Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ssbuild/fastdatasets
https://github.com/ssbuild/fastdatasets
Last synced: 4 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/ssbuild/fastdatasets
- Owner: ssbuild
- Created: 2022-03-02T10:49:24.000Z (over 2 years ago)
- Default Branch: dev
- Last Pushed: 2023-10-28T04:02:01.000Z (about 1 year ago)
- Last Synced: 2024-10-05T03:49:13.536Z (about 1 month ago)
- Language: Python
- Size: 255 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## The update statement
```text
2023-10-28 support more torch well known datatasets
2023-07-08: support some nested case
2023-07-02: support arrow parquet
2023-04-28: fix lmdb mutiprocess
2023-02-13: add TopDataset with iterable_dataset and patch
2022-12-07: modify a bug for randomdataset for batch reminder
2022-11-07: add numpy writer and parser,add memory writer and parser
2022-10-29: add kv dataset
```
## usage
[numpy_io](https://github.com/ssbuild/numpy_io)
## Install
```commandline
pip install -U fastdatasets
```
### 1. Record Write
```python
import data_serialize
from fastdatasets.record import load_dataset, gfile,TFRecordOptions, TFRecordCompressionType, TFRecordWriter
# Example Features结构兼容tensorflow.dataset
def test_write_featrue():
options = 'GZIP'
def test_write(filename, N=3, context='aaa'):
with TFRecordWriter(filename, options=options) as file_writer:
for _ in range(N):
val1 = data_serialize.Int64List(value=[1, 2, 3] * 20)
val2 = data_serialize.FloatList(value=[1, 2, 3] * 20)
val3 = data_serialize.BytesList(value=[b'The china', b'boy'])
featrue = data_serialize.Features(feature=
{
"item_0": data_serialize.Feature(int64_list=val1),
"item_1": data_serialize.Feature(float_list=val2),
"item_2": data_serialize.Feature(bytes_list=val3)
}
)
example = data_serialize.Example(features=featrue)
file_writer.write(example.SerializeToString())
test_write('d:/example.tfrecords0', 3, 'file0')
test_write('d:/example.tfrecords1', 10, 'file1')
test_write('d:/example.tfrecords2', 12, 'file2')
# 写任意字符串
def test_write_string():
options = 'GZIP'
def test_write(filename, N=3, context='aaa'):
with TFRecordWriter(filename, options=options) as file_writer:
for _ in range(N):
# x, y = np.random.random(), np.random.random()
file_writer.write(context + '____' + str(_))
test_write('d:/example.tfrecords0', 3, 'file0')
test_write('d:/example.tfrecords1', 10, 'file1')
test_write('d:/example.tfrecords2', 12, 'file2')
```
### 2. record Simple Writer Demo
```python
# @Time : 2022/9/18 23:27
import pickle
import data_serialize
import numpy as np
from fastdatasets.record import load_dataset
from fastdatasets.record import RECORD, WriterObject,FeatureWriter,StringWriter,PickleWriter,DataType,NumpyWriter
filename= r'd:\\example_writer.record'
def test_writer(filename):
print('test_feature ...')
options = RECORD.TFRecordOptions(compression_type='GZIP')
f = NumpyWriter(filename,options=options)
values = []
n = 30
for i in range(n):
train_node = {
"index": np.asarray(i, dtype=np.int64),
'image': np.random.rand(3, 4),
'labels': np.random.randint(0, 21128, size=(10), dtype=np.int64),
'bdata': np.asarray(b'11111111asdadasdasdaa')
}
values.append(train_node)
if (i + 1) % 10000 == 0:
f.write_batch( values)
values.clear()
if len(values):
f.write_batch(values)
f.close()
def test_iterable(filename):
options = RECORD.TFRecordOptions(compression_type='GZIP')
datasets = load_dataset.IterableDataset(filename, options=options).parse_from_numpy_writer()
for i, d in enumerate(datasets):
print(i, d)
def test_random(filename):
options = RECORD.TFRecordOptions(compression_type='GZIP')
datasets = load_dataset.RandomDataset(filename, options=options).parse_from_numpy_writer()
print(len(datasets))
for i in range(len(datasets)):
d = datasets[i]
print(i, d)
test_writer(filename)
test_iterable(filename)
```
### 3. IterableDataset demo
```python
import data_serialize
from fastdatasets.record import load_dataset, gfile, RECORD
data_path = gfile.glob('d:/example.tfrecords*')
options = RECORD.TFRecordOptions(compression_type=None)
base_dataset = load_dataset.IterableDataset(data_path, cycle_length=1,
block_length=1,
buffer_size=128,
options=options,
with_share_memory=True)
def test_batch():
num = 0
for _ in base_dataset:
num += 1
print('base_dataset num', num)
base_dataset.reset()
ds = base_dataset.repeat(2).repeat(2).repeat(3).map(lambda x: x + bytes('_aaaaaaaaaaaaaa', encoding='utf-8'))
num = 0
for _ in ds:
num += 1
print('repeat(2).repeat(2).repeat(3) num ', num)
def test_torch():
def filter_fn(x):
if x == b'file2____2':
return True
return False
base_dataset.reset()
dataset = base_dataset.filter(filter_fn).interval(2, 0)
i = 0
for d in dataset:
i += 1
print(i, d)
base_dataset.reset()
dataset = base_dataset.batch(3)
i = 0
for d in dataset:
i += 1
print(i, d)
# torch.utils.data.IterableDataset
from fastdatasets.torch_dataset import IterableDataset
dataset.reset()
ds = IterableDataset(dataset=dataset)
for d in ds:
print(d)
def test_mutiprocess():
print('mutiprocess 0...')
