diff --git a/.gitignore b/.gitignore
index 04bc2b8f4..1b4f1b656 100644
--- a/.gitignore
+++ b/.gitignore
@@ -152,3 +152,5 @@ debug*/
*.dat
*.tsv
*.gz
+
+cache/
diff --git a/app/image_text_match.py b/app/image_text_match.py
index 568acfefc..e7957384e 100644
--- a/app/image_text_match.py
+++ b/app/image_text_match.py
@@ -74,7 +74,7 @@ def app():
qry_tok = tokenizer(qry, return_tensors="pt").to(device)
gradcam, output = compute_gradcam(model, img, qry, qry_tok, block_num=layer_num)
- avg_gradcam = getAttMap(norm_img, gradcam[1], blur=True)
+ avg_gradcam = getAttMap(norm_img, gradcam[0][1], blur=True)
col2.image(avg_gradcam, use_column_width=True, clamp=True)
# output = model(img, question)
diff --git a/app/text_localization.py b/app/text_localization.py
index e62ff108f..d01655b97 100644
--- a/app/text_localization.py
+++ b/app/text_localization.py
@@ -73,7 +73,7 @@ def app():
gradcam, _ = compute_gradcam(model, img, qry, qry_tok, block_num=layer_num)
- avg_gradcam = getAttMap(norm_img, gradcam[1], blur=True)
+ avg_gradcam = getAttMap(norm_img, gradcam[0][1], blur=True)
col2.image(avg_gradcam, use_column_width=True, clamp=True)
num_cols = 4.0
@@ -81,7 +81,7 @@ def app():
num_rows = int(math.ceil(num_tokens / num_cols))
- gradcam_iter = iter(gradcam[2:-1])
+ gradcam_iter = iter(gradcam[0][2:-1])
token_id_iter = iter(qry_tok.input_ids[0][1:-1])
for _ in range(num_rows):
diff --git a/dataset_card/gqa.md b/dataset_card/gqa.md
new file mode 100644
index 000000000..5c65d1875
--- /dev/null
+++ b/dataset_card/gqa.md
@@ -0,0 +1,32 @@
+
+
+# GQA Dataset
+
+## Description
+(from https://cs.stanford.edu/people/dorarad/gqa/about.html)
+
+GQA is a VQA dataset for real-word images which requires visual, spatial and compositional reasoning.
+It consists of 22M questions and 110K images.
+
+## Task
+(from https://arxiv.org/abs/1902.09506)
+
+Given an image and a question, the model is required to output a correct answer.
+GQA questions require spatial understanding, multiple reasoning skills and multiple-step inference.
+
+## Metrics
+
+The metrics are accuracy, consistency, validity, plausibility. The commonly reported metric is accuracy.
+
+## Leaderboard
+
+TBD
+
+## Auto-Downloading
+
+```
+cd lavis/datasets/download_scripts && python download_gqa.py
+```
+
+## References
+"GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering", Drew A. Hudson, Christopher D. Manning
\ No newline at end of file
diff --git a/dataset_card/imgs/gqa.png b/dataset_card/imgs/gqa.png
new file mode 100644
index 000000000..1471ec171
Binary files /dev/null and b/dataset_card/imgs/gqa.png differ
diff --git a/examples/blip_text_localization.ipynb b/examples/blip_text_localization.ipynb
index cc4c12e46..5713293a3 100644
--- a/examples/blip_text_localization.ipynb
+++ b/examples/blip_text_localization.ipynb
@@ -150,7 +150,7 @@
"metadata": {},
"outputs": [],
"source": [
- "avg_gradcam = getAttMap(norm_img, gradcam[1], blur=True)"
+ "avg_gradcam = getAttMap(norm_img, gradcam[0][1], blur=True)"
]
},
{
@@ -214,7 +214,7 @@
"num_image = len(txt_tokens.input_ids[0]) - 2\n",
"fig, ax = plt.subplots(num_image, 1, figsize=(15, 5 * num_image))\n",
"\n",
- "gradcam_iter = iter(gradcam[2:-1])\n",
+ "gradcam_iter = iter(gradcam[0][2:-1])\n",
"token_id_iter = iter(txt_tokens.input_ids[0][1:-1])\n",
"\n",
"for i, (gradcam, token_id) in enumerate(zip(gradcam_iter, token_id_iter)):\n",
@@ -230,7 +230,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.8.10 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -244,9 +244,8 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.10"
+ "version": "3.8.13"
},
- "orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
diff --git a/lavis/common/vqa_tools/vqa_eval.py b/lavis/common/vqa_tools/vqa_eval.py
index b5d626892..ee808b349 100644
--- a/lavis/common/vqa_tools/vqa_eval.py
+++ b/lavis/common/vqa_tools/vqa_eval.py
@@ -16,7 +16,7 @@
class VQAEval:
- def __init__(self, vqa, vqaRes, n=2):
+ def __init__(self, vqa=None, vqaRes=None, n=2):
self.n = n
self.accuracy = {}
self.evalQA = {}
@@ -24,7 +24,8 @@ def __init__(self, vqa, vqaRes, n=2):
self.evalAnsType = {}
self.vqa = vqa
self.vqaRes = vqaRes
- self.params = {"question_id": vqa.getQuesIds()}
+ if vqa is not None:
+ self.params = {"question_id": vqa.getQuesIds()}
self.contractions = {
"aint": "ain't",
"arent": "aren't",
diff --git a/lavis/configs/datasets/gqa/balanced_testdev.yaml b/lavis/configs/datasets/gqa/balanced_testdev.yaml
new file mode 100644
index 000000000..86114fb96
--- /dev/null
+++ b/lavis/configs/datasets/gqa/balanced_testdev.yaml
@@ -0,0 +1,30 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+datasets:
+ gqa:
+ # data_dir: ${env.data_dir}/datasets
+ data_type: images # [images|videos|features]
+
+ build_info:
+ # Be careful not to append minus sign (-) before split to avoid itemizing
+ annotations:
+ train:
+ url:
+ - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json
+ storage:
+ - gqa/annotations/train_balanced_questions.json
+ val:
+ url:
+ - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/testdev_balanced_questions.json
+ storage:
+ - gqa/annotations/testdev_balanced_questions.json
+ test:
+ url:
+ - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json
+ storage:
+ - gqa/annotations/test_balanced_questions.json
+ images:
+ storage: gqa/images/
diff --git a/lavis/configs/datasets/gqa/balanced_val.yaml b/lavis/configs/datasets/gqa/balanced_val.yaml
new file mode 100644
index 000000000..ca420dfdc
--- /dev/null
+++ b/lavis/configs/datasets/gqa/balanced_val.yaml
@@ -0,0 +1,30 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+datasets:
+ gqa:
+ # data_dir: ${env.data_dir}/datasets
+ data_type: images # [images|videos|features]
+
+ build_info:
+ # Be careful not to append minus sign (-) before split to avoid itemizing
+ annotations:
+ train:
+ url:
+ - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/train_balanced_questions.json
+ storage:
+ - gqa/annotations/train_balanced_questions.json
+ val:
+ url:
+ - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/val_balanced_questions.json
+ storage:
+ - gqa/annotations/val_balanced_questions.json
+ test:
+ url:
+ - https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/gqa/test_balanced_questions.json
+ storage:
+ - gqa/annotations/test_balanced_questions.json
+ images:
+ storage: gqa/images/
diff --git a/lavis/configs/datasets/gqa/defaults.yaml b/lavis/configs/datasets/gqa/defaults.yaml
index f56ddc993..7c2d87cd7 100644
--- a/lavis/configs/datasets/gqa/defaults.yaml
+++ b/lavis/configs/datasets/gqa/defaults.yaml
@@ -33,4 +33,4 @@ datasets:
- aokvqa/annotations/aokvqa_v1p0_test.json
- aokvqa/annotations/large_vocab_train_lavis.json
images:
- storage: coco/images/
+ storage: gqa/images/
diff --git a/lavis/configs/models/pnp-vqa/pnp_vqa_3b.yaml b/lavis/configs/models/pnp-vqa/pnp_vqa_3b.yaml
new file mode 100644
index 000000000..e4c4a3929
--- /dev/null
+++ b/lavis/configs/models/pnp-vqa/pnp_vqa_3b.yaml
@@ -0,0 +1,59 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+
+ image_question_matching_model:
+ arch: blip_image_text_matching
+ load_finetuned: True
+
+ finetuned: "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco_train2014.pth"
+
+ # vit encoder
+ vit_type: "large"
+ vit_grad_ckpt: False
+ vit_ckpt_layer: 0
+
+ image_size: 384
+
+ # bert config
+ med_config_path: "configs/models/med_large_config.json"
+
+ embed_dim: 256
+
+ image_captioning_model:
+ arch: blip_caption
+ load_finetuned: True
+
+ finetuned: "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption_coco_train2014.pth"
+
+ vit_type: "large"
+ vit_grad_ckpt: True
+ vit_ckpt_layer: 5
+
+ image_size: 384
+
+ # bert config
+ med_config_path: "configs/models/med_large_config.json"
+
+ # generation configs
+ prompt: "a picture of "
+
+ question_answering_model:
+ arch: pnp_unifiedqav2_fid
+
+ pretrained: "allenai/unifiedqa-v2-t5-3b-1363200"
+
+ t5_config_path: "configs/models/pnp-vqa/unifiedqav2_3b_config.json"
+
+preprocess:
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_caption"
\ No newline at end of file
diff --git a/lavis/configs/models/pnp-vqa/pnp_vqa_base.yaml b/lavis/configs/models/pnp-vqa/pnp_vqa_base.yaml
new file mode 100644
index 000000000..b892c51d4
--- /dev/null
+++ b/lavis/configs/models/pnp-vqa/pnp_vqa_base.yaml
@@ -0,0 +1,59 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+
+ image_question_matching_model:
+ arch: blip_image_text_matching
+ load_finetuned: True
+
+ finetuned: "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco_train2014.pth"
+
+ # vit encoder
+ vit_type: "large"
+ vit_grad_ckpt: False
+ vit_ckpt_layer: 0
+
+ image_size: 384
+
+ # bert config
+ med_config_path: "configs/models/med_large_config.json"
+
+ embed_dim: 256
+
+ image_captioning_model:
+ arch: blip_caption
+ load_finetuned: True
+
+ finetuned: "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption_coco_train2014.pth"
