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eval_mme.py
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eval_mme.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from typing import Tuple
import os
import sys
import torch
import fire
import time
import json
from fairscale.nn.model_parallel.initialize import initialize_model_parallel
from lavin.eval_model import ModelArgs, Transformer
from lavin.tokenizer import Tokenizer
from lavin.generator import LaVIN_Generator
from lavin.mm_adapter import set_MMAdapter,set_Clip_Adapter
from util.base_prompt import build_prompt
from dataclasses import dataclass
import re
import random
import warnings
import pandas as pd
from PIL import Image
from torchvision.transforms import transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from pathlib import Path
import fairscale.nn.model_parallel.initialize as fs_init
import torch.distributed as dist
from util.apply_delta import apply_model_delta_online
warnings.filterwarnings('ignore')
@dataclass
class PromptArgs:
prompt_format='QCM-ALE'
use_caption=True
options=["A", "B", "C", "D", "E"]
def setup_model_parallel() -> Tuple[int, int]:
local_rank = int(os.environ.get("LOCAL_RANK", -1))
world_size = int(os.environ.get("WORLD_SIZE", -1))
torch.distributed.init_process_group("nccl")
initialize_model_parallel(world_size)
torch.cuda.set_device(local_rank)
# seed must be the same in all processes
torch.manual_seed(1)
return local_rank, world_size
def _load_and_redistribute_checkpoint(llama_model_path, model_name):
with open(Path(llama_model_path) / model_name / 'params.json') as f:
params = json.load(f)
tokenizer = Tokenizer(model_path=str(Path(llama_model_path) / 'tokenizer.model'))
print('Using model path: %s, model_name: %s' % (llama_model_path, model_name))
if model_name=='7B':
checkpoint = torch.load(llama_model_path + model_name + '/consolidated.00.pth', map_location="cpu")
return checkpoint, tokenizer, params
checkpoints = (Path(llama_model_path) / model_name).glob('*.pth')
checkpoints = sorted(checkpoints)
mp_world_size = fs_init.get_model_parallel_world_size()
mp_rank = fs_init.get_model_parallel_rank()
if mp_world_size == len(checkpoints):
print('same number of shards of checkpoints and training, loading directly...')
dist.barrier()
print('[rank=%d, mp_rank=%d] loading from %s' % (dist.get_rank(), mp_rank, checkpoints[mp_rank]))
checkpoint = torch.load(checkpoints[mp_rank], map_location='cpu')
else:
print('different number of shards of checkpoints and training, redistributing...')
if dist.get_rank() == 0:
loaded = []
for x in checkpoints:
print('loading from', x)
loaded.append(torch.load(x, map_location='cpu'))
full_state_dict = {}
split_dims = {}
def add_weight_with_split_dim(name, dim):
if dim < 0: # bcast without split
full_state_dict[name] = loaded[0][name].clone()
else:
full_state_dict[name] = torch.cat([x[name] for x in loaded], dim=dim)
for x in loaded:
del x[name]
split_dims[name] = dim
add_weight_with_split_dim('tok_embeddings.weight', 1)
add_weight_with_split_dim('norm.weight', -1)
add_weight_with_split_dim('output.weight', 0)
for i in range(params['n_layers']):
print('gathering layer %d of %d' % (i, params['n_layers']))
layer_prefix = f'layers.{i}.'
bcast_names = [
'attention_norm.weight',
'ffn_norm.weight',
]
column_parallel_names = [
'attention.wq.weight',
'attention.wk.weight',
'attention.wv.weight',
'feed_forward.w1.weight',
'feed_forward.w3.weight',
]
row_parallel_names = [
'attention.wo.weight',
'feed_forward.w2.weight',
]
for key in bcast_names:
add_weight_with_split_dim(layer_prefix + key, -1)
for key in column_parallel_names:
add_weight_with_split_dim(layer_prefix + key, 0)
for key in row_parallel_names:
add_weight_with_split_dim(layer_prefix + key, 1)
full_state_dict_meta = dict((k, v.shape) for k, v in full_state_dict.items())
dist.broadcast_object_list([full_state_dict_meta, split_dims], src=0)
else: # dist.get_rank() != 0
recv_objs = [None, None]
dist.broadcast_object_list(recv_objs, src=0)
full_state_dict_meta, split_dims = recv_objs
local_state_dict = {}
for k in sorted(full_state_dict_meta.keys()):
print('redistributing weights: %s' % k)
if dist.get_rank() == 0:
value = full_state_dict[k].cuda().half()
del full_state_dict[k]
else:
value = torch.empty(full_state_dict_meta[k], device='cuda', dtype=torch.half)
dist.broadcast(value, src=0)
value = value.cpu()
if split_dims[k] < 0:
local_state_dict[k] = value
else:
dim = split_dims[k]
