Merge branch 'main' of https://github.com/junchen14/MiniGPT-4_finetune into main

dataset/Evaluation.md

@@ -0,0 +1,26 @@
+
+### OKVQA 
+
+### GQA
+Images and question-answer pairs will be loaded during the evaluation.
+
+``` python run_eval.py xxxx ``` 
+
+### VSR
+Images and question-answer pairs will be loaded during the evaluation.
+
+``` python run_eval.py xxxx ``` 
+
+### IconVQA 
+
+### VizWiz 
+1. Download [`test.json`](https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip) and extract [`test.zip`](https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip) to `test`. Put them under `your_path/vizwiz`.
+2. Single-GPU inference.
+``` python run_eval.py xxxx ``` 
+
+### HM
+
+
+
+
+

dataset/README_MINIGPTv2_FINETUNE.md

@@ -1,133 +1,94 @@
 ## Download the COCO captions, RefCOCO, RefCOCO+. RefCOCOg, visual genome, textcaps, LLaVA, gqa, AOK-VQA, OK-VQA, OCR-VQA, filtered Flickr-30k, multi-task conversation, and Unnatural instruction datasets
 
+
 ### COCO captions
+- [train2017](http://images.cocodataset.org/zips/train2017.zip)
 
 
-### RefCOCO, RefCOCO+, RefCOCOg
-
 ### Visual genome
+- [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip)
 
-### textcaps
+### TextCaps
+
+- [TextCaps_0.1_train](https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_train.json)
+- [Images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)
+
+### RefCOCO, RefCOCO+, RefCOCOg
+Make sure you have the COCO 2014 images first. 
+
+Then,
+download RefCOCO, RefCOCO+, and RefCOCOg annotation files in the following links.
+
+- https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
+- https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
+- https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip
+
+Unzip these files to the location you like. It should have the structure like the following
+
+```
+Location_you_like
+├── refcoco
+│   ├── instances.json
+│   ├── refs(google).p
+│   └── refs(unc).p
+├── refcoco+
+│   ├── instances.json
+│   └── refs(unc).p
+└── refcocog
+    ├── instances.json
+    ├── refs(google).p
+    └── refs(umd).p
+```
+
+Set **image_path** in all the following dataset configuration files to the COCO 2014 image folder.
+Similarly, set **ann_path** in all the following configs to the above folder (Location_you_like) that contains refcoco, refcoco+, and refcocog.
+
+- [minigpt4/configs/datasets/coco_bbox/refcoco.yaml](../minigpt4/configs/datasets/coco_bbox/refcoco.yaml)
+- [minigpt4/configs/datasets/coco_bbox/refcocog.yaml](../minigpt4/configs/datasets/coco_bbox/refcocog.yaml) 
+- [minigpt4/configs/datasets/coco_bbox/refcocop.yaml](../minigpt4/configs/datasets/coco_bbox/refcocop.yaml)
+- [minigpt4/configs/datasets/coco_bbox/invrefcoco.yaml](../minigpt4/configs/datasets/coco_bbox/invrefcoco.yaml)
+- [minigpt4/configs/datasets/coco_bbox/invrefcocog.yaml](../minigpt4/configs/datasets/coco_bbox/invrefcocog.yaml) 
+- [minigpt4/configs/datasets/coco_bbox/invrefcocop.yaml](../minigpt4/configs/datasets/coco_bbox/invrefcocop.yaml)
 
 ### LLaVA
+Makesure you have the COCO 2014 images first. 
+
+Download Llava annotation files in the following link to the place you like.
+
+- https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/conversation_58k.json
+- https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/detail_23k.json
+- https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/complex_reasoning_77k.json
+
+Set **image_path** in all the following dataset configuration files to the COCO 2014 image folder.
+Similarly, set **ann_path** to the location of the previous downloaded conversation_58k.json, 
+detail_23k.json, and complex_reasoning_77k.json in conversation.yaml, detail.yaml, and reason.yaml, respectively.
+
+
+- [minigpt4/configs/datasets/llava/conversation.yaml](../minigpt4/configs/datasets/llava/conversation.yaml)
+- [minigpt4/configs/datasets/llava/detail.yaml](../minigpt4/configs/datasets/llava/detail.yaml) 
+- [minigpt4/configs/datasets/llava/reason.yaml](../minigpt4/configs/datasets/llava/reason.yaml)
 
