forked from huawei/mindspore2022
454 lines
18 KiB
Python
454 lines
18 KiB
Python
# Copyright 2020 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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"""Dataset help for minddata dataset"""
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import math
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import os
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from mindspore._checkparam import Validator
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from mindspore.common.dtype import pytype_to_dtype
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from .. import context, nn
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from ._utils import _exec_datagraph, _get_types_and_shapes, _construct_tensor_list
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from ..parallel._utils import _get_device_num, _get_global_rank, _need_to_full, _to_full_shapes
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from ..ops import operations as P
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def _send_data(dataset, epoch_num):
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"""Engine dataset to write data to tdt queue."""
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if not hasattr(dataset, '__has_sent__'):
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exec_dataset = dataset.__transfer_dataset__
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exec_dataset.send(epoch_num)
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dataset.__has_sent__ = True
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def _send_data_no_flag(dataset, epoch_num):
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"""Engine dataset to write data to tdt queue directly."""
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exec_dataset = dataset.__transfer_dataset__
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exec_dataset.send(epoch_num)
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def _dynamic_sink_scenario(dataset, dataset_iter):
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"""Special scenario with dynamic shape and sink_size=1."""
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flag = False
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ms_role = os.getenv("MS_ROLE")
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if hasattr(dataset_iter, "sink_size") and \
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dataset_iter.sink_size == 1 and \
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dataset.get_dataset_size() != 1 and \
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hasattr(dataset_iter, "sink_count") and \
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dataset_iter.sink_count == 1 and \
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context.get_context("device_target") == "Ascend" and \
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context.get_context("mode") == context.GRAPH_MODE and \
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ms_role != "MS_WORKER":
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flag = True
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return flag
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class _DataWrapper(nn.Cell):
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"""
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Wraps the input network with a dataset which automatically fetches data with 'GetNext' function from the
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dataset channel 'queue_name' and performs the forward computation.
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"""
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def __init__(self, network, dataset_types, dataset_shapes, queue_name, min_shapes=None, max_shapes=None):
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super(_DataWrapper, self).__init__(auto_prefix=False, flags=network.get_flags())
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# Also copy the flag in `network` construct
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flags = getattr(network.__class__.construct, "_mindspore_flags", {})
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self.info = (dataset_types, dataset_shapes)
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self.add_flags(**flags)
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self.get_next = P.GetNext(dataset_types, dataset_shapes, len(dataset_types), queue_name)
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if min_shapes is not None and max_shapes is not None:
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Validator.check_value_type("min_shapes", min_shapes, [list, tuple])
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Validator.check_value_type("max_shapes", max_shapes, [list, tuple])
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self.get_next.add_prim_attr("min_shapes", min_shapes)
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self.get_next.add_prim_attr("max_shapes", max_shapes)
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self.network = network
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def construct(self):
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outputs = self.get_next()
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return self.network(*outputs)
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def _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types, queue_name,
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min_shapes=None, max_shapes=None):
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if not isinstance(network, _DataWrapper):
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network = _DataWrapper(network, dataset_types, dataset_shapes, queue_name, min_shapes, max_shapes)
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return network
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def has_dynamic_shape(dataset_shapes):
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for shape in dataset_shapes:
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if -1 in shape:
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return True
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return False
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def _generate_network_with_dataset(network, dataset_helper, queue_name):
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dataset_types, dataset_shapes = dataset_helper.types_shapes()
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(min_shapes, max_shapes) = (None, None) if not has_dynamic_shape(dataset_shapes) \
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else dataset_helper.dynamic_min_max_shapes()
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network = _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types,
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queue_name, min_shapes, max_shapes)
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return network
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def connect_network_with_dataset(network, dataset_helper):
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"""
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Connect the `network` with dataset in `dataset_helper`.
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This function wraps the input network with 'GetNext' so that the data can be fetched automatically from the
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data channel corresponding to the 'queue_name' and passed to the input network during forward computation.
