mirror of
https://github.com/OpenBMB/MiniCPM-V.git
synced 2026-02-05 18:29:18 +08:00
Modify eval_mm for MiniCPM-V 2.6
This commit is contained in:
Haoyu Li
@@ -1,10 +1,13 @@
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import math
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import torch
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import random
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import numpy as np
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from PIL import Image
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from transformers import AutoModel, AutoTokenizer
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from .base import BaseModel
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from ..smp import *
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from ..utils import DATASET_TYPE
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from ..dataset import DATASET_TYPE
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class MiniCPM_V(BaseModel):
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@@ -59,10 +62,10 @@ class MiniCPM_V(BaseModel):
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return message
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def generate_inner(self, message, dataset=None):
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prompt, image_path = self.message_to_promptimg(message)
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prompt, image_path = self.message_to_promptimg(message, dataset=dataset)
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image = Image.open(image_path).convert('RGB')
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msgs = [{'role': 'user', 'content': prompt}]
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if DATASET_TYPE(dataset) == 'multi-choice':
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if DATASET_TYPE(dataset) == 'MCQ':
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max_new_tokens = 20
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elif DATASET_TYPE(dataset) == 'Y/N':
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max_new_tokens = 100
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@@ -83,3 +86,374 @@ class MiniCPM_V(BaseModel):
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**default_kwargs
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)
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return res
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class MiniCPM_Llama3_V(BaseModel):
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INSTALL_REQ = False
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INTERLEAVE = True
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def __init__(self, model_path='openbmb/MiniCPM-Llama3-V-2_5', **kwargs):
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assert model_path is not None
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self.model_path = model_path
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print(f'load from {self.model_path}')
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self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
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self.model = self.model.to(dtype=torch.float16)
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self.model.eval().cuda()
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self.kwargs = kwargs
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
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torch.cuda.empty_cache()
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self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3
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self.options_system_prompt = ('Carefully read the following question and select the letter corresponding '
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'to the correct answer. Highlight the applicable choices without giving '
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'explanations.')
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self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.'
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self.detail_system_prompt = 'Answer this question in detail.'
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self.vqa_prompt = 'Answer the question using a single word or phrase.'
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def use_custom_prompt(self, dataset):
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if listinstr(['MCQ', 'VQA'], DATASET_TYPE(dataset)):
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return True
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elif dataset is not None and listinstr(['HallusionBench'], dataset):
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return True
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return False
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def build_prompt(self, line, dataset=None):
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if isinstance(line, int):
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line = self.data.iloc[line]
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tgt_path = self.dump_image(line, dataset)
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system_prompt = ''
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question = line['question']
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if DATASET_TYPE(dataset) == 'MCQ':
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options = {
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cand: line[cand]
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for cand in string.ascii_uppercase
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if cand in line and not pd.isna(line[cand])
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}
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options_prompt = 'Options:\n'
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for key, item in options.items():
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options_prompt += f'{key}. {item}\n'
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hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
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prompt = ''
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if hint is not None:
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prompt += f'Hint: {hint}\n'
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prompt += f'Question: {question}\n'
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if len(options):
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prompt += options_prompt
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system_prompt = self.options_system_prompt + '\nPlease just indicate your choice.'
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else:
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system_prompt = self.wo_options_system_prompt
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if 'MMMU' in dataset: # Corner Case
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prompt = system_prompt + '\n' + prompt
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system_prompt = ''
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elif dataset is not None and listinstr(['HallusionBench'], dataset):
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question = line['question'] + ' Yes or No?'
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prompt = question
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elif dataset is not None and listinstr(['MME'], dataset):
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question = line['question'] + ' Yes or No?'
