[Feat] Support public dataset of visualglm and llava. (#265)

* [Feat] Add public dataset support of VisualGLM.

* [Feat] Refactor LLaVA.

* [Feat] Add public dataset support of LlaVA.

* [Fix] Add  arg.

configs/multimodal/llava/llava_7b_coco_caption.py

@@ -0,0 +1,50 @@
+from opencompass.multimodal.models.llava import LLaVABasePromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(type='mmpretrain.PackInputs', algorithm_keys=['image_id']),
+]
+
+
+dataset = dict(type='mmpretrain.COCOCaption',
+               data_root='data/coco',
+               data_prefix=dict(img_path='images'),
+               ann_file='annotations/coco_karpathy_val.json',
+               pipeline=val_pipeline)
+
+llava_coco_caption_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_coco_caption_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    is_caption_task=True,
+    prompt_constructor=dict(type=LLaVABasePromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_coco_caption_evaluator = [
+    dict(
+        type='mmpretrain.COCOCaption',
+        ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
+    )  # noqa
+]
+

configs/multimodal/llava/llava_7b_flickr30k.py

@@ -0,0 +1,52 @@
+from opencompass.multimodal.models.llava import LLaVABasePromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(type='mmpretrain.PackInputs', algorithm_keys=['image_id']),
+]
+
+
+dataset = dict(type='mmpretrain.Flickr30kCaption',
+               data_root='data/flickr30k',
+               ann_file='annotations/dataset_flickr30k.json',
+               data_prefix='images',
+               split='val',
+               pipeline=val_pipeline)
+
+llava_flickr30k_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_flickr30k_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    is_caption_task=True,
+    prompt_constructor=dict(type=LLaVABasePromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_flickr30k_evaluator = [
+    dict(
+        type='mmpretrain.COCOCaption',
+        ann_file='data/flickr30k/annotations/flickr30k_val_gt.json',
+    )  # noqa
+]
+
+

configs/multimodal/llava/llava_7b_gqa.py

@@ -0,0 +1,49 @@
+from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+
+dataset = dict(type='mmpretrain.GQA',
+               data_root='data/gqa',
+               data_prefix='images',
+               ann_file='annotations/testdev_balanced_questions.json',
+               pipeline=val_pipeline)
+
+llava_gqa_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_gqa_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')]
+
+

configs/multimodal/llava/llava_7b_mmbench.py

@@ -1,3 +1,5 @@
+from opencompass.multimodal.models.llava import LLaVAMMBenchPromptConstructor, LLaVABasePostProcessor
+
 # dataloader settings
 val_pipeline = [
     dict(type='mmpretrain.torchvision/Resize',
@@ -34,6 +36,8 @@ mmbench_dataloader = dict(
 llava_model = dict(
     type='llava',
     model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAMMBenchPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
 )  # noqa
 
 # evaluation settings

configs/multimodal/llava/llava_7b_ocr_vqa.py

@@ -0,0 +1,49 @@
+from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(type='mmpretrain.OCRVQA',
+               data_root='data/ocrvqa',
+               ann_file='annotations/dataset.json',
+               split='test',
+               data_prefix='images',
+               pipeline=val_pipeline)
+
+llava_ocrvqa_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_ocrvqa_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_ocrvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
+
+

configs/multimodal/llava/llava_7b_ok_vqa.py

@@ -0,0 +1,51 @@
+from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(
+    type='mmpretrain.COCOVQA',
+    data_root='data/okvqa',
+    question_file='annotations/OpenEnded_mscoco_val2014_questions.json',
+    ann_file='annotations/mscoco_val2014_annotations.json',
+    pipeline=val_pipeline,
+    data_prefix='images/val2014',
+)
+
+llava_okvqa_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_okvqa_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_okvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
+
+

configs/multimodal/llava/llava_7b_scienceqa.py

@@ -0,0 +1,50 @@
+from opencompass.multimodal.models.llava import LLaVAScienceQAPromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(type='mmpretrain.PackInputs',
+         algorithm_keys=[
+             'question', 'gt_answer', 'choices', 'hint', 'lecture', 'solution'
+         ])
+]
+
+dataset = dict(type='mmpretrain.ScienceQA',
+               data_root='./data/scienceqa',
+               split='val',
+               split_file='pid_splits.json',
+               ann_file='problems.json',
+               image_only=True,
+               data_prefix=dict(img_path='val'),
+               pipeline=val_pipeline)
+
+llava_scienceqa_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_scienceqa_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAScienceQAPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')]
+
+

