run_understanding_inference.py

# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import argparse
import base64
import io
from typing import Dict, List

import paddle
import PIL.Image
from paddlenlp.transformers import LlamaTokenizerFast

from paddlemix.models.janus import JanusMultiModalityCausalLM
from paddlemix.processors import JanusImageProcessor, JanusVLChatProcessor


def load_pil_images(conversations: List[Dict[str, str]]) -> List[PIL.Image.Image]:
    """

        Support file path or base64 images.

        Args:
            conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
                [
                    {
                        "role": "User",
                        "content": "<image_placeholder>
    Extract all information from this image and convert them into markdown format.",
                        "images": ["./examples/table_datasets.png"]
                    },
                    {"role": "Assistant", "content": ""},
                ]

        Returns:
            pil_images (List[PIL.Image.Image]): the list of PIL images.

    """
    pil_images = []
    for message in conversations:
        if "images" not in message:
            continue
        for image_data in message["images"]:
            if image_data.startswith("data:image"):
                _, image_data = image_data.split(",", 1)
                image_bytes = base64.b64decode(image_data)
                pil_img = PIL.Image.open(io.BytesIO(image_bytes))
            else:
                pil_img = PIL.Image.open(image_data)
            pil_img = pil_img.convert("RGB")
            pil_images.append(pil_img)
    return pil_images


parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="deepseek-ai/Janus-1.3B")
parser.add_argument("--image_file", type=str, required=True)
parser.add_argument("--question", type=str, default="What is shown in this image?")
parser.add_argument("--dtype", type=str, default="float16")

args = parser.parse_args()

vl_gpt = JanusMultiModalityCausalLM.from_pretrained(args.model_path, dtype=args.dtype)
tokenizer = LlamaTokenizerFast.from_pretrained(args.model_path)
image_processer = JanusImageProcessor.from_pretrained(args.model_path)
vl_chat_processor: JanusVLChatProcessor = JanusVLChatProcessor(image_processer, tokenizer)

conversation = [
    {
        "role": "User",
        "content": f"<image_placeholder>\n{args.question}",
        "images": [args.image_file],
    },
    {"role": "Assistant", "content": ""},
]

pil_images = load_pil_images(conversation)
prepare_inputs = vl_chat_processor(conversations=conversation, images=pil_images, force_batchify=True)
device = prepare_inputs["pixel_values"].place
prepare_inputs.to(device, dtype=args.dtype)

inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
bs, seq_len = prepare_inputs.attention_mask.shape
position_ids = paddle.arange(seq_len, dtype=paddle.int64).reshape([1, -1])
outputs = vl_gpt.language_model.generate(
    input_ids=prepare_inputs["input_ids"],
    inputs_embeds=inputs_embeds,
    position_ids=position_ids,
    attention_mask=prepare_inputs.attention_mask,
    pad_token_id=tokenizer.eos_token_id,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_new_tokens=128,  # 512,
    do_sample=False,
    use_cache=True,
)
answer = tokenizer.decode(outputs[0][0], skip_special_tokens=True)
print(f"{prepare_inputs['sft_format'][0]}", answer)