forked from huawei/mindspore2022
297 lines
9.6 KiB
Python
297 lines
9.6 KiB
Python
# Copyright 2019 Huawei Technologies Co., Ltd
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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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# ==============================================================================
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"""Built-in iterators.
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"""
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from abc import abstractmethod
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import copy
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import weakref
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from mindspore._c_dataengine import DEPipeline
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from mindspore._c_dataengine import OpName
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from mindspore import log as logger
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from . import datasets as de
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ITERATORS_LIST = list()
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def _cleanup():
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for itr_ref in ITERATORS_LIST:
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itr = itr_ref()
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if itr is not None:
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itr.release()
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def alter_tree(node):
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"""Traversing the python Dataset tree/graph to perform some alteration to some specific nodes."""
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if not node.input:
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return _alter_node(node)
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converted_children = []
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for input_op in node.input:
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converted_children.append(alter_tree(input_op))
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node.input = converted_children
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return _alter_node(node)
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def _alter_node(node):
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"""Performing some alteration to a dataset node. A common alteration is to insert a node."""
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if isinstance(node, (de.TFRecordDataset, de.TextFileDataset)) and node.shuffle_level == de.Shuffle.GLOBAL:
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# Remove the connection between the parent's node to the current node because we are inserting a node.
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if node.output:
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node.output.pop()
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# Perform a fast scan for average rows per file
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if isinstance(node, de.TFRecordDataset):
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avg_rows_per_file = node.get_dataset_size(True) // len(node.dataset_files)
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else:
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avg_rows_per_file = node.get_dataset_size() // len(node.dataset_files)
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# Shuffle between 4 files with a minimum size of 10000 rows
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new_shuffle = node.shuffle(max(avg_rows_per_file * 4, 10000))
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return new_shuffle
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if isinstance(node, de.MapDataset):
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if node.python_multiprocessing:
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# Bootstrap can only be performed on a copy of the original dataset node.
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# Bootstrap on original dataset node will make all iterators share the same process pool
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node.iterator_bootstrap()
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if node.columns_order is not None:
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# Remove the connection between the parent's node to the current node because we are inserting a node.
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if node.output:
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node.output.pop()
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return node.project(node.columns_order)
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return node
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class Iterator:
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"""
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General Iterator over a dataset.
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Attributes:
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dataset: Dataset to be iterated over
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"""
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def __init__(self, dataset):
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ITERATORS_LIST.append(weakref.ref(self))
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# create a copy of tree and work on it.
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self.dataset = copy.deepcopy(dataset)
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self.dataset = alter_tree(self.dataset)
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if not self.__is_tree():
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raise ValueError("The data pipeline is not a tree (i.e., one node has 2 consumers)")
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self.depipeline = DEPipeline()
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# for manifest temporary use
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self.__batch_node(self.dataset, 0)
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root = self.__convert_node_postorder(self.dataset)
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self.depipeline.AssignRootNode(root)
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self.depipeline.LaunchTreeExec()
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self._index = 0
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def __is_tree_node(self, node):
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"""Check if a node is tree node."""
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if not node.input:
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if len(node.output) > 1:
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return False
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if len(node.output) > 1:
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return False
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for input_node in node.input:
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cls = self.__is_tree_node(input_node)
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if not cls:
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return False
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return True
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def __is_tree(self):
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return self.__is_tree_node(self.dataset)
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@staticmethod
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def __get_dataset_type(dataset):
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"""Get the dataset type."""