-### gqa
 
 ### OKVQA
 
+- [OK-VQA Input Questions](https://okvqa.allenai.org/static/data/OpenEnded_mscoco_train2014_questions.json.zip)
+- [OK-VQA Annotations](https://okvqa.allenai.org/static/data/mscoco_train2014_annotations.json.zip)
+- [okvqa_train](https://storage.googleapis.com/sfr-vision-language-research/LAVIS/datasets/okvqa/okvqa_train.json)
+- Images are from COCO
+
 ### AOK-VQA
 
+```
+export AOKVQA_DIR=YOUR_DATASET_PATH
+mkdir -p ${AOKVQA_DIR}
+curl -fsSL https://prior-datasets.s3.us-east-2.amazonaws.com/aokvqa/aokvqa_v1p0.tar.gz | tar xvz -C ${AOKVQA_DIR}
+```
+
 ### OCR-VQA
+- [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing), **we save all files as `.jpg`**
 
 ### filtered Flickr-30k
 
 ### Multi-task conversation
 
 ### Unnatural instruction
-
-
-
-
-
-
-
-
-
-
-
-
-
-### Pre-training datasets download:
-We use the filtered synthetic captions prepared by BLIP. For more details about the dataset, please refer to [BLIP](https://github.com/salesforce/BLIP).
-
-It requires ~2.3T to store LAION and CC3M+CC12M+SBU datasets
-
-Image source | Filtered synthetic caption by ViT-L
---- | :---:
-CC3M+CC12M+SBU | <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_synthetic_filtered_large.json">Download</a>
-LAION115M |  <a href="https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/laion_synthetic_filtered_large.json">Download</a>
-
-This will download two json files 
-```
-ccs_synthetic_filtered_large.json
-laion_synthetic_filtered_large.json
-```
-
-## prepare the data step-by-step
-
-
-### setup the dataset folder and move the annotation file to the data storage folder
-```
-export MINIGPT4_DATASET=/YOUR/PATH/FOR/LARGE/DATASET/
-mkdir ${MINIGPT4_DATASET}/cc_sbu
-mkdir ${MINIGPT4_DATASET}/laion
-mv ccs_synthetic_filtered_large.json ${MINIGPT4_DATASET}/cc_sbu
-mv laion_synthetic_filtered_large.json ${MINIGPT4_DATASET}/laion
-```
-
-### Convert the scripts to data storate folder
-```
-cp convert_cc_sbu.py ${MINIGPT4_DATASET}/cc_sbu
-cp download_cc_sbu.sh ${MINIGPT4_DATASET}/cc_sbu
-cp convert_laion.py ${MINIGPT4_DATASET}/laion
-cp download_laion.sh ${MINIGPT4_DATASET}/laion
-```
-
-
-### Convert the laion and cc_sbu annotation file format to be img2dataset format
-```
-cd ${MINIGPT4_DATASET}/cc_sbu
-python convert_cc_sbu.py
-
-cd ${MINIGPT4_DATASET}/laion
-python convert_laion.py
-```
-
-### Download the datasets with img2dataset
-```
-cd ${MINIGPT4_DATASET}/cc_sbu
-sh download_cc_sbu.sh
-cd ${MINIGPT4_DATASET}/laion
-sh download_laion.sh
-```
-
-
-The final dataset structure
-
-```
-.
-├── ${MINIGPT4_DATASET}
-│   ├── cc_sbu
-│       ├── convert_cc_sbu.py
-│       ├── download_cc_sbu.sh
-│       ├── ccs_synthetic_filtered_large.json
-│       ├── ccs_synthetic_filtered_large.tsv
-│       └── cc_sbu_dataset
-│           ├── 00000.tar
-│           ├── 00000.parquet
-│           ...
-│   ├── laion
-│       ├── convert_laion.py
-│       ├── download_laion.sh
-│       ├── laion_synthetic_filtered_large.json
-│       ├── laion_synthetic_filtered_large.tsv
-│       └── laion_dataset
-│           ├── 00000.tar
-│           ├── 00000.parquet
-│           ...
-...   
-```
-
-
-## Set up the dataset configuration files
-
-Then, set up the LAION dataset loading path in 
-[here](../minigpt4/configs/datasets/laion/defaults.yaml#L5) at Line 5 as 
-${MINIGPT4_DATASET}/laion/laion_dataset/{00000..10488}.tar
-
-and the Conceptual Captoin and SBU datasets loading path in 
-[here](../minigpt4/configs/datasets/cc_sbu/defaults.yaml#L5) at Line 5 as 
-${MINIGPT4_DATASET}/cc_sbu/cc_sbu_dataset/{00000..01255}.tar
-
-
-

