[Feature]: Use multimodal (#73)

* [Feature]: Add minigpt-4

* [Feature]: Add mm local runner

* [Feature]: Add instructblip

* [Feature]: Delete redundant file

* [Feature]: Delete redundant file

* [Feature]: Add README to InstructBLIP

* [Feature]: Update MiniGPT-4

* [Fix]: Fix lint

* [Feature]add omnibenchmark readme (#49)

* add omnibenchmark readme

* fix

* Update OmniMMBench.md

* Update OmniMMBench.md

* Update OmniMMBench.md

* [Fix]: Refine name (#54)

* [Feature]: Unify out and err

* [Fix]: Fix lint

* [Feature]: Rename to mmbench and change weight path

* [Feature]: Delete Omni in instructblip

* [Feature]: Check the avaliablity of lavis

* [Fix]: Fix lint

* [Feature]: Refactor MM

* [Refactor]: Refactor path

* [Feature]: Delete redundant files

* [Refactor]: Delete redundant files

---------
Co-authored-by: Wangbo Zhao(黑色枷锁) <56866854+wangbo-zhao@users.noreply.github.com>

.gitignore

@@ -10,6 +10,7 @@ configs/datasets/log.json
 configs/eval_debug*.py
 configs/viz_*.py
 data
+work_dirs
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

configs/multimodal/instructblip/README.md

+# InstructBLIP
+
+### Prepare the environment
+
+```sh
+git clone https://github.com/salesforce/LAVIS.git
+cd ./LAVIS
+pip install -e .
+```
\ No newline at end of file

configs/multimodal/instructblip/instructblip-mmbench.py

+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(type='mmpretrain.PackInputs',
+         algorithm_keys=[
+             'question', 'answer', 'category', 'l2-category', 'context',
+             'index', 'options_dict', 'options', 'split'
+         ])
+]
+
+dataset = dict(type='opencompass.MMBench',
+               data_file='data/mmbench/mmbench_test_20230712.tsv',
+               pipeline=val_pipeline)
+
+dataloader = dict(batch_size=1,
+                  num_workers=4,
+                  dataset=dataset,
+                  collate_fn=dict(type='pseudo_collate'),
+                  sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+model = dict(
+    type='blip2-vicuna-instruct-mmbench',
+    freeze_vit=True,
+    low_resource=False,
+    llm_model='/path/to/vicuna-7b/',
+    sys_prompt=  # noqa: E251
+    '###Human: What is the capital of China? There are several options:\nA. Beijing\nB. Shanghai\nC. Guangzhou\nD. Shenzhen\n###Assistant: A\n'
+)
+
+# evaluation settings
+evaluator = [
+    dict(
+        type='opencompass.DumpResults',
+        save_path=  # noqa: E251
+        'work_dirs/instructblip_vicuna7b/instructblipvicuna_mmbench.xlsx')
+]
+
+load_from = '/path/to/instruct_blip_vicuna7b_trimmed.pth'  # noqa

configs/multimodal/minigpt_4/README.md

+# MiniGPT-4
+
+### Prepare the environment
+
+```sh
+cd opencompass/multimodal/models/minigpt_4
+git clone https://github.com/Vision-CAIR/MiniGPT-4.git
+```
+
+Then prepare the environement according to this [doc](https://github.com/Vision-CAIR/MiniGPT-4)
\ No newline at end of file

