forked from huawei/mindspore2022
99 lines
4.4 KiB
Python
99 lines
4.4 KiB
Python
# Copyright 2019-2021 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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"""Cache client
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"""
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import copy
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from mindspore._c_dataengine import CacheClient
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from ..core.validator_helpers import type_check, check_pos_int32, check_pos_uint32, check_uint64, check_positive, \
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check_value
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class DatasetCache:
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"""
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A client to interface with tensor caching service.
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For details, please check `Tutorial <https://www.mindspore.cn/docs/programming_guide/en/master/enable_cache.html>`_,
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`Programming guide <https://www.mindspore.cn/docs/programming_guide/en/master/cache.html>`_.
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Args:
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session_id (int): A user assigned session id for the current pipeline.
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size (int, optional): Size of the memory set aside for the row caching (default=0, which means unlimited,
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note that it might bring in the risk of running out of memory on the machine).
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spilling (bool, optional): Whether or not spilling to disk if out of memory (default=False).
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hostname (str, optional): Host name (default=None, use default hostname '127.0.0.1').
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port (int, optional): Port to connect to server (default=None, use default port 50052).
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num_connections (int, optional): Number of tcp/ip connections (default=None, use default value 12).
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prefetch_size (int, optional): The size of the cache queue between operations
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(default=None, use default value 20).
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Examples:
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>>> import mindspore.dataset as ds
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>>>
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>>> # create a cache instance, in which session_id is generated from command line `cache_admin -g`
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>>> some_cache = ds.DatasetCache(session_id=session_id, size=0)
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>>>
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>>> dataset_dir = "path/to/imagefolder_directory"
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>>> ds1 = ds.ImageFolderDataset(dataset_dir, cache=some_cache)
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"""
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def __init__(self, session_id, size=0, spilling=False, hostname=None, port=None, num_connections=None,
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prefetch_size=None):
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check_pos_uint32(session_id, "session_id")
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type_check(size, (int,), "size")
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if size != 0:
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check_positive(size, "size")
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check_uint64(size, "size")
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type_check(spilling, (bool,), "spilling")
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if hostname is not None:
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type_check(hostname, (str,), "hostname")
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if port is not None:
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type_check(port, (int,), "port")
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check_value(port, (1025, 65535), "port")
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if num_connections is not None:
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check_pos_int32(num_connections, "num_connections")
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if prefetch_size is not None:
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check_pos_int32(prefetch_size, "prefetch_size")
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self.session_id = session_id
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self.size = size
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self.spilling = spilling
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self.hostname = hostname
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self.port = port
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self.prefetch_size = prefetch_size
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self.num_connections = num_connections
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self.cache_client = CacheClient(session_id, size, spilling, hostname, port, num_connections, prefetch_size)
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def get_stat(self):
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"""Get the statistics from a cache."""
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return self.cache_client.GetStat()
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def __deepcopy__(self, memodict):
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if id(self) in memodict:
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return memodict[id(self)]
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cls = self.__class__
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new_cache = cls.__new__(cls)
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memodict[id(self)] = new_cache
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new_cache.session_id = copy.deepcopy(self.session_id, memodict)
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new_cache.spilling = copy.deepcopy(self.spilling, memodict)
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new_cache.size = copy.deepcopy(self.size, memodict)
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new_cache.hostname = copy.deepcopy(self.hostname, memodict)
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new_cache.port = copy.deepcopy(self.port, memodict)
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new_cache.prefetch_size = copy.deepcopy(self.prefetch_size, memodict)
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new_cache.num_connections = copy.deepcopy(self.num_connections, memodict)
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new_cache.cache_client = self.cache_client
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return new_cache
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