base_dataset.reset()
dataset = base_dataset.shard(num_shards=3, index=0)
i = 0
for d in dataset:
i += 1
print(i, d)
print('mutiprocess 1...')
base_dataset.reset()
dataset = base_dataset.shard(num_shards=3, index=1)
i = 0
for d in dataset:
i += 1
print(i, d)
print('mutiprocess 2...')
base_dataset.reset()
dataset = base_dataset.shard(num_shards=3, index=2)
i = 0
for d in dataset:
i += 1
print(i, d)
```
### 4. RandomDataset demo
```python
from fastdatasets.record import load_dataset, gfile, RECORD
data_path = gfile.glob('d:/example.tfrecords*')
options = RECORD.TFRecordOptions(compression_type=None)
dataset = load_dataset.RandomDataset(data_path, options=options,
with_share_memory=True)
dataset = dataset.map(lambda x: x + b"adasdasdasd")
print(len(dataset))
for i in range(len(dataset)):
print(i + 1, dataset[i])
print('batch...')
dataset = dataset.batch(7)
for i in range(len(dataset)):
print(i + 1, dataset[i])
print('unbatch...')
dataset = dataset.unbatch()
for i in range(len(dataset)):
print(i + 1, dataset[i])
print('shuffle...')
dataset = dataset.shuffle(10)
for i in range(len(dataset)):
print(i + 1, dataset[i])
print('map...')
dataset = dataset.map(transform_fn=lambda x: x + b'aa22222222222222222222222222222')
for i in range(len(dataset)):
print(i + 1, dataset[i])
print('torch Dataset...')
from fastdatasets.torch_dataset import Dataset
d = Dataset(dataset)
for i in range(len(d)):
print(i + 1, d[i])
```
### 5. leveldb dataset
```python
# @Time : 2022/10/27 20:37
# @Author : tk
import numpy as np
from tqdm import tqdm
from fastdatasets.leveldb import DB,load_dataset,WriterObject,DataType,StringWriter,JsonWriter,FeatureWriter,NumpyWriter
db_path = 'd:\\example_leveldb_numpy'
def test_write(db_path):
options = DB.LeveldbOptions(create_if_missing=True,error_if_exists=False)
f = NumpyWriter(db_path, options = options)
keys,values = [],[]
n = 30
for i in range(n):
train_node = {
"index":np.asarray(i,dtype=np.int64),
'image': np.random.rand(3,4),
'labels': np.random.randint(0,21128,size=(10),dtype=np.int64),
'bdata': np.asarray(b'11111111asdadasdasdaa')
}
keys.append('input{}'.format(i))
values.append(train_node)
if (i+1) % 10000 == 0:
f.put_batch(keys,values)
keys.clear()
values.clear()
if len(keys):
f.put_batch(keys, values)
f.get_writer.put('total_num',str(n))
f.close()
def test_random(db_path):
options = DB.LeveldbOptions(create_if_missing=False, error_if_exists=False)
dataset = load_dataset.RandomDataset(db_path,
data_key_prefix_list=('input',),
num_key='total_num',
options = options)
dataset = dataset.parse_from_numpy_writer().shuffle(10)
print(len(dataset))
for i in tqdm(range(len(dataset)),total=len(dataset)):
d = dataset[i]
print(i,d)
test_write(db_path)
test_random(db_path)
```
### 6. lmdb dataset
```python
# @Time : 2022/10/27 20:37
# @Author : tk
import numpy as np
from tqdm import tqdm
from fastdatasets.lmdb import DB,LMDB,load_dataset,WriterObject,DataType,StringWriter,JsonWriter,FeatureWriter,NumpyWriter
db_path = 'd:\\example_lmdb_numpy'
def test_write(db_path):
options = DB.LmdbOptions(env_open_flag = 0,
env_open_mode = 0o664, # 8进制表示
txn_flag = 0,
dbi_flag = 0,
put_flag = 0)
f = NumpyWriter(db_path, options = options,map_size=1024 * 1024 * 1024)
keys, values = [], []
n = 30
for i in range(n):
train_node = {
'image': np.random.rand(3, 4),
'labels': np.random.randint(0, 21128, size=(10), dtype=np.int64),
'bdata': np.asarray(b'11111111asdadasdasdaa')
}
keys.append('input{}'.format(i))
values.append(train_node)
if (i + 1) % 10000 == 0:
f.put_batch(keys, values)
keys.clear()