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Note:
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In the case of running the network on Ascend/GPU in graph mode, this function will wrap the input network with
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'GetNext', in other cases, the input network will be returned with no change.
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The 'GetNext' is required to get data only in sink mode, so this function is not applicable to no-sink mode.
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Args:
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network (Cell): The training network for dataset.
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dataset_helper (DatasetHelper): A class to process the MindData dataset, it provides the type, shape and queue
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name of the dataset to wrap the `GetNext`.
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Returns:
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Cell, a new network wrapped with 'GetNext' in the case of running the task on Ascend in graph mode, otherwise
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it is the input network.
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> from mindspore import DatasetHelper
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>>>
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>>> # call create_dataset function to create a regular dataset, refer to mindspore.dataset
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>>> train_dataset = create_custom_dataset()
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>>> dataset_helper = DatasetHelper(train_dataset, dataset_sink_mode=True)
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>>> net = Net()
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>>> net_with_get_next = connect_network_with_dataset(net, dataset_helper)
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"""
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dataset_iter = dataset_helper.iter
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dataset = dataset_iter.dataset
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if isinstance(dataset_iter, _DatasetIterNormal):
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raise RuntimeError("Dataset should be connected with network only in sink mode.")
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ms_role = os.getenv("MS_ROLE")
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if ms_role in ("MS_PSERVER", "MS_SCHED"):
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return network
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queue_name = dataset.__transfer_dataset__.queue_name
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if _dynamic_sink_scenario(dataset, dataset_iter):
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if not hasattr(dataset_iter, '__network__'):
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dataset_iter.__network__ = network
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network = dataset_iter.__network__
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dataset_types, dataset_shapes = dataset_helper.get_data_info()
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dataset_types = [pytype_to_dtype(x) for x in dataset_types]
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key = str(dataset_types) + str(dataset_shapes)
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if hasattr(dataset_iter, '__network_manage__') and key in dataset_iter.__network_manage__:
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network = dataset_iter.__network_manage__[key]
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else:
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if _need_to_full():
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device_num = _get_device_num()
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dataset_shapes = _to_full_shapes(dataset_shapes, device_num)
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network = _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types, queue_name)
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dataset_iter.__network_manage__ = dataset_iter.__network_manage__ if hasattr(
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dataset_iter, '__network_manage__') else dict()
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dataset_iter.__network_manage__[key] = network
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return network
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if not hasattr(dataset, '__me_inited__') and \
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not context.get_context("enable_ge") and \
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context.get_context("device_target") in ("Ascend", "GPU"):
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dataset.__me_inited__ = True
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network = _generate_network_with_dataset(network, dataset_helper, queue_name)
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if hasattr(dataset_iter, "sink_size") and \
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dataset_iter.sink_size == 1 and \
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dataset.get_dataset_size() != 1 and \
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hasattr(dataset_iter, "sink_count") and \
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dataset_iter.sink_count == 1 and \
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context.get_context("device_target") == "Ascend" and \
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context.get_context("mode") == context.PYNATIVE_MODE:
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dataset_helper.get_data_info()
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return network
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class DatasetHelper:
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"""
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DatasetHelper is a class to process the MindData dataset and it provides the information of dataset.
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According to different contexts, change the iterations of dataset and use the same iteration for loop in different
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contexts.
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Note:
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The iteration of DatasetHelper will provide one epoch data.
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Args:
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dataset (Dataset): The training dataset iterator.
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dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data
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from host. Default: True.
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sink_size (int): Control the amount of data in each sink.
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If sink_size=-1, sink the complete dataset for each epoch.
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If sink_size>0, sink sink_size data for each epoch.
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Default: -1.
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epoch_num (int): Control the number of epoch data to send. Default: 1.
