# Copyright 2019-2022 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.
# ==============================================================================
"""
This file contains standard format dataset loading classes.
You can convert a dataset to a standard format using the following steps:
1. Use mindspore.mindrecord.FileWriter / tf.io.TFRecordWriter api to
convert dataset to MindRecord / TFRecord.
2. Use MindDataset / TFRecordDataset to load MindRecord / TFRecrod files.
After declaring the dataset object, you can further apply dataset operations
(e.g. filter, skip, concat, map, batch) on it.
"""
import platform
import numpy as np
import mindspore._c_dataengine as cde
from .datasets import UnionBaseDataset, SourceDataset, MappableDataset, Shuffle, Schema, \
shuffle_to_shuffle_mode, shuffle_to_bool
from .datasets_user_defined import GeneratorDataset
from .obs.obs_mindrecord_dataset import MindRecordFromOBS
from .validators import check_csvdataset, check_minddataset, check_tfrecorddataset, check_obsminddataset
from ..core.validator_helpers import replace_none
from . import samplers
[文档]class CSVDataset(SourceDataset, UnionBaseDataset):
"""
A source dataset that reads and parses comma-separated values
`(CSV) <http://en.volupedia.org/wiki/Comma-separated_values>`_ files as dataset.
The columns of generated dataset depend on the source CSV files.
Args:
dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search
for a pattern of files. The list will be sorted in a lexicographical order.
field_delim (str, optional): A string that indicates the char delimiter to separate fields (default=',').
column_defaults (list, optional): List of default values for the CSV field (default=None). Each item
in the list is either a valid type (float, int, or string). If this is not provided, treats all
columns as string type.
column_names (list[str], optional): List of column names of the dataset (default=None). If this
is not provided, infers the column_names from the first row of CSV file.
num_samples (int, optional): The number of samples to be included in the dataset
(default=None, will include all images).
num_parallel_workers (int, optional): Number of workers to read the data
(default=None, number set in the config).
shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
(default=Shuffle.GLOBAL).
If shuffle is False, no shuffling will be performed.
If shuffle is True, performs global shuffle.
There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle.
- Shuffle.GLOBAL: Shuffle both the files and samples, same as setting shuffle to True.
- Shuffle.FILES: Shuffle files only.
num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
When this argument is specified, `num_samples` reflects the maximum sample number of per shard.
shard_id (int, optional): The shard ID within `num_shards` (default=None). This
argument can only be specified when `num_shards` is also specified.
cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
(default=None, which means no cache is used).
Raises:
RuntimeError: If dataset_files are not valid or do not exist.
ValueError: If field_delim is invalid.
ValueError: If `num_parallel_workers` exceeds the max thread numbers.
RuntimeError: If `num_shards` is specified but `shard_id` is None.
RuntimeError: If `shard_id` is specified but `num_shards` is None.
ValueError: If `shard_id` is invalid (< 0 or >= `num_shards`).
Examples:
>>> csv_dataset_dir = ["/path/to/csv_dataset_file"] # contains 1 or multiple csv files
>>> dataset = ds.CSVDataset(dataset_files=csv_dataset_dir, column_names=['col1', 'col2', 'col3', 'col4'])
"""
@check_csvdataset
def __init__(self, dataset_files, field_delim=',', column_defaults=None, column_names=None, num_samples=None,
num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None):
super().__init__(num_parallel_workers=num_parallel_workers, num_samples=num_samples, shuffle=shuffle,
num_shards=num_shards, shard_id=shard_id, cache=cache)
self.dataset_files = self._find_files(dataset_files)
self.dataset_files.sort()
self.field_delim = replace_none(field_delim, ',')
self.column_defaults = replace_none(column_defaults, [])
self.column_names = replace_none(column_names, [])
def parse(self, children=None):
return cde.CSVNode(self.dataset_files, self.field_delim, self.column_defaults, self.column_names,
self.num_samples, self.shuffle_flag, self.num_shards, self.shard_id)
[文档]class MindDataset(MappableDataset, UnionBaseDataset):
"""
A source dataset that reads and parses MindRecord dataset.
The columns of generated dataset depend on the source MindRecord files.
Args:
dataset_files (Union[str, list[str]]): If dataset_file is a str, it represents for
a file name of one component of a mindrecord source, other files with identical source
in the same path will be found and loaded automatically. If dataset_file is a list,
it represents for a list of dataset files to be read directly.
columns_list (list[str], optional): List of columns to be read (default=None).
num_parallel_workers (int, optional): The number of readers (default=None).
shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
(default=None, performs global shuffle).
If shuffle is False, no shuffling will be performed.
If shuffle is True, performs global shuffle.
