facebookresearch/ParlAI

Standardize batchifying image features

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#2,461 创建于 2020年3月11日

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 (4 评论) (1 反应) (0 负责人)Python (10,371 star) (2,089 fork)batch import
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描述

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.

贡献者指南

技术栈
python
领域
machine learningbackend
议题类型
refactor
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
half day
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
ParlAI agentsPyTorch DataParallelImage feature batchification
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
65
研究方向
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.