values.clear()
if len(keys):
f.put_batch(keys, values)
f.get_writer.put('total_num',str(n))
f.close()
def test_random(db_path):
options = DB.LmdbOptions(env_open_flag=DB.LmdbFlag.MDB_RDONLY,
env_open_mode=0o664, # 8进制表示
txn_flag=LMDB.LmdbFlag.MDB_RDONLY,
dbi_flag=0,
put_flag=0)
dataset = load_dataset.RandomDataset(db_path,
data_key_prefix_list=('input',),
num_key='total_num',
options = options)
dataset = dataset.parse_from_numpy_writer().shuffle(10)
print(len(dataset))
for i in tqdm(range(len(dataset)), total=len(dataset)):
d = dataset[i]
print(d)
test_write(db_path)
test_random(db_path)
```
### 7. arrow dataset
```python
from fastdatasets.arrow.writer import PythonWriter
from fastdatasets.arrow.dataset import load_dataset,arrow
path_file = 'd:/tmp/data.arrow'
with_stream = True
def test_write():
fs = PythonWriter(path_file,
schema={'id': 'int32',
'text': 'str',
'map': 'map',
'map2': 'map_list'
},
with_stream=with_stream,
options=None)
for i in range(2):
data = {
"id": list(range(i * 3,(i+ 1) * 3)),
'text': ['asdasdasdas' + str(i) for i in range(3)],
'map': [
{"a": "aa1" + str(i), "b": "bb1", "c": "ccccccc"},
{"a": "aa2", "b": "bb2", "c": "ccccccc"},
{"a": "aa3", "b": "bb3", "c": "ccccccc"},
],
'map2': [
[
{"a": "11" + str(i), "b": "bb", "c": "ccccccc"},
{"a": "12", "b": "bb", "c": "ccccccc"},
{"a": "13", "b": "bb", "c": "ccccccc"},
],
[
{"a": "21", "b": "bb", "c": "ccccccc"},
{"a": "22", "b": "bb", "c": "ccccccc"},
],
[
{"a": "31", "b": "bb", "c": "ccccccc"},
{"a": "32", "b": "bb", "c": "ccccccc"},
{"a": "32", "b": "bb", "c": "ccccccc22222222222222"},
]
]
}
# fs.write_batch(data.keys(),data.values())
status = fs.write_batch(data.keys(),data.values())
assert status.ok(),status.message()
fs.close()
def test_random():
dataset = load_dataset.RandomDataset(path_file,with_share_memory=not with_stream)
print('total', len(dataset))
for i in range(len(dataset)):
print(i,dataset[i])
def test_read_iter():
dataset = load_dataset.IterableDataset(path_file,with_share_memory=not with_stream,batch_size=1)
for d in dataset:
print('iter',d)
test_write()
test_random()
test_read_iter()
```
### 8. parquet dataset
```python
from fastdatasets.parquet.writer import PythonWriter
from fastdatasets.parquet.dataset import load_dataset
from tfrecords.python.io.arrow import ParquetReader,arrow
path_file = 'd:/tmp/data.parquet'
def test_write():
fs = PythonWriter(path_file,
schema={'id': 'int32',
'text': 'str',
'map': 'map',
'map2': 'map_list'
},
parquet_options=dict(write_batch_size = 10))
for i in range(2):
data = {
"id": list(range(i * 3, (i + 1) * 3)),
'text': ['asdasdasdas' + str(i) for i in range(3)],
'map': [
{"a": "aa1", "b": "bb1", "c": "ccccccc"},
{"a": "aa2", "b": "bb2", "c": "ccccccc"},
{"a": "aa3", "b": "bb3", "c": "ccccccc"},
],
'map2': [
[
{"a": "11", "b": "bb", "c": "ccccccc"},
{"a": "12", "b": "bb", "c": "ccccccc"},
{"a": "13", "b": "bb", "c": "ccccccc"},
],
[
{"a": "21", "b": "bb", "c": "ccccccc"},
{"a": "22", "b": "bb", "c": "ccccccc"},
],
[
{"a": "31", "b": "bb", "c": "ccccccc"},
{"a": "32", "b": "bb", "c": "ccccccc"},
{"a": "32", "b": "bb", "c": "ccccccc22222222222222"},
]
]
}
# fs.write_batch(data.keys(),data.values())
fs.write_table(data.keys(),data.values())
fs.close()
def test_random():
dataset = load_dataset.RandomDataset(path_file)
print('total', len(dataset))
for i in range(len(dataset)):
print(dataset[i])
def test_read_iter():
dataset = load_dataset.IterableDataset(path_file,batch_size=1)
for d in dataset:
print('iter',d)
test_write()
test_random()
test_read_iter()
```