# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Dataset help for minddata dataset"""
from mindspore._checkparam import check_bool
from .. import context
from .parallel_utils import ParallelMode
from ._utils import _exec_datagraph, _get_types_and_shapes, _to_tensor, \
_construct_tensor_list, _to_full_shapes, _to_full_tensor
from ..nn.wrap import GetNextSingleOp
from ..parallel._utils import _get_device_num, _get_global_rank, _get_parallel_mode
[docs]class DatasetHelper:
"""
Help function to use the Minddata dataset.
According to different context, change the iter of dataset, to use the same for loop in different context.
Note:
The iter of DatasetHelper will give one epoch data.
Args:
dataset (DataSet): The dataset.
dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data from host.
Default: True.
Examples:
>>> dataset_helper = DatasetHelper(dataset)
>>> for inputs in dataset_helper:
>>> outputs = network(*inputs)
"""
def __init__(self, dataset, dataset_sink_mode=True):
check_bool(dataset_sink_mode)
iterclass = _DatasetIterGE
if not dataset_sink_mode:
iterclass = _DatasetIterFeed
elif not context.get_context("enable_ge"):
if context.get_context("enable_loop_sink"):
iterclass = _DatasetIterMSLoopSink
else:
iterclass = _DatasetIterMS
self.iter = iterclass(dataset)
def __iter__(self):
return self.iter.__iter__()
# A temp solution for loop sink. Delete later
[docs] def types_shapes(self):
"""Get the types and shapes from dataset on current config."""
return self.iter.types_shapes()
[docs] def loop_size(self):
"""Get loop_size for every iteration."""
return self.iter.loop_size
class _DatasetIter:
"""Base iter for dataset help"""
def __init__(self, dataset):
self.loop_size = 1
if not hasattr(dataset, '__ME_INITED__'):
if not hasattr(dataset, '__loop_size__'):
self.loop_size = dataset.get_dataset_size()
else:
self.loop_size = dataset.__loop_size__
dataset.__ME_INITED__ = _exec_datagraph(dataset, self.loop_size).queue_name
self.ind = 0
self.dataset = dataset
dataset_types, dataset_shapes = _get_types_and_shapes(dataset)
self.dataset_types, self.dataset_shapes = dataset_types, dataset_shapes
# for self._parallel_mode equal to semi_auto_parallel or auto_parallel, use a complete tensor to
# compile, and slice tensor to run. The batch dimension of tensors for compile is device_number
# times the batch dimension of tensors for run
if _get_parallel_mode() in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
device_num = _get_device_num()
dataset_shapes = _to_full_shapes(dataset_shapes, device_num)
def __iter__(self):
self.ind = 0
return self
def __next__(self):
if self.ind >= self.loop_count:
raise StopIteration()
self.ind += 1
return self.op()
def types_shapes(self):
return self.dataset_types, self.dataset_shapes
def get_loop_count(self, dataset):
loop_count = 1
if hasattr(dataset, '__loop_size__'):
loop_size = dataset.__loop_size__
if dataset.get_dataset_size() % loop_size != 0:
raise ValueError(f'Dataset size {dataset.get_dataset_size()} and '
f'loop_size {loop_size} are not matched.')
loop_count = int(dataset.get_dataset_size()/loop_size)
return loop_count
class _DatasetIterMSLoopSink(_DatasetIter):
"""Iter for context (enable_loop_sink=True)"""
def __init__(self, dataset):
super(_DatasetIterMSLoopSink, self).__init__(dataset)
self.loop_count = self.get_loop_count(dataset)
def op():
return tuple()
self.op = op
def types_shapes(self):
parallel_mode = _get_parallel_mode()
if parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
batch_expand_num = _get_device_num()
self.dataset_shapes = _to_full_shapes(self.dataset_shapes, batch_expand_num)
return self.dataset_types, self.dataset_shapes
class _DatasetIterMS(_DatasetIter):
"""Iter for context (enable_loop_sink=False)"""
def __init__(self, dataset):
super(_DatasetIterMS, self).__init__(dataset)
self.loop_count = dataset.get_dataset_size()
self.loop_size = 1
queue_name = dataset.__ME_INITED__
self.op = GetNextSingleOp(self.dataset_types, self.dataset_shapes, queue_name)
class _DatasetIterGE(_DatasetIter):
"""Iter for ge"""
def __init__(self, dataset):
super(_DatasetIterGE, self).__init__(dataset)
self.loop_count = self.get_loop_count(dataset)
parallel_mode = _get_parallel_mode()
self.need_to_full = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
batch_expand_num = 1
if self.need_to_full:
batch_expand_num = _get_device_num()
tensor_list_run = _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num)
def op():
return tensor_list_run
self.op = op
class _DatasetIterFeed:
"""Iter for feed data"""
def __init__(self, dataset):
self.dataset = dataset
self.device_num = _get_device_num()
self.global_rank = _get_global_rank()
self.repeat_count = dataset.get_repeat_count()
self.repeat_ind = 0
self.loop_count = dataset.get_dataset_size()
self.ind = 0
parallel_mode = context.get_auto_parallel_context("parallel_mode")
self.need_to_full = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
def __iter__(self):
if self.repeat_ind % self.repeat_count == 0:
self.iter = self.dataset.__iter__()
self.repeat_ind += 1
self.ind = 0
return self
def __next__(self):
if self.ind >= self.loop_count:
raise StopIteration()
self.ind += 1
data = self.iter.__next__()
if self.need_to_full:
return _to_full_tensor(data, self.device_num, self.global_rank)
return _to_tensor(data)