usrbinenv python codingutf8 2022 the huggingface inc team 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 orglicenseslicense2 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 pretraining a transformers model for simple masked image modeling simmim any model supported by the automodelformaskedimagemodeling api can be used will error if the minimal version of transformers is not installed remove at your own risks arguments pertaining to what data we are going to input our model for training and eval using hfargumentparser we can turn this class into argparse arguments to be able to specify them on the command line arguments pertaining to which modelconfigimage processor we are going to pretrain a class to generate boolean masks for the pretraining task a mask is a 1d tensor of shape modelpatchsize2 where the value is either 0 or 1 where 1 indicates masked see all possible arguments in srctransformerstrainingargs py or by passing the help flag to this script we now keep distinct sets of args for a cleaner separation of concerns if we pass only one argument to the script and it s the path to a json file let s parse it to get our arguments sending telemetry tracking the example usage helps us better allocate resources to maintain them the information sent is the one passed as arguments along with your pythonpytorch versions setup logging the default of trainingargs loglevel is passive so we set log level at info here to have that default log on each process the small summary detecting last checkpoint initialize our dataset if we don t have a validation split split off a percentage of train as validation create config distributed training the frompretrained methods guarantee that only one local process can concurrently download model vocab make sure the decodertype is simmim only relevant for beit adapt config create image processor create model transformations as done in original simmim paper source https github commicrosoftsimmimblobmaindatadatasimmim py create mask generator preprocess a batch of images by applying transforms creating a corresponding mask indicating which patches to mask examplespixelvalues transformsimage for image in examplesimagecolumnname examplesmask maskgenerator for i in rangelenexamplesimagecolumnname return examples if trainingargs dotrain if train not in ds raise valueerrordotrain requires a train dataset if dataargs maxtrainsamples is not none dstrain dstrain shuffleseedtrainingargs seed selectrangedataargs maxtrainsamples set the training transforms dstrain settransformpreprocessimages if trainingargs doeval if validation not in ds raise valueerrordoeval requires a validation dataset if dataargs maxevalsamples is not none dsvalidation dsvalidation shuffleseedtrainingargs seed selectrangedataargs maxevalsamples set the validation transforms dsvalidation settransformpreprocessimages initialize our trainer trainer trainer modelmodel argstrainingargs traindatasetdstrain if trainingargs dotrain else none evaldatasetdsvalidation if trainingargs doeval else none tokenizerimageprocessor datacollatorcollatefn training if trainingargs dotrain checkpoint none if trainingargs resumefromcheckpoint is not none checkpoint trainingargs resumefromcheckpoint elif lastcheckpoint is not none checkpoint lastcheckpoint trainresult trainer trainresumefromcheckpointcheckpoint trainer savemodel trainer logmetricstrain trainresult metrics trainer savemetricstrain trainresult metrics trainer savestate evaluation if trainingargs doeval metrics trainer evaluate trainer logmetricseval metrics trainer savemetricseval metrics write model card and optionally push to hub kwargs finetunedfrom modelargs modelnameorpath tasks maskedimagemodeling dataset dataargs datasetname tags maskedimagemodeling if trainingargs pushtohub trainer pushtohubkwargs else trainer createmodelcardkwargs if name main main usr bin env python coding utf 8 2022 the huggingface inc team 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 pre training a transformers model for simple masked image modeling simmim any model supported by the automodelformaskedimagemodeling api can be used will error if the minimal version of transformers is not installed remove at your own risks arguments pertaining to what data we are going to input our model for training and eval using hfargumentparser we can turn this class into argparse arguments to be able to specify them on the command line arguments pertaining to which model config image processor we are going to pre train a class to generate boolean masks for the pretraining task a mask is a 1d tensor of shape model_patch_size 2 where the value is either 0 or 1 where 1 indicates masked see all possible arguments in src transformers training_args py or by passing the help flag to this script we now keep distinct sets of args for a cleaner separation of concerns if we pass only one argument to the script and it s the path to a json file let s parse it to get our arguments sending telemetry tracking the example usage helps us better allocate resources to maintain them the information sent is the one passed as arguments along with your python pytorch versions setup logging the default of training_args log_level is passive so we set log level at info here to have that default log on each process the small summary detecting last checkpoint initialize our dataset if we don t have a validation split split off a percentage of train as validation create config distributed training the from_pretrained methods guarantee that only one local process can concurrently download model vocab make sure the decoder_type is simmim only relevant for beit adapt config create image processor create model transformations as done in original simmim paper source https github com microsoft simmim blob main data data_simmim py create mask generator preprocess a batch of images by applying transforms creating a corresponding mask indicating which patches to mask set the training transforms set the validation transforms initialize our trainer training evaluation write model card and optionally push to hub
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version