configs/multimodal/llava/llava_7b_textvqa.py

@@ -0,0 +1,50 @@
+from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(
+    type='mmpretrain.TextVQA',
+    data_root='data/textvqa',
+    ann_file='annotations/TextVQA_0.5.1_val.json',
+    pipeline=val_pipeline,
+    data_prefix='images/train_images',
+)
+
+llava_textvqa_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_textvqa_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_textvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
+
+

configs/multimodal/llava/llava_7b_vizwiz.py

@@ -0,0 +1,48 @@
+from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(type='mmpretrain.VizWiz',
+               data_root='data/vizwiz/',
+               data_prefix='Images/val',
+               ann_file='Annotations/val.json',
+               pipeline=val_pipeline)
+
+llava_vizwiz_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_vizwiz_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_vizwiz_evaluator = [dict(type='mmpretrain.VQAAcc')]
+
+

configs/multimodal/llava/llava_7b_vqav2.py

@@ -0,0 +1,50 @@
+from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(
+    type='mmpretrain.COCOVQA',
+    data_root='data/coco',
+    data_prefix='images/val2014',
+    question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
+    ann_file='annotations/v2_mscoco_val2014_annotations.json',
+    pipeline=val_pipeline)
+
+llava_vqav2_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_vqav2_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
+    post_processor=dict(type=LLaVABasePostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_vqav2_evaluator = [dict(type='mmpretrain.VQAAcc')]
+
+

configs/multimodal/llava/llava_7b_vsr.py

@@ -0,0 +1,48 @@
+from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVAVSRPostProcessor
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(
+        type='mmpretrain.torchvision/Normalize',
+        mean=(0.48145466, 0.4578275, 0.40821073),
+        std=(0.26862954, 0.26130258, 0.27577711),
+    ),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(type='mmpretrain.VSR',
+               data_root='data/vsr/',
+               data_prefix='images/',
+               ann_file='annotations/test.json',
+               pipeline=val_pipeline)
+
+llava_vsr_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    dataset=dataset,
+    collate_fn=dict(type='pseudo_collate'),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+
+# model settings
+llava_vsr_model = dict(
+    type='llava',
+    model_path='/path/to/llava',
+    prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
+    post_processor=dict(type=LLaVAVSRPostProcessor)
+)  # noqa
+
+# evaluation settings
+llava_vsr_evaluator = [dict(type='mmpretrain.GQAAcc')]
+
+

configs/multimodal/visualglm/visualglm_6b_coco_caption.py

@@ -0,0 +1,45 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMBasePromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(type='mmpretrain.PackInputs', algorithm_keys=['image_id'])
+]
+
+
+dataset = dict(type='mmpretrain.COCOCaption',
+               data_root='data/coco',
+               data_prefix=dict(img_path='images'),
+               ann_file='annotations/coco_karpathy_val.json',
+               pipeline=val_pipeline)
+
+visualglm_coco_caption_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_coco_caption_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    is_caption_task=True,
+    prompt_constructor=dict(type=VisualGLMBasePromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_coco_caption_evaluator = [
+    dict(
+        type='mmpretrain.COCOCaption',
+        ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
+    )  # noqa
+]

configs/multimodal/visualglm/visualglm_6b_flickr30k.py

@@ -0,0 +1,46 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMBasePromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(type='mmpretrain.PackInputs', algorithm_keys=['image_id'])
+]
+
+
+dataset = dict(type='mmpretrain.Flickr30kCaption',
+               data_root='data/flickr30k',
+               ann_file='annotations/dataset_flickr30k.json',
+               data_prefix='images',
+               split='val',
+               pipeline=val_pipeline)
+
+visualglm_flickr30k_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_flickr30k_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    is_caption_task=True,
+    prompt_constructor=dict(type=VisualGLMBasePromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_flickr30k_evaluator = [
+    dict(
+        type='mmpretrain.COCOCaption',
+        ann_file='data/flickr30k/annotations/flickr30k_val_gt.json',
+    )  # noqa
+]

