docs/transformers/examples/pytorch/image-pretraining/run_mim.py

#!/usr/bin/env python
# Copyright 2022 The HuggingFace Inc. team. 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
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# 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

import logging
import os
import sys
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


""" Pre-training a 🤗 Transformers model for simple masked image modeling (SimMIM).
Any model supported by the AutoModelForMaskedImageModeling API can be used.
"""

logger = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.52.0.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:
    """
    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.
    """

    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:
    """
    Arguments pertaining to which model/config/image processor we are going to pre-train.
    """

    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`)."
            )
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to trust the execution of code from datasets/models defined on the Hub."
                " 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:
    """
    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".
    """

    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():
    # 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.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # 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.
        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()

    # 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.
    send_example_telemetry("run_mim", model_args, data_args)

    # Setup logging
    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:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        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()

    # Log on each process the small summary:
    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}")

    # Detecting last checkpoint.
    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."
            )

    # Initialize our dataset.
    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,
        trust_remote_code=model_args.trust_remote_code,
    )

    # If we don't have a validation split, split off a percentage of train as validation.
    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"]

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    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}")

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
    if hasattr(config, "decoder_type"):
        config.decoder_type = "simmim"

    # adapt config
    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,
        }
    )

    # create image processor
    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][-1]()

    # create model
    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]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    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),
        ]
    )

    # create mask generator
    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):
        """Preprocess a batch of images by applying transforms + creating a corresponding mask, indicating
        which patches to mask."""

        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))
        # Set the training transforms
        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))
            )
        # Set the validation transforms
        ds["validation"].set_transform(preprocess_images)

    # Initialize our trainer
    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,
        processing_class=image_processor,
        data_collator=collate_fn,
    )

    # Training
    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()

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

    # Write model card and (optionally) push to hub
    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()