logger = logging.getLogger(__name__)


check_min_version("4.36.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class DataTrainingArguments:
    

    dataset_name: Optional[str] = field(
        default="cifar10", metadata={"help": "Name of a dataset from the datasets package"}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    image_column_name: Optional[str] = field(
        default=None,
        metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."},
    )
    train_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the training data."})
    validation_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the validation data."})
    train_val_split: Optional[float] = field(
        default=0.15, metadata={"help": "Percent to split off of train for validation."}
    )
    mask_patch_size: int = field(default=32, metadata={"help": "The size of the square patches to use for masking."})
    mask_ratio: float = field(
        default=0.6,
        metadata={"help": "Percentage of patches to mask."},
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )

    def __post_init__(self):
        data_files = {}
        if self.train_dir is not None:
            data_files["train"] = self.train_dir
        if self.validation_dir is not None:
            data_files["val"] = self.validation_dir
        self.data_files = data_files if data_files else None


@dataclass
class ModelArguments:
    

    model_name_or_path: str = field(
        default=None,
        metadata={
            "help": (
                "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
                "checkpoint identifier on the hub. "
                "Don't set if you want to train a model from scratch."
            )
        },
    )
    model_type: Optional[str] = field(
        default=None,
        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
    )
    config_name_or_path: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    config_overrides: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        },
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
    token: str = field(
        default=None,
        metadata={
            "help": (
                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
            )
        },
    )
    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
                "execute code present on the Hub on your local machine."
            )
        },
    )
    image_size: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
            )
        },
    )
    patch_size: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
            )
        },
    )
    encoder_stride: Optional[int] = field(
        default=None,
        metadata={"help": "Stride to use for the encoder."},
    )


class MaskGenerator:
    

    def __init__(self, input_size=192, mask_patch_size=32, model_patch_size=4, mask_ratio=0.6):
        self.input_size = input_size
        self.mask_patch_size = mask_patch_size
        self.model_patch_size = model_patch_size
        self.mask_ratio = mask_ratio

        if self.input_size % self.mask_patch_size != 0:
            raise ValueError("Input size must be divisible by mask patch size")
        if self.mask_patch_size % self.model_patch_size != 0:
            raise ValueError("Mask patch size must be divisible by model patch size")

        self.rand_size = self.input_size // self.mask_patch_size
        self.scale = self.mask_patch_size // self.model_patch_size

        self.token_count = self.rand_size**2
        self.mask_count = int(np.ceil(self.token_count * self.mask_ratio))

    def __call__(self):
        mask_idx = np.random.permutation(self.token_count)[: self.mask_count]
        mask = np.zeros(self.token_count, dtype=int)
        mask[mask_idx] = 1

        mask = mask.reshape((self.rand_size, self.rand_size))
        mask = mask.repeat(self.scale, axis=0).repeat(self.scale, axis=1)

        return torch.tensor(mask.flatten())


def collate_fn(examples):
    pixel_values = torch.stack([example["pixel_values"] for example in examples])
    mask = torch.stack([example["mask"] for example in examples])
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def main():
    
    
    

