tfrecord下載tfrecord源代碼下載

Tfrecord讀者和作家

該庫允許在Python中有效地讀取和編寫Tfrecord文件。該庫還為Pytorch提供了TfreCord文件的iTerabledataSet讀取器。目前支持未壓縮和壓縮的Gzip Tfrecords。

安裝

 pip3 install 'tfrecord[torch]'

用法

建議為每個Tfrecord文件創建一個索引文件。使用多個工人時必須提供索引文件，否則加載程序可以返回重複記錄。您可以使用此實用程序程序為單個tfrecord文件創建索引文件：

 python3 -m tfrecord.tools.tfrecord2idx <tfrecord path> <index path>

在目錄運行中創建所有“ .tfidnex”文件的“ .tfidnex”文件：

 tfrecord2idx <data dir>

閱讀和寫作tf.train.example

讀取tf。示例記錄pytorch

使用tfrecorddataset讀取pytorch中的tfrecord文件。

 import torch
from tfrecord . torch . dataset import TFRecordDataset

tfrecord_path = "/tmp/data.tfrecord"
index_path = None
description = { "image" : "byte" , "label" : "float" }
dataset = TFRecordDataset ( tfrecord_path , index_path , description )
loader = torch . utils . data . DataLoader ( dataset , batch_size = 32 )

data = next ( iter ( loader ))
print ( data )

使用MultitFrecordDataSet讀取多個Tfrecord文件。此類從給定的tfrecord文件中的示例帶有給定的概率。

 import torch
from tfrecord . torch . dataset import MultiTFRecordDataset

tfrecord_pattern = "/tmp/{}.tfrecord"
index_pattern = "/tmp/{}.index"
splits = {
    "dataset1" : 0.8 ,
    "dataset2" : 0.2 ,
}
description = { "image" : "byte" , "label" : "int" }
dataset = MultiTFRecordDataset ( tfrecord_pattern , index_pattern , splits , description )
loader = torch . utils . data . DataLoader ( dataset , batch_size = 32 )

data = next ( iter ( loader ))
print ( data )

無限和有限的Pytorch數據集

默認情況下， MultiTFRecordDataset是無限的，這意味著它將永遠採樣數據。您可以通過提供適當的標誌來使其有限

 dataset = MultiTFRecordDataset(..., infinite=False)

改組數據

當您提供隊列尺寸時，TfrecordDataSet和MultitFrecordDataSet都會自動將數據供電。

 dataset = TFRecordDataset(..., shuffle_queue_size=1024)

轉換輸入數據

您可以選擇將函數作為transform參數傳遞，以在返回之前執行功能的後處理。例如，這可以用於解碼圖像或將顏色標準化為特定範圍或墊變量長度序列。

 import tfrecord
import cv2

def decode_image ( features ):
    # get BGR image from bytes
    features [ "image" ] = cv2 . imdecode ( features [ "image" ], - 1 )
    return features


description = {
    "image" : "bytes" ,
}

dataset = tfrecord . torch . TFRecordDataset ( "/tmp/data.tfrecord" ,
                                         index_path = None ,
                                         description = description ,
                                         transform = decode_image )

data = next ( iter ( dataset ))
print ( data )

在python中編寫tf.xample記錄

 import tfrecord

writer = tfrecord . TFRecordWriter ( "/tmp/data.tfrecord" )
writer . write ({
    "image" : ( image_bytes , "byte" ),
    "label" : ( label , "float" ),
    "index" : ( index , "int" )
})
writer . close ()

讀取python中的tf。示例記錄

 import tfrecord

loader = tfrecord . tfrecord_loader ( "/tmp/data.tfrecord" , None , {
    "image" : "byte" ,
    "label" : "float" ,
    "index" : "int"
})
for record in loader :
    print ( record [ "label" ])

閱讀和寫作tf.train.sequenceexample

可以使用上面顯示的相同方法讀取和編寫sequenceExamples，並帶有額外的參數（用於讀取的sequence_description和sequence_datum用於寫作），從而導致相應的讀/寫功能將數據視為sequenceExample。

編寫序列化示例以歸檔

 import tfrecord

writer = tfrecord . TFRecordWriter ( "/tmp/data.tfrecord" )
writer . write ({ 'length' : ( 3 , 'int' ), 'label' : ( 1 , 'int' )},
             { 'tokens' : ([[ 0 , 0 , 1 ], [ 0 , 1 , 0 ], [ 1 , 0 , 0 ]], 'int' ), 'seq_labels' : ([ 0 , 1 , 1 ], 'int' )})
writer . write ({ 'length' : ( 3 , 'int' ), 'label' : ( 1 , 'int' )},
             { 'tokens' : ([[ 0 , 0 , 1 ], [ 1 , 0 , 0 ]], 'int' ), 'seq_labels' : ([ 0 , 1 ], 'int' )})
writer . close ()

在Python中讀取sequenceexamples

從序列樣本yeilds讀取一個包含兩個元素的元組。

 import tfrecord

context_description = { "length" : "int" , "label" : "int" }
sequence_description = { "tokens" : "int" , "seq_labels" : "int" }
loader = tfrecord . tfrecord_loader ( "/tmp/data.tfrecord" , None ,
                                  context_description ,
                                  sequence_description = sequence_description )

for context , sequence_feats in loader :
    print ( context [ "label" ])
    print ( sequence_feats [ "seq_labels" ])

閱讀pytorch中的序列示例

如有關Transforming Input的部分所述，可以將函數作為transform參數傳遞，以執行特徵的後處理。對於序列特徵，應該使用它，因為這些是可變的長度序列，並且需要在批處理之前填充。

 import torch
import numpy as np
from tfrecord . torch . dataset import TFRecordDataset

PAD_WIDTH = 5
def pad_sequence_feats ( data ):
    context , features = data
    for k , v in features . items ():
        features [ k ] = np . pad ( v , (( 0 , PAD_WIDTH - len ( v )), ( 0 , 0 )), 'constant' )
    return ( context , features )

context_description = { "length" : "int" , "label" : "int" }
sequence_description = { "tokens" : "int " , "seq_labels" : "int" }
dataset = TFRecordDataset ( "/tmp/data.tfrecord" ,
                          index_path = None ,
			  description = context_description ,
			  transform = pad_sequence_feats ,
			  sequence_description = sequence_description )
loader = torch . utils . data . DataLoader ( dataset , batch_size = 32 )
data = next ( iter ( loader ))
print ( data )

另外，您可以選擇實現自定義collate_fn ，以組裝批處理，例如執行動態填充。

 import torch
import numpy as np
from tfrecord . torch . dataset import TFRecordDataset

def collate_fn ( batch ):
    from torch . utils . data . _utils import collate
    from torch . nn . utils import rnn
    context , feats = zip ( * batch )
    feats_ = { k : [ torch . Tensor ( d [ k ]) for d in feats ] for k in feats [ 0 ]}
    return ( collate . default_collate ( context ),
            { k : rnn . pad_sequence ( f , True ) for ( k , f ) in feats_ . items ()})

context_description = { "length" : "int" , "label" : "int" }
sequence_description = { "tokens" : "int " , "seq_labels" : "int" }
dataset = TFRecordDataset ( "/tmp/data.tfrecord" ,
                          index_path = None ,
			  description = context_description ,
			  transform = pad_sequence_feats ,
			  sequence_description = sequence_description )
loader = torch . utils . data . DataLoader ( dataset , batch_size = 32 , collate_fn = collate_fn )
data = next ( iter ( loader ))
print ( data )