configs/multimodal/visualglm/visualglm_6b_gqa.py

@@ -0,0 +1,42 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(type='mmpretrain.GQA',
+               data_root='data/gqa',
+               data_prefix='images',
+               ann_file='annotations/testdev_balanced_questions.json',
+               pipeline=val_pipeline)
+
+visualglm_gqa_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_gqa_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')]

configs/multimodal/visualglm/visualglm_6b_mmbench.py

@@ -1,4 +1,4 @@
-from opencompass.multimodal.models.visualglm import (VisualGLMPostProcessor, VisualGLMPromptConstructor)
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMMMBenchPromptConstructor)
 
 # dataloader settings
 val_pipeline = [
@@ -30,8 +30,8 @@ mmbench_dataloader = dict(batch_size=1,
 visualglm_model = dict(
     type='visualglm',
     pretrained_path='/path/to/visualglm',  # or Huggingface repo id
-    prompt_constructor=dict(type=VisualGLMPromptConstructor),
-    post_processor=dict(type=VisualGLMPostProcessor)
+    prompt_constructor=dict(type=VisualGLMMMBenchPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
 )
 
 # evaluation settings

configs/multimodal/visualglm/visualglm_6b_ocr_vqa.py

@@ -0,0 +1,43 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(type='mmpretrain.OCRVQA',
+               data_root='data/ocrvqa',
+               ann_file='annotations/dataset.json',
+               split='test',
+               data_prefix='images',
+               pipeline=val_pipeline)
+
+visualglm_ocrvqa_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_ocrvqa_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_ocrvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]

configs/multimodal/visualglm/visualglm_6b_ok_vqa.py

@@ -0,0 +1,45 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(
+    type='mmpretrain.COCOVQA',
+    data_root='data/okvqa',
+    question_file='annotations/OpenEnded_mscoco_val2014_questions.json',
+    ann_file='annotations/mscoco_val2014_annotations.json',
+    pipeline=val_pipeline,
+    data_prefix='images/val2014',
+)
+
+visualglm_okvqa_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_okvqa_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_okvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]

configs/multimodal/visualglm/visualglm_6b_scienceqa.py

@@ -0,0 +1,44 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMScienceQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(type='mmpretrain.PackInputs',
+         algorithm_keys=[
+             'question', 'gt_answer', 'choices', 'hint', 'lecture', 'solution'
+         ])
+]
+
+dataset = dict(type='mmpretrain.ScienceQA',
+               data_root='./data/scienceqa',
+               split='val',
+               split_file='pid_splits.json',
+               ann_file='problems.json',
+               image_only=True,
+               data_prefix=dict(img_path='val'),
+               pipeline=val_pipeline)
+
+visualglm_vizwiz_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_scienceqa_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMScienceQAPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')]

configs/multimodal/visualglm/visualglm_6b_textvqa.py

@@ -0,0 +1,44 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(
+    type='mmpretrain.TextVQA',
+    data_root='data/textvqa',
+    ann_file='annotations/TextVQA_0.5.1_val.json',
+    pipeline=val_pipeline,
+    data_prefix='images/train_images',
+)
+
+visualglm_textvqa_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_textvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]

configs/multimodal/visualglm/visualglm_6b_vizwiz.py

@@ -0,0 +1,42 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(type='mmpretrain.VizWiz',
+               data_root='data/vizwiz/',
+               data_prefix='Images/val',
+               ann_file='Annotations/val.json',
+               pipeline=val_pipeline)
+
+visualglm_vizwiz_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_vizwiz_evaluator = [dict(type='mmpretrain.VQAAcc')]

configs/multimodal/visualglm/visualglm_6b_vqav2.py

@@ -0,0 +1,44 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+dataset = dict(
+    type='mmpretrain.COCOVQA',
+    data_root='data/coco',
+    data_prefix='images/val2014',
+    question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
+    ann_file='annotations/v2_mscoco_val2014_annotations.json',
+    pipeline=val_pipeline)
+
+visualglm_vqav2_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
+    post_processor=dict(type=VisualGLMBasePostProcessor)
+)
+
+# evaluation settings
+visualglm_vqav2_evaluator = [dict(type='mmpretrain.VQAAcc')]

