)
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self.batch_size = batch_size
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self.shuffle = shuffle
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self.drop_last = drop_last
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self.use_shared_memory = use_shared_memory
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self.kwargs = kwargs
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def __call__(self,
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dataset,
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worker_num,
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batch_sampler=None,
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return_list=False):
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self.dataset = dataset
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self.dataset.check_or_download_dataset()
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self.dataset.parse_dataset()
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# get data
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self.dataset.set_transform(self._sample_transforms)
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# set kwargs
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self.dataset.set_kwargs(**self.kwargs)
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# batch sampler
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if batch_sampler is None:
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self._batch_sampler = DistributedBatchSampler(
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self.dataset,
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batch_size=self.batch_size,
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shuffle=self.shuffle,
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drop_last=self.drop_last)
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else:
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self._batch_sampler = batch_sampler
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# DataLoader do not start sub-process in Windows and Mac
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# system, do not need to use shared memory
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use_shared_memory = self.use_shared_memory and \
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sys.platform not in ['win32', 'darwin']
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# check whether shared memory size is bigger than 1G(1024M)
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if use_shared_memory:
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shm_size = _get_shared_memory_size_in_M()
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if shm_size is not None and shm_size < 1024.:
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logger.warning("Shared memory size is less than 1G, "
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"disable shared_memory in DataLoader")
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use_shared_memory = False
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self.dataloader = DataLoader(
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dataset=self.dataset,
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batch_sampler=self._batch_sampler,
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collate_fn=self._batch_transforms,
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num_workers=worker_num,
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return_list=return_list,
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use_shared_memory=use_shared_memory)
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self.loader = iter(self.dataloader)
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return self
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def __len__(self):
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return len(self._batch_sampler)
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def __iter__(self):
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return self
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def __next__(self):
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try:
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return next(self.loader)
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except StopIteration:
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self.loader = iter(self.dataloader)
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six.reraise(*sys.exc_info())
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def next(self):
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# python2 compatibility
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return self.__next__()
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@register
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class TrainReader(BaseDataLoader):
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__shared__ = ['num_classes']
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def __init__(self,
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sample_transforms=[],
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batch_transforms=[],
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batch_size=1,
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shuffle=True,
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drop_last=True,
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num_classes=80,
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collate_batch=True,
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**kwargs):
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super(TrainReader, self).__init__(sample_transforms, batch_transforms,
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batch_size, shuffle, drop_last,
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num_classes, collate_batch, **kwargs)
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@register
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class EvalReader(BaseDataLoader):
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__shared__ = ['num_classes']
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def __init__(self,
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sample_transforms=[],
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batch_transforms=[],
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batch_size=1,
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shuffle=False,
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drop_last=True,
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num_classes=80,
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**kwargs):
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super(EvalReader, self).__init__(sample_transforms, batch_transforms,
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batch_size, shuffle, drop_last,
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num_classes, **kwargs)
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@register
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class TestReader(BaseDataLoader):
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__shared__ = ['num_classes']
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def __init__(self,
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sample_transforms=[],
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batch_transforms=[],
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batch_size=1,
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shuffle=False,
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drop_last=False,
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num_classes=80,
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**kwargs):
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super(TestReader, self).__init__(sample_transforms, batch_transforms,
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batch_size, shuffle, drop_last,
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num_classes, **kwargs)
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@register
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class EvalMOTReader(BaseDataLoader):
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__shared__ = ['num_classes']
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def __init__(self,
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sample_transforms=[],
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batch_transforms=[],
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batch_size=1,
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shuffle=False,
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drop_last=False,
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num_classes=1,
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**kwargs):
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super(EvalMOTReader, self).__init__(sample_transforms, batch_transforms,
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batch_size, shuffle, drop_last,
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num_classes, **kwargs)
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@register
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class TestMOTReader(BaseDataLoader):
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__shared__ = ['num_classes']
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def __init__(self,
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sample_transforms=[],
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batch_transforms=[],
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batch_size=1,
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shuffle=False,
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drop_last=False,
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num_classes=1,
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**kwargs):
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super(TestMOTReader, self).__init__(sample_transforms, batch_transforms,
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batch_size, shuffle, drop_last,
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num_classes, **kwargs)
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