There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle.
- Shuffle.GLOBAL: Global shuffle of all rows of data in dataset, same as setting shuffle to True.
- Shuffle.FILES: Shuffle the file sequence but keep the order of data within each file.
- Shuffle.INFILE: Keep the file sequence the same but shuffle the data within each file.
num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
When this argument is specified, 'num_samples' reflects the maximum sample number of per shard.
shard_id (int, optional): The shard ID within `num_shards` (default=None). This
argument can only be specified when `num_shards` is also specified.
sampler (Sampler, optional): Object used to choose samples from the
dataset (default=None, sampler is exclusive
with shuffle and block_reader). Support list: SubsetRandomSampler,
PkSampler, RandomSampler, SequentialSampler, DistributedSampler.
padded_sample (dict, optional): Samples will be appended to dataset, where
keys are the same as column_list.
num_padded (int, optional): Number of padding samples. Dataset size
plus num_padded should be divisible by num_shards.
num_samples (int, optional): The number of samples to be included in the dataset
(default=None, all samples).
cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
(default=None, which means no cache is used).
Raises:
ValueError: If dataset_files are not valid or do not exist.
ValueError: If `num_parallel_workers` exceeds the max thread numbers.
RuntimeError: If `num_shards` is specified but `shard_id` is None.
RuntimeError: If `shard_id` is specified but `num_shards` is None.
ValueError: If `shard_id` is invalid (< 0 or >= `num_shards`).
Note:
- This dataset can take in a `sampler`. `sampler` and `shuffle` are mutually exclusive.
The table below shows what input arguments are allowed and their expected behavior.
.. list-table:: Expected Order Behavior of Using `sampler` and `shuffle`
:widths: 25 25 50
:header-rows: 1
* - Parameter `sampler`
- Parameter `shuffle`
- Expected Order Behavior
* - None
- None
- random order
* - None
- True
- random order
* - None
- False
- sequential order
* - Sampler object
- None
- order defined by sampler
* - Sampler object
- True
- not allowed
* - Sampler object
- False
- not allowed
Examples:
>>> mind_dataset_dir = ["/path/to/mind_dataset_file"] # contains 1 or multiple MindRecord files
>>> dataset = ds.MindDataset(dataset_files=mind_dataset_dir)
"""
def parse(self, children=None):
return cde.MindDataNode(self.dataset_files, self.columns_list, self.sampler, self.new_padded_sample,
self.num_padded, shuffle_to_shuffle_mode(self.shuffle_option))
@check_minddataset
def __init__(self, dataset_files, columns_list=None, num_parallel_workers=None, shuffle=None, num_shards=None,
shard_id=None, sampler=None, padded_sample=None, num_padded=None, num_samples=None, cache=None):
super().__init__(num_parallel_workers=num_parallel_workers, sampler=sampler, num_samples=num_samples,
shuffle=shuffle_to_bool(shuffle), num_shards=num_shards, shard_id=shard_id, cache=cache)
if num_samples and shuffle in (Shuffle.FILES, Shuffle.INFILE):
raise ValueError("'Shuffle.FILES' or 'Shuffle.INFILE' and 'num_samples' "
"cannot be specified at the same time.")
self.shuffle_option = shuffle
self.load_dataset = True
if isinstance(dataset_files, list):
self.load_dataset = False
self.dataset_files = dataset_files
if platform.system().lower() == "windows":
if isinstance(dataset_files, list):
file_tuple = []
for item in dataset_files:
item.replace("\\", "/")
file_tuple.append(item)
self.dataset_files = file_tuple
else:
self.dataset_files = dataset_files.replace("\\", "/")
self.columns_list = replace_none(columns_list, [])
if sampler is not None:
if isinstance(sampler, (
samplers.SubsetRandomSampler, samplers.SubsetSampler, samplers.PKSampler,
samplers.DistributedSampler,
samplers.RandomSampler, samplers.SequentialSampler)) is False:
raise ValueError("The sampler is not supported yet.")
self.padded_sample = padded_sample
self.num_padded = replace_none(num_padded, 0)
self.new_padded_sample = {}
if padded_sample:
for k, v in padded_sample.items():
if isinstance(v, np.ndarray):
self.new_padded_sample[k] = v.tobytes()
else:
self.new_padded_sample[k] = v
[文档]class TFRecordDataset(SourceDataset, UnionBaseDataset):
"""
A source dataset that reads and parses datasets stored on disk in TFData format.
The columns of generated dataset depend on the source TFRecord files.
Args:
dataset_files (Union[str, list[str]]): String or list of files to be read or glob strings to search for a
pattern of files. The list will be sorted in a lexicographical order.
schema (Union[str, Schema], optional): Path to the JSON schema file or schema object (default=None).
