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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from dc.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from dc.conversation import conv_templates, SeparatorStyle
from dc.model.builder import load_pretrained_model
from dc.utils import disable_torch_init
from dc.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import math
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
# Custom dataset class
class CustomDataset(Dataset):
def __init__(self, questions, image_folder, tokenizer, image_processor, model_config):
self.questions = questions
self.image_folder = image_folder
self.tokenizer = tokenizer
self.image_processor = image_processor
self.model_config = model_config
def __getitem__(self, index):
line = self.questions[index]
image_file = line["image"]
qs = line["text"]
# self.model_config.mm_use_im_start_end=False
if self.model_config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
# llama3 !!!!!
# prompt += '<|start_header_id|>assistant<|end_header_id|>\n\n'
# print([prompt])
image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB')
image_tensor = process_images([image], self.image_processor, self.model_config)[0]
input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
return input_ids, image_tensor
def __len__(self):
return len(self.questions)
# DataLoader
def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, batch_size=1, num_workers=4):
assert batch_size == 1, "batch_size must be 1"
dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config)
data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
return data_loader
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, load_8bit=args.load_8bit)
terminators = [
tokenizer.eos_token_id
]
if args.conv_mode == 'llama_3':
if tokenizer.unk_token is None:
tokenizer.unk_token = "<|reserved_special_token_0|>"
tokenizer.pad_token = tokenizer.unk_token
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
if args.projection_path is not None and os.path.exists(args.projection_path):
print(f"Loading weights from {args.projection_path}")
status = model.load_state_dict(torch.load(args.projection_path, map_location='cpu'), strict=False)
if status.unexpected_keys:
print(f"Unexpected Keys: {status.unexpected_keys}.\nThe Video-ChatGPT weights are not loaded correctly.")
print(f"Weights loaded from {args.projection_path}")
for n, p in model.named_parameters():
if "model.local_enc.0.b1.conv1.conv.weight" in n:
print(p[:5, :1])
import time
time.sleep(20)
questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
args.conv_mode = args.conv_mode + '_mmtag'
print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
data_loader = create_data_loader(questions, args.image_folder, tokenizer, image_processor, model.config)
for (input_ids, image_tensor), line in tqdm(zip(data_loader, questions), total=len(questions)):
idx = line["question_id"]
cur_prompt = line["text"]
input_ids = input_ids.to(device='cuda', non_blocking=True)
# print(input_ids)
# terminators = [
# tokenizer.eos_token_id,
# tokenizer.convert_tokens_to_ids("<|eot_id|>")
# ]
with torch.inference_mode():
# outputs_ = model.generate(
# input_ids,
# max_new_tokens=256,
# eos_token_id=terminators,
# do_sample=True,
# temperature=0.6,
# top_p=0.9,
# )
output_ids = model.generate(
input_ids,
images=image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True),
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
eos_token_id=terminators,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=args.max_new_tokens,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
# print(tokenizer.batch_decode(output_ids))
# input_token_len = input_ids.shape[1]
# n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
# if n_diff_input_output > 0:
# print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
# outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
# outputs = outputs.strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"prompt": cur_prompt,
"text": outputs,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
# ans_file.flush()
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="llava_v1")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument('--load_8bit', type=bool, default=False)
parser.add_argument("--max_new_tokens", type=int, default=128)
parser.add_argument("--projection_path", type=str, required=False)
args = parser.parse_args()
eval_model(args)
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