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inference_image.py
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inference_image.py
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import re
import argparse
import torch
import math
import numpy as np
from typing import Dict, Optional, Sequence, List
import transformers
from transformers import AutoConfig
from PIL import Image
from oryx.conversation import conv_templates, SeparatorStyle
from oryx.model.builder import load_pretrained_model
from oryx.utils import disable_torch_init
from oryx.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria, process_anyres_video_genli, process_anyres_highres_image_genli
from oryx.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
from decord import VideoReader, cpu
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]
def preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:
roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"}
im_start, im_end = tokenizer.additional_special_tokens_ids
nl_tokens = tokenizer("\n").input_ids
_system = tokenizer("system").input_ids + nl_tokens
_user = tokenizer("user").input_ids + nl_tokens
_assistant = tokenizer("assistant").input_ids + nl_tokens
# Apply prompt templates
input_ids, targets = [], []
source = sources
if roles[source[0]["from"]] != roles["human"]:
source = source[1:]
input_id, target = [], []
system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
input_id += system
target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens
assert len(input_id) == len(target)
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
if has_image and sentence["value"] is not None and "<image>" in sentence["value"]:
num_image = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"]))
texts = sentence["value"].split('<image>')
_input_id = tokenizer(role).input_ids + nl_tokens
for i,text in enumerate(texts):
_input_id += tokenizer(text).input_ids
if i<len(texts)-1:
_input_id += [IMAGE_TOKEN_INDEX] + nl_tokens
_input_id += [im_end] + nl_tokens
assert sum([i==IMAGE_TOKEN_INDEX for i in _input_id])==num_image
else:
if sentence["value"] is None:
_input_id = tokenizer(role).input_ids + nl_tokens
else:
_input_id = tokenizer(role).input_ids + nl_tokens + tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
input_id += _input_id
if role == "<|im_start|>user":
_target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens
elif role == "<|im_start|>assistant":
_target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens
else:
raise NotImplementedError
target += _target
input_ids.append(input_id)
targets.append(target)
input_ids = torch.tensor(input_ids, dtype=torch.long)
targets = torch.tensor(targets, dtype=torch.long)
return input_ids
def eval_model(args):
# Model
disable_torch_init()
model_path = args.model_path
model_name = get_model_name_from_path(model_path)
if args.overwrite == True:
overwrite_config = {}
overwrite_config["mm_resampler_type"] = "dynamic_compressor"
overwrite_config["patchify_video_feature"] = False
overwrite_config["attn_implementation"] = "sdpa" if torch.__version__ >= "2.1.2" else "eager"
cfg_pretrained = AutoConfig.from_pretrained(model_path)
if '7b' in model_path:
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, device_map="cuda:0", overwrite_config=overwrite_config)
elif '34b' in model_path:
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, device_map="auto", overwrite_config=overwrite_config)
model.to('cuda').eval()
image_file = "image.png"
visual = Image.open(image_file)
image_sizes = [visual.size]
args.conv_mode = "qwen_1_5"
question = "Describe this image."
question = "<image>\n" + question
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
if '7b' in model_path:
input_ids = preprocess_qwen([{'from': 'human','value': question},{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda()
elif '34b' in model_path:
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda:0')
image_tensor, image_highres_tensor = [], []
image_processor.do_resize = False
image_processor.do_center_crop = False
image_tensor_, image_highres_tensor_ = process_anyres_highres_image_genli(visual, image_processor)
image_tensor.append(image_tensor_)
image_highres_tensor.append(image_highres_tensor_)
image_tensor = torch.stack(image_tensor, dim=0)
image_highres_tensor = torch.stack(image_highres_tensor, dim=0)
image_tensor = image_tensor.to(dtype=torch.bfloat16, device=self.device)
image_highres_tensor = image_highres_tensor.to(dtype=torch.bfloat16, device=self.device)
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = self.model.generate(
input_ids,
modalities=['image'],
images=image_tensor,
images_highres=image_highres_tensor,
image_sizes=image_sizes,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=128,
use_cache=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
print(outputs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--frames-upbound", type=int, default=64)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--overwrite", type=bool, default=True)
parser.add_argument("--num_beams", type=int, default=1)
args = parser.parse_args()
eval_model(args)