assert dim >= 0 and dim < value.ndim and value.size(dim) % mp_world_size == 0
shard_size = value.size(dim) // mp_world_size
shard_st, shard_ed = shard_size * mp_rank, shard_size * (mp_rank + 1)
# TODO: make more general
if dim == 0:
value = value[shard_st: shard_ed]
elif dim == 1:
value = value[:, shard_st: shard_ed]
else:
raise NotImplementedError()
local_state_dict[k] = value.clone()
checkpoint = local_state_dict
return checkpoint, tokenizer, params
def get_acc_with_contion(res_pd, key, values):
if isinstance(values, list):
total_pd = res_pd[res_pd[key].isin(values)]
else:
total_pd = res_pd[res_pd[key] == values]
correct_pd = total_pd[total_pd['true_false'] == True]
acc = "{:.2f}".format(len(correct_pd) / len(total_pd) * 100)
return acc
def get_scores(result_file, data_file):
# read result file
results = json.load(open(result_file))
num = len(results)
assert num == 4241
sqa_data = json.load(open(data_file))
# construct pandas data
sqa_pd = pd.DataFrame(sqa_data).T
res_pd = sqa_pd[sqa_pd['split'] == 'test'] # test set
# update data
for index, row in res_pd.iterrows():
res_pd.loc[index, 'no_context'] = True if (not row['hint'] and not row['image']) else False
res_pd.loc[index, 'has_text'] = True if row['hint'] else False
res_pd.loc[index, 'has_image'] = True if row['image'] else False
res_pd.loc[index, 'has_text_image'] = True if (row['hint'] and row['image']) else False
label = row['answer']
pred = int(results[index])
res_pd.loc[index, 'pred'] = pred
res_pd.loc[index, 'true_false'] = (label == pred)
# accuracy scores
acc_average = len(res_pd[res_pd['true_false'] == True]) / num * 100
scores = {
'acc_natural':
get_acc_with_contion(res_pd, 'subject', 'natural science'),
'acc_social':
get_acc_with_contion(res_pd, 'subject', 'social science'),
'acc_language':
get_acc_with_contion(res_pd, 'subject', 'language science'),
'acc_has_text':
get_acc_with_contion(res_pd, 'has_text', True),
'acc_has_image':
get_acc_with_contion(res_pd, 'has_image', True),
'acc_no_context':
get_acc_with_contion(res_pd, 'no_context', True),
'acc_grade_1_6':
get_acc_with_contion(res_pd, 'grade', ['grade1', 'grade2', 'grade3', 'grade4', 'grade5', 'grade6']),
'acc_grade_7_12':
get_acc_with_contion(res_pd, 'grade', ['grade7', 'grade8', 'grade9', 'grade10', 'grade11', 'grade12']),
'acc_average':
"{:.2f}".format(acc_average),
}
return scores
def print_scores(scores):
latex_output = ""
for key, score in scores.items():
print(f"{key[4:]}: \t{score}")
latex_output += f"& {score} "
latex_output += "\\\\"
print(latex_output)
def load(
ckpt_dir: str,
llm_model:str,
tokenizer_path: str,
adapter_path: str,
local_rank: int,
world_size: int,
max_seq_len: int,
max_batch_size: int,
adapter_type: str,
adapter_dim:int,
adapter_scale:float,
hidden_proj:int,
visual_adapter_type: str,
temperature: float,
use_vicuna: bool
) -> LaVIN_Generator:
start_time = time.time()
checkpoint, tokenizer, params = _load_and_redistribute_checkpoint(ckpt_dir, llm_model)
print("Loading")
adapter_checkpoint = torch.load(adapter_path, map_location="cpu")
model_args: ModelArgs = ModelArgs(
max_seq_len=max_seq_len, max_batch_size=max_batch_size,hidden_proj=hidden_proj, **params
)
model_args.vocab_size = tokenizer.n_words
torch.set_default_tensor_type(torch.cuda.HalfTensor)
model = Transformer(model_args)
set_MMAdapter(model, adapter_type, dim=adapter_dim, s=adapter_scale,t=temperature)
set_Clip_Adapter(model.backbone.visual, visual_adapter_type, dim=adapter_dim, s=adapter_scale,t=temperature)
torch.set_default_tensor_type(torch.FloatTensor)
model.load_state_dict(checkpoint, strict=False)
if use_vicuna:
apply_model_delta_online(model,'../data/weights/vicuna_'+llm_model)
state_dict={}
for key in adapter_checkpoint['model']:
state_dict[key.replace('module.','')]=adapter_checkpoint['model'][key]
model.load_state_dict(state_dict, strict=False)
generator = LaVIN_Generator(model, tokenizer)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
return generator
def get_pred_idx(prediction, choices, options):
"""
Get the index (e.g. 2) from the prediction (e.g. 'C')
"""
if prediction in options[:len(choices)]:
return options.index(prediction)
else:
return random.choice(range(len(choices)))
def prepare_data(dir):
if os.path.exists(os.path.join(dir,'images')):
image_dir=os.path.join(dir,'images')
else:
image_dir=dir
if os.path.exists(os.path.join(dir,'questions_answers_YN')):
ann_dir=os.path.join(dir,'questions_answers_YN')
else:
ann_dir=dir
image_list=[]