If the schema is not provided, the meta data from the TFData file is considered the schema.
columns_list (list[str], optional): List of columns to be read (default=None, read all columns).
num_samples (int, optional): The number of samples (rows) to be included in the dataset (default=None).
If num_samples is None and numRows(parsed from schema) does not exist, read the full dataset;
If num_samples is None and numRows(parsed from schema) is greater than 0, read numRows rows;
If both num_samples and numRows(parsed from schema) are greater than 0, read num_samples rows.
num_parallel_workers (int, optional): Number of workers to read the data
(default=None, number set in the config).
shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
(default=Shuffle.GLOBAL).
If shuffle is False, no shuffling will be performed.
If shuffle is True, performs global shuffle.
There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle.
- Shuffle.GLOBAL: Shuffle both the files and samples, same as setting shuffle to True.
- Shuffle.FILES: Shuffle files only.
num_shards (int, optional): Number of shards that the dataset will be divided
into (default=None). When this argument is specified, `num_samples` reflects
the maximum sample number of per shard.
shard_id (int, optional): The shard ID within `num_shards` (default=None). This
argument can only be specified when `num_shards` is also specified.
shard_equal_rows (bool, optional): Get equal rows for all shards(default=False). If shard_equal_rows
is false, number of rows of each shard may be not equal, and may lead to a failure in distributed training.
When the number of samples of per TFRecord file are not equal, it is suggested to set to true.
This argument should only be specified when `num_shards` is also specified.
cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
(default=None, which means no cache is used).
Raises:
ValueError: If dataset_files are not valid or do not exist.
ValueError: If `num_parallel_workers` exceeds the max thread numbers.
RuntimeError: If `num_shards` is specified but `shard_id` is None.
RuntimeError: If `shard_id` is specified but `num_shards` is None.
ValueError: If `shard_id` is invalid (< 0 or >= `num_shards`).
Examples:
>>> from mindspore import dtype as mstype
>>>
>>> tfrecord_dataset_dir = ["/path/to/tfrecord_dataset_file"] # contains 1 or multiple TFRecord files
>>> tfrecord_schema_file = "/path/to/tfrecord_schema_file"
>>>
>>> # 1) Get all rows from tfrecord_dataset_dir with no explicit schema.
>>> # The meta-data in the first row will be used as a schema.
>>> dataset = ds.TFRecordDataset(dataset_files=tfrecord_dataset_dir)
>>>
>>> # 2) Get all rows from tfrecord_dataset_dir with user-defined schema.
>>> schema = ds.Schema()
>>> schema.add_column(name='col_1d', de_type=mstype.int64, shape=[2])
>>> dataset = ds.TFRecordDataset(dataset_files=tfrecord_dataset_dir, schema=schema)
>>>
>>> # 3) Get all rows from tfrecord_dataset_dir with schema file.
>>> dataset = ds.TFRecordDataset(dataset_files=tfrecord_dataset_dir, schema=tfrecord_schema_file)
"""
@check_tfrecorddataset
def __init__(self, dataset_files, schema=None, columns_list=None, num_samples=None, num_parallel_workers=None,
shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, shard_equal_rows=False, cache=None):
super().__init__(num_parallel_workers=num_parallel_workers, num_samples=num_samples, shuffle=shuffle,
num_shards=num_shards, shard_id=shard_id, cache=cache)
self.dataset_files = self._find_files(dataset_files)
self.dataset_files.sort()
self.schema = schema
self.columns_list = replace_none(columns_list, [])
self.shard_equal_rows = replace_none(shard_equal_rows, False)
if self.schema is not None and (self.num_samples is None or self.num_samples == 0):
self.num_samples = Schema.get_num_rows(self.schema)
def parse(self, children=None):
schema = self.schema.cpp_schema if isinstance(self.schema, Schema) else self.schema
return cde.TFRecordNode(self.dataset_files, schema, self.columns_list, self.num_samples, self.shuffle_flag,
self.num_shards, self.shard_id, self.shard_equal_rows)
[文档]class OBSMindDataset(GeneratorDataset):
"""
A source dataset that reads and parses MindRecord dataset which stored in cloud storage
such as OBS, Minio or AWS S3.
The columns of generated dataset depend on the source MindRecord files.
Args:
dataset_files (list[str]): List of files in cloud storage to be read and file path is in
the format of s3://bucketName/objectKey.
server (str): Endpoint for accessing cloud storage.
If it's OBS Service of Huawei Cloud，the endpoint is
like <obs.cn-north-4.myhuaweicloud.com> (Region cn-north-4).