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Examples:
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>>> from mindspore import DatasetHelper
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>>>
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>>> train_dataset = create_custom_dataset()
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>>> set_helper = DatasetHelper(train_dataset, dataset_sink_mode=False)
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>>> for next_element in set_helper:
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... print(next_element)
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"""
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def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1):
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dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
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Validator.check_is_int(sink_size)
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if sink_size < -1 or sink_size == 0:
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raise ValueError("The sink_size must be -1 or positive, but got sink_size {}.".format(sink_size))
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if sink_size == -1:
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sink_size = dataset.get_dataset_size()
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if dataset_sink_mode:
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if context.get_context("enable_ge"):
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iterclass = _DatasetIterGE
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else:
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if context.get_context("mode") == context.GRAPH_MODE:
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ms_role = os.getenv("MS_ROLE")
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if ms_role in ("MS_PSERVER", "MS_SCHED"):
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iterclass = _DatasetIterPSServer
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elif ms_role == "MS_WORKER":
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iterclass = _DatasetIterPSWork
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elif (context.get_context("device_target") == "Ascend") or \
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(context.get_context("device_target") == "GPU"):
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iterclass = _DatasetIterMSLoopSink
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elif context.get_context("device_target") == "CPU":
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raise RuntimeError(
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"Currently dataset sink mode is not supported when the device target is CPU.")
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else:
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iterclass = _DatasetIterPyNative
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self.iter = iterclass(dataset, sink_size, epoch_num)
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else:
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iterclass = _DatasetIterNormal
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self.iter = iterclass(dataset, epoch_num=epoch_num)
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def __iter__(self):
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return self.iter.__iter__()
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# A temp solution for loop sink. Delete later
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def types_shapes(self):
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"""Get the types and shapes from dataset on the current configuration."""
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return self.iter.types_shapes()
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def sink_size(self):
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"""Get sink_size for each iteration."""
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return self.iter.get_sink_size()
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def stop_send(self):
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"""stop send data about data sink."""
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self.iter.stop_send()
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def release(self):
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"""Free up resources about data sink."""
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self.iter.release()
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def continue_send(self):
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"""Continue send data to device at the beginning of epoch."""
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self.iter.continue_send()
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def get_data_info(self):
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"""Get the types and shape of current batch."""
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return self.iter.get_data_info()
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def dynamic_min_max_shapes(self):
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"""Get shape range(min shape, max shape) of dynamic data."""
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return self.iter.dynamic_min_max_shapes()
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class _DatasetIter:
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"""Base iter for dataset helper"""
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def __init__(self, dataset, sink_size, epoch_num):
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self.dataset = dataset
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self.sink_size = sink_size
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self.sink_count = self.get_sink_count(dataset)
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if not hasattr(dataset, '__transfer_dataset__'):
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if hasattr(dataset, '__loop_size__'):
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ms_role = os.getenv("MS_ROLE")
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# PS mode does not support loop sink and need get the real sink size.
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if ms_role != "MS_WORKER":
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self.sink_size = dataset.__loop_size__
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create_data_info_queue = (sink_size == 1 and self.sink_count == 1 and context.get_context(
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"device_target") == "Ascend")
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dataset.__transfer_dataset__ = _exec_datagraph(dataset, self.sink_size,
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create_data_info_queue=create_data_info_queue)
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if not hasattr(dataset, '__no_send__'):
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_send_data(dataset, epoch_num)
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else:
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_send_data_no_flag(dataset, epoch_num)
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self.stop_send = dataset.__transfer_dataset__.stop_send
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self.release = dataset.__transfer_dataset__.release
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self.continue_send = dataset.__transfer_dataset__.continue_send
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self.get_data_info = dataset.__transfer_dataset__.get_data_info
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self.dynamic_min_max_shapes = dataset.dynamic_min_max_shapes
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self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
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def __iter__(self):
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self.index = 0
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return self
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def __next__(self):
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if self.index >= self.sink_count:
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raise StopIteration()
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self.index += 1
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return self.op()
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def types_shapes(self):
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return self.dataset_types, self.dataset_shapes
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def get_sink_count(self, dataset):
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sink_count = 1
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if hasattr(dataset, '__loop_size__'):
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loop_size = dataset.__loop_size__
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if loop_size <= dataset.get_dataset_size() and dataset.get_dataset_size() % loop_size != 0:
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raise ValueError(f'Dataset size {dataset.get_dataset_size()} and '
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f'sink_size {loop_size} are not matched.')