configs/multimodal/minigpt_4/minigpt_4_7b_mmbench.py

+# dataloader settings
+val_pipeline = [
+    dict(type='mmpretrain.torchvision/Resize',
+         size=(224, 224),
+         interpolation=3),
+    dict(type='mmpretrain.torchvision/ToTensor'),
+    dict(type='mmpretrain.torchvision/Normalize',
+         mean=(0.48145466, 0.4578275, 0.40821073),
+         std=(0.26862954, 0.26130258, 0.27577711)),
+    dict(type='mmpretrain.PackInputs',
+         algorithm_keys=[
+             'question', 'answer', 'category', 'l2-category', 'context',
+             'index', 'options_dict', 'options', 'split'
+         ])
+]
+
+dataset = dict(type='opencompass.MMBenchDataset',
+               data_file='data/mmbench/mmbench_test_20230712.tsv',
+               pipeline=val_pipeline)
+
+minigpt_4_dataloader = dict(batch_size=1,
+                            num_workers=4,
+                            dataset=dataset,
+                            collate_fn=dict(type='pseudo_collate'),
+                            sampler=dict(type='DefaultSampler', shuffle=False))
+
+# model settings
+minigpt_4_model = dict(
+    type='minigpt-4-mmbench',
+    low_resource=True,
+    llama_model='/path/to/vicuna',
+    sys_prompt=  # noqa: E251
+    '###Human: What is the capital of China? There are several options:\nA. Beijing\nB. Shanghai\nC. Guangzhou\nD. Shenzhen\n###Assistant: A\n'
+)
+
+# evaluation settings
+minigpt_4_evaluator = [
+    dict(type='opencompass.DumpResults',
+         save_path='work_dirs/minigpt-4-7b-mmbench.xlsx')
+]
+
+minigpt_4_load_from = '/path/to/minigpt-4'  # noqa

configs/multimodal/tasks.py

+from mmengine.config import read_base
+
+with read_base():
+    from .minigpt_4.minigpt_4_7b_mmbench import (minigpt_4_dataloader,
+                                                 minigpt_4_evaluator,
+                                                 minigpt_4_load_from,
+                                                 minigpt_4_model)
+
+models = [minigpt_4_model]
+datasets = [minigpt_4_dataloader]
+evaluators = [minigpt_4_evaluator]
+load_froms = [minigpt_4_load_from]
+num_gpus = 1
+num_procs = 1
+launcher = 'slurm'

docs/en/MMBench.md

@@ -76,7 +76,6 @@ class MMBenchDataset(Dataset):
             'context': hint,
         }
         return data
-
    def load_from_df(self, idx, key):
         if key in self.df.iloc[idx] and not pd.isna(self.df.iloc[idx][key]):
             return self.df.iloc[idx][key]

opencompass/metrics/__init__.py

+from .dump_results import DumpResults
+
+__all__ = ['DumpResults']

opencompass/metrics/dump_results.py

+import os
+from typing import Optional
+
+import pandas as pd
+from mmengine.evaluator import BaseMetric
+
+from opencompass.registry import METRICS
+
+
+@METRICS.register_module()
+class DumpResults(BaseMetric):
+    """Dump model's prediction to a file.
+
+    Args:
+        save_path (str): the path to save model's prediction.
+        collect_device (str): Device name used for collecting results from
+            different ranks during distributed training. Must be 'cpu' or
+            'gpu'. Defaults to 'cpu'.
+        prefix (str, optional): The prefix that will be added in the metric
+            names to disambiguate homonymous metrics of different evaluators.
+            If prefix is not provided in the argument, self.default_prefix
+            will be used instead. Default: None.
+    """
+
+    def __init__(self,
+                 save_path: str,
+                 collect_device: str = 'cpu',
+                 prefix: Optional[str] = None) -> None:
+        super().__init__(collect_device, prefix)
+        self.save_path = save_path
+        if not os.path.exists(os.path.dirname(self.save_path)):
+            os.makedirs(os.path.dirname(self.save_path), exist_ok=True)
+
+    def process(self, data_batch, data_samples) -> None:
+        for data_sample in data_samples:
+            result = dict()
+
+            result['question'] = data_sample.get('question')
+            result.update(data_sample.get('options_dict'))
+            result['prediction'] = data_sample.get('pred_answer')
+            if data_sample.get('category') is not None:
+                result['category'] = data_sample.get('category')
+            if data_sample.get('l2-category') is not None:
+                result['l2-category'] = data_sample.get('l2-category')
+            result['index'] = data_sample.get('index')
+            result['split'] = data_sample.get('split')
+            self.results.append(result)
+
+    def compute_metrics(self, results: list) -> dict:
+        df = pd.DataFrame(results)
+        with pd.ExcelWriter(self.save_path, engine='openpyxl') as writer:
+            df.to_excel(writer, index=False)
+        return {}