+
+ vit_type: "large"
+ vit_grad_ckpt: True
+ vit_ckpt_layer: 5
+
+ image_size: 384
+
+ # bert config
+ med_config_path: "configs/models/med_large_config.json"
+
+ # generation configs
+ prompt: "a picture of "
+
+ question_answering_model:
+ arch: pnp_unifiedqav2_fid
+
+ pretrained: "allenai/unifiedqa-v2-t5-base-1363200"
+
+ t5_config_path: "configs/models/pnp-vqa/unifiedqav2_base_config.json"
+
+preprocess:
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_caption"
\ No newline at end of file
diff --git a/lavis/configs/models/pnp-vqa/pnp_vqa_large.yaml b/lavis/configs/models/pnp-vqa/pnp_vqa_large.yaml
new file mode 100644
index 000000000..c5d676a16
--- /dev/null
+++ b/lavis/configs/models/pnp-vqa/pnp_vqa_large.yaml
@@ -0,0 +1,59 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+
+ image_question_matching_model:
+ arch: blip_image_text_matching
+ load_finetuned: True
+
+ finetuned: "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_retrieval_coco_train2014.pth"
+
+ # vit encoder
+ vit_type: "large"
+ vit_grad_ckpt: False
+ vit_ckpt_layer: 0
+
+ image_size: 384
+
+ # bert config
+ med_config_path: "configs/models/med_large_config.json"
+
+ embed_dim: 256
+
+ image_captioning_model:
+ arch: blip_caption
+ load_finetuned: True
+
+ finetuned: "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption_coco_train2014.pth"
+
+ vit_type: "large"
+ vit_grad_ckpt: True
+ vit_ckpt_layer: 5
+
+ image_size: 384
+
+ # bert config
+ med_config_path: "configs/models/med_large_config.json"
+
+ # generation configs
+ prompt: "a picture of "
+
+ question_answering_model:
+ arch: pnp_unifiedqav2_fid
+
+ pretrained: "allenai/unifiedqa-v2-t5-large-1363200"
+
+ t5_config_path: "configs/models/pnp-vqa/unifiedqav2_large_config.json"
+
+preprocess:
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_caption"
\ No newline at end of file
diff --git a/lavis/configs/models/pnp-vqa/unifiedqav2_3b_config.json b/lavis/configs/models/pnp-vqa/unifiedqav2_3b_config.json
new file mode 100644
index 000000000..e5220dc59
--- /dev/null
+++ b/lavis/configs/models/pnp-vqa/unifiedqav2_3b_config.json
@@ -0,0 +1,60 @@
+{
+ "architectures": [
+ "T5ForConditionalGeneration"
+ ],
+ "d_ff": 16384,
+ "d_kv": 128,
+ "d_model": 1024,
+ "decoder_start_token_id": 0,
+ "dense_act_fn": "relu",
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "feed_forward_proj": "relu",
+ "gradient_checkpointing": false,
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": false,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "n_positions": 512,
+ "num_decoder_layers": 24,
+ "num_heads": 32,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_max_distance": 128,
+ "relative_attention_num_buckets": 32,
+ "task_specific_params": {
+ "summarization": {
+ "early_stopping": true,
+ "length_penalty": 2.0,
+ "max_length": 200,
+ "min_length": 30,
+ "no_repeat_ngram_size": 3,
+ "num_beams": 4,
+ "prefix": "summarize: "
+ },
+ "translation_en_to_de": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to German: "
+ },
+ "translation_en_to_fr": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to French: "
+ },
+ "translation_en_to_ro": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to Romanian: "
+ }
+ },
+ "torch_dtype": "float32",
+ "transformers_version": "4.21.3",
+ "use_cache": true,
+ "vocab_size": 32128
+}
\ No newline at end of file
diff --git a/lavis/configs/models/pnp-vqa/unifiedqav2_base_config.json b/lavis/configs/models/pnp-vqa/unifiedqav2_base_config.json
new file mode 100644
index 000000000..24ffa8d18
--- /dev/null
+++ b/lavis/configs/models/pnp-vqa/unifiedqav2_base_config.json
@@ -0,0 +1,59 @@
+{
+ "architectures": [
+ "T5ForConditionalGeneration"
+ ],
+ "d_ff": 3072,
+ "d_kv": 64,
+ "d_model": 768,
+ "decoder_start_token_id": 0,
+ "dense_act_fn": "relu",
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "feed_forward_proj": "relu",
+ "gradient_checkpointing": false,
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": false,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "n_positions": 512,
+ "num_decoder_layers": 12,
+ "num_heads": 12,
+ "num_layers": 12,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_max_distance": 128,
+ "relative_attention_num_buckets": 32,
+ "task_specific_params": {
+ "summarization": {
+ "early_stopping": true,
+ "length_penalty": 2.0,
+ "max_length": 200,
+ "min_length": 30,
+ "no_repeat_ngram_size": 3,
+ "num_beams": 4,
+ "prefix": "summarize: "
+ },
+ "translation_en_to_de": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to German: "
+ },
+ "translation_en_to_fr": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to French: "
+ },
+ "translation_en_to_ro": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to Romanian: "
+ }
+ },
+ "transformers_version": "4.21.3",
+ "use_cache": true,
+ "vocab_size": 32128
+}
\ No newline at end of file
diff --git a/lavis/configs/models/pnp-vqa/unifiedqav2_large_config.json b/lavis/configs/models/pnp-vqa/unifiedqav2_large_config.json
new file mode 100644
index 000000000..4f87ec697
--- /dev/null
+++ b/lavis/configs/models/pnp-vqa/unifiedqav2_large_config.json
@@ -0,0 +1,59 @@
+{
+ "architectures": [
+ "T5ForConditionalGeneration"
+ ],
+ "d_ff": 4096,
+ "d_kv": 64,
+ "d_model": 1024,
+ "decoder_start_token_id": 0,
+ "dense_act_fn": "relu",
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "feed_forward_proj": "relu",
+ "gradient_checkpointing": false,
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": false,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "n_positions": 512,
+ "num_decoder_layers": 24,
+ "num_heads": 16,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_max_distance": 128,
+ "relative_attention_num_buckets": 32,
+ "task_specific_params": {
+ "summarization": {
+ "early_stopping": true,
+ "length_penalty": 2.0,
+ "max_length": 200,
+ "min_length": 30,
+ "no_repeat_ngram_size": 3,
+ "num_beams": 4,
+ "prefix": "summarize: "
+ },
+ "translation_en_to_de": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to German: "
+ },
+ "translation_en_to_fr": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to French: "
+ },
+ "translation_en_to_ro": {
+ "early_stopping": true,
+ "max_length": 300,
+ "num_beams": 4,
+ "prefix": "translate English to Romanian: "
+ }
+ },
+ "transformers_version": "4.21.3",
+ "use_cache": true,
+ "vocab_size": 32128
+}
\ No newline at end of file
diff --git a/lavis/datasets/builders/__init__.py b/lavis/datasets/builders/__init__.py
index ea07a7598..43d540d62 100644
--- a/lavis/datasets/builders/__init__.py
+++ b/lavis/datasets/builders/__init__.py
@@ -28,6 +28,7 @@
COCOVQABuilder,
OKVQABuilder,
VGVQABuilder,
+ GQABuilder,
)
from lavis.datasets.builders.retrieval_builder import (
MSRVTTRetrievalBuilder,
@@ -47,6 +48,7 @@
"ConceptualCaption3MBuilder",
"DiDeMoRetrievalBuilder",
"Flickr30kBuilder",
+ "GQABuilder",
"ImageNetBuilder",
"MSRVTTCapBuilder",
"MSRVTTQABuilder",
diff --git a/lavis/datasets/builders/vqa_builder.py b/lavis/datasets/builders/vqa_builder.py
index 9c366fa94..c08a6d140 100644
--- a/lavis/datasets/builders/vqa_builder.py
+++ b/lavis/datasets/builders/vqa_builder.py
@@ -11,6 +11,7 @@
from lavis.datasets.datasets.aok_vqa_datasets import AOKVQADataset, AOKVQAEvalDataset
from lavis.datasets.datasets.coco_vqa_datasets import COCOVQADataset, COCOVQAEvalDataset
from lavis.datasets.datasets.vg_vqa_datasets import VGVQADataset
+from lavis.datasets.datasets.gqa_datasets import GQADataset, GQAEvalDataset
@registry.register_builder("coco_vqa")
@@ -43,3 +44,15 @@ class AOKVQABuilder(BaseDatasetBuilder):
eval_dataset_cls = AOKVQAEvalDataset
DATASET_CONFIG_DICT = {"default": "configs/datasets/aokvqa/defaults.yaml"}
+
+
+@registry.register_builder("gqa")
+class GQABuilder(BaseDatasetBuilder):
+ train_dataset_cls = GQADataset
+ eval_dataset_cls = GQAEvalDataset
+
+ DATASET_CONFIG_DICT = {
+ "default": "configs/datasets/gqa/defaults.yaml",
+ "balanced_val": "configs/datasets/gqa/balanced_val.yaml",
+ "balanced_testdev": "configs/datasets/gqa/balanced_testdev.yaml",
+ }
\ No newline at end of file
diff --git a/lavis/datasets/datasets/gqa_datasets.py b/lavis/datasets/datasets/gqa_datasets.py
new file mode 100644
index 000000000..073c57040
--- /dev/null
+++ b/lavis/datasets/datasets/gqa_datasets.py
@@ -0,0 +1,101 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import os
+import json
+
+from PIL import Image
+
+from lavis.datasets.datasets.vqa_datasets import VQADataset, VQAEvalDataset
+
+from collections import OrderedDict
+
+
+class __DisplMixin:
+ def displ_item(self, index):
+ sample, ann = self.__getitem__(index), self.annotation[index]
+
+ return OrderedDict(
+ {
+ "file": ann["image"],
+ "question": ann["question"],
+ "question_id": ann["question_id"],
+ "answers": "; ".join(ann["answer"]),
+ "image": sample["image"],
+ }
+ )
+
+