configs/multimodal/visualglm/visualglm_6b_vsr.py

@@ -0,0 +1,43 @@
+from opencompass.multimodal.models.visualglm import (VisualGLMVSRPostProcessor, VisualGLMVQAPromptConstructor)
+
+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.LoadImageFromFile'),
+    dict(type='mmpretrain.ToPIL', to_rgb=True),
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(
+        type='mmpretrain.PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    )
+]
+
+
+dataset = dict(type='mmpretrain.VSR',
+               data_root='data/vsr/',
+               data_prefix='images/',
+               ann_file='annotations/test.json',
+               pipeline=val_pipeline)
+
+visualglm_vsr_dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+visualglm_model = dict(
+    type='visualglm',
+    pretrained_path='/path/to/visualglm',  # or Huggingface repo id
+    prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
+    post_processor=dict(type=VisualGLMVSRPostProcessor)
+)
+
+# evaluation settings
+visualglm_vsr_evaluator = [dict(type='mmpretrain.GQAAcc')]

opencompass/multimodal/models/llava/__init__.py

@@ -1,3 +1,12 @@
 from .llava import LLaVA
+from .post_processor import LLaVABasePostProcessor, LLaVAVSRPostProcessor
+from .prompt_constructor import (LLaVABasePromptConstructor,
+                                 LLaVAMMBenchPromptConstructor,
+                                 LLaVAScienceQAPromptConstructor,
+                                 LLaVAVQAPromptConstructor)
 
-__all__ = ['LLaVA']
+__all__ = [
+    'LLaVA', 'LLaVABasePromptConstructor', 'LLaVAMMBenchPromptConstructor',
+    'LLaVABasePostProcessor', 'LLaVAVQAPromptConstructor',
+    'LLaVAScienceQAPromptConstructor', 'LLaVAVSRPostProcessor'
+]

opencompass/multimodal/models/llava/llava.py

@@ -2,6 +2,7 @@ import importlib
 import os
 import sys
 
+import mmengine
 import torch
 import torch.nn as nn
 from mmengine.device import get_device
@@ -9,8 +10,6 @@ from transformers import StoppingCriteria
 
 from opencompass.registry import MM_MODELS
 
-from .prompt_constructor import LLaVAMMBenchPromptConstructor
-
 IMAGE_TOKEN_INDEX = -200
 
 
@@ -53,19 +52,27 @@ class LLaVA(nn.Module):
 
     Args:
         model_path (str): The path of llava checkpoint.
+        prompt_constructor (dict): The config of prompt constructor.
+        post_processor (dict): The config of post processor.
+        is_caption_task (bool): Whether the task is caption task.
+            Defaults to False.
     """
 
-    def __init__(self, model_path: str) -> None:
+    def __init__(
+        self,
+        model_path: str,
+        prompt_constructor: dict,
+        post_processor: dict,
+        is_caption_task: bool = False,
+    ) -> None:
         super().__init__()
         self.dtype = torch.float16
+        self.is_caption_task = is_caption_task
 
         # load LLaVA modules
         load_package()
         mm_utils = importlib.import_module('llava.mm_utils')
         builder = importlib.import_module('llava.model.builder')
-        conversation = importlib.import_module('llava.conversation')
-        self.SeparatorStyle = conversation.SeparatorStyle
-        self.conv_templates = conversation.conv_templates
 
         # load pretrained LLaVA
         # Note: When encounters with device related errors,
@@ -86,13 +93,16 @@ class LLaVA(nn.Module):
             conv_mode = 'multimodal'
         mm_use_im_start_end = getattr(model.config, 'mm_use_im_start_end',
                                       False)
-
+        prompt_constructor.update({
+            'conv_mode': conv_mode,
+            'mm_use_im_start_end': mm_use_im_start_end
+        })
+        self.prompt_constructor = mmengine.registry.build_from_cfg(
+            prompt_constructor, MM_MODELS)
+        self.post_processor = mmengine.registry.build_from_cfg(
+            post_processor, MM_MODELS)
         self.model = model
         self.tokenizer = tokenizer
-        self.prompt_constructor = LLaVAMMBenchPromptConstructor(
-            conv_templates=conversation.conv_templates,
-            conv_mode=conv_mode,
-            mm_use_im_start_end=mm_use_im_start_end)
 
     def generate(self, batch):
 