opencompass/multimodal/datasets/__init__.py

+from .mmbench import MMBenchDataset
+
+__all__ = ['MMBenchDataset']

opencompass/multimodal/datasets/mmbench.py

+import base64
+import io
+from typing import List, Optional
+
+import pandas as pd
+from mmengine.dataset import Compose
+from PIL import Image
+from torch.utils.data import Dataset
+
+from opencompass.registry import DATASETS
+
+
+def decode_base64_to_image(base64_string) -> Image:
+    """Convert raw data into Pillow image."""
+    image_data = base64.b64decode(base64_string)
+    image = Image.open(io.BytesIO(image_data))
+    return image
+
+
+@DATASETS.register_module()
+class MMBenchDataset(Dataset):
+    """Dataset to load MMBench dataset.
+
+    Args:
+        data_file (str): The path of the dataset.
+        pipeline (dict): The data augmentation.
+        sys_prompt (str): The system prompt added to the head
+            of these options. Defaults to
+            There are several options:
+    """
+
+    def __init__(self,
+                 data_file: str,
+                 pipeline: List[dict],
+                 sys_prompt: str = 'There are several options:') -> None:
+        self.df = pd.read_csv(data_file, sep='\t')
+        self.pipeline = Compose(pipeline)
+        self.sys_prompt = sys_prompt
+
+    def __len__(self) -> None:
+        return len(self.df)
+
+    def __getitem__(self, idx: str) -> dict:
+        index = self.df.iloc[idx]['index']
+        image = self.df.iloc[idx]['image']
+        image = decode_base64_to_image(image)
+        question = self.df.iloc[idx]['question']
+        catetory = self.df.iloc[idx]['category']
+        l2_catetory = self.df.iloc[idx]['l2-category']
+
+        option_candidate = ['A', 'B', 'C', 'D', 'E']
+        options = {
+            cand: self.load_from_df(idx, cand)
+            for cand in option_candidate
+            if self.load_from_df(idx, cand) is not None
+        }
+        options_prompt = f'{self.sys_prompt}\n'
+        for key, item in options.items():
+            options_prompt += f'{key}. {item}\n'
+
+        hint = self.load_from_df(idx, 'hint')
+        data = {
+            'img': image,
+            'question': question,
+            'options': options_prompt,
+            'category': catetory,
+            'l2-category': l2_catetory,
+            'options_dict': options,
+            'index': index,
+            'context': hint,
+        }
+        data = self.pipeline(data)
+        return data
+
+    def load_from_df(self, idx: int, key: str) -> Optional[str]:
+        if key in self.df.iloc[idx] and not pd.isna(self.df.iloc[idx][key]):
+            return self.df.iloc[idx][key]
+        else:
+            return None

opencompass/multimodal/models/__init__.py

+from opencompass.utils import satisfy_requirement
+
+if satisfy_requirement('salesforce-lavis'):
+    from .instructblip import *  # noqa: F401, F403
+from .minigpt_4 import *  # noqa: F401, F403

opencompass/multimodal/models/instructblip/__init__.py

+from .blip2_vicuna_instruct import Blip2VicunaInstructMMBench
+
+__all__ = ['Blip2VicunaInstructMMBench']

