File size: 2,889 Bytes
b8f1589 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import os
import torch
import dataset4eo as eodata
import jsonlines
import numpy as np
import pdb
from mistral_inference.transformer import Transformer #type:ignore
from mistral_inference.generate import generate #type:ignore
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer #type:ignore
from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageChunk #type:ignore
from mistral_common.protocol.instruct.request import ChatCompletionRequest #type:ignore
def get_caption_from_id(model, captions, id):
caption_item = captions[id]
caption = caption_item["caption"]
#pdb.set_trace()
prompt = ("You are an AI assistant tasked with creating a concise",
f"30-word caption that effectively summarizes the key points of the following content: {caption}")
prompt = "\n".join(prompt)
completion_request = ChatCompletionRequest(messages=[UserMessage(content=[TextChunk(text=prompt)])])
encoded = tokenizer.encode_chat_completion(completion_request)
images = encoded.images
tokens = encoded.tokens
out_tokens, _ = generate([tokens], model, images=[images], max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
result = result.replace("\"","")
return result
if __name__=="__main__":
import tqdm #type: ignore
import json
import argparse
parser = argparse.ArgumentParser(description="Process some integers.")
# Add the --part argument
parser.add_argument('--part', type=int, required=True, help="Specify the part number as an integer.")
# Parse the command-line arguments
args = parser.parse_args()
# Access the --part argument
part = args.part
# Generate partitions
n_total = 15000
n_parts = 5
step = n_total // n_parts
partitions = {
i: {"start": (i - 1) * step, "end": i * step} for i in range(1, n_parts + 1)
}
index_dict = partitions[part]
# load model
mistral_models_path = "../Pixtral-12B"
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)
# load data
filename = "hyper_id_text_nlcd.jsonl"
#Read the captions
captions = []
with open(filename, 'r', encoding='utf-8') as file:
for line in file:
# Parse each line as JSON
captions.append(json.loads(line.strip()))
out_filename = f"caption_hyper_id_text_nlcd_part{part}.jsonl"
for id in tqdm.tqdm(range(index_dict['start'], index_dict['end'])):
caption = get_caption_from_id(model, captions, id)
record = {"id": id, "caption": caption} # Create a structured record
with jsonlines.open(out_filename, mode='a') as writer:
writer.write(record) # Write one record per line
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