+class GQADataset(VQADataset, __DisplMixin):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
+ super().__init__(vis_processor, text_processor, vis_root, ann_paths)
+
+ def __getitem__(self, index):
+ ann = self.annotation[index]
+
+ image_path = os.path.join(self.vis_root, ann["image"])
+ image = Image.open(image_path).convert("RGB")
+
+ image = self.vis_processor(image)
+ question = self.text_processor(ann["question"])
+
+ answers = [ann["answer"]]
+ weights = [1]
+
+ return {
+ "image": image,
+ "text_input": question,
+ "answers": answers,
+ "weights": weights,
+ }
+
+
+class GQAEvalDataset(VQAEvalDataset, __DisplMixin):
+ def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
+ """
+ vis_root (string): Root directory of images (e.g. gqa/images/)
+ ann_root (string): directory to store the annotation file
+ """
+
+ self.vis_root = vis_root
+
+ self.annotation = json.load(open(ann_paths[0]))
+
+ ## TODO: support inference method == 'ranking'
+ answer_list_path = ann_paths[1] if len(ann_paths) > 1 else ''
+ if os.path.exists(answer_list_path):
+ self.answer_list = json.load(open(answer_list_path))
+ else:
+ self.answer_list = None
+
+ self.vis_processor = vis_processor
+ self.text_processor = text_processor
+
+ self._add_instance_ids()
+
+ def __getitem__(self, index):
+ ann = self.annotation[index]
+
+ image_path = os.path.join(self.vis_root, ann["image"])
+ image = Image.open(image_path).convert("RGB")
+
+ image = self.vis_processor(image)
+ question = self.text_processor(ann["question"])
+
+ if "answer" in ann:
+ # answer is a string
+ answer = ann["answer"]
+ else:
+ answer = None
+
+ return {
+ "image": image,
+ "text_input": question,
+ "answer": answer,
+ "question_id": ann["question_id"],
+ "instance_id": ann["instance_id"],
+ }
diff --git a/lavis/datasets/download_scripts/download_gqa.py b/lavis/datasets/download_scripts/download_gqa.py
new file mode 100644
index 000000000..0bce71408
--- /dev/null
+++ b/lavis/datasets/download_scripts/download_gqa.py
@@ -0,0 +1,51 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import os
+from pathlib import Path
+
+from omegaconf import OmegaConf
+
+from lavis.common.utils import (
+ cleanup_dir,
+ download_and_extract_archive,
+ get_abs_path,
+ get_cache_path,
+)
+
+
+DATA_URL = "https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip"
+
+
+def download_datasets(root, url):
+ download_and_extract_archive(url=url, download_root=root, extract_root=storage_dir.parent)
+
+
+if __name__ == "__main__":
+
+ config_path = get_abs_path("configs/datasets/gqa/defaults.yaml")
+
+ storage_dir = OmegaConf.load(
+ config_path
+ ).datasets.gqa.build_info.images.storage
+
+ download_dir = Path(get_cache_path(storage_dir)).parent / "download"
+ storage_dir = Path(get_cache_path(storage_dir))
+
+ if storage_dir.exists():
+ print(f"Dataset already exists at {storage_dir}. Aborting.")
+ exit(0)
+
+ try:
+ print("Downloading {}".format(DATA_URL))
+ download_datasets(download_dir, DATA_URL)
+ except Exception as e:
+ # remove download dir if failed
+ cleanup_dir(download_dir)
+ print("Failed to download or extracting datasets. Aborting.")
+
+ cleanup_dir(download_dir)
diff --git a/lavis/models/__init__.py b/lavis/models/__init__.py
index c14de7360..4eb0f33a0 100644
--- a/lavis/models/__init__.py
+++ b/lavis/models/__init__.py
@@ -29,6 +29,8 @@
from lavis.models.blip_models.blip_pretrain import BlipPretrain
from lavis.models.blip_models.blip_retrieval import BlipRetrieval
from lavis.models.blip_models.blip_vqa import BlipVQA
+from lavis.models.pnp_vqa_models.pnp_vqa import PNPVQA
+from lavis.models.pnp_vqa_models.pnp_unifiedqav2_fid import PNPUnifiedQAv2FiD
from lavis.models.med import XBertLMHeadDecoder
from lavis.models.vit import VisionTransformerEncoder
from lavis.models.clip_models.model import CLIP
@@ -58,6 +60,8 @@
"BlipPretrain",
"BlipRetrieval",
"BlipVQA",
+ "PNPVQA",
+ "PNPUnifiedQAv2FiD",
"CLIP",
"VisionTransformerEncoder",
"XBertLMHeadDecoder",
diff --git a/lavis/models/blip_models/blip_image_text_matching.py b/lavis/models/blip_models/blip_image_text_matching.py
index 2b77e37c9..164dcee80 100644
--- a/lavis/models/blip_models/blip_image_text_matching.py
+++ b/lavis/models/blip_models/blip_image_text_matching.py
@@ -133,7 +133,7 @@ def compute_gradcam(model, visual_input, text_input, tokenized_text, block_num=6
mask = tokenized_text.attention_mask.view(
tokenized_text.attention_mask.size(0), 1, -1, 1, 1
) # (bsz,1,token_len, 1,1)
- token_length = mask.sum() - 2
+ token_length = tokenized_text.attention_mask.sum(dim=-1) - 2
token_length = token_length.cpu()
# grads and cams [bsz, num_head, seq_len, image_patch]
grads = model.text_encoder.base_model.base_model.encoder.layer[
@@ -150,16 +150,21 @@ def compute_gradcam(model, visual_input, text_input, tokenized_text, block_num=6
* mask
)
- gradcam = cams * grads
- gradcam = gradcam[0].mean(0).cpu().detach()
- # [enc token gradcam, average gradcam across token, gradcam for individual token]
- gradcam = torch.cat(
- (
- gradcam[0:1, :],
- gradcam[1 : token_length + 1, :].sum(dim=0, keepdim=True)
- / token_length,
- gradcam[1:, :],
+ gradcams = cams * grads
+ gradcam_list = []
+
+ for ind in range(visual_input.size(0)):
+ token_length_ = token_length[ind]
+ gradcam = gradcams[ind].mean(0).cpu().detach()
+ # [enc token gradcam, average gradcam across token, gradcam for individual token]
+ gradcam = torch.cat(
+ (
+ gradcam[0:1, :],
+ gradcam[1 : token_length_ + 1, :].sum(dim=0, keepdim=True)
+ / token_length_,
+ gradcam[1:, :],
+ )
)
- )
-
- return gradcam, output
+ gradcam_list.append(gradcam)
+
+ return gradcam_list, output
diff --git a/lavis/models/med.py b/lavis/models/med.py
index a6b3a2c29..e963ffb3b 100644
--- a/lavis/models/med.py
+++ b/lavis/models/med.py
@@ -3,6 +3,7 @@
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
Based on huggingface code base
https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
"""
diff --git a/lavis/models/pnp_vqa_models/__init__.py b/lavis/models/pnp_vqa_models/__init__.py
new file mode 100644
index 000000000..44178e550
--- /dev/null
+++ b/lavis/models/pnp_vqa_models/__init__.py
@@ -0,0 +1,29 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import torch
+
+
+def prepare_qa_input(sample, num_captions, num_captions_fid):
+ sample_question_captions = []
+
+ for question, captions in zip(sample['text_input'], sample['captions']):
+ assert isinstance(captions, list)
+ question_captions = []
+ question_caption = ''
+ for cap_id, cap_ in enumerate(captions[0:num_captions]):
+ question_caption += (cap_.strip() + '. ')
+ if (cap_id + 1) != num_captions and ((cap_id + 1) % num_captions_fid == 0):
+ question_caption = question.lower().strip() + " \\n " + question_caption.lower().strip()
+ question_captions.append(question_caption)
+ question_caption = ''
+ if (cap_id + 1) == num_captions:
+ question_caption = question.lower().strip() + " \\n " + question_caption.lower().strip()
+ question_captions.append(question_caption)
+ sample_question_captions.append(question_captions)
+
+ sample['question_captions'] = sample_question_captions
diff --git a/lavis/models/pnp_vqa_models/pnp_unifiedqav2_fid.py b/lavis/models/pnp_vqa_models/pnp_unifiedqav2_fid.py
new file mode 100644
index 000000000..43da9ac14
--- /dev/null
+++ b/lavis/models/pnp_vqa_models/pnp_unifiedqav2_fid.py
@@ -0,0 +1,87 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+ Based on facebookresearch code base
+ https://github.com/facebookresearch/FiD
+"""
+
+import torch
+import torch.nn as nn
+from lavis.common.registry import registry
+from lavis.models.base_model import BaseModel
+from lavis.common.utils import get_abs_path
+from transformers import T5Config, T5Tokenizer, T5ForConditionalGeneration
+
+
+@registry.register_model("pnp_unifiedqav2_fid")
+class PNPUnifiedQAv2FiD(T5ForConditionalGeneration, BaseModel):
+
+ PRETRAINED_MODEL_CONFIG_DICT = {}
+
+ def __init__(self, config, model_path):
+ super().__init__(config)
+
+ self.tokenizer = T5Tokenizer.from_pretrained(model_path)
+
+ def forward(self, input_ids=None, attention_mask=None, **kwargs):
+ if input_ids != None:
+ if input_ids.dim() == 3:
+ self.encoder.num_contexts = input_ids.size(1)
+ input_ids = input_ids.view(input_ids.size(0), -1)
+ if attention_mask != None:
+ attention_mask = attention_mask.view(attention_mask.size(0), -1)
+
+ return super().forward(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ **kwargs
+ )
+
+ def generate(self, input_ids, attention_mask, num_beams=1, min_length=0, max_length=20):
+ self.encoder.num_contexts = input_ids.size(1)
+
+ return super().generate(
+ input_ids=input_ids.view(input_ids.size(0), -1),
+ attention_mask=attention_mask.view(attention_mask.size(0), -1),
+ num_beams=num_beams,
+ min_length=min_length,
+ max_length=max_length
+ )
+
+ def load_unifiedqa(self, state_dict):
+ self.load_state_dict(state_dict)