opencompass/multimodal/models/instructblip/blip2_vicuna_instruct.py

+"""Requires Transformer 4.28 and above, implementation may change according the
+Llama implementation."""
+import logging
+import re
+
+import torch
+import torch.nn as nn
+from lavis.models.blip2_models.blip2 import Blip2Base, disabled_train
+from mmengine.device import get_device
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+from opencompass.registry import MM_MODELS
+
+
+@MM_MODELS.register_module('blip2-vicuna-instruct-mmbench')
+class Blip2VicunaInstructMMBench(Blip2Base):
+
+    def __init__(
+        self,
+        vit_model='eva_clip_g',
+        img_size=224,
+        drop_path_rate=0,
+        use_grad_checkpoint=False,
+        vit_precision='fp16',
+        freeze_vit=True,
+        num_query_token=32,
+        llm_model='',
+        sys_prompt='',
+        prompt='',
+        max_txt_len=128,
+        max_output_txt_len=256,
+        qformer_text_input=True,
+        low_resource=False,
+    ):
+        super().__init__()
+        self.tokenizer = self.init_tokenizer(truncation_side='left')
+
+        self.visual_encoder, self.ln_vision = self.init_vision_encoder(
+            vit_model, img_size, drop_path_rate, use_grad_checkpoint,
+            vit_precision)
+        if freeze_vit:
+            for name, param in self.visual_encoder.named_parameters():
+                param.requires_grad = False
+            self.visual_encoder = self.visual_encoder.eval()
+            self.visual_encoder.train = disabled_train
+            logging.info('freeze vision encoder')
+
+        self.Qformer, self.query_tokens = self.init_Qformer(
+            num_query_token, self.visual_encoder.num_features)
+
+        if not qformer_text_input:
+            self.Qformer.bert.embeddings.word_embeddings = None
+            self.Qformer.bert.embeddings.position_embeddings = None
+            for layer in self.Qformer.bert.encoder.layer:
+                layer.output = None
+                layer.intermediate = None
+        else:
+            self.Qformer.resize_token_embeddings(len(self.tokenizer))
+        self.Qformer.cls = None
+
+        self.llm_tokenizer = LlamaTokenizer.from_pretrained(
+            llm_model, use_fast=False, truncation_side='left')
+
+        if low_resource:
+            self.llm_model = LlamaForCausalLM.from_pretrained(
+                llm_model,
+                torch_dtype=torch.float16,
+                load_in_8bit=True,
+                device_map={'': 0})
+        else:
+            self.llm_model = LlamaForCausalLM.from_pretrained(
+                llm_model, torch_dtype=torch.float16)
+        self.llm_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+        self.llm_tokenizer.add_special_tokens({'bos_token': '</s>'})
+        self.llm_tokenizer.add_special_tokens({'eos_token': '</s>'})
+        self.llm_tokenizer.add_special_tokens({'unk_token': '</s>'})
+
+        self.llm_model.resize_token_embeddings(len(self.llm_tokenizer))
+
+        for name, param in self.llm_model.named_parameters():
+            param.requires_grad = False
+
+        self.llm_proj = nn.Linear(self.Qformer.config.hidden_size,
+                                  self.llm_model.config.hidden_size)
+
+        self.max_txt_len = max_txt_len
+        self.max_output_txt_len = max_output_txt_len
+        self.sys_prompt = sys_prompt
+        self.prompt = prompt
+
+        self._lemmatizer = None
+
+        self.qformer_text_input = qformer_text_input
+
+    def concat_text_input_output(self, input_ids, input_atts, output_ids,
+                                 output_atts):
+        input_part_targets_len = []
+        llm_tokens = {'input_ids': [], 'attention_mask': []}
+        for i in range(input_ids.size(0)):
+            this_input_ones = input_atts[i].sum()
+            input_part_targets_len.append(this_input_ones)
+            llm_tokens['input_ids'].append(
+                torch.cat([
+                    input_ids[i][:this_input_ones], output_ids[i][1:],
+                    input_ids[i][this_input_ones:]
+                ]))
+            llm_tokens['attention_mask'].append(
+                torch.cat([
+                    input_atts[i][:this_input_ones], output_atts[i][1:],
+                    input_atts[i][this_input_ones:]
+                ]))
+        llm_tokens['input_ids'] = torch.stack(llm_tokens['input_ids'])
+        llm_tokens['attention_mask'] = torch.stack(
+            llm_tokens['attention_mask'])
+        return llm_tokens, input_part_targets_len
+
+    def pack_inputs(self, batch):
+        images = [image.unsqueeze(0) for image in batch['inputs']]
+        data_samples = [data_sample for data_sample in batch['data_samples']]
+        images = torch.cat(images, dim=0).to(get_device())
+        inputs = {'image': images, 'data_samples': data_samples}
+        return inputs
+
+    @torch.no_grad()
