基于区块链的毕业设计🚀 Feature request – 🚀 功能请求

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🚀 Feature request

As can be seen here: https://github.com/huggingface/transformers/blob/11655fafdd42eb56ad94e09ecd84d4dc2d1041ae/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L47,

the function _compute_mask_indices (responsible for spec augment) of Wav2Vec2 is written in numpy which means that the function is not GPU compatible.
The function could simply be rewritten in PyTorch

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some recordings have low volume so the output can be sometimes really quiet. how about we add a volume bar so we can make the output louder/quieter?

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We keep this issue open to collect feature requests from users and hear your voice. Our monthly release plan is also available here.

You can either:

  1. Suggest a new feature by leaving a comment.
  2. Vote for a feature request with 👍 or be against with 👎. (Remember that developers are busy and cannot respond to all feature requests, so vote for your most favorable one!)
  3. Tell us that
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🚀 Feature

It would be nice to have a possibility to rename currently automatically set metrics postfix dataloader_idx_X to the custom name for better metrics visualization.
Say I have an imagenet and cifar datasets and accuracy metrics. Now I got accuracy_dataloader_idx_0 and
accuracy_dataloader_idx_1 metrics postfix

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Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

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Add a new API for converting a model to external data. Today the conversion happens in 2 steps
external_data_helper.convert_model_to_external_data(<model>, <all_tensors_to_one_file>, <size_threshold>) save_model(model, output_path)
We want to add another api which combines the 2 steps
`
save_model_to_external_data(, <output_

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While setting train_parameters to False very often we also may consider disabling dropout/batchnorm, in other words, to run the pretrained model in eval mode.
We’ve done a little modification to PretrainedTransformerEmbedder that allows providing whether the token embedder should be forced to eval mode during the training phase.

Do you this feature might be handy? Should I open a PR?

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I’m using mxnet to do some work, but there is nothing when I search the mxnet trial and example.

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Current pytorch implementation ignores the argument split_f in the function train_batch_ch13 as shown below.

def train_batch_ch13(net, X, y, loss, trainer, devices):     if isinstance(X, list):         # Required for BERT Fine-tuning (to be covered later)         X = [x.to(devices[0]) for x in X]     else:         X = X.to(devices[0]) ...

Todo: Define the argument `

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Please can you train ghostnet.
(i don’t have the imagenet dataset)

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Is it possible to run this on a (recent) Mac, which does not support CUDA? I would have guessed setting –GPU 0 would not attempt to call CUDA, but it fails.

File "/Users/../Desktop/bopbtl/venv/lib/python3.7/site-packages/torch/cuda/__init__.py", line 61, in _check_driver     raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enable 
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Python 8,995


🚀 功能请求

如图所示:https://github.com/huggingface/transformers/blob/11655fafdd42eb56ad94e09ecd84d4dc2d1041ae/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L47,

Wav2Vec2的函数computeu masku index(负责规范扩充)是用numpy编写的,这意味着该函数与GPU不兼容。
该函数可以简单地用PyTorch重写

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一些录音的音量很小,因此有时输出可能非常安静。不如我们加一个音量条,这样我们可以使输出声音更大/更安静?

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我们将此问题公开,以收集用户的功能请求并倾听您的声音。我们的每月发布计划也在这里提供。

您可以:

  1. 通过留下评论来建议新功能。
  2. 投票支持具有👍 或者反对👎. (请记住,开发人员很忙,无法响应所有功能请求,所以请选择您最喜欢的功能!)告诉我们
  3. Tell us that
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🚀 功能

如果能够将当前自动设置的metrics postfix dataloaderu idxu X重命名为自定义名称,以实现更好的度量可视化,那就太好了。
假设我有一个imagenet和cifar数据集以及准确度度量。现在我得到了accurityu dataloaderu idxu 0和
accurityu dataloaderu idxu 1 metrics postfix

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更改张量数据至张量分离()由于
pytorch/pytorch#6990(评论)
张量分离()比张量数据.

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添加用于将模型转换为外部数据的新API。如今,转换分两步进行
外部数据_helper.convert model to external数据(&lt;model&gt;,&lt;allu tensorsu to u oneu file&gt;,&lt;sizeu threshold&gt;)saveu model(model,outputu path)
我们想添加另一个api,它结合了两个步骤
`
saveu modelu to u externalu data(,&lt;输出

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在经常将列参数设置为False时,我们还可以考虑禁用dropout/batchnorm,换句话说,在评估模式下运行预训练模型。
我们对预训练TransformerEmbedder做了一些修改,允许提供在训练阶段是否应强制令牌嵌入器进入评估模式。

您认为这个功能可能很方便吗?我应该开公关吗?

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我正在使用mxnet做一些工作,但是在搜索mxnet试用版和示例时却什么都没有。

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当前的pytorch实现忽略函数列batch ch13中的参数split,如下所示。

def train_batch_ch13(net, X, y, loss, trainer, devices):     if isinstance(X, list):         # Required for BERT Fine-tuning (to be covered later)         X = [x.to(devices[0]) for x in X]     else:         X = X.to(devices[0]) ...

Todo:定义参数`

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请训练ghostnet。
(我没有imagenet数据集)

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是否可以在(最近的)不支持CUDA的Mac上运行此操作?我猜设置——GPU0不会尝试调用CUDA,但它失败了。

File "/Users/../Desktop/bopbtl/venv/lib/python3.7/site-packages/torch/cuda/__init__.py", line 61, in _check_driver     raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enable 
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Python 8,995

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