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        
        
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if model_args.use_auth_token is not None:
        warnings.warn(
            "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
            FutureWarning,
        )
        if model_args.token is not None:
            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
        model_args.token = model_args.use_auth_token

    
    
    send_example_telemetry("run_mim", model_args, data_args)

    
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    
    ds = load_dataset(
        data_args.dataset_name,
        data_args.dataset_config_name,
        data_files=data_args.data_files,
        cache_dir=model_args.cache_dir,
        token=model_args.token,
    )

    
    data_args.train_val_split = None if "validation" in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
        split = ds["train"].train_test_split(data_args.train_val_split)
        ds["train"] = split["train"]
        ds["validation"] = split["test"]

    
    
    
    
    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "token": model_args.token,
        "trust_remote_code": model_args.trust_remote_code,
    }
    if model_args.config_name_or_path:
        config = AutoConfig.from_pretrained(model_args.config_name_or_path, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")

    
    if hasattr(config, "decoder_type"):
        config.decoder_type = "simmim"

    
    model_args.image_size = model_args.image_size if model_args.image_size is not None else config.image_size
    model_args.patch_size = model_args.patch_size if model_args.patch_size is not None else config.patch_size
    model_args.encoder_stride = (
        model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
    )

    config.update(
        {
            "image_size": model_args.image_size,
            "patch_size": model_args.patch_size,
            "encoder_stride": model_args.encoder_stride,
        }
    )

    
    if model_args.image_processor_name:
        image_processor = AutoImageProcessor.from_pretrained(model_args.image_processor_name, **config_kwargs)
    elif model_args.model_name_or_path:
        image_processor = AutoImageProcessor.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        IMAGE_PROCESSOR_TYPES = {
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        image_processor = IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    
    if model_args.model_name_or_path:
        model = AutoModelForMaskedImageModeling.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForMaskedImageModeling.from_config(config, trust_remote_code=model_args.trust_remote_code)

    if training_args.do_train:
        column_names = ds["train"].column_names
    else:
        column_names = ds["validation"].column_names

    if data_args.image_column_name is not None:
        image_column_name = data_args.image_column_name
    elif "image" in column_names:
        image_column_name = "image"
    elif "img" in column_names:
        image_column_name = "img"
    else:
        image_column_name = column_names[0]

    
    
    transforms = Compose(
        [
            Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
            RandomResizedCrop(model_args.image_size, scale=(0.67, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
        ]
    )

    
    mask_generator = MaskGenerator(
        input_size=model_args.image_size,
        mask_patch_size=data_args.mask_patch_size,
        model_patch_size=model_args.patch_size,
        mask_ratio=data_args.mask_ratio,
    )

    def preprocess_images(examples):
        

        examples["pixel_values"] = [transforms(image) for image in examples[image_column_name]]
        examples["mask"] = [mask_generator() for i in range(len(examples[image_column_name]))]

        return examples

    if training_args.do_train:
        if "train" not in ds:
            raise ValueError("--do_train requires a train dataset")
        if data_args.max_train_samples is not None:
            ds["train"] = ds["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
        
        ds["train"].set_transform(preprocess_images)

    if training_args.do_eval:
        if "validation" not in ds:
            raise ValueError("--do_eval requires a validation dataset")
        if data_args.max_eval_samples is not None:
            ds["validation"] = (
                ds["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
            )
        
        ds["validation"].set_transform(preprocess_images)

    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=ds["train"] if training_args.do_train else None,
        eval_dataset=ds["validation"] if training_args.do_eval else None,
        tokenizer=image_processor,
        data_collator=collate_fn,
    )

    
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()
        trainer.log_metrics("train", train_result.metrics)
        trainer.save_metrics("train", train_result.metrics)
        trainer.save_state()

    
    if training_args.do_eval:
        metrics = trainer.evaluate()
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    
    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "masked-image-modeling",
        "dataset": data_args.dataset_name,
        "tags": ["masked-image-modeling"],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)


if __name__ == "__main__":
    main()