prefetch_generator的使用可以加速。pip install prefetch_generator用法如下,用DataLoaderX替换Dataloader。 预期可能有10-15%的性能增益。 # 新建DataLoaderX类fromtorch.utils.dataimportDataLoaderfromprefetch_generatorimportBackgroundGeneratorclassDataLoaderX(DataLoader):def__iter__(self):returnBackgroundGenerator(super(...
prefetch_generator==1.0.1 nltk==3.5 transformers==3.4.0 xlrd==1.2.0 torch==1.7.1 XlsxWriter==1.3.7 numpy==1.19.4 requests==2.25.1 scikit_learn==0.24.1一键安装/批量安装多个依赖包【温馨提示】 最好先用conda建一个新环境,做好环境隔离。不然,很有可能会污染你原来的环境,会出现以前跑通的代码...
batch_sampler=None,num_workers=0,collate_fn=None,pin_memory=False,drop_last=False,timeout=0,worker_init_fn=None,multiprocessing_context=None,generator=None,*,prefetch_factor=2,persistent_workers=False,pin_memory_device='')
[_collate_fn_t] = None, pin_memory: bool = False, drop_last: bool = False, timeout: float = 0, worker_init_fn: Optional[_worker_init_fn_t] = None, multiprocessing_context=None, generator=None, *, prefetch_factor: int = 2, persistent_workers: bool = False, pin_memory_device: ...
drop_last=False,timeout=0,worker_init_fn=None,multiprocessing_context=None,generator=None,*,prefetch_factor=None,persistent_workers=False,pin_memory_device='') Data loader. Combines a dataset and a sampler, and provides an iterable over the given dataset. TheDataLoadersupports both map-style and...
generator=None, *, prefetch_factor=2, persistent_workers=False ) [source] Data loader. Combines a dataset and a sampler, and provides an iterable over the given dataset. The DataLoader supports both map-style and iterable-style datasets with single- or multi-process loading, customizing loading...
collate_fn=None, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None, multiprocessing_context=None, generator=None, *, prefetch_factor=2, persistent_workers=False, pin_memory_device='') 1. 2. 3. 4. 其实我们只要知道DataLoader接收一个dataset对象并生成一个DataLoader对象便可,我...
prefetch_generator==1.0.3 < prettytable==3.11.0 | prettytable==3.12.0 prometheus_client==0.20.0 | prometheus_client==0.21.1 prompt_toolkit==3.0.47 | prompt_toolkit==3.0.48 prophet==1.1.5 | propcache==0.2.1 proto-plus==1.24.0 | prophet==1.1.6 ...
prefetch_generator==1.0.3 < prettytable==3.11.0 | prettytable==3.12.0 prometheus_client==0.20.0 | prometheus_client==0.21.1 prompt_toolkit==3.0.47 | prompt_toolkit==3.0.48 prophet==1.1.5 | propcache==0.2.1 proto-plus==1.24.0 | prophet==1.1.6 > proto-plus==1.25.0 psycopg2=...
['label']batch_size = 4if is_train_dataset is not None:#tf.data.experimental.AUTOTUNE#根据计算机性能进行运算速度的调整dataset = dataset.map(_parse_example).shuffle(buffer_size=2000).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)else:dataset = dataset.map(_parse_example)dataset = ...