@@ -133,11 +143,12 @@ class LLaVA(nn.Module):
             )
         outputs = self.tokenizer.batch_decode(output_ids[:, input_token_len:],
                                               skip_special_tokens=True)[0]
-        outputs = outputs.strip()
-        if outputs.endswith(stop_str):
-            outputs = outputs[:-len(stop_str)]
-        output_text = outputs.strip()
 
+        output_text = self.post_processor(outputs, stop_str)
+
+        if self.is_caption_task:
+            data_sample.pred_caption = output_text
+        else:
             data_sample.pred_answer = output_text
         return data_sample
 

opencompass/multimodal/models/llava/post_processor.py

@@ -0,0 +1,28 @@
+class LLaVABasePostProcessor:
+    """Base post processor for LLaVA on MMBench."""
+
+    def __init__(self) -> None:
+        pass
+
+    def __call__(self, outputs: str, stop_str: str) -> str:
+        outputs = outputs.strip()
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        output_text = outputs.strip()
+        return output_text
+
+
+class LLaVAVSRPostProcessor(LLaVABasePostProcessor):
+    """VSR post processor for LLaVA on MMBench."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def __call__(self, outputs: str, stop_str: str) -> str:
+        output_text = super().__call__(outputs, stop_str)
+        if 'yes' in output_text.lower():
+            return 'yes'
+        elif 'no' in output_text.lower():
+            return 'no'
+        else:
+            return 'unknown'

opencompass/multimodal/models/llava/prompt_constructor.py

@@ -1,5 +1,4 @@
 import importlib
-from typing import Any
 
 DEFAULT_IMAGE_TOKEN = '<image>'
 DEFAULT_IMAGE_PATCH_TOKEN = '<im_patch>'
@@ -7,23 +6,26 @@ DEFAULT_IM_START_TOKEN = '<im_start>'
 DEFAULT_IM_END_TOKEN = '<im_end>'
 
 
-class LLaVAMMBenchPromptConstructor:
-    """Prompt constructor for LLaVA on MMBench.
+class LLaVABasePromptConstructor:
+    """Base prompt constructor for LLaVA.
 
     Args:
-        conv_templates (Any): Conversation class to build prompt.
         conv_mode (str): Version control args for different version of LLaVA.
         mm_use_im_start_end (bool):
             Config arg. Use start and end token when build prompt or not.
+        reply_prompt (str): Reply prompt added at the end. (Default: '')
     """
 
-    def __init__(self, conv_templates: Any, conv_mode: str,
-                 mm_use_im_start_end: bool) -> None:
-        self.conv_templates = conv_templates
+    def __init__(self,
+                 conv_mode: str,
+                 mm_use_im_start_end: bool,
+                 reply_prompt: str = '') -> None:
+        conversation = importlib.import_module('llava.conversation')
+        self.conv_templates = conversation.conv_templates
         self.conv_mode = conv_mode
         self.mm_use_im_start_end = mm_use_im_start_end
-        conversation = importlib.import_module('llava.conversation')
         self.SeparatorStyle = conversation.SeparatorStyle
+        self.reply_prompt = reply_prompt
 
     def __call__(self, inputs: dict) -> tuple:
         """Construct prompt.
@@ -36,13 +38,7 @@ class LLaVAMMBenchPromptConstructor:
         """
         data_samples = inputs['data_samples']
         assert len(data_samples) == 1
-        question = data_samples[0].get('question')
-        options = data_samples[0].get('options')
-        context = data_samples[0].get('context')
-        if context is not None:
-            prompt = context + ' ' + question + ' ' + options
-        else:
-            prompt = question + ' ' + options
+        prompt = self._build_prompt(data_samples[0])
         if self.mm_use_im_start_end:
             prompt = (DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN +
                       DEFAULT_IM_END_TOKEN + '\n' + prompt)
@@ -57,3 +53,87 @@ class LLaVAMMBenchPromptConstructor:
         stop_str = conv.sep if conv.sep_style != self.SeparatorStyle.TWO else conv.sep2  # noqa
 
