Standardize batchifying image features · facebookresearch/ParlAI#2461

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説明

Currently, ImageSeq2seqAgent and ImagePolyencoderAgent have two separate .batchify_image_features() methods for preparing image features for encoding. The main advantage of ImagePolyencoderAgent's implementation of this method is that the images are packed into a Tensor for data_parallel. It's worth testing whether ImageSeq2seqAgent really requires a separate .batchify_image_features() method now: if not, it should be removed, and ImagePolyencoderAgent.batchify_image_features() should be moved into TorchImageAgent.

コントリビューターガイド

技術スタック: python
領域: machine learningbackend
Issue 種別: refactor
難度: 3
推定時間: half day
活動状況: stale
明確さ: clear
前提条件: ParlAI agentsPyTorch DataParallelImage feature batchification
初心者向け度: 65
調査方針: Examine the implementation of batchify image features in both ImageSeq2seqAgent and ImagePolyencoderAgent within the ParlAI codebase. Determine if the differences are necessary due to architectural constraints or if the Polyencoder version can be generalized. If possible, move the Polyencoder's method to TorchImageAgent and remove the duplicate in Seq2seq. Refer to the discussion in issue #2461 for context.