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#!/usr/bin/env python3
import argparse
import codecs
import datasets
import json
import os
import requests
import sys
import torch
from glob import glob
from PIL import Image
from transformers import AutoProcessor, AutoModelForImageTextToText
def clean_str(s):
return codecs.decode(s.removeprefix("```json").removesuffix("```").replace("\n", "").strip(), "unicode_escape")
def captioning_prompt(image):
return [
{
"role": "system",
"content": [{"type": "text", "text": "You are a professional English-German translator and also a renowned photography critic."}]
},
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": "Write a detailed caption for this image in a single sentence. Translate the caption into German. The output needs to be JSON, the keys being 'English' and 'German' for the respective captions. Only output the JSON, nothing else."}
]
}
]
def captioning_prompt_with_source(image, source):
prompt = captioning_prompt(image)
prefix = json.dumps({"English": source, ensure_ascii=False)}).removesuffix("}") + ', "German": "'
prompt.append({"role": "assistant", "content": [{"type": "text", "text": prefix}]})
return prompt
def translation_prompt(source):
return [
{
"role": "system",
"content": [{"type": "text", "text": "You are a professional English-German translator."}]
},
{
"role": "user",
"content": [
{"type": "text", "text": f"Translate the following caption into German. The output needs to be JSON, the only being 'Translation' for the translation. Only output the JSON, nothing else. Caption: {source}"}
]
}
]
def make_inputs(processor,
messages,
device):
return processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(device, dtype=torch.bfloat16)
def make_inputs_with_prefix(processor,
messages,
device):
prefix = processor.apply_chat_template(
messages,
add_generation_prompt=False,
tokenize=False,
return_dict=True,
return_tensors="pt"
).removesuffix("<end_of_turn>\n")
ret = processor(
text=prefix,
images=messages[1]['content'][0]['image'], # FIXME: That's not great
return_tensors="pt"
).to(device, dtype=torch.bfloat16)
return ret, prefix
def generate_and_parse(model,
processor,
messages,
args,
example_id):
sys.stderr.write(f"Processing {example_id=}\n")
if args.mode == "with_prefix":
inputs, prefix = make_inputs_with_prefix(processor, messages, model.device)
else:
inputs = make_inputs(processor, messages, model.device)
input_len = inputs["input_ids"].shape[-1]
stop_token_ids = [processor.tokenizer.eos_token_id, processor.tokenizer.convert_tokens_to_ids("<end_of_turn>")]
with torch.inference_mode():
generation = model.generate(
**inputs,
max_new_tokens=args.max_new_tokens,
do_sample=not args.do_not_sample,
temperature=args.temperature,
top_p=args.top_p,
top_k=args.top_k,
eos_token_id=stop_token_ids,
disable_compile=True,
)
output_tokens = generation[0][input_len:]
output_text = clean_str(processor.decode(output_tokens, skip_special_tokens=True))
if args.mode == "with_prefix":
output_text = prefix[prefix.index("{"):] + output_text
try:
return json.loads(output_text)
except Exception:
sys.stderr.write(f"Error loading JSON from string '{output_text}' for id={example_id}\n")
return output_text
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="google/gemma-3-4b-it", type=str)
parser.add_argument("--attention-implementation", default="eager", type=str)
parser.add_argument("--lora-adapter", default=None, type=str)
parser.add_argument("--mode", choices=["from_scratch", "with_prefix", "translate"], type=str, required=True)
parser.add_argument("--dataset", default="asdf2k/caption_translation", type=str)
parser.add_argument("--data-subset", choices=["train", "dev", "test"], default="test", type=str)
parser.add_argument("--max-new-tokens", default=300, type=int)
parser.add_argument("--top-p", default=1.0, type=int)
parser.add_argument("--top-k", default=50, type=int)
parser.add_argument("--temperature", default=0.8, type=int)
parser.add_argument("--do-not-sample", action="store_true")
args = parser.parse_args()
model = AutoModelForImageTextToText.from_pretrained(
args.model,
device_map="cuda",
dtype=torch.bfloat16,
attn_implementation=args.attention_implementation,
).eval()
processor = AutoProcessor.from_pretrained(args.model, use_fast=True)
if args.lora_adapter:
from peft import PeftModel
model = PeftModel.from_pretrained(model, args.lora_adapter)
dataset = datasets.load_dataset(args.dataset)[args.data_subset]
if args.mode == "from_scratch": # Generate caption & translation from scratch
for x in dataset:
output = generate_and_parse(
model,
processor,
captioning_prompt(x["image"]),
args,
example_id=x["id"],
)
print(f"{x['id']}\t{json.dumps(output, ensure_ascii=False)}")
elif args.mode == "translate": # Generate German translation given English source
for x in dataset:
input_data = json.loads(x["assistant"])
output = generate_and_parse(
model,
processor,
translation_prompt(input_data["English"]),
args,
example_id=x["id"],
)
output = {"English": input_data["English"], "German": output["Translation"]}
print(f"{x['id']}\t{json.dumps(output, ensure_ascii=False)}")
elif args.mode == "with_prefix": # Generate German translation given English caption and image
for x in dataset:
assistant_output_as_input = json.loads(x["assistant"])
output = generate_and_parse(
model,
processor,
captioning_prompt_with_source(x["image"], assistant_output_as_input["English"]),
args,
example_id=x["id"],
)
print(f"{x['id']}\t{json.dumps(output, ensure_ascii=False)}")
else:
sys.stderr.write(f"Unkown mode '{args.mode}'")
if __name__ == "__main__":
main()
|