+    def generate(
+        self,
+        batch,
+        use_nucleus_sampling=False,
+        num_beams=5,
+        max_length=256,
+        min_length=1,
+        top_p=0.9,
+        repetition_penalty=1.5,
+        length_penalty=1,
+        num_captions=1,
+        temperature=1,
+    ):
+        inputs = self.pack_inputs(batch)
+        image = inputs.pop('image')
+        data_samples = inputs['data_samples']
+        samples = {'image': image}
+        questions = [
+            data_sample.get('question') for data_sample in data_samples
+        ]
+        options = [data_sample.get('options') for data_sample in data_samples]
+        if data_samples[0].get('context') is not None:
+            contexts = [
+                data_sample.get('context') for data_sample in data_samples
+            ]
+            prompt = [
+                context + ' ' + question + ' ' + option for context, question,
+                option in zip(contexts, questions, options)
+            ]
+        else:
+            prompt = [
+                question + ' ' + option
+                for question, option in zip(questions, options)
+            ]
+
+        self.llm_tokenizer.padding_side = 'left'
+
+        image = samples['image']
+
+        bs = image.size(0)
+
+        if isinstance(prompt, str):
+            prompt = [prompt] * bs
+        else:
+            assert len(
+                prompt
+            ) == bs, 'The number of prompts must be equal to the batch size.'
+
+        query_tokens = self.query_tokens.expand(bs, -1, -1)
+        if self.qformer_text_input:
+            text_Qformer = self.tokenizer(
+                prompt,
+                padding='longest',
+                truncation=True,
+                max_length=self.max_txt_len,
+                return_tensors='pt',
+            ).to(image.device)
+            query_atts = torch.ones(query_tokens.size()[:-1],
+                                    dtype=torch.long).to(image.device)
+            Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask],
+                                     dim=1)
+
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image))
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        if self.qformer_text_input:
+            query_output = self.Qformer.bert(
+                text_Qformer.input_ids,
+                attention_mask=Qformer_atts,
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+        else:
+            query_output = self.Qformer.bert(
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+
+        inputs_llm = self.llm_proj(
+            query_output.last_hidden_state[:, :query_tokens.size(1), :])
+        atts_llm = torch.ones(inputs_llm.size()[:-1],
+                              dtype=torch.long).to(image.device)
+
+        prompt = ['###Human: ' + p + '###Assistant:' for p in prompt]
+        prompt = [self.sys_prompt + p for p in prompt]
+        llm_tokens = self.llm_tokenizer(prompt,
+                                        padding='longest',
+                                        return_tensors='pt').to(image.device)
+
+        with self.maybe_autocast():
+            inputs_embeds = self.llm_model.get_input_embeddings()(
+                llm_tokens.input_ids)
+            inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1)
+            attention_mask = torch.cat([atts_llm, llm_tokens.attention_mask],
+                                       dim=1)
+
+            outputs = self.llm_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                do_sample=use_nucleus_sampling,
+                top_p=top_p,
+                temperature=temperature,
+                num_beams=num_beams,
+                max_length=max_length,
+                min_length=min_length,
+                repetition_penalty=repetition_penalty,
+                length_penalty=length_penalty,
+                num_return_sequences=num_captions,
+            )
+        outputs[outputs == 0] = 2  # convert output id 0 to 2 (eos_token_id)
+        output_text = self.llm_tokenizer.batch_decode(outputs,
+                                                      skip_special_tokens=True)
+        output_text = [text.strip() for text in output_text]
+        output_text = self.post_process(output_text[0])
+        data_sample = data_samples[0]
+        data_sample.pred_answer = output_text
+
+        return data_sample
+
+    def post_process(self, output_text):
+        output_text = output_text.split('###')[0]
+        output_text = output_text.split('Assistant:')[-1].strip()
+        output_text = output_text.strip('</s><s>')
+        output_text = output_text.strip('</Img>')
+        output_text = output_text.strip()
+        pattern = re.compile(r'([A-Z]\.)')
+        res = pattern.findall(output_text)
+        if len(res) > 0:
+            output_text = res[0][:-1]
+        return output_text

