run_instance_segmentation_no_trainer.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 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
#
# 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

"""Finetuning 🤗 Transformers model for instance segmentation with Accelerate 🚀."""

import argparse
import json
import logging
import math
import os
import sys
from functools import partial
from pathlib import Path
from typing import Any, Mapping

import albumentations as A
import datasets
import numpy as np
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from torchmetrics.detection.mean_ap import MeanAveragePrecision
from tqdm import tqdm

import transformers
from transformers import (
    AutoImageProcessor,
    AutoModelForUniversalSegmentation,
    SchedulerType,
    get_scheduler,
)
from transformers.image_processing_utils import BatchFeature
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


logger = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.48.0.dev0")

require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/instance-segmentation/requirements.txt")


def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model for instance segmentation task")

    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to a pretrained model or model identifier from huggingface.co/models.",
        default="facebook/mask2former-swin-tiny-coco-instance",
    )
    parser.add_argument(
        "--dataset_name",
        type=str,
        help="Name of the dataset on the hub.",
        default="qubvel-hf/ade20k-mini",
    )
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        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."
        ),
    )
    parser.add_argument(
        "--image_height",
        type=int,
        default=384,
        help="The height of the images to feed the model.",
    )
    parser.add_argument(
        "--image_width",
        type=int,
        default=384,
        help="The width of the images to feed the model.",
    )
    parser.add_argument(
        "--do_reduce_labels",
        action="store_true",
        help="Whether to reduce the number of labels by removing the background class.",
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        help="Path to a folder in which the model and dataset will be cached.",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=4,
        help="Number of workers to use for the dataloaders.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--adam_beta1",
        type=float,
        default=0.9,
        help="Beta1 for AdamW optimizer",
    )
    parser.add_argument(
        "--adam_beta2",
        type=float,
        default=0.999,
        help="Beta2 for AdamW optimizer",
    )
    parser.add_argument(
        "--adam_epsilon",
        type=float,
        default=1e-8,
        help="Epsilon for AdamW optimizer",
    )
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        required=False,
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    args = parser.parse_args()

    # Sanity checks
    if args.push_to_hub or args.with_tracking:
        if args.output_dir is None:
            raise ValueError(
                "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified."
            )

    if args.output_dir is not None:
        os.makedirs(args.output_dir, exist_ok=True)

    return args


def augment_and_transform_batch(
    examples: Mapping[str, Any], transform: A.Compose, image_processor: AutoImageProcessor
) -> BatchFeature:
    batch = {
        "pixel_values": [],
        "mask_labels": [],
        "class_labels": [],
    }

    for pil_image, pil_annotation in zip(examples["image"], examples["annotation"]):
        image = np.array(pil_image)
        semantic_and_instance_masks = np.array(pil_annotation)[..., :2]

        # Apply augmentations
        output = transform(image=image, mask=semantic_and_instance_masks)

        aug_image = output["image"]
        aug_semantic_and_instance_masks = output["mask"]
        aug_instance_mask = aug_semantic_and_instance_masks[..., 1]

        # Create mapping from instance id to semantic id
        unique_semantic_id_instance_id_pairs = np.unique(aug_semantic_and_instance_masks.reshape(-1, 2), axis=0)
        instance_id_to_semantic_id = {
            instance_id: semantic_id for semantic_id, instance_id in unique_semantic_id_instance_id_pairs
        }

        # Apply the image processor transformations: resizing, rescaling, normalization
        model_inputs = image_processor(
            images=[aug_image],
            segmentation_maps=[aug_instance_mask],
            instance_id_to_semantic_id=instance_id_to_semantic_id,
            return_tensors="pt",
        )

        batch["pixel_values"].append(model_inputs.pixel_values[0])
        batch["mask_labels"].append(model_inputs.mask_labels[0])
        batch["class_labels"].append(model_inputs.class_labels[0])

    return batch


def collate_fn(examples):
    batch = {}
    batch["pixel_values"] = torch.stack([example["pixel_values"] for example in examples])
    batch["class_labels"] = [example["class_labels"] for example in examples]
    batch["mask_labels"] = [example["mask_labels"] for example in examples]
    if "pixel_mask" in examples[0]:
        batch["pixel_mask"] = torch.stack([example["pixel_mask"] for example in examples])
    return batch


