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transformers/examples/pytorch/image-classification/run_image_classification.py Executable file
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#!/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.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
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()
# 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_image_classification", 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."
)
# 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
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": "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()