Source code for mindspore.train.dataset_helper

# 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
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# Unless required by applicable law or agreed to in writing, software
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"""Dataset help for minddata dataset"""
import math
import os

from mindspore._checkparam import check_bool, check_int
from .. import context
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, _need_to_full


def _send_data(dataset, epoch_num):
    """Engine dataset to write data to tdt queue."""
    if not hasattr(dataset, '__has_sent__'):
        exec_dataset = dataset.__TRANSFER_DATASET__
        exec_dataset.send(epoch_num)
        dataset.__has_sent__ = True

def _send_data_no_flag(dataset, epoch_num):
    """Engine dataset to write data to tdt queue directly."""
    exec_dataset = dataset.__TRANSFER_DATASET__
    exec_dataset.send(epoch_num)


[docs]class DatasetHelper: """ Help function to use the MindData dataset. According to different contexts, change the iterations of dataset and use the same iteration for loop in different contexts. Note: The iteration of DatasetHelper will provide one epoch data. Args: dataset (DataSet): The training dataset iterator. dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data from host. Default: True. sink_size (int): Control the amount of data in each sink. If sink_size=-1, sink the complete dataset for each epoch. If sink_size>0, sink sink_size data for each epoch. Default: -1. epoch_num (int): Control the number of epoch data to send. Default: 1. Examples: >>> dataset_helper = DatasetHelper(dataset) >>> for inputs in dataset_helper: >>> outputs = network(*inputs) """ def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1): check_bool(dataset_sink_mode) check_int(sink_size) if sink_size < -1 or sink_size == 0: raise ValueError("The sink_size must be -1 or positive, but got sink_size {}.".format(sink_size)) if dataset_sink_mode: if context.get_context("enable_ge"): iterclass = _DatasetIterGE else: if context.get_context("device_target") == "Ascend": iterclass = _DatasetIterMSLoopSink elif context.get_context("device_target") == "GPU": ms_role = os.getenv("MS_ROLE") if ms_role in ("MS_PSERVER", "MS_SCHED"): iterclass = _DatasetIterPSLite else: iterclass = _DatasetIterMS elif context.get_context("device_target") == "CPU": raise RuntimeError("Currently dataset sink mode is not supported when the device target is CPU.") self.iter = iterclass(dataset, sink_size, epoch_num) else: iterclass = _DatasetIterNormal 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 the current configuration.""" return self.iter.types_shapes()
[docs] def sink_size(self): """Get sink_size for each iteration.""" return self.iter.get_sink_size()
[docs] def stop_send(self): """Free up resources about data sink.""" self.iter.stop_send()
class _DatasetIter: """Base iter for dataset helper""" def __init__(self, dataset, sink_size, epoch_num): self.dataset = dataset self.sink_size = sink_size self.sink_count = 1 if not hasattr(dataset, '__TRANSFER_DATASET__'): if hasattr(dataset, '__loop_size__'): self.sink_size = dataset.__loop_size__ dataset.__TRANSFER_DATASET__ = _exec_datagraph(dataset, self.sink_size) dataset.__ME_INITED__ = dataset.__TRANSFER_DATASET__.queue_name if not hasattr(dataset, '__no_send__'): _send_data(dataset, epoch_num) else: _send_data_no_flag(dataset, epoch_num) self.stop_send = dataset.__TRANSFER_DATASET__.stop_send self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset) def __iter__(self): self.index = 0 return self def __next__(self): if self.index >= self.sink_count: raise StopIteration() self.index += 1 return self.op() def types_shapes(self): return self.dataset_types, self.dataset_shapes def get_sink_count(self, dataset): sink_count = 1 if hasattr(dataset, '__loop_size__'): loop_size = dataset.__loop_size__ if loop_size <= dataset.get_dataset_size() and dataset.get_dataset_size() % loop_size != 0: raise ValueError(f'Dataset size {dataset.get_dataset_size()} and ' f'sink_size {loop_size} are not matched.') sink_count = math.ceil(dataset.get_dataset_size() / loop_size) return sink_count def get_sink_size(self): """get sink_size to device""" sink_size = 1 if hasattr(self.dataset, '__loop_size__'): sink_size = self.dataset.__loop_size__ else: if context.get_context("enable_ge") or context.get_context("device_target") == "Ascend": if self.sink_size > 0: sink_size = self.sink_size else: sink_size = self.dataset.get_dataset_size() return sink_size class _DatasetIterGE(_DatasetIter): """Iter for GE.""" def __init__(self, dataset, sink_size, epoch_num): super().__init__(dataset, sink_size, epoch_num) self.sink_count = self.get_sink_count(dataset) batch_expand_num = 1 if _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 _DatasetIterMSLoopSink(_DatasetIter): """Iter for context (device_target=Ascend)""" def __init__(self, dataset, sink_size, epoch_num): super().__init__(dataset, sink_size, epoch_num) self.sink_count = self.get_sink_count(dataset) ms_role = os.getenv("MS_ROLE") if ms_role in ("MS_PSERVER", "MS_SCHED"): self.sink_count = 1 # for self._parallel_mode equal to semi_auto_parallel or auto_parallel, and not using full_batch, # 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. Now only support LoopSink. if _need_to_full(): device_num = _get_device_num() self.dataset_shapes = _to_full_shapes(self.dataset_shapes, device_num) def op(): return tuple() self.op = op class _DatasetIterMS(_DatasetIter): """Iter for MS(enable_loop_sink=False).""" def __init__(self, dataset, sink_size, epoch_num): super().__init__(dataset, sink_size, epoch_num) if sink_size > 0: self.sink_count = sink_size else: self.sink_count = dataset.get_dataset_size() queue_name = dataset.__ME_INITED__ self.op = GetNextSingleOp(self.dataset_types, self.dataset_shapes, queue_name) class _DatasetIterPSLite(_DatasetIter): """Iter for context (device_target=GPU) on MS_PSERVER or MS_SCHED""" def __init__(self, dataset, sink_size, epoch_num): super().__init__(dataset, sink_size, epoch_num) self.sink_count = 1 self.sink_size = 1 self.op = None def op(): return _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num=1) self.op = op class _DatasetIterNormal: """Iter for normal(non sink) mode, feed the data from host.""" def __init__(self, dataset): self.dataset = dataset self.device_num = _get_device_num() self.global_rank = _get_global_rank() self.iter = self.dataset.create_tuple_iterator() def __iter__(self): return self def __next__(self): data = self.iter.__next__() if _need_to_full(): return _to_full_tensor(data, self.device_num, self.global_rank) return _to_tensor(data)