minigpt4/models/minigpt_base.py

@@ -178,7 +178,6 @@ class MiniGPTBase(BaseModel):
             answers = [self.llama_tokenizer(a + self.end_sym,
                                             return_tensors="pt",
                                             add_special_tokens=False).to(self.device) for a in answers]
-
             cur_id = []
             cur_target = []
             for i in range(len(questions)):
@@ -226,8 +225,6 @@ class MiniGPTBase(BaseModel):
 
             conv_q = [[self.prompt_template.format(item) for item in items] for items in conv_q]
 
-        
-
             cond_embeds, cond_atts = self.prompt_wrap(img_embeds, img_atts, [q[0] for q in conv_q])
             regress_token_ids, regress_atts, part_targets = self.tokenize_conversation(conv_q, conv_a)
 

minigpt4/models/modeling_llama.py

@@ -75,7 +75,7 @@ class LlamaForCausalLM(LlamaForCausalLMOrig):
         )
 
         hidden_states = outputs[0]
-        if self.config.pretraining_tp > 1:
+        if hasattr(self.config, 'pretraining_tp') and self.config.pretraining_tp > 1:
             lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
             logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
             logits = torch.cat(logits, dim=-1)

train.py

@@ -12,6 +12,7 @@ import random
 import numpy as np
 import torch
 import torch.backends.cudnn as cudnn
+import wandb
 
 import minigpt4.tasks as tasks
 from minigpt4.common.config import Config
@@ -30,7 +31,6 @@ from minigpt4.models import *
 from minigpt4.processors import *
 from minigpt4.runners import *
 from minigpt4.tasks import *
-import wandb
 
 
 def parse_args():
@@ -44,12 +44,10 @@ def parse_args():
         "in xxx=yyy format will be merged into config file (deprecate), "
         "change to --cfg-options instead.",
     )
-    parser.add_argument("--wandb_log", default=False)
+    parser.add_argument("--job_name", default="minigpt_v2",type=str)
-    parser.add_argument("--job_name",default="minigpt_v2",type=str)
 
     args = parser.parse_args()
 
-
     return args
 
 
@@ -80,16 +78,13 @@ def main():
     # set before init_distributed_mode() to ensure the same job_id shared across all ranks.
     job_id = now()
     args = parse_args()
-
+    cfg = Config(args)
-    cfg = Config(parse_args())
 
     init_distributed_mode(cfg.run_cfg)
-
     setup_seeds(cfg)
 
     # set after init_distributed_mode() to only log on master.
     setup_logger()
-
     cfg.pretty_print()
 
     task = tasks.setup_task(cfg)
@@ -98,10 +93,9 @@ def main():
 
     if cfg.run_cfg.wandb_log:
         wandb.login()
-        wandb.init(project="minigptv2",name=args.job_name)
+        wandb.init(project="minigptv", name=cfg.run_cfg.job_name)
         wandb.watch(model)
 
-
     runner = get_runner_class(cfg)(
         cfg=cfg, job_id=job_id, task=task, model=model, datasets=datasets
     )

train_configs/minigpt4_llama2_stage1_pretrain.yaml

@@ -53,3 +53,6 @@ run:
   world_size: 1
   dist_url: "env://"
   distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_llama2_pretrain

train_configs/minigpt4_llama2_stage2_finetune.yaml

@@ -47,3 +47,6 @@ run:
   world_size: 1
   dist_url: "env://"
   distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_llama2_finetune

train_configs/minigpt4_stage1_pretrain.yaml

@@ -53,3 +53,6 @@ run:
   world_size: 1
   dist_url: "env://"
   distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_pretrain

train_configs/minigpt4_stage2_finetune.yaml

@@ -47,3 +47,6 @@ run:
   world_size: 1
   dist_url: "env://"
   distributed: True
+
+  wandb_log: True
+  job_name: minigpt4_finetune

train_configs/minigpt_v2_finetune.yaml

@@ -276,7 +276,6 @@ run:
   init_lr: 1e-5
   min_lr: 8e-5
   warmup_lr: 1e-6
-  wandb_log: True
 
   weight_decay: 0.05
   max_epoch: 50
@@ -297,3 +296,6 @@ run:
   world_size: 1
   dist_url: "env://"
   distributed: True
+
+  wandb_log: True
+  job_name: minigptv2_finetune