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op_type = None
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if isinstance(dataset, de.ShuffleDataset):
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op_type = OpName.SHUFFLE
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elif isinstance(dataset, de.MindDataset):
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op_type = OpName.MINDRECORD
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elif isinstance(dataset, de.BatchDataset):
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op_type = OpName.BATCH
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elif isinstance(dataset, de.SyncWaitDataset):
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op_type = OpName.BARRIER
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elif isinstance(dataset, de.ZipDataset):
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op_type = OpName.ZIP
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elif isinstance(dataset, de.MapDataset):
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op_type = OpName.MAP
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elif isinstance(dataset, de.FilterDataset):
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op_type = OpName.FILTER
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elif isinstance(dataset, de.RepeatDataset):
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op_type = OpName.REPEAT
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elif isinstance(dataset, de.SkipDataset):
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op_type = OpName.SKIP
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elif isinstance(dataset, de.TakeDataset):
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op_type = OpName.TAKE
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elif isinstance(dataset, de.StorageDataset):
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op_type = OpName.STORAGE
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elif isinstance(dataset, de.ImageFolderDatasetV2):
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op_type = OpName.IMAGEFOLDER
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elif isinstance(dataset, de.GeneratorDataset):
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op_type = OpName.GENERATOR
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elif isinstance(dataset, de.TransferDataset):
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op_type = OpName.DEVICEQUEUE
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elif isinstance(dataset, de.RenameDataset):
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op_type = OpName.RENAME
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elif isinstance(dataset, de.TFRecordDataset):
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op_type = OpName.TFREADER
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elif isinstance(dataset, de.ProjectDataset):
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op_type = OpName.PROJECT
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elif isinstance(dataset, de.MnistDataset):
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op_type = OpName.MNIST
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elif isinstance(dataset, de.ManifestDataset):
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op_type = OpName.MANIFEST
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elif isinstance(dataset, de.VOCDataset):
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op_type = OpName.VOC
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elif isinstance(dataset, de.Cifar10Dataset):
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op_type = OpName.CIFAR10
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elif isinstance(dataset, de.Cifar100Dataset):
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op_type = OpName.CIFAR100
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elif isinstance(dataset, de.CelebADataset):
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op_type = OpName.CELEBA
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elif isinstance(dataset, de.TextFileDataset):
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op_type = OpName.TEXTFILE
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else:
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raise ValueError("Unsupported DatasetOp")
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return op_type
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# Convert python node into C node and add to C layer execution tree in postorder traversal.
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def __convert_node_postorder(self, node):
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op_type = self.__get_dataset_type(node)
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c_node = self.depipeline.AddNodeToTree(op_type, node.get_args())
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for py_child in node.input:
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c_child = self.__convert_node_postorder(py_child)
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self.depipeline.AddChildToParentNode(c_child, c_node)
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return c_node
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def __batch_node(self, dataset, level):
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"""Recursively get batch node in the dataset tree."""
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if isinstance(dataset, de.BatchDataset):
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return
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for input_op in dataset.input:
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self.__batch_node(input_op, level + 1)
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@staticmethod
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def __print_local(dataset, level):
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"""Recursively print the name and address of nodes in the dataset tree."""
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name = dataset.__class__.__name__
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ptr = hex(id(dataset))
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for _ in range(level):
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logger.info("\t", end='')
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if not dataset.input:
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logger.info("-%s (%s)", name, ptr)
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else:
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logger.info("+%s (%s)", name, ptr)
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for input_op in dataset.input:
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Iterator.__print_local(input_op, level + 1)
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def print(self):
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"""Print the dataset tree"""
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self.__print_local(self.dataset, 0)
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def release(self):
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if hasattr(self, 'depipeline') and self.depipeline:
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del self.depipeline
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@abstractmethod
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def get_next(self):
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pass
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def __next__(self):
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data = self.get_next()
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if not data:
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if self._index == 0:
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logger.warning("No records available.")
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raise StopIteration
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self._index += 1
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return data
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def get_output_shapes(self):
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return [t for t in self.depipeline.GetOutputShapes()]
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def get_output_types(self):
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return [t for t in self.depipeline.GetOutputTypes()]
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def get_dataset_size(self):
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return self.depipeline.GetDatasetSize()
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def get_batch_size(self):
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return self.depipeline.GetBatchSize()
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def get_repeat_count(self):
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return self.depipeline.GetRepeatCount()
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def num_classes(self):
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return self.depipeline.GetNumClasses()
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def __deepcopy__(self, memo):
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return self
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class DictIterator(Iterator):
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"""
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The derived class of Iterator with dict type.
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"""
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def __iter__(self):
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return self
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def get_next(self):
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"""
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Returns the next record in the dataset as dictionary
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Returns:
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Dict, the next record in the dataset.
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"""
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return {k: v.as_array() for k, v in self.depipeline.GetNextAsMap().items()}
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class TupleIterator(Iterator):
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"""
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The derived class of Iterator with list type.
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"""
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def __init__(self, dataset, columns=None):
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if columns is not None:
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if not isinstance(columns, list):
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columns = [columns]
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dataset = dataset.project(columns)
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super().__init__(dataset)
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def __iter__(self):
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return self
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def get_next(self):
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"""
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Returns the next record in the dataset as a list
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Returns:
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List, the next record in the dataset.
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"""
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return [t.as_array() for t in self.depipeline.GetNextAsList()]
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