def nested_cpu(tensors):
    if isinstance(tensors, (list, tuple)):
        return type(tensors)(nested_cpu(t) for t in tensors)
    elif isinstance(tensors, Mapping):
        return type(tensors)({k: nested_cpu(t) for k, t in tensors.items()})
    elif isinstance(tensors, torch.Tensor):
        return tensors.cpu().detach()
    else:
        return tensors


def evaluation_loop(model, image_processor, accelerator: Accelerator, dataloader, id2label):
    metric = MeanAveragePrecision(iou_type="segm", class_metrics=True)

    for inputs in tqdm(dataloader, total=len(dataloader), disable=not accelerator.is_local_main_process):
        with torch.no_grad():
            outputs = model(**inputs)

        inputs = accelerator.gather_for_metrics(inputs)
        inputs = nested_cpu(inputs)

        outputs = accelerator.gather_for_metrics(outputs)
        outputs = nested_cpu(outputs)

        # For metric computation we need to provide:
        #  - targets in a form of list of dictionaries with keys "masks", "labels"
        #  - predictions in a form of list of dictionaries with keys "masks", "labels", "scores"

        post_processed_targets = []
        post_processed_predictions = []
        target_sizes = []

        # Collect targets
        for masks, labels in zip(inputs["mask_labels"], inputs["class_labels"]):
            post_processed_targets.append(
                {
                    "masks": masks.to(dtype=torch.bool),
                    "labels": labels,
                }
            )
            target_sizes.append(masks.shape[-2:])

        # Collect predictions
        post_processed_output = image_processor.post_process_instance_segmentation(
            outputs,
            threshold=0.0,
            target_sizes=target_sizes,
            return_binary_maps=True,
        )

        for image_predictions, target_size in zip(post_processed_output, target_sizes):
            if image_predictions["segments_info"]:
                post_processed_image_prediction = {
                    "masks": image_predictions["segmentation"].to(dtype=torch.bool),
                    "labels": torch.tensor([x["label_id"] for x in image_predictions["segments_info"]]),
                    "scores": torch.tensor([x["score"] for x in image_predictions["segments_info"]]),
                }
            else:
                # for void predictions, we need to provide empty tensors
                post_processed_image_prediction = {
                    "masks": torch.zeros([0, *target_size], dtype=torch.bool),
                    "labels": torch.tensor([]),
                    "scores": torch.tensor([]),
                }
            post_processed_predictions.append(post_processed_image_prediction)

        # Update metric for batch targets and predictions
        metric.update(post_processed_predictions, post_processed_targets)

    # Compute metrics
    metrics = metric.compute()

    # Replace list of per class metrics with separate metric for each class
    classes = metrics.pop("classes")
    map_per_class = metrics.pop("map_per_class")
    mar_100_per_class = metrics.pop("mar_100_per_class")
    for class_id, class_map, class_mar in zip(classes, map_per_class, mar_100_per_class):
        class_name = id2label[class_id.item()] if id2label is not None else class_id.item()
        metrics[f"map_{class_name}"] = class_map
        metrics[f"mar_100_{class_name}"] = class_mar

    metrics = {k: round(v.item(), 4) for k, v in metrics.items()}

    return metrics


def setup_logging(accelerator: Accelerator) -> None:
    """Setup logging according to `training_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 accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
        logger.setLevel(logging.INFO)
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()


def handle_repository_creation(accelerator: Accelerator, args: argparse.Namespace):
    """Create a repository for the model and dataset if `args.push_to_hub` is set."""

    repo_id = None
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    return repo_id


def main():
    args = parse_args()

    # 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_instance_segmentation_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
    setup_logging(accelerator)

    # If passed along, set the training seed now.
    # We set device_specific to True as we want different data augmentation per device.
    if args.seed is not None:
        set_seed(args.seed, device_specific=True)

    # Create repository if push ot hub is specified
    repo_id = handle_repository_creation(accelerator, args)

    if args.push_to_hub:
        api = HfApi()

    # ------------------------------------------------------------------------------------------------
    # Load dataset, prepare splits
    # ------------------------------------------------------------------------------------------------

    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir, trust_remote_code=args.trust_remote_code)