+ self.encoder = T5EncoderWrapper(self.encoder)
+
+ @classmethod
+ def from_config(cls, cfg):
+ model_path = cfg.get('pretrained')
+ t5_config_path = get_abs_path(cfg.get("t5_config_path"))
+ t5_config = T5Config.from_json_file(t5_config_path)
+ model = cls(t5_config, model_path)
+ model.load_unifiedqa(T5ForConditionalGeneration.from_pretrained(model_path).state_dict())
+
+ return model
+
+
+class T5EncoderWrapper(torch.nn.Module):
+
+ def __init__(self, encoder):
+ super().__init__()
+
+ self.encoder = encoder
+ self.block = self.encoder.block
+ self.parallelize = self.encoder.parallelize
+ self.main_input_name = encoder.main_input_name
+
+ def forward(self, input_ids=None, attention_mask=None, **kwargs):
+ bsz, total_length = input_ids.shape
+ context_length = total_length // self.num_contexts
+ input_ids = input_ids.view(bsz*self.num_contexts, context_length)
+ attention_mask = attention_mask.view(bsz*self.num_contexts, context_length)
+ outputs = self.encoder(input_ids, attention_mask, **kwargs)
+ outputs = (outputs[0].view(bsz, self.num_contexts*context_length, -1), ) + outputs[1:]
+
+ return outputs
\ No newline at end of file
diff --git a/lavis/models/pnp_vqa_models/pnp_vqa.py b/lavis/models/pnp_vqa_models/pnp_vqa.py
new file mode 100644
index 000000000..59b9d888b
--- /dev/null
+++ b/lavis/models/pnp_vqa_models/pnp_vqa.py
@@ -0,0 +1,340 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import torch
+import torch.nn as nn
+from itertools import chain
+from lavis.common.registry import registry
+from lavis.models.base_model import BaseModel
+from torch.nn import CrossEntropyLoss, MSELoss
+from transformers import T5ForConditionalGeneration
+from lavis.models.pnp_vqa_models import prepare_qa_input
+from lavis.models.blip_models.blip_image_text_matching import compute_gradcam
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+
+
+@registry.register_model("pnp_vqa")
+class PNPVQA(BaseModel):
+ """
+ PNPVQA model consists of three submodels for zero-shot VQA:
+ 1. Image-questioning matching model
+ 2. Image captioning model
+ 3. Question answering model
+
+ Supported model types:
+ - base: BLIPITM, BLIPCaption, PNPUnifiedQAv2FiD (t5-base)
+ - large: BLIPITM, BLIPCaption, PNPUnifiedQAv2FiD (t5-large)
+ - 3b: BLIPITM, BLIPCaption, PNPUnifiedQAv2FiD (t5-3b)
+
+ Usage:
+ >>> from lavis.models import load_model
+ >>> model = load_model("pnp_vqa", "base", is_eval=True)
+ >>> model = load_model("pnp_vqa", "large", is_eval=True)
+ >>> model = load_model("pnp_vqa", "3b", is_eval=True)
+ """
+
+ PRETRAINED_MODEL_CONFIG_DICT = {"base": "configs/models/pnp-vqa/pnp_vqa_base.yaml",
+ "large": "configs/models/pnp-vqa/pnp_vqa_large.yaml",
+ "3b": "configs/models/pnp-vqa/pnp_vqa_3b.yaml",
+ }
+
+ def __init__(self, image_question_matching_model, image_captioning_model,
+ question_answering_model, offload_model=False):
+ super().__init__()
+
+ self.image_question_matching_model = image_question_matching_model
+ self.image_captioning_model = image_captioning_model
+ self.question_answering_model = question_answering_model
+ self.offload_model = offload_model
+
+ def forward_itm(self, samples, block_num=7):
+ """
+ Args:
+ samples (dict): A dictionary containing the following keys:
+ - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
+ - text_input (list): A list of strings of length batch_size
+ block_num (int): The index of cross-attention block for gradcam computation.
+
+ Returns:
+ samples (dict): A dictionary containing the following keys:
+ - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
+ - text_input (list): A list of strings of length batch_size
+ - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
+ """
+ image = samples['image']
+ question = [text.strip('?') for text in samples['text_input']]
+ tokenized_text = self.image_question_matching_model.tokenizer(question, padding='longest', truncation=True,
+ return_tensors="pt").to(self.image_question_matching_model.device)
+ with torch.set_grad_enabled(True):
+ gradcams, _ = compute_gradcam(model=self.image_question_matching_model,
+ visual_input=image,
+ text_input=question,
+ tokenized_text=tokenized_text,
+ block_num=block_num)
+
+ gradcams = [gradcam_[1] for gradcam_ in gradcams]
+ samples['gradcams'] = torch.stack(gradcams).reshape(samples['image'].size(0), -1)
+
+ return samples
+
+ def forward_cap(
+ self,
+ samples,
+ cap_max_length=20,
+ cap_min_length=0,
+ top_p=1,
+ top_k=50,
+ repetition_penalty=1.0,
+ num_captions=100,
+ num_patches=20,
+ ):
+ """
+ Args:
+ samples (dict): A dictionary containing the following keys:
+ - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
+ - text_input (list): A list of strings of length batch_size
+ - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
+ cap_max_length (int): The maximum length of the caption to be generated.
+ cap_min_length (int): The minimum length of the caption to be generated.
+ top_p (float): The cumulative probability for nucleus sampling.
+ top_k (float): The number of the highest probability tokens for top-k sampling.
+ repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty.
+ num_captions (int): Number of captions generated for each image.
+ num_patches (int): Number of patches sampled for each image.
+
+ Returns:
+ samples (dict): A dictionary containing the following keys:
+ - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
+ - text_input (list): A list of strings of length batch_size
+ - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
+ - captions (nested list): A nested list of strings of total length batch_size * num_captions
+ """
+ encoder_out = self.image_captioning_model.forward_encoder(samples)
+ captions = [[] for _ in range(encoder_out.size(0))]
+
+ min_num_captions = 0
+
+ while min_num_captions < num_captions:
+ encoder_out_samples = []
+ for i in range(num_captions):
+ patch_id = torch.multinomial(samples['gradcams'].to(self.image_captioning_model.device),
+ num_patches).reshape(encoder_out.size(0), -1) + 1
+ patch_id = patch_id.sort(dim=1).values.unsqueeze(-1).expand(-1, -1, encoder_out.size(2))
+ encoder_out_sample = torch.gather(encoder_out, 1, patch_id)
+ encoder_out_samples.append(encoder_out_sample)
+
+ stacked = torch.stack(encoder_out_samples, dim=1)
+ image_embeds = torch.flatten(stacked, start_dim=0, end_dim=1) #(bsz*num_seq, num_patch, dim)
+
+ image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.image_captioning_model.device)
+ model_kwargs = {
+ "encoder_hidden_states": image_embeds,
+ "encoder_attention_mask": image_atts,
+ }
+
+ prompt = [self.image_captioning_model.prompt] * image_embeds.size(0)
+ prompt = self.image_captioning_model.tokenizer(prompt,
+ return_tensors="pt").to(self.image_captioning_model.device)
+ prompt.input_ids[:, 0] = self.image_captioning_model.tokenizer.bos_token_id
+ prompt.input_ids = prompt.input_ids[:, :-1]
+
+ decoder_out = self.image_captioning_model.text_decoder.generate(
+ input_ids=prompt.input_ids,
+ max_length=cap_max_length,
+ min_length=cap_min_length,
+ do_sample=True,
+ top_p=top_p,
+ top_k=top_k,
+ num_return_sequences=1,
+ eos_token_id=self.image_captioning_model.tokenizer.sep_token_id,
+ pad_token_id=self.image_captioning_model.tokenizer.pad_token_id,
+ repetition_penalty=repetition_penalty,
+ **model_kwargs)
+
+ outputs = self.image_captioning_model.tokenizer.batch_decode(decoder_out, skip_special_tokens=True)
+
+ for counter, output in enumerate(outputs):
+ ind = counter//num_captions
+ if len(captions[ind]) < num_captions:
+ caption = output[len(self.image_captioning_model.prompt):]
+ overlap_caption = [1 for caps in captions[ind] if caption in caps]
+ if len(overlap_caption) == 0:
+ captions[ind].append(caption)
+
+ min_num_captions = min([len(i) for i in captions])
+
+ samples['captions'] = captions
+
+ return samples
+
+ def forward_qa(
+ self,
+ samples,
+ num_beams=1,
+ max_len=20,
+ min_len=0,
+ internal_bsz_fid=1,
+ num_captions=100,
+ num_captions_fid=1,
+ ):
+ """
+ Args:
+ samples (dict): A dictionary containing the following keys:
+ - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
+ - text_input (list): A list of strings of length batch_size
+ - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
+ - captions (nested list): A nested list of strings of total length batch_size * num_captions
+ - question_captions (nested list): A nested list of concatenated strings of questions and captions
+ num_beams (int): Number of beams for beam search. 1 means no beam search.
+ max_len (int): Maximum length of generated answers.
+ min_len (int): Minimum length of generated answers.
+ internal_bsz_fid (int): Internal batch size when using FiD decoding.
+ num_captions (int): Number of captions generated for each image.
+ num_captions_fid (int): Number of captions concatenated with a question during FiD decoding.
+
+ Returns:
+ List: A list of strings, each string is an answer.
