run_predict.py

# Copyright (c) 2023 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
# limitations under the License.

import os
from dataclasses import dataclass, field
from types import SimpleNamespace

import paddle
from paddlenlp.trainer import PdArgumentParser
from PIL import Image

from paddlemix import ImageBindModel, ImageBindProcessor
from paddlemix.datasets import *  # noqa: F401,F403
from paddlemix.models import *  # noqa: F401,F403
from paddlemix.models.imagebind.utils import *  # noqa: F401,F403
from paddlemix.utils.log import logger
from ppdiffusers.utils import load_image

ModalityType = SimpleNamespace(
    VISION="vision",
    TEXT="text",
    AUDIO="audio",
    THERMAL="thermal",
    DEPTH="depth",
    IMU="imu",
)


class Predictor:
    def __init__(self, model_args):
        self.processor = ImageBindProcessor.from_pretrained(model_args.model_name_or_path)
        self.predictor = ImageBindModel.from_pretrained(model_args.model_name_or_path)
        self.predictor.eval()

    def run(self, inputs):
        with paddle.no_grad():
            embeddings = self.predictor(inputs)

        return embeddings


def main(model_args, data_args):

    # build model
    logger.info("imagebind_model: {}".format(model_args.model_name_or_path))

    url = data_args.input_image
    if os.path.isfile(url):
        # read image
        image_pil = Image.open(data_args.input_image).convert("RGB")
    elif url:
        image_pil = load_image(url)
    else:
        image_pil = None

    url = data_args.input_audio
    if os.path.isfile(url):
        # read image
        input_audio = data_args.input_audio
    elif url:
        os.system("wget {}".format(url))
        input_audio = os.path.basename(data_args.input_audio)
    else:
        input_audio = None

    predictor = Predictor(model_args)

    encoding = predictor.processor(
        images=image_pil,
        text=data_args.input_text,
        audios=input_audio,
        return_tensors="pd",
    )
    inputs = {}
    if data_args.input_text:
        tokenized_processor = encoding["input_ids"]
        inputs.update({ModalityType.TEXT: tokenized_processor})
        # input.update()
    if image_pil:
        image_processor = encoding["pixel_values"]
        inputs.update({ModalityType.VISION: image_processor})
    if input_audio:
        audio_processor = encoding["audio_values"]
        inputs.update({ModalityType.AUDIO: audio_processor})

    embeddings = predictor.run(inputs)

    if data_args.input_text:
        logger.info("Generate text: {}".format(embeddings[ModalityType.TEXT]))
    if image_pil:
        logger.info("Generate vision: {}".format(embeddings[ModalityType.VISION]))
    if input_audio:
        logger.info("Generate audio: {}".format(embeddings[ModalityType.AUDIO]))


@dataclass
class DataArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    Using `PdArgumentParser` we can turn this class
    into argparse arguments to be able to specify them on
    the command line.
    """

    input_text: str = field(default="A dog.", metadata={"help": "The name of imagebind text input."})
    input_image: str = field(
        default="",
        # wget https://github.com/facebookresearch/ImageBind/blob/main/.assets/bird_image.jpg
        metadata={"help": "The name of imagebind image input."},
    )
    input_audio: str = field(
        default=None,
        # wget https://github.com/facebookresearch/ImageBind/blob/main/.assets/bird_audio.wav
        metadata={"help": "The name of imagebind audio input."},
    )


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        default="imagebind-1.2b/",
        metadata={"help": "Path to pretrained model or model identifier"},
    )

    device: str = field(
        default="GPU",
        metadata={"help": "Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU."},
    )


if __name__ == "__main__":

    parser = PdArgumentParser((ModelArguments, DataArguments))
    model_args, data_args = parser.parse_args_into_dataclasses()

    model_args.device = model_args.device.upper()
    assert model_args.device in [
        "CPU",
        "GPU",
        "XPU",
        "NPU",
    ], "device should be CPU, GPU, XPU or NPU"

    main(model_args, data_args)