[tests] Fix build by fixing tqdm version #62
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This PR implements MMBT model described in https://arxiv.org/pdf/1909.02950.pdf. Salient features: - Two training modes: pretraining and classification - Works with both images as well as features - Can use any underlying encoder - Starter configurations provided for Hateful Memes and MaskedCOCO We provide two modes of training mmbt, pretraining and classification which are configurable through `training_head_type` configuration parameters. We also provide starter configuration that can be used to compose training configs for your use case. Sample configurations are provided for Hateful Memes and Masked COCO dataset. The model can work with either direct images or features provided from faster rcnn as is used in other Pythia models. A base is provided for easy building of further baselines. Follow the steps in #62 to build the setup for hateful memes dataset and then test following commands: - For training on hateful memes with images: ```sh python -u tools/run.py training.batch_size=16 config=projects/mmbt/configs/hateful_memes/defaults.yaml dataset=hateful_memes model=mmbt training.log_interval=10 training.find_unused_parameters=True training.num_workers=2 ``` - For training on hateful memes but with preextracted features: ```sh python -u tools/run.py training.batch_size=16 config=projects/mmbt/configs/hateful_memes/with_features.yaml dataset=hateful_memes model=mmbt training.log_interval=10 training.find_unused_parameters=True training.num_workers=2 ``` Both of the above commands invoke the classification MMBT model and #62 to be landed before they are run - For running pretraining on masked_coco with preextracted features run: ```sh python -u tools/run.py training.batch_size=16 config=projects/mmbt/configs/masked_coco/defaults.yaml dataset=masked_coco model=mmbt training.log_interval=10 training.find_unused_parameters=True training.num_workers=2 ```
apsdehal
added a commit
that referenced
this pull request
May 8, 2020
This PR implements MMBT model described in https://arxiv.org/pdf/1909.02950.pdf. Salient features: - Two training modes: pretraining and classification - Works with both images as well as features - Can use any underlying encoder - Starter configurations provided for Hateful Memes and MaskedCOCO We provide two modes of training mmbt, pretraining and classification which are configurable through `training_head_type` configuration parameters. We also provide starter configuration that can be used to compose training configs for your use case. Sample configurations are provided for Hateful Memes and Masked COCO dataset. The model can work with either direct images or features provided from faster rcnn as is used in other Pythia models. A base is provided for easy building of further baselines. Follow the steps in #62 to build the setup for hateful memes dataset and then test following commands: - For training on hateful memes with images: ```sh python -u tools/run.py training.batch_size=16 config=projects/mmbt/configs/hateful_memes/defaults.yaml dataset=hateful_memes model=mmbt training.log_interval=10 training.find_unused_parameters=True training.num_workers=2 ``` - For training on hateful memes but with preextracted features: ```sh python -u tools/run.py training.batch_size=16 config=projects/mmbt/configs/hateful_memes/with_features.yaml dataset=hateful_memes model=mmbt training.log_interval=10 training.find_unused_parameters=True training.num_workers=2 ``` Both of the above commands invoke the classification MMBT model and #62 to be landed before they are run - For running pretraining on masked_coco with preextracted features run: ```sh python -u tools/run.py training.batch_size=16 config=projects/mmbt/configs/masked_coco/defaults.yaml dataset=masked_coco model=mmbt training.log_interval=10 training.find_unused_parameters=True training.num_workers=2 ```
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