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prompt = question
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elif dataset is not None and listinstr(['OCRBench'], dataset):
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system_prompt = self.vqa_prompt
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question = line['question']
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prompt = question
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elif DATASET_TYPE(dataset) == 'VQA':
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if listinstr(['LLaVABench', 'MMLongBench_DOC'], dataset):
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system_prompt = ''
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prompt = question
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elif listinstr(['MMVet'], dataset):
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system_prompt = self.detail_system_prompt
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prompt = question
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else:
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system_prompt = self.vqa_prompt
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prompt = question
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msgs = []
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if system_prompt:
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msgs.append(dict(type='text', value=system_prompt))
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if isinstance(tgt_path, list):
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msgs.extend([dict(type='image', value=p) for p in tgt_path])
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else:
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msgs = [dict(type='image', value=tgt_path)]
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msgs.append(dict(type='text', value=prompt))
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return msgs
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def generate_inner(self, message, dataset=None):
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if DATASET_TYPE(dataset) == 'MCQ':
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max_new_tokens = 200
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elif DATASET_TYPE(dataset) == 'Y/N':
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max_new_tokens = 3
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else:
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max_new_tokens = 1024
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default_kwargs = dict(
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max_new_tokens=max_new_tokens,
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sampling=False,
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num_beams=self.num_beams,
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)
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default_kwargs.update(self.kwargs)
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content = []
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for x in message:
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if x['type'] == 'text':
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content.append(x['value'])
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elif x['type'] == 'image':
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image = Image.open(x['value']).convert('RGB')
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content.append(image)
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msgs = [{'role': 'user', 'content': content}]
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res = self.model.chat(
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msgs=msgs,
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context=None,
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image=None,
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tokenizer=self.tokenizer,
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**default_kwargs
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)
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if isinstance(res, tuple) and len(res) > 0:
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res = res[0]
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return res
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def chat_inner(self, message, dataset=None):
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max_new_tokens = 1024
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default_kwargs = dict(
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max_new_tokens=max_new_tokens,
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sampling=False,
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num_beams=self.num_beams,
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)
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default_kwargs.update(self.kwargs)
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msgs = []
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for msg in message:
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content = []
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if len(msg['content']) == 1 and msg['content'][0]['type'] == 'text':
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msg_new = {'role': msg['role'], 'content': msg['content'][0]['value']}
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msgs.append(msg_new)
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continue
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for x in msg['content']:
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if x['type'] == 'text':
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content.append(x['value'])
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elif x['type'] == 'image':
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image = Image.open(x['value']).convert('RGB')
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content.append(image)
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msg_new = {'role': msg['role'], 'content': content}
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msgs.append(msg_new)
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res = self.model.chat(
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msgs=msgs,
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context=None,
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image=None,
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tokenizer=self.tokenizer,
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**default_kwargs)
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if isinstance(res, tuple) and len(res) > 0:
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res = res[0]
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return res
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class MiniCPM_V_2_6(BaseModel):
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INSTALL_REQ = False
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INTERLEAVE = True
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def __init__(self, model_path='openbmb/MiniCPM-V', **kwargs):
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random.seed(0)
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np.random.seed(0)
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torch.manual_seed(0)
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torch.cuda.manual_seed_all(0)
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assert model_path is not None
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self.model_path = model_path
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print(f'load from path {self.model_path}')
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self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
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self.model = self.model.to(dtype=torch.bfloat16)
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self.model.eval().cuda()
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self.kwargs = kwargs
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
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torch.cuda.empty_cache()
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self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3
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self.options_suffix_prompt = '''\nAnswer with the option's letter from the given choices directly.'''
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self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly.'
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self.detail_system_prompt = 'Answer this question in detail.'
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self.vqa_prompt = 'Answer the question using a single word or phrase.'
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self.multi_choice_cot_prompt = ('''Carefully read the following multichoice question, solve it step '''
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'''by step and finally pick the option associated with the correct '''
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'''answer in the format of "Answer: selected option\n\n''')
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self.short_ans_cot_prompt = ('''Read the following question carefully, solve it step by step, and '''
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'''then output the final answer in the format of "Answer: single number '''
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'''or single word or phrase".\n\n''')
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def use_custom_prompt(self, dataset=None):
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if dataset is None:
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return False
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if listinstr(['MCQ', 'VQA', 'Y/N'], DATASET_TYPE(dataset)):
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return True
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return False
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def use_cot(self, dataset=None):
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if dataset is None:
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return False
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if listinstr(['MMMU', 'HallusionBench', 'OCRBench', 'ChartQA'], dataset):
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return True
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elif listinstr(['MathVista', 'MMVet', 'MMBench', 'MMStar', 'AI2D', 'RealWorldQA',
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'POPE', 'ScienceQA', 'TextVQA', 'DocVQA'], dataset):
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return False
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else:
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return False
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def use_upsize(self, dataset=None):
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if dataset is None:
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return False
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if listinstr(['MMVet', 'MMBench', 'MMStar', 'AI2D', 'OCRBench'], dataset):
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return True
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else:
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return False
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def build_prompt(self, line, dataset=None):
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if isinstance(line, int):
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line = self.data.iloc[line]
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tgt_path = self.dump_image(line, dataset)
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system_prompt, prompt = '', ''
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question = line['question']
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if not self.use_cot(dataset):
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if DATASET_TYPE(dataset) == 'MCQ':
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options = {
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cand: line[cand]
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for cand in string.ascii_uppercase
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if cand in line and not pd.isna(line[cand])
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}
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options_prompt = 'Options:\n'
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for key, item in options.items():
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options_prompt += f'{key}. {item}\n'
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hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
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if hint is not None:
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prompt += f'Hint: {hint}\n'
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prompt += f'Question: {question}\n'
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if len(options):
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prompt += options_prompt
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prompt += self.options_suffix_prompt
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else:
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system_prompt = self.wo_options_system_prompt
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if 'MMMU' in dataset:
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if len(system_prompt) > 0:
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prompt = system_prompt + '\n' + prompt
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system_prompt = ''
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elif dataset is not None and listinstr(['HallusionBench'], dataset):
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question += ' Yes or No?'