opencompass/multimodal/models/minigpt_4/__init__.py

+from .minigpt_4 import MiniGPT4MMBench
+
+__all__ = ['MiniGPT4MMBench']

opencompass/multimodal/models/minigpt_4/minigpt_4.py

+import os
+import re
+import sys
+
+import torch
+import torch.nn as nn
+from mmengine.device import get_device
+from transformers import StoppingCriteriaList
+
+from opencompass.registry import MM_MODELS
+
+from .utils import StoppingCriteriaSub
+
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+def load_package():
+    """Load required packages from MiniGPT-4."""
+    current_file_path = os.path.abspath(__file__)
+    current_folder_path = os.path.dirname(current_file_path)
+
+    sys.path.append(os.path.join(current_folder_path, 'MiniGPT-4'))  # noqa
+    from minigpt4.models.mini_gpt4 import MiniGPT4
+
+    sys.path.pop(-1)
+
+    return MiniGPT4
+
+
+MiniGPT4 = load_package()
+
+
+@MM_MODELS.register_module('minigpt-4-mmbench')
+class MiniGPT4MMBench(MiniGPT4):
+    """Inference code of MiniGPT-4 on MMBench.
+
+    Args:
+        llama_model (str): The path of vicuna path.
+        sys_prompt (str): The prompt added to the beginning
+            of each query. Defaults to ''.
+        low_resource (bool): Whether loaded in low precision.
+            Defaults to False.
+    """
+
+    def __init__(self,
+                 llama_model: str,
+                 sys_prompt: str = '',
+                 low_resource: bool = False) -> None:
+        super().__init__(llama_model=llama_model, low_resource=low_resource)
+
+        cur_device = get_device()
+        stop_words_ids = [
+            torch.tensor([835]).to(cur_device),
+            torch.tensor([2277, 29937]).to(cur_device),
+        ]
+        self.stopping_criteria = StoppingCriteriaList(
+            [StoppingCriteriaSub(stops=stop_words_ids)])
+        self.sys_prompt = sys_prompt
+
+    def encode_img(self, image):
+        device = image.device
+
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(
+                self.visual_encoder(image)).to(device)
+            image_atts = torch.ones(image_embeds.size()[:-1],
+                                    dtype=torch.long).to(device)
+
+            query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1,
+                                                    -1)
+            query_output = self.Qformer.bert(
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+
+            inputs_llama = self.llama_proj(query_output.last_hidden_state)
+            atts_llama = torch.ones(inputs_llama.size()[:-1],
+                                    dtype=torch.long).to(image.device)
+        return inputs_llama, atts_llama
+
+    def pack_inputs(self, batch):
+        images = [image.unsqueeze(0) for image in batch['inputs']]
+        data_samples = [data_sample for data_sample in batch['data_samples']]
+        images = torch.cat(images, dim=0).to(get_device())
+        inputs = {'image': images, 'data_samples': data_samples}
+        return inputs
+
+    def generate(self, batch):
+        inputs = self.pack_inputs(batch)
+        image = inputs.pop('image')
+        data_samples = inputs['data_samples']
+        samples = {'image': image}
+        question = [
+            data_sample.get('question') for data_sample in data_samples
+        ]
+        options = [data_sample.get('options') for data_sample in data_samples]
+        samples.update({'question': question[0]})
+        samples.update({'options': options[0]})
+        if data_samples[0].get('context') is not None:
+            context = [
+                data_sample.get('context') for data_sample in data_samples
+            ]
+            samples.update({'context': context})
+        data_sample = data_samples[0]
+        img_prompt = '###Human: <Img><ImageHere></Img> '
+        if 'context' in samples:
+            context_prompt = samples['context'][0]
+
+        question = samples['question']
+        options = samples['options']
+        if 'context' in samples:
+            prompt = img_prompt + ' ' + context_prompt + ' ' + question + ' ' + options  # noqa
+        else:
+            prompt = img_prompt + ' ' + question + ' ' + options
+
+        # prompt = self.sys_prompt + prompt
+        prompt = prompt + '###Assistant:'
+
+        image = samples['image']
+        img_embeds, _ = self.encode_img(image)
+
+        prompt_segs = prompt.split('<ImageHere>')
+        prompt_seg_tokens = [
+            self.llama_tokenizer(seg,
+                                 return_tensors='pt',
+                                 add_special_tokens=i == 0).
+            to(self.llama_model.model.embed_tokens.weight.device).input_ids
+            for i, seg in enumerate(prompt_segs)
+        ]
+        prompt_seg_embs = [
+            self.llama_model.model.embed_tokens(seg)
+            for seg in prompt_seg_tokens
+        ]
+        prompt_seg_embs = [prompt_seg_embs[0], img_embeds, prompt_seg_embs[1]]
+        prompt_embs = torch.cat(prompt_seg_embs, dim=1)
+
+        # generate output
+        outputs = self.llama_model.generate(
+            inputs_embeds=prompt_embs,
+            max_new_tokens=20,
+            num_beams=5,
+            do_sample=False,
+            min_length=1,
+            top_p=0.9,
+            repetition_penalty=1.0,
+            length_penalty=-1.0,
+            temperature=1.0,
+            stopping_criteria=self.stopping_criteria,
+            num_return_sequences=1)
+
+        output_token = outputs[0]
+        if output_token[0] == 0:
+            output_token = output_token[1:]
+        if output_token[0] == 1:
+            output_token = output_token[1:]
+        output_text = self.llama_tokenizer.decode(output_token,
+                                                  add_special_tokens=False)
+        output_text = self.post_process(output_text)
+        data_sample.pred_answer = output_text
+        return data_sample
+
+    def post_process(self, output_text):
+        output_text = output_text.split('###')[0]
+        output_text = output_text.split('Assistant:')[-1].strip()
+        output_text = output_text.strip('</s><s>')
+        output_text = output_text.strip('</Img>')
+        output_text = output_text.strip()
+        pattern = re.compile(r'([A-Z]\.)')
+        res = pattern.findall(output_text)
+        if len(res) > 0:
+            output_text = res[0][:-1]
+        return output_text