    # We need to specify the label2id mapping for the model
    # it is a mapping from semantic class name to class index.
    # In case your dataset does not provide it, you can create it manually:
    # label2id = {"background": 0, "cat": 1, "dog": 2}
    label2id = dataset["train"][0]["semantic_class_to_id"]

    if args.do_reduce_labels:
        label2id = {name: idx for name, idx in label2id.items() if idx != 0}  # remove background class
        label2id = {name: idx - 1 for name, idx in label2id.items()}  # shift class indices by -1

    id2label = {v: k for k, v in label2id.items()}

    # ------------------------------------------------------------------------------------------------
    # Load pretrained model and image processor
    # ------------------------------------------------------------------------------------------------
    model = AutoModelForUniversalSegmentation.from_pretrained(
        args.model_name_or_path,
        label2id=label2id,
        id2label=id2label,
        ignore_mismatched_sizes=True,
        token=args.hub_token,
    )

    image_processor = AutoImageProcessor.from_pretrained(
        args.model_name_or_path,
        do_resize=True,
        size={"height": args.image_height, "width": args.image_width},
        do_reduce_labels=args.do_reduce_labels,
        reduce_labels=args.do_reduce_labels,  # TODO: remove when mask2former support `do_reduce_labels`
        token=args.hub_token,
    )

    # ------------------------------------------------------------------------------------------------
    # Define image augmentations and dataset transforms
    # ------------------------------------------------------------------------------------------------
    train_augment_and_transform = A.Compose(
        [
            A.HorizontalFlip(p=0.5),
            A.RandomBrightnessContrast(p=0.5),
            A.HueSaturationValue(p=0.1),
        ],
    )
    validation_transform = A.Compose(
        [A.NoOp()],
    )

    # Make transform functions for batch and apply for dataset splits
    train_transform_batch = partial(
        augment_and_transform_batch, transform=train_augment_and_transform, image_processor=image_processor
    )
    validation_transform_batch = partial(
        augment_and_transform_batch, transform=validation_transform, image_processor=image_processor
    )

    with accelerator.main_process_first():
        dataset["train"] = dataset["train"].with_transform(train_transform_batch)
        dataset["validation"] = dataset["validation"].with_transform(validation_transform_batch)

    dataloader_common_args = {
        "num_workers": args.dataloader_num_workers,
        "persistent_workers": True,
        "collate_fn": collate_fn,
    }
    train_dataloader = DataLoader(
        dataset["train"], shuffle=True, batch_size=args.per_device_train_batch_size, **dataloader_common_args
    )
    valid_dataloader = DataLoader(
        dataset["validation"], shuffle=False, batch_size=args.per_device_eval_batch_size, **dataloader_common_args
    )

    # ------------------------------------------------------------------------------------------------
    # Define optimizer, scheduler and prepare everything with the accelerator
    # ------------------------------------------------------------------------------------------------

    # Optimizer
    optimizer = torch.optim.AdamW(
        list(model.parameters()),
        lr=args.learning_rate,
        betas=[args.adam_beta1, args.adam_beta2],
        eps=args.adam_epsilon,
    )

    # Figure out how many steps we should save the Accelerator states
    checkpointing_steps = args.checkpointing_steps
    if checkpointing_steps is not None and checkpointing_steps.isdigit():
        checkpointing_steps = int(checkpointing_steps)

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_warmup_steps=args.num_warmup_steps * accelerator.num_processes,
        num_training_steps=args.max_train_steps
        if overrode_max_train_steps
        else args.max_train_steps * accelerator.num_processes,
    )

    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, valid_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, lr_scheduler
    )

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if args.with_tracking:
        experiment_config = vars(args)
        # TensorBoard cannot log Enums, need the raw value
        experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
        accelerator.init_trackers("instance_segmentation_no_trainer", experiment_config)

    # ------------------------------------------------------------------------------------------------
    # Run training with evaluation on each epoch
    # ------------------------------------------------------------------------------------------------

    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(dataset['train'])}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")

    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    completed_steps = 0
    starting_epoch = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            checkpoint_path = args.resume_from_checkpoint
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
            dirs.sort(key=os.path.getctime)
            path = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            checkpoint_path = path
            path = os.path.basename(checkpoint_path)

        accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
        accelerator.load_state(checkpoint_path)
        # Extract `epoch_{i}` or `step_{i}`
        training_difference = os.path.splitext(path)[0]

        if "epoch" in training_difference:
            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
            resume_step = None
            completed_steps = starting_epoch * num_update_steps_per_epoch
        else:
            # need to multiply `gradient_accumulation_steps` to reflect real steps
            resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
            starting_epoch = resume_step // len(train_dataloader)
            completed_steps = resume_step // args.gradient_accumulation_steps
            resume_step -= starting_epoch * len(train_dataloader)