         return output_prompt, stop_str
+
+    def _build_prompt(self, data_sample):
+        return self.reply_prompt
+
+
+class LLaVAMMBenchPromptConstructor(LLaVABasePromptConstructor):
+    """MMBench prompt constructor for LLaVA.
+
+    Args:
+        conv_mode (str): Version control args for different version of LLaVA.
+        mm_use_im_start_end (bool):
+            Config arg. Use start and end token when build prompt or not.
+        reply_prompt (str): Reply prompt added at the end. (Default: '')
+    """
+
+    def __init__(self,
+                 conv_mode: str,
+                 mm_use_im_start_end: bool,
+                 reply_prompt: str = '') -> None:
+        super().__init__(conv_mode, mm_use_im_start_end, reply_prompt)
+
+    def _build_prompt(self, data_sample):
+        question = data_sample.get('question')
+        options = data_sample.get('options')
+        context = data_sample.get('context')
+        if context is not None:
+            prompt = context + ' ' + question + ' ' + options
+        else:
+            prompt = question + ' ' + options
+        prompt += self.reply_prompt
+        return prompt
+
+
+class LLaVAVQAPromptConstructor(LLaVABasePromptConstructor):
+    """VQA prompt constructor for LLaVA.
+
+    Args:
+        conv_mode (str): Version control args for different version of LLaVA.
+        mm_use_im_start_end (bool):
+            Config arg. Use start and end token when build prompt or not.
+        reply_prompt (str): Reply prompt added at the end. (Default: '')
+    """
+
+    def __init__(self,
+                 conv_mode: str,
+                 mm_use_im_start_end: bool,
+                 reply_prompt: str = '') -> None:
+        super().__init__(conv_mode, mm_use_im_start_end, reply_prompt)
+
+    def _build_prompt(self, data_sample):
+        prompt = data_sample.get('question')
+        prompt += self.reply_prompt
+        return prompt
+
+
+class LLaVAScienceQAPromptConstructor(LLaVABasePromptConstructor):
+    """ScienceQA prompt constructor for LLaVA.
+
+    Args:
+        conv_mode (str): Version control args for different version of LLaVA.
+        mm_use_im_start_end (bool):
+            Config arg. Use start and end token when build prompt or not.
+        reply_prompt (str): Reply prompt added at the end. (Default: '')
+    """
+
+    choice_mapping = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F'}
+
+    def __init__(self,
+                 conv_mode: str,
+                 mm_use_im_start_end: bool,
+                 reply_prompt: str = '') -> None:
+        super().__init__(conv_mode, mm_use_im_start_end, reply_prompt)
+
+    def _build_prompt(self, data_sample):
+        question = data_sample.get('question')
+        choices = data_sample.get('choices')
+        choices = [
+            f'({self.choice_mapping[i]}) ' + item
+            for i, item in enumerate(choices)
+        ]
+        choices = 'Choices: ' + ' '.join(choices) + '\n'
+        context = 'Context: ' + data_sample.get('hint') + '\n'
+        prompt = context + question + choices + self.reply_prompt
+        return prompt

opencompass/multimodal/models/visualglm/__init__.py

@@ -1,5 +1,15 @@
-from .post_processor import VisualGLMPostProcessor
-from .prompt_constructor import VisualGLMPromptConstructor
+from .post_processor import (VisualGLMBasePostProcessor,
+                             VisualGLMVSRPostProcessor)
+from .prompt_constructor import (VisualGLMBasePromptConstructor,
+                                 VisualGLMIconQAPromptConstructor,
+                                 VisualGLMMMBenchPromptConstructor,
+                                 VisualGLMScienceQAPromptConstructor,
+                                 VisualGLMVQAPromptConstructor)
 from .visualglm import VisualGLM
 
-__all__ = ['VisualGLM', 'VisualGLMPostProcessor', 'VisualGLMPromptConstructor']
+__all__ = [
+    'VisualGLM', 'VisualGLMBasePostProcessor', 'VisualGLMVSRPostProcessor',
+    'VisualGLMBasePromptConstructor', 'VisualGLMMMBenchPromptConstructor',
+    'VisualGLMVQAPromptConstructor', 'VisualGLMScienceQAPromptConstructor',
+    'VisualGLMIconQAPromptConstructor'
+]

opencompass/multimodal/models/visualglm/post_processor.py

@@ -3,8 +3,8 @@ from typing import Any
 import torch
 
 
-class VisualGLMPostProcessor:
-    """"Post processor for VisualGLM on MMBench."""
+class VisualGLMBasePostProcessor:
+    """Base post processor for VisualGLM."""
 
