#!/usr/bin/env python # Copyright 2021 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 # /// script # dependencies = [ # "transformers @ git+https://github.com/huggingface/transformers.git", # "accelerate>=0.12.0", # "torch>=1.5.0", # "torchvision>=0.6.0", # "datasets>=2.14.0", # "evaluate", # "scikit-learn", # ] # /// import logging import os import sys from dataclasses import dataclass, field from typing import Optional import evaluate import numpy as np import torch from datasets import load_dataset from PIL import Image from torchvision.transforms import ( CenterCrop, Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor, ) import transformers from transformers import ( MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoImageProcessor, AutoModelForImageClassification, HfArgumentParser, TimmWrapperImageProcessor, Trainer, TrainingArguments, set_seed, ) from transformers.utils import check_min_version from transformers.utils.versions import require_version """ Fine-tuning a 🤗 Transformers model for image classification""" logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.57.0.dev0") require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt") MODEL_CONFIG_CLASSES = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def pil_loader(path: str): with open(path, "rb") as f: im = Image.open(f) return im.convert("RGB") @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=None, metadata={ "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)." }, ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) 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."} ) 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." ) }, ) image_column_name: str = field( default="image", metadata={"help": "The name of the dataset column containing the image data. Defaults to 'image'."}, ) label_column_name: str = field( default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'."}, ) def __post_init__(self): if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None): raise ValueError( "You must specify either a dataset name from the hub or a train and/or validation directory." ) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default="google/vit-base-patch16-224-in21k", metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) 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: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) 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 `hf auth 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." ) }, ) ignore_mismatched_sizes: bool = field( default=False, metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, ) 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() # 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_process_index}, 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}") # Set seed before initializing model. set_seed(training_args.seed) # Initialize our dataset and prepare it for the 'image-classification' task. if data_args.dataset_name is not None: dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) else: data_files = {} if data_args.train_dir is not None: data_files["train"] = os.path.join(data_args.train_dir, "**") if data_args.validation_dir is not None: data_files["validation"] = os.path.join(data_args.validation_dir, "**") dataset = load_dataset( "imagefolder", data_files=data_files, cache_dir=model_args.cache_dir, ) dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names if data_args.image_column_name not in dataset_column_names: raise ValueError( f"--image_column_name {data_args.image_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--image_column_name` to the correct audio column - one of " f"{', '.join(dataset_column_names)}." ) if data_args.label_column_name not in dataset_column_names: raise ValueError( f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--label_column_name` to the correct text column - one of " f"{', '.join(dataset_column_names)}." ) def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) labels = torch.tensor([example[data_args.label_column_name] for example in examples]) return {"pixel_values": pixel_values, "labels": labels} # 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 dataset else data_args.train_val_split if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0: split = dataset["train"].train_test_split(data_args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. labels = dataset["train"].features[data_args.label_column_name].names label2id, id2label = {}, {} for i, label in enumerate(labels): label2id[label] = str(i) id2label[str(i)] = label # Load the accuracy metric from the datasets package metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir) # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(p): """Computes accuracy on a batch of predictions""" return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids) config = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path, num_labels=len(labels), label2id=label2id, id2label=id2label, finetuning_task="image-classification", cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) model = AutoModelForImageClassification.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, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, ) image_processor = AutoImageProcessor.from_pretrained( model_args.image_processor_name or model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) # Define torchvision transforms to be applied to each image. if isinstance(image_processor, TimmWrapperImageProcessor): _train_transforms = image_processor.train_transforms _val_transforms = image_processor.val_transforms else: if "shortest_edge" in image_processor.size: size = image_processor.size["shortest_edge"] else: size = (image_processor.size["height"], image_processor.size["width"]) # Create normalization transform if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std"): normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std) else: normalize = Lambda(lambda x: x) _train_transforms = Compose( [ RandomResizedCrop(size), RandomHorizontalFlip(), ToTensor(), normalize, ] ) _val_transforms = Compose( [ Resize(size), CenterCrop(size), ToTensor(), normalize, ] ) def train_transforms(example_batch): """Apply _train_transforms across a batch.""" example_batch["pixel_values"] = [ _train_transforms(pil_img.convert("RGB")) for pil_img in example_batch[data_args.image_column_name] ] return example_batch def val_transforms(example_batch): """Apply _val_transforms across a batch.""" example_batch["pixel_values"] = [ _val_transforms(pil_img.convert("RGB")) for pil_img in example_batch[data_args.image_column_name] ] return example_batch if training_args.do_train: if "train" not in dataset: raise ValueError("--do_train requires a train dataset") if data_args.max_train_samples is not None: dataset["train"] = ( dataset["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples)) ) # Set the training transforms dataset["train"].set_transform(train_transforms) if training_args.do_eval: if "validation" not in dataset: raise ValueError("--do_eval requires a validation dataset") if data_args.max_eval_samples is not None: dataset["validation"] = ( dataset["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples)) ) # Set the validation transforms dataset["validation"].set_transform(val_transforms) # Initialize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset["train"] if training_args.do_train else None, eval_dataset=dataset["validation"] if training_args.do_eval else None, compute_metrics=compute_metrics, 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 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": "image-classification", "dataset": data_args.dataset_name, "tags": ["image-classification", "vision"], } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()