    # update the progress_bar if load from checkpoint
    progress_bar.update(completed_steps)

    for epoch in range(starting_epoch, args.num_train_epochs):
        model.train()
        if args.with_tracking:
            total_loss = 0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We skip the first `n` batches in the dataloader when resuming from a checkpoint
            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
        else:
            active_dataloader = train_dataloader

        for step, batch in enumerate(active_dataloader):
            with accelerator.accumulate(model):
                outputs = model(**batch)
                loss = outputs.loss
                # We keep track of the loss at each epoch
                if args.with_tracking:
                    total_loss += loss.detach().float()
                accelerator.backward(loss)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                completed_steps += 1

            if isinstance(checkpointing_steps, int):
                if completed_steps % checkpointing_steps == 0 and accelerator.sync_gradients:
                    output_dir = f"step_{completed_steps}"
                    if args.output_dir is not None:
                        output_dir = os.path.join(args.output_dir, output_dir)
                    accelerator.save_state(output_dir)

                    if args.push_to_hub and epoch < args.num_train_epochs - 1:
                        accelerator.wait_for_everyone()
                        unwrapped_model = accelerator.unwrap_model(model)
                        unwrapped_model.save_pretrained(
                            args.output_dir,
                            is_main_process=accelerator.is_main_process,
                            save_function=accelerator.save,
                        )
                        if accelerator.is_main_process:
                            image_processor.save_pretrained(args.output_dir)
                            api.upload_folder(
                                repo_id=repo_id,
                                commit_message=f"Training in progress epoch {epoch}",
                                folder_path=args.output_dir,
                                repo_type="model",
                                token=args.hub_token,
                            )

            if completed_steps >= args.max_train_steps:
                break

        logger.info("***** Running evaluation *****")
        metrics = evaluation_loop(model, image_processor, accelerator, valid_dataloader, id2label)

        logger.info(f"epoch {epoch}: {metrics}")

        if args.with_tracking:
            accelerator.log(
                {
                    "train_loss": total_loss.item() / len(train_dataloader),
                    **metrics,
                    "epoch": epoch,
                    "step": completed_steps,
                },
                step=completed_steps,
            )

        if args.push_to_hub and epoch < args.num_train_epochs - 1:
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            unwrapped_model.save_pretrained(
                args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
            )
            if accelerator.is_main_process:
                image_processor.save_pretrained(args.output_dir)
                api.upload_folder(
                    commit_message=f"Training in progress epoch {epoch}",
                    folder_path=args.output_dir,
                    repo_id=repo_id,
                    repo_type="model",
                    token=args.hub_token,
                )

        if args.checkpointing_steps == "epoch":
            output_dir = f"epoch_{epoch}"
            if args.output_dir is not None:
                output_dir = os.path.join(args.output_dir, output_dir)
            accelerator.save_state(output_dir)

    # ------------------------------------------------------------------------------------------------
    # Run evaluation on test dataset and save the model
    # ------------------------------------------------------------------------------------------------

    logger.info("***** Running evaluation on test dataset *****")
    metrics = evaluation_loop(model, image_processor, accelerator, valid_dataloader, id2label)
    metrics = {f"test_{k}": v for k, v in metrics.items()}

    logger.info(f"Test metrics: {metrics}")

    if args.with_tracking:
        accelerator.end_training()

    if args.output_dir is not None:
        accelerator.wait_for_everyone()
        unwrapped_model = accelerator.unwrap_model(model)
        unwrapped_model.save_pretrained(
            args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
        )
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                json.dump(metrics, f, indent=2)

            image_processor.save_pretrained(args.output_dir)

            if args.push_to_hub:
                api.upload_folder(
                    commit_message="End of training",
                    folder_path=args.output_dir,
                    repo_id=repo_id,
                    repo_type="model",
                    token=args.hub_token,
                    ignore_patterns=["epoch_*"],
                )


if __name__ == "__main__":
    main()