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sink_count = math.ceil(dataset.get_dataset_size() / loop_size)
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return sink_count
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def get_sink_size(self):
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"""get sink_size to device"""
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sink_size = 1
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ms_role = os.getenv("MS_ROLE")
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if hasattr(self.dataset, '__loop_size__'):
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sink_size = self.dataset.__loop_size__
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elif ms_role == "MS_WORKER":
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# PS mode does not support loop sink.
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sink_size = 1
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else:
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if context.get_context("enable_ge") or context.get_context("device_target") == "Ascend" \
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or context.get_context("device_target") == "GPU":
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if self.sink_size > 0:
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sink_size = self.sink_size
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else:
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sink_size = self.dataset.get_dataset_size()
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return sink_size
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class _DatasetIterGE(_DatasetIter):
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"""Iter for GE."""
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def __init__(self, dataset, sink_size, epoch_num):
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super().__init__(dataset, sink_size, epoch_num)
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self.sink_count = self.get_sink_count(dataset)
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batch_expand_num = 1
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if _need_to_full():
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batch_expand_num = _get_device_num()
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tensor_list_run = _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num)
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def op():
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return tensor_list_run
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self.op = op
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class _DatasetIterPyNative(_DatasetIter):
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"""Iter for context (mode=PYNATIVE_MODE)."""
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def __init__(self, dataset, sink_size, epoch_num):
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super().__init__(dataset, sink_size, epoch_num)
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if sink_size > 0:
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self.sink_count = sink_size
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else:
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self.sink_count = dataset.get_dataset_size()
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def op():
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return tuple()
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self.op = op
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class _DatasetIterMSLoopSink(_DatasetIter):
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"""Iter for context (device_target=Ascend)"""
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def __init__(self, dataset, sink_size, epoch_num):
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super().__init__(dataset, sink_size, epoch_num)
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self.sink_count = self.get_sink_count(dataset)
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# for self._parallel_mode equal to semi_auto_parallel or auto_parallel, and not using full_batch,
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# use a complete tensor to compile, and slice tensor to run. The batch dimension of tensors for
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# compile is device_number times the batch dimension of tensors for run. Now only support LoopSink.
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if _need_to_full():
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device_num = _get_device_num()
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self.dataset_shapes = _to_full_shapes(self.dataset_shapes, device_num)
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def op():
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return tuple()
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self.op = op
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class _DatasetIterPSServer(_DatasetIter):
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"""Iter for context on MS_PSERVER or MS_SCHED"""
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def __init__(self, dataset, sink_size, epoch_num):
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super().__init__(dataset, sink_size, epoch_num)
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self.sink_count = 1
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self.sink_size = 1
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self.op = None
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def op():
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return _construct_tensor_list(self.dataset_types, self.dataset_shapes, batch_expand_num=1)
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self.op = op
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class _DatasetIterPSWork(_DatasetIter):
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"""Iter for context on MS_WORKER"""
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def __init__(self, dataset, sink_size, epoch_num):
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super().__init__(dataset, sink_size, epoch_num)
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if sink_size > 0:
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self.sink_count = sink_size
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else:
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self.sink_count = dataset.get_dataset_size()
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def op():
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return tuple()
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self.op = op
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class _DatasetIterNormal:
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"""Iter for normal(non sink) mode, feed the data from host."""
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def __init__(self, dataset, epoch_num=-1):
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self.dataset = dataset
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self.device_num = _get_device_num()
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self.global_rank = _get_global_rank()
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self.iter = self.dataset.create_tuple_iterator(num_epochs=epoch_num, do_copy=True)
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def __iter__(self):
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return self
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def __next__(self):
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data = self.iter.__next__()
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return data
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__all__ = ["DatasetHelper", "connect_network_with_dataset"]
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