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prompt = question
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elif dataset is not None and listinstr(['OCRBench'], dataset):
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system_prompt = self.vqa_prompt
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prompt = question
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elif DATASET_TYPE(dataset) == 'VQA':
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if listinstr(['LLaVABench'], dataset):
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system_prompt = ''
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elif listinstr(['MMVet'], dataset):
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system_prompt = self.detail_system_prompt
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else:
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system_prompt = self.vqa_prompt
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prompt = question
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else:
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prompt = question
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else:
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has_options = True
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if DATASET_TYPE(dataset) == 'MCQ':
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options = {
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cand: line[cand]
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for cand in string.ascii_uppercase
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if cand in line and not pd.isna(line[cand])
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}
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options_prompt = ''
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for key, item in options.items():
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options_prompt += f'{key}. {item}\n'
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hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
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if hint is not None:
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prompt += f'Hint: {hint}\n'
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prompt += f'{question}\n'
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if len(options):
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prompt += options_prompt
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else:
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has_options = False
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if 'MMMU' in dataset:
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if len(system_prompt) > 0:
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prompt = system_prompt + '\n' + prompt
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system_prompt = ''
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else:
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prompt = question
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if DATASET_TYPE(dataset) in ['MCQ', 'Y/N', 'VQA']:
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if DATASET_TYPE(dataset) == 'MCQ':
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if has_options:
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prompt = self.multi_choice_cot_prompt + prompt
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else:
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prompt = self.short_ans_cot_prompt + prompt
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elif DATASET_TYPE(dataset) == 'Y/N':
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prompt = self.short_ans_cot_prompt + prompt
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else:
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prompt = self.short_ans_cot_prompt + prompt
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msgs = []
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if system_prompt:
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msgs.append(dict(type='text', value=system_prompt))
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if isinstance(tgt_path, list):
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msgs.extend([dict(type='image', value=p) for p in tgt_path])
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else:
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msgs = [dict(type='image', value=tgt_path)]
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msgs.append(dict(type='text', value=prompt))
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return msgs
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def generate_inner(self, message, dataset=None):
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max_new_tokens = 2048
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default_kwargs = dict(
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max_new_tokens=max_new_tokens,
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sampling=False,
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num_beams=self.num_beams,
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)
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default_kwargs.update(self.kwargs)
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content = []
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for x in message:
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if x['type'] == 'text':
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content.append(x['value'])
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elif x['type'] == 'image':
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image = Image.open(x['value']).convert('RGB')
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if not self.use_upsize(dataset):
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content.append(image)
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else:
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img_width, img_height = image.width, image.height
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if (img_width * img_height) >= (1344 * 1344):
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content.append(image)
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else:
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ratio = math.sqrt((1344 * 1344) / (img_width * img_height))
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max_img_width = int(img_width * ratio)
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new_img_width = random.randint(img_width, max_img_width)
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new_img_height = int(new_img_width / img_width * img_height)
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resized_image = image.resize((new_img_width, new_img_height))
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content.append(resized_image)
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msgs = [{'role': 'user', 'content': content}]
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res = self.model.chat(
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image=None,
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msgs=msgs,
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context=None,
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tokenizer=self.tokenizer,
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max_inp_length=8192,
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**default_kwargs
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)
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if isinstance(res, tuple) and len(res) > 0:
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res = res[0]
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return res
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