+ """
+ prepare_qa_input(samples, num_captions=num_captions, num_captions_fid=num_captions_fid)
+
+ pred_answers = []
+ question_captions = samples['question_captions']
+ question_captions_chunk = [question_captions[i:i + internal_bsz_fid]
+ for i in range(0, len(question_captions), internal_bsz_fid)]
+ question_captions_chunk = list(chain(*question_captions_chunk))
+
+ for question_caption in question_captions_chunk:
+ question_caption_input = self.question_answering_model.tokenizer(question_caption, padding='longest',
+ truncation=True, return_tensors="pt").to(self.question_answering_model.device)
+
+ question_caption_input.input_ids = question_caption_input.input_ids.reshape(
+ internal_bsz_fid, -1, question_caption_input.input_ids.size(1))
+ question_caption_input.attention_mask = question_caption_input.attention_mask.reshape(
+ internal_bsz_fid, -1, question_caption_input.attention_mask.size(1))
+
+ outputs = self.question_answering_model.generate(input_ids=question_caption_input.input_ids,
+ attention_mask=question_caption_input.attention_mask,
+ num_beams=num_beams,
+ min_length=min_len,
+ max_length=max_len,
+ )
+
+ for output in outputs:
+ pred_answer = self.question_answering_model.tokenizer.decode(output, skip_special_tokens=True)
+ pred_answers.append(pred_answer)
+
+ return pred_answers
+
+ def predict_answers(
+ self,
+ samples,
+ num_beams=1,
+ inference_method="generate",
+ max_len=20,
+ min_len=0,
+ internal_bsz_fid=1,
+ num_captions=50,
+ num_captions_fid=1,
+ cap_max_length=20,
+ cap_min_length=10,
+ top_k=50,
+ top_p=1,
+ repetition_penalty=1,
+ num_patches=50,
+ block_num=7,
+ ):
+ """
+ Args:
+ samples (dict): A dictionary containing the following keys:
+ - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W). Default H=480, W=480.
+ - text_input (str or [str]): String or a list of strings, each string is a question.
+ The number of questions must be equal to the batch size. If a single string, will be converted to a list of string, with length 1 first.
+ num_beams (int): Number of beams for beam search. 1 means no beam search.
+ inference_method (str): Inference method. Must be "generate". The model will generate answers.
+ max_len (int): Maximum length of generated answers.
+ min_len (int): Minimum length of generated answers.
+ internal_bsz_fid (int): Internal batch size when using FiD decoding.
+ num_captions (int): Number of captions generated for each image.
+ num_captions_fid (int): Number of captions concatenated with a question during FiD decoding.
+ cap_max_length (int): The maximum length of the caption to be generated.
+ cap_min_length (int): The minimum length of the caption to be generated.
+ top_k (float): The number of the highest probability tokens for top-k sampling.
+ top_p (float): The cumulative probability for nucleus sampling.
+ repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty.
+ num_patches (int): Number of patches sampled for each image.
+ block_num (int): The index of cross-attention block for gradcam computation.
+
+ Returns:
+ List: A list of strings, each string is an answer.
+ gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
+ captions (nested list): A nested list of strings of total length batch_size * num_captions
+ """
+ assert inference_method in [
+ "generate",
+ ], "Inference method must be 'generate', got {}.".format(
+ inference_method
+ )
+
+ if isinstance(samples["text_input"], str):
+ samples["text_input"] = [samples["text_input"]]
+
+ assert len(samples["text_input"]) == samples["image"].size(
+ 0
+ ), "The number of questions must be equal to the batch size."
+
+ samples = self.forward_itm(samples, block_num=block_num)
+
+ samples = self.forward_cap(samples,
+ cap_max_length=cap_max_length,
+ cap_min_length=cap_min_length,
+ top_k=top_k,
+ top_p=top_p,
+ repetition_penalty=repetition_penalty,
+ num_captions=num_captions,
+ num_patches=num_patches)
+
+ if self.offload_model:
+ samples['image'] = samples['image'].to('cpu')
+ self.image_question_matching_model.to('cpu')
+ self.image_captioning_model.to('cpu')
+ torch.cuda.empty_cache()
+
+ pred_answers = self.forward_qa(samples,
+ num_beams=num_beams,
+ max_len=max_len,
+ min_len=min_len,
+ internal_bsz_fid=internal_bsz_fid,
+ num_captions=num_captions,
+ num_captions_fid=num_captions_fid)
+
+ if self.offload_model:
+ self.image_question_matching_model.to(self.question_answering_model.device)
+ self.image_captioning_model.to(self.question_answering_model.device)
+
+ return pred_answers, samples['captions'], samples['gradcams']
+
+ @classmethod
+ def from_config(cls, model_config):
+ itm_config = model_config.image_question_matching_model
+ cap_config = model_config.image_captioning_model
+ qa_config = model_config.question_answering_model
+
+ itm_cls = registry.get_model_class(itm_config.arch)
+ cap_cls = registry.get_model_class(cap_config.arch)
+ qa_cls = registry.get_model_class(qa_config.arch)
+
+ image_question_matching_model = itm_cls.from_config(itm_config)
+ image_captioning_model = cap_cls.from_config(cap_config)
+ question_answering_model = qa_cls.from_config(qa_config)
+
+ model = cls(image_question_matching_model=image_question_matching_model,
+ image_captioning_model=image_captioning_model,
+ question_answering_model=question_answering_model,
+ offload_model= True if model_config.model_type == '3b' else False,
+ )
+
+ return model
\ No newline at end of file
diff --git a/lavis/models/vit.py b/lavis/models/vit.py
index 12411840f..f35b7bb68 100644
--- a/lavis/models/vit.py
+++ b/lavis/models/vit.py
@@ -3,6 +3,7 @@
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
Based on timm code base
https://github.com/rwightman/pytorch-image-models/tree/master/timm
"""
diff --git a/lavis/projects/pnp-vqa/eval/gqa_eval.yaml b/lavis/projects/pnp-vqa/eval/gqa_eval.yaml
new file mode 100644
index 000000000..d8c97adfd
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/gqa_eval.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: base
+
+datasets:
+ gqa: # name of the dataset builder
+ type: balanced_testdev
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: gqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 16
+ batch_size_eval: 16
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 5
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA/GQA"
+
+ evaluate: True
+ test_splits: ["val"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/gqa_eval_3b.yaml b/lavis/projects/pnp-vqa/eval/gqa_eval_3b.yaml
new file mode 100644
index 000000000..933691c2c
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/gqa_eval_3b.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: 3b
+
+datasets:
+ gqa: # name of the dataset builder
+ type: balanced_testdev
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: gqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 4
+ batch_size_eval: 4
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 5
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-3b/GQA"
+
+ evaluate: True
+ test_splits: ["val"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/gqa_eval_large.yaml b/lavis/projects/pnp-vqa/eval/gqa_eval_large.yaml
new file mode 100644
index 000000000..537936aa4
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/gqa_eval_large.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: large
+
+datasets:
+ gqa: # name of the dataset builder
+ type: balanced_testdev
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: gqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 12
+ batch_size_eval: 12
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 5
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-large/GQA"
+
+ evaluate: True
+ test_splits: ["val"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/okvqa_eval.yaml b/lavis/projects/pnp-vqa/eval/okvqa_eval.yaml
new file mode 100644
index 000000000..f29e95db6
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/okvqa_eval.yaml
@@ -0,0 +1,59 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: base
+
+datasets:
+ ok_vqa: # name of the dataset builder
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 16
+ batch_size_eval: 16
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA/OKVQA"
+
+ evaluate: True
+ test_splits: ["test"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/okvqa_eval_3b.yaml b/lavis/projects/pnp-vqa/eval/okvqa_eval_3b.yaml
new file mode 100644
index 000000000..3a0c2dd26
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/okvqa_eval_3b.yaml
@@ -0,0 +1,59 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: 3b
+
+datasets:
+ ok_vqa: # name of the dataset builder
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 4
+ batch_size_eval: 4
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-3b/OKVQA"
+
+ evaluate: True
+ test_splits: ["test"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/okvqa_eval_large.yaml b/lavis/projects/pnp-vqa/eval/okvqa_eval_large.yaml
new file mode 100644
index 000000000..a55d7f25f
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/okvqa_eval_large.yaml
@@ -0,0 +1,59 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: large
+
+datasets:
+ ok_vqa: # name of the dataset builder
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 12
+ batch_size_eval: 12
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-large/OKVQA"
+
+ evaluate: True
+ test_splits: ["test"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/vqav2_eval.yaml b/lavis/projects/pnp-vqa/eval/vqav2_eval.yaml
new file mode 100644
index 000000000..e6f26b6e3
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/vqav2_eval.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: base
+
+datasets:
+ coco_vqa: # name of the dataset builder
+ type: eval
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 16
+ batch_size_eval: 16
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA/VQAv2_val"
+
+ evaluate: True
+ test_splits: ["val"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/vqav2_eval_3b.yaml b/lavis/projects/pnp-vqa/eval/vqav2_eval_3b.yaml
new file mode 100644
index 000000000..dc0db0176
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/vqav2_eval_3b.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: 3b
+
+datasets:
+ coco_vqa: # name of the dataset builder
+ type: eval
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 4
+ batch_size_eval: 4
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-3b/VQAv2_val"
+
+ evaluate: True
+ test_splits: ["val"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/vqav2_eval_large.yaml b/lavis/projects/pnp-vqa/eval/vqav2_eval_large.yaml
new file mode 100644
index 000000000..3e59eae99
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/vqav2_eval_large.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: large
+
+datasets:
+ coco_vqa: # name of the dataset builder
+ type: eval
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 12
+ batch_size_eval: 12
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-large/VQAv2_val"
+
+ evaluate: True
+ test_splits: ["val"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/vqav2_test_eval.yaml b/lavis/projects/pnp-vqa/eval/vqav2_test_eval.yaml
new file mode 100644
index 000000000..a5f3d387a
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/vqav2_test_eval.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: base
+
+datasets:
+ coco_vqa: # name of the dataset builder
+ type: default
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 16
+ batch_size_eval: 16
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA/VQAv2_test"
+
+ evaluate: True
+ test_splits: ["test"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/vqav2_test_eval_3b.yaml b/lavis/projects/pnp-vqa/eval/vqav2_test_eval_3b.yaml
new file mode 100644
index 000000000..799ad5633
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/vqav2_test_eval_3b.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: 3b
+
+datasets:
+ coco_vqa: # name of the dataset builder
+ type: default
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 4
+ batch_size_eval: 4
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-3b/VQAv2_test"
+
+ evaluate: True
+ test_splits: ["test"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/projects/pnp-vqa/eval/vqav2_test_eval_large.yaml b/lavis/projects/pnp-vqa/eval/vqav2_test_eval_large.yaml
new file mode 100644
index 000000000..10aa447ed
--- /dev/null
+++ b/lavis/projects/pnp-vqa/eval/vqav2_test_eval_large.yaml
@@ -0,0 +1,60 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+model:
+ arch: pnp_vqa
+ model_type: large
+
+datasets:
+ coco_vqa: # name of the dataset builder
+ type: default
+ vis_processor:
+ eval:
+ name: "blip_image_eval"
+ image_size: 384
+ text_processor:
+ eval:
+ name: "blip_question"
+
+run:
+ task: vqa_reading_comprehension
+
+ # optimization-specific
+ batch_size_train: 12
+ batch_size_eval: 12
+ num_workers: 4
+
+ # image question matching specific
+ block_num: 7
+
+ # image captioning specific
+ top_k: 50
+ top_p: 1
+ cap_min_length: 10
+ cap_max_length: 20
+ repetition_penalty: 1
+ num_patches: 20
+ num_captions: 100
+ prompt: 'a picture of '
+
+ # question answering specific
+ internal_bsz_fid: 1
+ num_captions_fid: 1
+ min_len: 0
+ max_len: 20
+ num_beams: 1
+ inference_method: "generate"
+
+ seed: 42
+ output_dir: "output/PNP-VQA-large/VQAv2_test"
+
+ evaluate: True
+ test_splits: ["test"]
+
+ # distribution-specific
+ device: "cuda"
+ world_size: 1
+ dist_url: "env://"
+ distributed: True
diff --git a/lavis/tasks/__init__.py b/lavis/tasks/__init__.py
index cd827b099..7b8ad9d4d 100644
--- a/lavis/tasks/__init__.py
+++ b/lavis/tasks/__init__.py
@@ -14,6 +14,7 @@
)
from lavis.tasks.retrieval import RetrievalTask
from lavis.tasks.vqa import VQATask, AOKVQATask
+from lavis.tasks.vqa_reading_comprehension import VQARCTask, GQARCTask
from lavis.tasks.dialogue import DialogueTask
@@ -33,6 +34,8 @@ def setup_task(cfg):
"RetrievalTask",
"CaptionTask",
"VQATask",
+ "VQARCTask",
+ "GQARCTask",
"MultimodalClassificationTask",
# "VideoQATask",
# "VisualEntailmentTask",
diff --git a/lavis/tasks/vqa.py b/lavis/tasks/vqa.py
index 01c2bab79..ff6b2ce5f 100644
--- a/lavis/tasks/vqa.py
+++ b/lavis/tasks/vqa.py
@@ -153,7 +153,12 @@ def _report_metrics(self, result_file, split):
"%s : %.02f"
% (ans_type, vqa_scorer.accuracy["perAnswerType"][ans_type])
)
- metrics["ansType"] = vqa_scorer.accuracy["perAnswerType"][ans_type]
+ metrics[ans_type] = vqa_scorer.accuracy["perAnswerType"][ans_type]
+
+ with open(
+ os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
+ ) as f:
+ f.write(json.dumps(metrics) + "\n")
return metrics
diff --git a/lavis/tasks/vqa_reading_comprehension.py b/lavis/tasks/vqa_reading_comprehension.py
new file mode 100644
index 000000000..254f464b8
--- /dev/null
+++ b/lavis/tasks/vqa_reading_comprehension.py
@@ -0,0 +1,248 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import json
+import os
+import torch
+import torch.distributed as dist
+from itertools import chain
+
+import lavis.common.dist_utils as dist_utils
+from lavis.common.dist_utils import get_rank, get_world_size, is_main_process
+from lavis.common.registry import registry
+from lavis.common.vqa_tools.vqa_eval import VQAEval as VQATool
+from lavis.tasks.vqa import VQATask
+
+
+@registry.register_task("vqa_reading_comprehension")
+class VQARCTask(VQATask):
+ def __init__(
+ self,
+ num_beams,
+ max_len,
+ min_len,
+ evaluate,
+ num_ans_candidates,
+ inference_method="rank",
+ **kwargs,
+ ):
+ super().__init__(num_beams, max_len, min_len, evaluate, num_ans_candidates, inference_method)
+
+ self.config = kwargs.get('config')
+
+ @classmethod
+ def setup_task(cls, cfg):
+ run_cfg = cfg.run_cfg
+
+ num_beams = run_cfg.get("num_beams", 3)
+ max_len = run_cfg.get("max_len", 10)
+ min_len = run_cfg.get("min_len", 1)
+
+ evaluate = run_cfg.get("evaluate", False)
+
+ inference_method = run_cfg.get("inference_method", "rank")
+ num_ans_candidates = run_cfg.get("num_ans_candidates", 128)
+
+ return cls(
+ num_beams=num_beams,
+ max_len=max_len,
+ min_len=min_len,
+ evaluate=evaluate,
+ num_ans_candidates=num_ans_candidates,
+ inference_method=inference_method,
+ config=run_cfg,
+ )
+
+ def valid_step(self, model, samples):
+ answers, captions, gradcams = model.predict_answers(
+ samples=samples,
+ inference_method=self.inference_method,
+ num_beams=self.num_beams,
+ max_len=self.max_len,
+ min_len=self.min_len,
+ internal_bsz_fid=self.config['internal_bsz_fid'],
+ num_captions=self.config['num_captions'],
+ num_captions_fid=self.config['num_captions_fid'],
+ cap_max_length=self.config['cap_max_length'],
+ cap_min_length=self.config['cap_min_length'],
+ top_k=self.config['top_k'],
+ top_p=self.config['top_p'],
+ repetition_penalty=self.config['repetition_penalty'],
+ num_patches=self.config['num_patches'],
+ block_num=self.config['block_num'],
+ )
+
+ pred_qa_pairs = []
+ sample_captions = []
+ sample_gradcams = []
+
+ question_id = samples["question_id"]
+ for answer, caption, gradcam, ques_id in zip(answers, captions, gradcams, question_id):
+ ques_id = int(ques_id.item())
+ pred_qa_pairs.append({"question_id": ques_id, "answer": answer})
+ sample_captions.append({"question_id": ques_id, "caption": caption})
+ sample_gradcams.append({"question_id": ques_id, "gradcam": gradcam})
+
+ return [sample_gradcams, sample_captions, pred_qa_pairs]
+
+ def after_evaluation(self, val_result, split_name, **kwargs):
+ result_ = list(chain(*val_result[0::3]))
+ result_file = self.save_gradcam(
+ result_,
+ result_dir=registry.get_path("result_dir"),
+ filename=f"{split_name}_gradcam_result",
+ remove_duplicate="question_id",
+ )
+
+ result_ = list(chain(*val_result[1::3]))
+ result_file = self.save_result(
+ result_,
+ result_dir=registry.get_path("result_dir"),
+ filename=f"{split_name}_caption_result",
+ remove_duplicate="question_id",
+ )
+
+ result_ = list(chain(*val_result[2::3]))
+ result_file = self.save_result(
+ result_,
+ result_dir=registry.get_path("result_dir"),
+ filename=f"{split_name}_vqa_result",
+ remove_duplicate="question_id",
+ )
+
+ metrics = self._report_metrics(result_file=result_file, split=split_name)
+
+ return metrics
+
+ def save_gradcam(self, result, result_dir, filename, remove_duplicate=""):
+ result_file = os.path.join(result_dir, '%s_rank%d.pth' % (filename, get_rank()))
+ final_result_file = os.path.join(result_dir, '%s.pth' % filename)
+ torch.save({'result': result}, result_file)
+
+ dist.barrier()
+
+ if is_main_process():
+ logging.warning("rank %d starts merging results." % get_rank())
+ # combine results from all processes
+ result = []
+
+ for rank in range(get_world_size()):
+ result_file = os.path.join(result_dir, '%s_rank%d.pth' % (filename, rank))
+ res_ckpt = torch.load(result_file, map_location='cpu')
+ res = res_ckpt['result']
+
+ result += res
+
+ if remove_duplicate:
+ result_new = []
+ id_list = []
+ for res in result:
+ if res[remove_duplicate] not in id_list:
+ id_list.append(res[remove_duplicate])
+ result_new.append(res)
+ result = result_new
+
+ torch.save({'result': result}, final_result_file)
+ print("result file saved to %s" % final_result_file)
+
+ return final_result_file
+
+
+@registry.register_task("gqa_reading_comprehension")
+class GQARCTask(VQARCTask):
+ def valid_step(self, model, samples):
+ answers, captions, gradcams = model.predict_answers(
+ samples=samples,
+ inference_method=self.inference_method,
+ num_beams=self.num_beams,
+ max_len=self.max_len,
+ min_len=self.min_len,
+ internal_bsz_fid=self.config['internal_bsz_fid'],
+ num_captions=self.config['num_captions'],
+ num_captions_fid=self.config['num_captions_fid'],
+ cap_max_length=self.config['cap_max_length'],
+ cap_min_length=self.config['cap_min_length'],
+ top_k=self.config['top_k'],
+ top_p=self.config['top_p'],
+ repetition_penalty=self.config['repetition_penalty'],
+ num_patches=self.config['num_patches'],
+ block_num=self.config['block_num'],
+ )
+
+ pred_qa_pairs = []
+ sample_captions = []
+ sample_gradcams = []
+
+ question_id = samples["question_id"]
+ gt_answers = samples["answer"]
+
+ for pred_answer, caption, gradcam, ques_id, gt_answer in zip(answers, captions, gradcams, question_id, gt_answers):
+ ques_id = int(ques_id.item())
+ pred_qa_pairs.append({"question_id": ques_id, "pred_ans": pred_answer, "gt_ans": gt_answer})
+ sample_captions.append({"question_id": ques_id, "caption": caption})
+ sample_gradcams.append({"question_id": ques_id, "gradcam": gradcam})
+
+ return [sample_gradcams, sample_captions, pred_qa_pairs]
+
+ @dist_utils.main_process
+ def _report_metrics(self, result_file, split):
+ """
+ TODO: add other evaluation metrics for GQA
+ """
+
+ results = json.load(open(result_file, "r"))
+ acc = []
+ vqa_tool = VQATool()
+
+ for res in results:
+ if res["gt_ans"] is None:
+ # prepare test results for leaderboard evaluation
+ self._save_result_leaderboard(results)
+ return
+
+ gt_ans = res["gt_ans"]
+ pred = res["pred_ans"]
+
+ if self.inference_method == "generate":
+ pred = vqa_tool.processPunctuation(pred)
+ pred = vqa_tool.processDigitArticle(pred)
+
+ vqa_acc = 1 if pred == gt_ans else 0
+
+ acc.append(vqa_acc)
+
+ accuracy = sum(acc) / len(acc) * 100
+ metrics = {"agg_metrics": accuracy, "acc": accuracy}
+
+ with open(
+ os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
+ ) as f:
+ f.write(json.dumps(metrics) + "\n")
+
+ logging.info(metrics)
+
+ return metrics
+
+ @dist_utils.main_process
+ def _save_result_leaderboard(self, results):
+ """
+ Saving the results in the format required for leaderboard evaluation.