     def __init__(self) -> None:
         pass
@@ -12,3 +12,20 @@ class VisualGLMPostProcessor:
     def __call__(self, output_token: torch.tensor, tokenizer: Any,
                  input_len: int) -> str:
         return tokenizer.decode(output_token[input_len:])
+
+
+class VisualGLMVSRPostProcessor(VisualGLMBasePostProcessor):
+    """VSR post processor for VisualGLM."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def __call__(self, output_token: torch.tensor, tokenizer: Any,
+                 input_len: int) -> str:
+        output_text = tokenizer.decode(output_token[input_len:])
+        if 'yes' in output_text.lower():
+            return 'yes'
+        elif 'no' in output_text.lower():
+            return 'no'
+        else:
+            return 'unknown'

opencompass/multimodal/models/visualglm/prompt_constructor.py

@@ -1,8 +1,8 @@
 import torch
 
 
-class VisualGLMPromptConstructor:
-    """Prompt constructor for VisualGLM.
+class VisualGLMMMBenchPromptConstructor:
+    """MMBench prompt constructor for VisualGLM.
 
     The overall prompt will be formulated as
     "system_prompt"+"human_prompt"+"image_prompt"+question+"assistant+prompt".
@@ -30,7 +30,7 @@ class VisualGLMPromptConstructor:
             batch (dict): Input data containing image and data_samples.
 
         Returns:
-            tuple: A tuple containing prompt, images and data_samples.
+            A tuple containing images, prompt, data_samples and image_position.
         """
 
         images = batch.pop('inputs')
@@ -53,3 +53,168 @@ class VisualGLMPromptConstructor:
         image_position = 5
 