opencompass/multimodal/models/minigpt_4/utils.py

+import os
+import re
+
+import timm.models.hub as timm_hub
+import torch
+import torch.distributed as dist
+from mmengine.dist import is_distributed, is_main_process
+from transformers import StoppingCriteria
+
+
+class StoppingCriteriaSub(StoppingCriteria):
+
+    def __init__(self, stops=[], encounters=1):
+        super().__init__()
+        self.stops = stops
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
+        for stop in self.stops:
+            if torch.all((stop == input_ids[0][-len(stop):])).item():
+                return True
+
+        return False
+
+
+def download_cached_file(url, check_hash=True, progress=False):
+    """Download a file from a URL and cache it locally.
+
+    If the file already exists, it is not downloaded again. If distributed,
+    only the main process downloads the file, and the other processes wait for
+    the file to be downloaded.
+    """
+
+    def get_cached_file_path():
+        # a hack to sync the file path across processes
+        parts = torch.hub.urlparse(url)
+        filename = os.path.basename(parts.path)
+        cached_file = os.path.join(timm_hub.get_cache_dir(), filename)
+
+        return cached_file
+
+    if is_main_process():
+        timm_hub.download_cached_file(url, check_hash, progress)
+
+    if is_distributed():
+        dist.barrier()
+
+    return get_cached_file_path()
+
+
+def is_url(input_url):
+    """Check if an input string is a url.
+
+    look for http(s):// and ignoring the case
+    """
+    is_url = re.match(r'^(?:http)s?://', input_url, re.IGNORECASE) is not None
+    return is_url

opencompass/openicl/icl_evaluator/icl_hf_evaluator.py

@@ -120,7 +120,7 @@ class AccEvaluator(HuggingfaceEvaluator):
 
