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Running
on
Zero
File size: 8,115 Bytes
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import argparse
import json
from io import BytesIO
import os
import requests
import torch
from PIL import Image
from tqdm import tqdm
from llava.constants import (
IMAGE_TOKEN_INDEX,
DEFAULT_IMAGE_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
)
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import (
tokenizer_image_token,
process_images,
load_image_from_base64,
get_model_name_from_path,
KeywordsStoppingCriteria
)
def load_image(image_file):
if image_file.startswith('http') or image_file.startswith('https'):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.open(image_file).convert('RGB')
return image
def eval_model(args):
# Model
disable_torch_init()
model_name = get_model_name_from_path(args.model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(
args.model_path, args.model_base, model_name)#, True)
with open(args.questions_file) as f:
llvqa_data = json.load(f)
for i, llddata in enumerate(tqdm(llvqa_data)):
image_file = llddata["img_path"]
if args.lang == "en":
message = llddata["question"] + \
"\nChoose between one of the options as follows:\n"
elif args.lang == "zh":
message = llddata["question"] + "\在下列选项中选择一个:\n"
else:
raise NotImplementedError(
"Q-Bench does not support languages other than English (en) and Chinese (zh) yet. Contact us (https://github.com/VQAssessment/Q-Bench/) to convert Q-Bench into more languages.")
for choice, ans in zip(["A.", "B.", "C.", "D."], llddata["candidates"]):
message += f"{choice} {ans}\n"
qs = message
if 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
# if 'llama-2' in model_name.lower():
# conv_mode = "share4v_llama_2"
# elif "v1" in model_name.lower():
# conv_mode = "share4v_v1"
# elif "mpt" in model_name.lower():
# conv_mode = "mpt"
# else:
# conv_mode = "share4v_v0"
# if args.conv_mode is not None and conv_mode != args.conv_mode:
# print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(
# conv_mode, args.conv_mode, args.conv_mode))
# else:
# args.conv_mode = conv_mode
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()
image = Image.open(os.path.join(args.image_folder, image_file))
if isinstance(image_processor, list):
image_tensor_0 = image_processor[0].preprocess(
image, return_tensors="pt"
)["pixel_values"][0]
image_tensor_1 = image_processor[1].preprocess(
image, return_tensors="pt"
)["pixel_values"][0]
image_tensor = torch.cat((image_tensor_0, image_tensor_1), dim=0)
if len(image_processor)>2:
image_tensor_2 = image_processor[2].preprocess(
image, return_tensors="pt"
)["pixel_values"][0]
high_image_tensor = image_tensor_2
else:
high_image_tensor = image_tensor_0
if len(image_processor)>3:
image_tensor_3 = image_processor[3].preprocess(
image, return_tensors="pt"
)["pixel_values"][0]
flattened_image_tensor = image_tensor_3
else:
flattened_image_tensor = image_tensor_0
else:
image_tensor = image_processor.preprocess(image, return_tensors="pt")[
"pixel_values"
][0]
high_image_tensor = image_tensor
flattened_image_tensor = image_tensor
images = image_tensor.unsqueeze(0).bfloat16().cuda()
high_images = high_image_tensor.unsqueeze(0).bfloat16().cuda()
flattened_patches = flattened_image_tensor.unsqueeze(0).bfloat16().cuda()
input_ids = (
tokenizer_image_token(
prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
)
.unsqueeze(0)
.cuda()
)
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = (
[KeywordsStoppingCriteria(keywords, tokenizer, input_ids)]
if conv.version == "v0"
else None
)
if "qformer" in model_name or 'gate' in model_name:
prompts = [["question 0: <q>\n\n".replace("<q>", qs.replace("<image>\n", "").lower())]]
else:
prompts = None
if "gate-mask" in model_name:
routing_weights = torch.Tensor([1, 0, 1, 0, 0, 0, 0])
routing_weights = routing_weights.unsqueeze(0).bfloat16().cuda()
else:
routing_weights = None
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=images,
high_images=high_images if isinstance(image_processor, list) and len(image_processor)>2 else None,
flattened_patches=flattened_patches if isinstance(image_processor, list) and len(image_processor)>3 else None,
routing_weights=routing_weights,
prompts=prompts,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
# no_repeat_ngram_size=3,
max_new_tokens=1024,
use_cache=True,
)
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()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
llddata["response"] = outputs
with open(args.answers_file, "a") as wf:
json.dump(llddata, wf)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="share4v-7b")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str,
default="./playground/data/qbench/images_llvisionqa")
parser.add_argument("--questions-file", type=str,
default="./playground/data/qbench/llvisionqa_dev.json")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="llava_v1")
parser.add_argument("--lang", type=str, default="en")
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--regen", action="store_true", default=False)
args = parser.parse_args()
if os.path.exists(args.answers_file) and not args.regen:
print("{} already exists, won't regen again.".format(args.answers_file))
else:
eval_model(args)
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