image_path=[]
for root, dirs, files in os.walk(image_dir):
for file in files:
# 检查文件后缀名是否为.jpg或.png
if file.endswith(".jpg") or file.endswith(".png"):
# 拼接文件的完整路径
image_list.append(file)
image_path.append(os.path.join(image_dir,file))
ann_list=[]
for img_id in image_list:
ann_file=img_id.replace('.jpg','.txt').replace('.png','.txt')
ann={}
with open(os.path.join(ann_dir,ann_file)) as f:
pos=f.readline().split('\t')[0]
neg=f.readline().split('\t')[0]
ann['pos']=pos
ann['neg']=neg
ann_list.append(ann)
return image_path,ann_list
def main(
ckpt_dir: str,
tokenizer_path: str,
adapter_path: str,
data_root:str,
caption_file:str,
max_seq_len: int,
max_batch_size: int,
llm_model:str='7B',
generation_temperature: float = 0.1,
top_p: float = 0.75,
split='val',
prompt_format='QCM-ALE',
use_caption=False,
options=["A", "B", "C", "D", "E"],
adapter_type='repattn',
adapter_dim=8,
adapter_scale=1,
n_prompt=10,
hidden_proj=128,
visual_adapter_type='normal',
temperature=10.,
use_vicuna=False,
root_dir_='../data/mme'
):
print(max_batch_size,max_seq_len)
print('use caption: ',use_caption)
local_rank, world_size = setup_model_parallel()
if local_rank > 0:
sys.stdout = open(os.devnull, "w")
generator = load(
ckpt_dir,llm_model, tokenizer_path, adapter_path, local_rank, world_size, max_seq_len, max_batch_size,
adapter_type,adapter_dim,adapter_scale,hidden_proj,visual_adapter_type,
temperature,use_vicuna)
subsets=os.listdir(root_dir_)
total_score=0
cognition_score=0
perception_score=0
for subset in subsets:
root_dir=os.path.join(root_dir_,subset)
print('split: ', subset)
img_list,ann_list=prepare_data(root_dir)
qids=range(len(img_list))
total_items=len(img_list)
print('total_items: ',total_items)
image_transforms=transforms.Compose([transforms.Resize((224, 224), interpolation=Image.BICUBIC),transforms.ToTensor(), transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)])
prompt_args=PromptArgs()
prompt_args.prompt_format = prompt_format
prompt_args.use_caption = use_caption
prompt_args.options = options
pattern = re.compile(r'The answer is ([A-Z]).')
answers = []
preds=[]
max_batch_size=8
for i in range(total_items//max_batch_size+1):
batch_qids=qids[i*max_batch_size:(i+1)*max_batch_size]
if len(batch_qids)==0:
break
indicators = []
prompts=[]
images = []
for qid in batch_qids:
#pos
prompt= 'Instruction: '+ ann_list[qid]['pos']+'\n'+\
'Response: '
prompt = prompt.replace(" ", " ").strip()
answer='yes'
image = Image.open(img_list[qid]).convert('RGB')
image = image_transforms(image)
indicator = 1
prompts.append(prompt)
answers.append(answer)
images.append(image.unsqueeze(0))
indicators.append(indicator)
#neg
prompt= 'Instruction: '+ ann_list[qid]['neg']+'\n'+\
'Response: '
prompt = prompt.replace(" ", " ").strip()
answer='no'
indicator = 1
prompts.append(prompt)
answers.append(answer)
images.append(image.unsqueeze(0))
indicators.append(indicator)
images=torch.cat(images,0)
results = generator.generate(
prompts,images=images,indicators=indicators, max_gen_len=20, temperature=generation_temperature, top_p=top_p,n_feats=n_prompt
)
for result in results:
result=result.lower().strip().split('response:')[1]
if 'yes' in result[:4]:
pred='yes'
elif 'no' in result[:4]:
pred='no'
else:
pred='other'
preds.append(pred)
#evaluations
correct=0
corrects=[]
assert len(preds)==len(answers)
for i, prediction in enumerate(preds):
if prediction == answers[i]:
correct += 1
corrects.append(1)
else:
corrects.append(0)
import numpy as np
corrects=np.array(corrects)
acc = correct / len(preds) * 100
acc_plus= (corrects.reshape(-1,2).sum(1)==2).sum()/ (len(preds)//2)* 100
total_score+=acc
total_score+=acc_plus
if subset in ['commonsense_reasoning','numerical_calculation','text_translation','code_reasoning']:
cognition_score+=acc_plus
cognition_score+=acc
else:
perception_score+=acc_plus
perception_score+=acc
print('subset: ', subset)
print('overall accuracy: ', acc)
print('overall accuracy+: ', acc_plus)
with open('mme_eval.txt','a') as f:
f.write('subset: '+ subset+'\n')
f.write('accuracy: '+ str(acc)+'\n')
f.write('accuracy+: '+ str(acc_plus)+'\n')
print('total_score: ',total_score)
print('perception_score: ',perception_score)
print('cognition_score: ',cognition_score)
with open('mme_eval.txt', 'a') as f:
f.write('total_score: ' + str(total_score) + '\n')
f.write('perception_score: ' + str(perception_score) + '\n')
f.write('cognition_score: ' + str(cognition_score) + '\n')
if __name__ == "__main__":
fire.Fire(main)