 @ICL_EVALUATORS.register_module()
 class RougeEvaluator(HuggingfaceEvaluator):
-    """Rouge evaluator."""
+    """Rouge evaluator."""  # noqa
 
     def __init__(self) -> None:
         super().__init__(metric='rouge')

opencompass/partitioners/__init__.py

+from .mm_naive import *  # noqa: F401, F403
 from .naive import *  # noqa: F401, F403
 from .size import *  # noqa: F401, F403

opencompass/partitioners/mm_naive.py

+from copy import deepcopy
+from typing import Dict, List
+
+from mmengine.config import Config, ConfigDict
+
+from opencompass.registry import PARTITIONERS
+
+from .base import BasePartitioner
+
+
+@PARTITIONERS.register_module()
+class MultimodalNaivePartitioner(BasePartitioner):
+    """Multimodal naive task partitioner.
+
+    This partitioner will generate a task for each
+    model-dataset-evaluator pair.
+
+    Args:
+        config (ConfigDict): The full config dict.
+    """
+
+    def partition(self, models: List[ConfigDict], datasets: List[ConfigDict],
+                  evaluators: List[ConfigDict], load_froms: List[ConfigDict],
+                  work_dir: str, num_gpus: int, num_procs: int,
+                  launcher: str) -> List[Dict]:
+        """Partition model-dataset pairs into tasks. Each task is defined as a
+        dict and will run independently as a unit. Its structure is as follows:
+
+        .. code-block:: python
+
+            {
+                'models': [],  # a list of model configs
+                'datasets': [],  # a list of dataset configs
+                'evaluators': [], # a list of evaluator configs
+                'load_froms': [], # a list of load_from paths
+                'work_dir': '',  # the work dir
+                'num_gpus': int, # integer, number of gpus for each task
+                'num_procs': int, # integer, number of gpus on single machine
+                'launcher': str, # string, how to launch distributed training
+            }
+
+        Args:
+            models (List[ConfigDict]): A list of model configs.
+            datasets (List[ConfigDict]): A list of dataset configs.
+            evaluators (List[ConfigDict]): A list of evaluator configs.
+            load_froms (List[ConfigDict]): A list of load_from paths.
+            work_dir (str): The work dir for the task.
+            num_gpus (int): Number of gpus for each task.
+            num_procs (int): Number of gpus on single machine.
+            launcher (str): How to launch distributed training.
+                Only `slurm`, `pytorch` and `mpi` are available.
+
+        Returns:
+            List[Dict]: A list of tasks.
+        """
+
+        tasks = []
+        for model, dataset, evaluator, load_from in zip(
+                models, datasets, evaluators, load_froms):
+            task = Config({
+                'model': model,
+                'dataset': dataset,
+                'evaluator': evaluator,
+                'load_from': load_from,
+                'work_dir': work_dir,
+                'num_gpus': num_gpus,
+                'num_procs': num_procs,
+                'launcher': launcher
+            })
+            tasks.append(task)
+
+        return tasks
+
+    def __call__(self, cfg: ConfigDict) -> List[Dict]:
+        """Generate tasks from config. Each task is defined as a
+        dict and will run independently as a unit. Its structure is as
+        follows:
+
+        .. code-block:: python
+
+            {
+                'models': [],  # a list of model configs
+                'datasets': [],  # a list of dataset configs
+                'evaluators': [], # a list of evaluator configs
+                'load_froms': [], # a list of load_from paths
+                'work_dir': '',  # the work dir
+                'num_gpus': int, # integer, number of gpus for each task
+                'num_procs': int, # integer, number of gpus on single machine
+            }
+
+        Args:
+            cfg (ConfigDict): The config dict, containing "models", "dataset"
+                and "work_dir" keys.
+
+        Returns:
+            List[Dict]: A list of tasks.
+        """
+        cfg = deepcopy(cfg)
+        models = cfg['models']
+        datasets = cfg['datasets']
+        evaluators = cfg['evaluators']
+        load_froms = cfg['load_froms']
+        work_dir = cfg['work_dir']
+        num_gpus = cfg['num_gpus']
+        num_procs = cfg['num_procs']
+        launcher = cfg['launcher']
+
+        tasks = self.partition(models, datasets, evaluators, load_froms,
+                               work_dir, num_gpus, num_procs, launcher)
+
+        self.logger.info(f'Partitioned into {len(tasks)} tasks.')
+        for i, task in enumerate(tasks):
+            model_name = task['model']['type']
+            dataset_name = task['dataset']['dataset']['type']
+            evaluator_name = task['evaluator'][0]['type']
+            self.logger.debug(
+                f'Task {i}: {model_name}-{dataset_name}-{evaluator_name}')
+
+        return tasks