If it's Minio which starts locally, the endpoint is like <https://127.0.0.1:9000>.
ak (str): Access key ID of cloud storage.
sk (str): Secret key ID of cloud storage.
sync_obs_path (str): Remote dir path used for synchronization, users need to
create it on cloud storage in advance. Path is in the format of s3://bucketName/objectKey.
columns_list (list[str], optional): List of columns to be read (default=None, read all columns).
shuffle (Union[bool, Shuffle level], optional): Perform reshuffling of the data every epoch
(default=None, performs global shuffle).
If shuffle is False, no shuffling will be performed.
If shuffle is True, performs global shuffle.
There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle.
- Shuffle.GLOBAL: Global shuffle of all rows of data in dataset, same as setting shuffle to True.
- Shuffle.FILES: Shuffle the file sequence but keep the order of data within each file.
- Shuffle.INFILE: Keep the file sequence the same but shuffle the data within each file.
num_shards (int, optional): Number of shards that the dataset will be divided
into (default=None).
shard_id (int, optional): The shard ID within num_shards (default=None). This
argument can only be specified when num_shards is also specified.
shard_equal_rows (bool, optional): Get equal rows for all shards(default=True). If shard_equal_rows
is false, number of rows of each shard may be not equal, and may lead to a failure in distributed training.
When the number of samples of per MindRecord file are not equal, it is suggested to set to true.
This argument should only be specified when num_shards is also specified.
Raises:
RuntimeError: If `sync_obs_path` do not exist.
ValueError: If `columns_list` is invalid.
RuntimeError: If `num_shards` is specified but `shard_id` is None.
RuntimeError: If `shard_id` is specified but `num_shards` is None.
ValueError: If `shard_id` is invalid (< 0 or >= `num_shards`).
Note:
- It's necessary to create a synchronization directory on cloud storage in
advance which be defined by parameter: `sync_obs_path` .
- If training is offline(no cloud), it's recommended to set the
environment variable `BATCH_JOB_ID`.
- In distributed training, if there are multiple nodes(servers), all 8
devices must be used in each node(server). If there is only one
node(server), there is no such restriction.
Examples:
>>> # OBS
>>> bucket = "iris" # your obs bucket name
>>> # the bucket directory structure is similar to the following:
>>> # - imagenet21k
>>> # | - mr_imagenet21k_01
>>> # | - mr_imagenet21k_02
>>> # - sync_node
>>> dataset_obs_dir = ["s3://" + bucket + "/imagenet21k/mr_imagenet21k_01",
... "s3://" + bucket + "/imagenet21k/mr_imagenet21k_02"]
>>> sync_obs_dir = "s3://" + bucket + "/sync_node"
>>> num_shards = 8
>>> shard_id = 0
>>> dataset = ds.OBSMindDataset(dataset_obs_dir, "obs.cn-north-4.myhuaweicloud.com",
... "AK of OBS", "SK of OBS",
... sync_obs_dir, shuffle=True, num_shards=num_shards, shard_id=shard_id)
"""
@check_obsminddataset
def __init__(self, dataset_files, server, ak, sk, sync_obs_path,
columns_list=None,
shuffle=Shuffle.GLOBAL,
num_shards=None,
shard_id=None,
shard_equal_rows=True):
from .obs.config_loader import config
config.AK = ak
config.SK = sk
config.SERVER = server
config.SYNC_OBS_PATH = sync_obs_path
if shuffle is not None and not isinstance(shuffle, (bool, Shuffle)):
raise TypeError("shuffle must be of boolean or enum of 'Shuffle' values like 'Shuffle.GLOBAL' or "
"'Shuffle.FILES'.")
self.num_shards = replace_none(num_shards, 1)
self.shard_id = replace_none(shard_id, 0)
self.shuffle = replace_none(shuffle, True)
dataset = MindRecordFromOBS(dataset_files, columns_list, shuffle, self.num_shards, self.shard_id,
shard_equal_rows, config.DATASET_LOCAL_PATH)
if not columns_list:
columns_list = dataset.get_col_names()
else:
full_columns_list = dataset.get_col_names()
if not set(columns_list).issubset(full_columns_list):
raise ValueError("columns_list: {} can not found in MindRecord fields: {}".format(columns_list,
full_columns_list))
super().__init__(source=dataset, column_names=columns_list, num_shards=None, shard_id=None, shuffle=False)
def add_sampler(self, new_sampler):
raise NotImplementedError("add_sampler is not supported for OBSMindDataset.")
def use_sampler(self, new_sampler):
raise NotImplementedError("use_sampler is not supported for OBSMindDataset.")