+ """
+ result_leaderboard = []
+ for res in results:
+ result_leaderboard.append({
+ "questionId": str(res['question_id']),
+ "prediction": str(res["pred_ans"]),
+ })
+
+ result_file = registry.get_path("result_dir") + "_leaderboard.json"
+
+ with open(result_file, "w") as f:
+ json.dump(result_leaderboard, f)
+
+ logging.info(f"Saved results for leaderboard evaluation at {result_file}")
diff --git a/projects/pnp-vqa/README.md b/projects/pnp-vqa/README.md
index 1537eeddd..e61f54ba1 100644
--- a/projects/pnp-vqa/README.md
+++ b/projects/pnp-vqa/README.md
@@ -1,20 +1,112 @@
-# Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
-Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese, Steven C.H. Hoi
+## Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
-[paper](https://arxiv.org/abs/2210.08773), accepted by EMNLP 22 Findings 🎉.
+
-code, notebook example, coming soon
+This is the code for PNP-VQA paper. We integrate the implementation into LAVIS.
+### Demo
+We include an interactive demo [Colab notebook](https://colab.research.google.com/github/salesforce/LAVIS/blob/main/projects/pnp-vqa/pnp_vqa.ipynb)
+to show PNP-VQA inference workflow:
+1. Image-question matching: compute the relevancy score of the image patches wrt the question.
+2. Image captioning: generate question-guided captions based on the relevancy score.
+3. Question answering: answer the question by using the captions.
-## Citation
+### Evaluation
+
+
+
+ | Model |
+ VQAv2 val |
+ VQAv2 test |
+ OK-VQA test |
+ GQA test-dev |
+
+
+ | Paper |
+ LAVIS |
+ Paper |
+ LAVIS |
+ Paper |
+ LAVIS |
+ Paper |
+ LAVIS |
+
+
+
+
+ | PNP-VQAbase |
+ 54.3 |
+ 54.2 |
+ 55.2 |
+ 55.3 |
+ 23.0 |
+ 23.3 |
+ 34.6 |
+ 34.9 |
+
+
+ | PNP-VQAlarge |
+ 57.5 |
+ 57.5 |
+ 58.8 |
+ 58.9 |
+ 27.1 |
+ 27.1 |
+ 38.4 |
+ 38.4 |
+
+
+ | PNP-VQA3B |
+ 62.1 |
+ 62.1 |
+ 63.5 |
+ 63.5 |
+ 34.1 |
+ 34.0 |
+ 42.3 |
+ 42.3 |
+
+
+
-```bibtex
-@misc{tiong2022plug,
+Evaluate PNP-VQA of different size using the respective scripts. The details are as following:
+
+#### VQAv2 Val
+```
+bash run_scripts/pnp-vqa/eval/eval_vqav2.sh ## 54.2
+bash run_scripts/pnp-vqa/eval/eval_vqav2_large.sh ## 57.5
+bash run_scripts/pnp-vqa/eval/eval_vqav2_3b.sh ## 62.1
+```
+
+#### VQAv2 Test
+```
+bash run_scripts/pnp-vqa/eval/eval_vqav2_test.sh ## 55.3
+bash run_scripts/pnp-vqa/eval/eval_vqav2_test_large.sh ## 58.9
+bash run_scripts/pnp-vqa/eval/eval_vqav2_test_3b.sh ## 63.5
+```
+
+#### OK-VQA Test
+```
+bash run_scripts/pnp-vqa/eval/eval_okvqa.sh ## 23.3
+bash run_scripts/pnp-vqa/eval/eval_okvqa_large.sh ## 27.1
+bash run_scripts/pnp-vqa/eval/eval_okvqa_3b.sh ## 34.0
+```
+
+#### GQA Test-dev
+```
+bash run_scripts/pnp-vqa/eval/eval_gqa.sh ## 34.9
+bash run_scripts/pnp-vqa/eval/eval_gqa_large.sh ## 38.4
+bash run_scripts/pnp-vqa/eval/eval_gqa_3b.sh ## 42.3
+```
+
+### Citation
+If you find this code to be useful for your research, please consider citing.
+
+@misc{tiong2022pnpvqa,
title={Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training},
- author={Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese, Steven C. H. Hoi},
+ author={Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese, and Steven C.H. Hoi},
year={2022},
eprint={2210.08773},
archivePrefix={arXiv},
primaryClass={cs.CV}
-}
-```
+}
diff --git a/projects/pnp-vqa/pnp_vqa.ipynb b/projects/pnp-vqa/pnp_vqa.ipynb
new file mode 100644
index 000000000..2ebb005a8
--- /dev/null
+++ b/projects/pnp-vqa/pnp_vqa.ipynb
@@ -0,0 +1,414 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0746610a",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "_qxQ4_bEhkrd",
+ "metadata": {
+ "id": "_qxQ4_bEhkrd"
+ },
+ "source": [
+ "## PNP-VQA: Inference Demo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dq8t0LJThhuJ",
+ "metadata": {
+ "id": "dq8t0LJThhuJ"
+ },
+ "outputs": [],
+ "source": [
+ "# install requirements\n",
+ "import sys\n",
+ "if 'google.colab' in sys.modules:\n",
+ " print('Running in Colab.')\n",
+ " !git clone https://github.com/salesforce/LAVIS\n",
+ " %cd LAVIS\n",
+ " !pip install ."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "838168ff",
+ "metadata": {
+ "id": "838168ff"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import requests\n",
+ "from PIL import Image\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "from lavis.common.gradcam import getAttMap\n",
+ "from lavis.models import load_model_and_preprocess"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ffeec4e",
+ "metadata": {
+ "id": "2ffeec4e"
+ },
+ "source": [
+ "### Load an example image and question"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2da65ed1",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 335
+ },
+ "id": "2da65ed1",
+ "outputId": "31b7806a-95eb-418f-fed9-e55ee9cc51fd"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "What is the black objects on the salad called?\n"
+ ]
+ }
+ ],
+ "source": [
+ "img_url = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/projects/pnp-vqa/demo.png' \n",
+ "raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') \n",
+ "# raw_image = Image.open(\"./demo.png\").convert(\"RGB\")\n",
+ "display(raw_image.resize((400, 300)))\n",
+ "question = \"What is the black objects on the salad called?\"\n",
+ "print(question)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "5ba89c01",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5ba89c01",
+ "outputId": "04e3ab39-475b-4c8c-b335-45e0b739cc6e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "device(type='cuda')"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# setup device to use\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "076f55e8",
+ "metadata": {
+ "id": "076f55e8"
+ },
+ "source": [
+ "### Load PNP-VQA model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "01be941e",
+ "metadata": {
+ "id": "01be941e",
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "model, vis_processors, txt_processors = load_model_and_preprocess(name=\"pnp_vqa\", model_type=\"base\", is_eval=True, device=device)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0e27d11",
+ "metadata": {
+ "id": "f0e27d11"
+ },
+ "source": [
+ "### Preprocess image and text inputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "1c47f415",
+ "metadata": {
+ "id": "1c47f415"
+ },
+ "outputs": [],
+ "source": [
+ "image = vis_processors[\"eval\"](raw_image).unsqueeze(0).to(device)\n",
+ "question = txt_processors[\"eval\"](question)\n",
+ "\n",
+ "samples = {\"image\": image, \"text_input\": [question]}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba7a0e77",
+ "metadata": {
+ "id": "ba7a0e77"
+ },
+ "source": [
+ "### PNP-VQA utilizes 3 submodels to perform VQA:\n",
+ "#### 1. Image-Question Matching \n",
+ "Compute the relevancy score of image patches with respect to the question using GradCAM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6e1615fc",
+ "metadata": {
+ "id": "6e1615fc"
+ },
+ "outputs": [],
+ "source": [
+ "samples = model.forward_itm(samples=samples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "46c50f9c",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 262
+ },
+ "id": "46c50f9c",
+ "outputId": "1588f183-c786-4de0-abc8-ecbfffe2f9a6"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Question: what is the black objects on the salad called?\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Gradcam visualisation\n",
+ "dst_w = 720\n",
+ "w, h = raw_image.size\n",
+ "scaling_factor = dst_w / w\n",
+ "\n",
+ "resized_img = raw_image.resize((int(w * scaling_factor), int(h * scaling_factor)))\n",
+ "norm_img = np.float32(resized_img) / 255\n",
+ "gradcam = samples['gradcams'].reshape(24,24)\n",
+ "\n",
+ "avg_gradcam = getAttMap(norm_img, gradcam, blur=True)\n",
+ "\n",
+ "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
+ "ax.imshow(avg_gradcam)\n",
+ "ax.set_yticks([])\n",
+ "ax.set_xticks([])\n",
+ "print('Question: {}'.format(question))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2d7cf41",
+ "metadata": {
+ "id": "c2d7cf41"
+ },
+ "source": [
+ "#### 2. Image Captioning\n",
+ "Generate question-guided captions based on the relevancy score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "49ac307b",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "49ac307b",
+ "outputId": "93535276-fdd0-4620-a833-cc7da0ff4776"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Examples of question-guided captions: \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['a salad in a lunch box with olives, olives and black olives',\n",
+ " 'a salad and olive salad with black olives on the computer desk',\n",
+ " 'a salad with black olives and a plate of olives and a keyboard cl',\n",
+ " 'salad salad black salad bowl salad salad salad salad, black salad salad and black salad',\n",
+ " 'a salad and salad platter with black olives, olives and olives']"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "samples = model.forward_cap(samples=samples, num_captions=50, num_patches=20)\n",
+ "print('Examples of question-guided captions: ')\n",
+ "samples['captions'][0][:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b708efb6",
+ "metadata": {
+ "id": "b708efb6"
+ },
+ "source": [
+ "#### 3. Question Answering\n",
