✨ Add PyTorch image classification example (#13134)
* ✨ add pytorch image classification example * 🔥 remove utils.py * 💄 fix flake8 style issues * 🔥 remove unnecessary line * ✨ limit dataset sizes * 📌 update reqs * 🎨 restructure - use datasets lib * 🎨 import transforms directly * 📝 add comments * 💄 style * 🔥 remove flag * 📌 update requirement warning * 📝 add vision README.md * 📝 update README.md * 📝 update README.md * 🎨 add image-classification tag to model card * 🚚 rename vision ➡️ image-classification * 📝 update image-classification README.md
This commit is contained in:
Nathan Raw
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import logging
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import os
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import sys
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from dataclasses import dataclass, field
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from typing import Optional
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import datasets
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import numpy as np
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import torch
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from datasets import load_dataset
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from PIL import Image
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from torchvision.transforms import (
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CenterCrop,
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Compose,
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Normalize,
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RandomHorizontalFlip,
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RandomResizedCrop,
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Resize,
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ToTensor,
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)
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import transformers
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from transformers import (
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MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
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AutoConfig,
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AutoFeatureExtractor,
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AutoModelForImageClassification,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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)
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from transformers.trainer_utils import get_last_checkpoint
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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""" Fine-tuning a 🤗 Transformers model for image classification"""
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logger = logging.getLogger(__name__)
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.10.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
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MODEL_CONFIG_CLASSES = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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def pil_loader(path: str):
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with open(path, "rb") as f:
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im = Image.open(f)
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return im.convert("RGB")
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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Using `HfArgumentParser` we can turn this class
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into argparse arguments to be able to specify them on
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the command line.
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"""
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dataset_name: Optional[str] = field(
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default="nateraw/image-folder", metadata={"help": "Name of a dataset from the datasets package"}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the training data."})
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validation_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the validation data."})
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train_val_split: Optional[float] = field(
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default=0.15, metadata={"help": "Percent to split off of train for validation."}
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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},
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)
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image_size: Optional[int] = field(default=224, metadata={"help": " The size (resolution) of each image."})
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def __post_init__(self):
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data_files = dict()
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if self.train_dir is not None:
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data_files["train"] = self.train_dir
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if self.validation_dir is not None:
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data_files["val"] = self.validation_dir
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self.data_files = data_files if data_files else None
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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default="google/vit-base-patch16-224-in21k",
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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def collate_fn(examples):
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pixel_values = torch.stack([example["pixel_values"] for example in examples])
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labels = torch.tensor([example["labels"] for example in examples])
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return {"pixel_values": pixel_values, "labels": labels}
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Setup logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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logger.info(f"Training/evaluation parameters {training_args}")
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to overcome."
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)
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elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
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logger.info(
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f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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# Initialize our dataset and prepare it for the 'image-classification' task.
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ds = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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data_files=data_args.data_files,
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cache_dir=model_args.cache_dir,
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task="image-classification",
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)
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# Define torchvision transforms to be applied to each image.
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normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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_train_transforms = Compose(
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[
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RandomResizedCrop(data_args.image_size),
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RandomHorizontalFlip(),
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ToTensor(),
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normalize,
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]
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)
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_val_transforms = Compose(
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[
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Resize(data_args.image_size),
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CenterCrop(data_args.image_size),
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ToTensor(),
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normalize,
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]
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)
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def train_transforms(example_batch):
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"""Apply _train_transforms across a batch."""
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example_batch["pixel_values"] = [_train_transforms(pil_loader(f)) for f in example_batch["image_file_path"]]
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return example_batch
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def val_transforms(example_batch):
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"""Apply _val_transforms across a batch."""
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example_batch["pixel_values"] = [_val_transforms(pil_loader(f)) for f in example_batch["image_file_path"]]
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return example_batch
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# If we don't have a validation split, split off a percentage of train as validation.
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data_args.train_val_split = None if "validation" in ds.keys() else data_args.train_val_split
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if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
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split = ds["train"].train_test_split(data_args.train_val_split)
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ds["train"] = split["train"]
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ds["validation"] = split["test"]
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# Prepare label mappings.
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# We'll include these in the model's config to get human readable labels in the Inference API.
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labels = ds["train"].features["labels"].names
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label2id, id2label = dict(), dict()
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for i, label in enumerate(labels):
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label2id[label] = str(i)
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id2label[str(i)] = label
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# Load the accuracy metric from the datasets package
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metric = datasets.load_metric("accuracy")
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# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
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# predictions and label_ids field) and has to return a dictionary string to float.
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def compute_metrics(p):
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"""Computes accuracy on a batch of predictions"""
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return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)
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config = AutoConfig.from_pretrained(
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model_args.config_name or model_args.model_name_or_path,
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num_labels=len(labels),
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label2id=label2id,
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id2label=id2label,
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finetuning_task="image-classification",
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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model = AutoModelForImageClassification.from_pretrained(
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model_args.model_name_or_path,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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# NOTE - We aren't directly using this feature extractor since we defined custom transforms above.
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# We initialize this instance below and pass it to Trainer to ensure that the feature extraction
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# config, preprocessor_config.json, is included in output directories.
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# This way if we push a model to the hub, the inference widget will work.
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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model_args.feature_extractor_name or model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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size=data_args.image_size,
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image_mean=normalize.mean,
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image_std=normalize.std,
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)
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if training_args.do_train:
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if "train" not in ds:
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raise ValueError("--do_train requires a train dataset")
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if data_args.max_train_samples is not None:
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ds["train"] = ds["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
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# Set the training transforms
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ds["train"].set_transform(train_transforms)
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if training_args.do_eval:
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if "validation" not in ds:
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raise ValueError("--do_eval requires a validation dataset")
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if data_args.max_eval_samples is not None:
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ds["validation"] = (
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ds["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
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)
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# Set the validation transforms
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ds["validation"].set_transform(val_transforms)
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# Initalize our trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=ds["train"] if training_args.do_train else None,
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eval_dataset=ds["validation"] if training_args.do_eval else None,
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compute_metrics=compute_metrics,
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tokenizer=feature_extractor,
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data_collator=collate_fn,
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)
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# Training
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if training_args.do_train:
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checkpoint = None
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if training_args.resume_from_checkpoint is not None:
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checkpoint = training_args.resume_from_checkpoint
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elif last_checkpoint is not None:
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checkpoint = last_checkpoint
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train_result = trainer.train(resume_from_checkpoint=checkpoint)
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trainer.save_model()
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trainer.log_metrics("train", train_result.metrics)
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trainer.save_metrics("train", train_result.metrics)
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trainer.save_state()
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# Evaluation
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if training_args.do_eval:
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metrics = trainer.evaluate()
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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# Write model card and (optionally) push to hub
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kwargs = {
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"finetuned_from": model_args.model_name_or_path,
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"tasks": "image-classification",
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"dataset": data_args.dataset_name,
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"tags": ["image-classification"],
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}
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if training_args.push_to_hub:
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trainer.push_to_hub(**kwargs)
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else:
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trainer.create_model_card(**kwargs)
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if __name__ == "__main__":
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main()
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