         return images, prompt, data_samples, image_position
+
+
+class VisualGLMBasePromptConstructor:
+    """Base prompt constructor for VisualGLM.
+
+    The prompt will concat <img> and the given system prompt.
+    Args:
+        system_prompt (str): System prompt. (Default: '')
+    """
+
+    def __init__(self, system_prompt='') -> None:
+        self.prompt = system_prompt
+
+    def __call__(self, batch: dict) -> tuple:
+        """Construct prompt.
+
+        Args:
+            batch (dict): Input data containing image and data_samples.
+
+        Returns:
+            A tuple containing images, prompt, data_samples and image_position.
+        """
+
+        images = batch.pop('inputs')
+        images = torch.stack(images, dim=0)
+        data_samples = batch.pop('data_samples')
+
+        # generate text prompt
+        img_prompt = '<img></img>'
+        prompt = img_prompt + self.prompt
+        image_position = prompt.rfind('<img>') + 5
+
+        image_position = 5
+
+        return images, prompt, data_samples, image_position
+
+
+class VisualGLMVQAPromptConstructor(VisualGLMBasePromptConstructor):
+    """VQA prompt constructor for VisualGLM.
+
+    The prompt will concat <img>, the question and the system prompt.
+    Args:
+        system_prompt (str): System prompt. (Default: '')
+    """
+
+    def __init__(self, system_prompt='') -> None:
+        super().__init__(system_prompt)
+
+    def __call__(self, batch: dict) -> tuple:
+        """Construct prompt.
+
+        Args:
+            batch (dict): Input data containing image and data_samples.
+
+        Returns:
+            A tuple containing images, prompt, data_samples and image_position.
+        """
+
+        images = batch.pop('inputs')
+        images = torch.stack(images, dim=0)
+        data_samples = batch.pop('data_samples')
+        questions = [sample.get('question') for sample in data_samples]
+
+        # generate text prompt
+        prompt = [
+            '<img></img>Q:{} {}\nA:'.format(question, self.prompt)
+            for question in questions
+        ]
+        image_position = 5
+
+        return images, prompt, data_samples, image_position
+
+
+class VisualGLMScienceQAPromptConstructor(VisualGLMBasePromptConstructor):
+    """ScienceQA prompt constructor for VisualGLM.
+
+    The prompt will concat image and all terms in a question.
+    Args:
+        system_prompt (str): System prompt. (Default: '')
+    """
+
+    choice_mapping = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F'}
+
+    def __init__(self, system_prompt='') -> None:
+        super().__init__(system_prompt)
+
+    def __call__(self, batch: dict) -> tuple:
+        """Construct prompt.
+
+        Args:
+            batch (dict): Input data containing image and data_samples.
+
+        Returns:
+            A tuple containing images, prompt, data_samples and image_position.
+        """
+
+        images = batch.pop('inputs')
+        images = torch.stack(images, dim=0)
+        data_samples = batch.pop('data_samples')
+        questions = [
+            'Q: ' + sample.get('question') + '\n' for sample in data_samples
+        ]
+        choices = [sample.get('choices') for sample in data_samples]
+        choices = [[
+            f'({self.choice_mapping[i]}) ' + item
+            for i, item in enumerate(choice)
+        ] for choice in choices]
+        choices = [
+            'Choices: ' + ' '.join(choice) + '\n' for choice in choices
+        ]  # noqa
+        contexts = [
+            'Context: ' + data_sample.get('hint') + '\n'
+            for data_sample in data_samples
+        ]  # noqa
+
+        # generate text prompt
+        prompt = [
+            '<img></img>' + context + question + choice + self.prompt
+            for context, question, choice in zip(contexts, questions, choices)
+        ]
+        image_position = 5
+
+        return images, prompt, data_samples, image_position
+
+
+class VisualGLMIconQAPromptConstructor(VisualGLMBasePromptConstructor):
+    """IconQA prompt constructor for VisualGLM.
+
+    The prompt will concat <img>, the question and the system prompt.
+    Args:
+        system_prompt (str): System prompt. (Default: '')
+    """
+
+    def __init__(self, system_prompt='') -> None:
+        super().__init__(system_prompt)
+
+    def __call__(self, batch: dict) -> tuple:
+        """Construct prompt.
+
+        Args:
+            batch (dict): Input data containing image and data_samples.
+
+        Returns:
+            A tuple containing images, prompt, data_samples and image_position.
+        """
+
+        images = batch.pop('inputs')
+        images = torch.stack(images, dim=0)
+        data_samples = batch.pop('data_samples')
+        questions = [
+            'Q: ' + sample.get('question') + '\n' for sample in data_samples
+        ]
+        choices = [sample.get('choices') for sample in data_samples]
+        choices = [
+            'Options: ' + ', '.join(choice) + '.\n' for choice in choices
+        ]  # noqa
+
+        # generate text prompt
+        prompt = [
+            '<img></img>' + question + choice + self.prompt
+            for question, choice in zip(questions, choices)
+        ]
+        image_position = 5
+
+        return images, prompt, data_samples, image_position

opencompass/multimodal/models/visualglm/visualglm.py

@@ -18,13 +18,17 @@ class VisualGLM(nn.Module):
         pretrained_path (str): Path to visualGLM checkpoint or repo id.
         prompt_constructor (dict): The config of prompt constructor.
         post_processor (dict): The config of post processor.
+        is_caption_task (bool): Whether the task is caption task.
+            Defaults to False.
         gen_kwargs (dict): Customize generate function arguments.
+            Defaults to None.
     """
 
     def __init__(self,
                  pretrained_path: str,
                  prompt_constructor: dict,
                  post_processor: dict,
+                 is_caption_task: bool = False,
                  gen_kwargs: Optional[dict] = None) -> None:
         super().__init__()
         self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path,
@@ -40,6 +44,7 @@ class VisualGLM(nn.Module):
             self.gen_kwargs = gen_kwargs
         else:
             self.gen_kwargs = dict()
+        self.is_caption_task = is_caption_task
 
     def encode_by_tokenizer(self, multi_prompts, image_position):
         input_ids = []
@@ -89,8 +94,12 @@ class VisualGLM(nn.Module):
         # format output
         outputs = outputs.tolist()
         for i, sample in enumerate(data_sample):
-            data_sample[i].pred_answer = self.post_processor(
-                outputs[i], self.tokenizer, input_all.shape[1])
+            answer = self.post_processor(outputs[i], self.tokenizer,
+                                         input_all.shape[1])
+            if self.is_caption_task:
+                data_sample[i].pred_caption = answer
+            else:
+                data_sample[i].pred_answer = answer
 
         return data_sample