+ "Answer the question by using the captions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f065b898",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "f065b898",
+ "outputId": "152ef7d0-9071-4397-f68f-00534a274784"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Question: what is the black objects on the salad called? \n",
+ "Predicted answer: olives\n"
+ ]
+ }
+ ],
+ "source": [
+ "pred_answers = model.forward_qa(samples, num_captions=50)\n",
+ "print('Question: {} \\nPredicted answer: {}'.format(question, pred_answers[0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47fb5774",
+ "metadata": {
+ "id": "47fb5774"
+ },
+ "source": [
+ "### Generate answer by calling `predict_answers()` directly\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "8d9a4de5",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "8d9a4de5",
+ "outputId": "c057146e-009a-4272-8bfb-36f92e610518"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Question: what is the black objects on the salad called? \n",
+ "Predicted answer: olives\n"
+ ]
+ }
+ ],
+ "source": [
+ "pred_answers, caption, gradcam = model.predict_answers(samples, num_captions=50, num_patches=20)\n",
+ "print('Question: {} \\nPredicted answer: {}'.format(question, pred_answers[0]))"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "include_colab_link": true,
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/pnp-vqa/pnp_vqa.png b/projects/pnp-vqa/pnp_vqa.png
new file mode 100644
index 000000000..03ee15459
Binary files /dev/null and b/projects/pnp-vqa/pnp_vqa.png differ
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 099a6cfb8..b6db6c957 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -24,3 +24,4 @@ tqdm
transformers>=4.15.0,<4.22.0
webdataset
wheel
+sentencepiece
diff --git a/run_scripts/pnp-vqa/eval/eval_gqa.sh b/run_scripts/pnp-vqa/eval/eval_gqa.sh
new file mode 100644
index 000000000..719d9d3c4
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_gqa.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/gqa_eval.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_gqa_3b.sh b/run_scripts/pnp-vqa/eval/eval_gqa_3b.sh
new file mode 100644
index 000000000..d5c15805a
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_gqa_3b.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/gqa_eval_3b.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_gqa_large.sh b/run_scripts/pnp-vqa/eval/eval_gqa_large.sh
new file mode 100644
index 000000000..4491cd8d4
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_gqa_large.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/gqa_eval_large.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_okvqa.sh b/run_scripts/pnp-vqa/eval/eval_okvqa.sh
new file mode 100644
index 000000000..27344bdfe
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_okvqa.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/okvqa_eval.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_okvqa_3b.sh b/run_scripts/pnp-vqa/eval/eval_okvqa_3b.sh
new file mode 100644
index 000000000..1bf5d6bef
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_okvqa_3b.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/okvqa_eval_3b.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_okvqa_large.sh b/run_scripts/pnp-vqa/eval/eval_okvqa_large.sh
new file mode 100644
index 000000000..75f55d784
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_okvqa_large.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/okvqa_eval_large.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_vqav2.sh b/run_scripts/pnp-vqa/eval/eval_vqav2.sh
new file mode 100644
index 000000000..3e2f013fa
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_vqav2.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/vqav2_eval.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_vqav2_3b.sh b/run_scripts/pnp-vqa/eval/eval_vqav2_3b.sh
new file mode 100644
index 000000000..fc1f803c6
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_vqav2_3b.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/vqav2_eval_3b.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_vqav2_large.sh b/run_scripts/pnp-vqa/eval/eval_vqav2_large.sh
new file mode 100644
index 000000000..af54a8221
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_vqav2_large.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/vqav2_eval_large.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_vqav2_test.sh b/run_scripts/pnp-vqa/eval/eval_vqav2_test.sh
new file mode 100644
index 000000000..695f985bd
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_vqav2_test.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/vqav2_test_eval.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_vqav2_test_3b.sh b/run_scripts/pnp-vqa/eval/eval_vqav2_test_3b.sh
new file mode 100644
index 000000000..48e061fc2
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_vqav2_test_3b.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/vqav2_test_eval_3b.yaml
\ No newline at end of file
diff --git a/run_scripts/pnp-vqa/eval/eval_vqav2_test_large.sh b/run_scripts/pnp-vqa/eval/eval_vqav2_test_large.sh
new file mode 100644
index 000000000..fc01ebb69
--- /dev/null
+++ b/run_scripts/pnp-vqa/eval/eval_vqav2_test_large.sh
@@ -0,0 +1 @@
+python -m torch.distributed.run --nproc_per_node=8 evaluate.py --cfg-path lavis/projects/pnp-vqa/eval/vqav2_test_eval_large.yaml
\ No newline at end of file
diff --git a/tests/models/test_pnp_vqa.py b/tests/models/test_pnp_vqa.py
new file mode 100644
index 000000000..c43bb108a
--- /dev/null
+++ b/tests/models/test_pnp_vqa.py
@@ -0,0 +1,119 @@
+"""
+#
+# Copyright (c) 2022 salesforce.com, inc.
+# All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+#
+
+Integration tests for PNP-VQA model.
+"""
+
+import pytest
+import torch
+from lavis.models import load_model, load_model_and_preprocess
+from PIL import Image
+
+# setup device to use
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# load sample image
+raw_image = Image.open("docs/_static/merlion.png").convert("RGB")
+
+precision = 1e-1
+
+
+class TestPNPVQA:
+ def test_vqa(self):
+ # loads PNP-VQA base model, with BLIP_itm_large, BLIP_caption_large, Unifiedqav2_base
+ # this also loads the associated image processors and text processors
+ model, vis_processors, txt_processors = load_model_and_preprocess(
+ name="pnp_vqa", model_type="base", is_eval=True, device=device
+ )
+
+ # ask a random question.
+ question = "Which city is this photo taken?"
+
+ image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
+ question = txt_processors["eval"](question)
+
+ samples = {"image": image, "text_input": [question]}
+
+ answer, caption, gradcam = model.predict_answers(
+ samples=samples,
+ inference_method="generate",
+ num_captions=5,
+ num_patches=20,
+ )
+ assert isinstance(answer, list)
+ assert isinstance(caption, list)
+ assert isinstance(gradcam, torch.Tensor)
+
+ assert len(answer) == 1
+ assert len(caption) == 1
+ assert len(caption[0]) == 5
+ assert gradcam.size() == torch.Size([1,576])
+
+ def test_itm(self):
+ # loads PNP-VQA base model, with BLIP_itm_large, BLIP_caption_large, Unifiedqav2_base
+ # this also loads the associated image processors and text processors
+ model, vis_processors, txt_processors = load_model_and_preprocess(
+ name="pnp_vqa", model_type="base", is_eval=True, device=device
+ )
+
+ # ask a random question.
+ question = "Which city is this photo taken?"
+
+ image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
+ question = txt_processors["eval"](question)
+
+ samples = {"image": image, "text_input": [question]}
+
+ samples = model.forward_itm(samples=samples)
+
+ assert samples['gradcams'].size() == torch.Size([1,576])
+
+ def test_caption(self):
+ # loads PNP-VQA base model, with BLIP_itm_large, BLIP_caption_large, Unifiedqav2_base
+ # this also loads the associated image processors and text processors
+ model, vis_processors, txt_processors = load_model_and_preprocess(
+ name="pnp_vqa", model_type="base", is_eval=True, device=device
+ )
+
+ # ask a random question.
+ question = "Which city is this photo taken?"
+
+ image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
+ question = txt_processors["eval"](question)
+
+ samples = {"image": image, "text_input": [question]}
+
+ samples['gradcams'] = torch.rand(1,576)
+
+ samples = model.forward_cap(samples=samples, num_captions=5, num_patches=20)
+
+ assert len(samples['captions']) == 1
+ assert len(samples['captions'][0]) == 5
+
+ def test_qa(self):
+ # loads PNP-VQA base model, with BLIP_itm_large, BLIP_caption_large, Unifiedqav2_base
+ # this also loads the associated image processors and text processors
+ model, vis_processors, txt_processors = load_model_and_preprocess(
+ name="pnp_vqa", model_type="base", is_eval=True, device=device
+ )
+
+ # ask a random question.
+ question = "Which city is this photo taken?"
+
+ image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
+ question = txt_processors["eval"](question)
+
+ samples = {"image": image, "text_input": [question]}
+
+ samples['captions'] = [['the city is singapore', 'the picture is taken in singapore']]
+
+ answer = model.forward_qa(samples=samples, num_captions=2)
+
+ assert isinstance(answer, list)
+ assert len(answer) == 1
+ assert answer[0]== 'singapore'
\ No newline at end of file