#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace 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
# limitations under the License.
"""
Training a CLIP like dual encoder models using text and vision encoders in the library.
The script can be used to train CLIP like models for languages other than English by using
a text encoder pre-trained in the desired language. Currently this script supports the following vision
and text models:
Vision models: ViT(https://huggingface.co/models?filter=vit), CLIP (https://huggingface.co/models?filter=clip)
Text models: BERT, ROBERTa (https://huggingface.co/models?filter=fill-mask)
"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import tensorflow as tf
from datasets import load_dataset
from PIL import Image
import transformers
from transformers import (
AutoImageProcessor,
AutoTokenizer,
HfArgumentParser,
PushToHubCallback,
TFAutoModel,
TFTrainingArguments,
TFVisionTextDualEncoderModel,
create_optimizer,
)
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("4.28.0")
require_version(
"datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/contrastive-image-text/requirements.txt"
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, default=None
)
vision_model_name_or_path: str = field(
metadata={"help": "Path to pretrained image model or model identifier from huggingface.co/models"},
default=None,
)
text_model_name_or_path: str = field(
metadata={"help": "Path to pretrained text model or model identifier from huggingface.co/models"}, default=None
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
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)."},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
freeze_vision_model: bool = field(
default=False, metadata={"help": "Whether to freeze the vision model parameters or not."}
)
freeze_text_model: bool = field(
default=False, metadata={"help": "Whether to freeze the text model parameters or not."}
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input files."})
image_column: Optional[str] = field(
default="image_path",
metadata={"help": "The name of the column in the datasets containing the full image file paths."},
)
caption_column: Optional[str] = field(
default="caption",
metadata={"help": "The name of the column in the datasets containing the image captions."},
)
train_file: Optional[str] = field(
default=None, metadata={"help": "The input training data file (a jsonlines file)."}
)
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input testing data file (a jsonlines file)."},
)
max_seq_length: Optional[int] = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
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."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
def __post_init__(self):
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension == "json", "`validation_file` should be a json file."
dataset_name_mapping = {
"image_caption_dataset.py": ("image_path", "caption"),
}
def crop_to_square(image):
height, width = tf.shape(image)[0], tf.shape(image)[1]
if height > width:
image = tf.image.crop_to_bounding_box(image, (height - width) // 2, 0, width, width)
elif width > height:
image = tf.image.crop_to_bounding_box(image, 0, (width - height) // 2, height, height)
return image
def load_as_tf_dataset(dataset, image_column, image_size, mean, std, batch_size, shuffle):
dataset = dataset.with_format("tensorflow")[:] # Load the dataset as tensor slices, but not the images yet!
tf_dataset = tf.data.Dataset.from_tensor_slices(dataset)
def load_image(sample):
image_path = sample[image_column]
image = tf.io.read_file(image_path)
image = tf.image.decode_image(image, channels=3, expand_animations=False)
image = crop_to_square(image)
image = tf.image.resize(image, [image_size, image_size], method="bicubic", antialias=True)
image = image / 255.0
image = (image - mean) / std
image = tf.transpose(image, perm=[2, 0, 1]) # Convert to channels-first
sample["pixel_values"] = image
del sample[image_column]
return sample
if shuffle:
tf_dataset = tf_dataset.shuffle(len(tf_dataset))
tf_dataset = tf_dataset.map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
tf_dataset = tf_dataset.batch(batch_size, drop_remainder=shuffle)
tf_dataset = tf_dataset.prefetch(tf.data.experimental.AUTOTUNE)
return tf_dataset
def main():
# 1. Parse input arguments
# 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, TFTrainingArguments))
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()
if model_args.model_name_or_path is not None:
if model_args.vision_model_name_or_path is not None or model_args.text_model_name_or_path is not None:
raise ValueError(
"If using model_name_or_path, you cannot specify separate image/text model paths as well!"
)
if model_args.vision_model_name_or_path is not None or model_args.text_model_name_or_path is not None:
if model_args.model_name_or_path is not None:
raise ValueError(
"If using separate image/text model paths, you cannot specify model_name_or_path as well!"
)
if not (model_args.vision_model_name_or_path is not None and model_args.text_model_name_or_path is not None):
raise ValueError(
"If using separate image/text model paths, you must specify both vision_model_name_or_path "
"and text_model_name_or_path!"
)
# 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/TensorFlow versions.
send_example_telemetry("run_clip", model_args, data_args, framework="tensorflow")
# 2. 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)],
)
# 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.info(f"Training/evaluation parameters {training_args}")
# 3. Detecting last checkpoint and eventualy continue from 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:
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."
)
# 4. Load dataset
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files this script will use the first column for the full image path and the second column for the
# captions (unless you specify column names for this with the `image_column` and `caption_column` arguments).
#
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
keep_in_memory=False,
data_dir=data_args.data_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
dataset = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# 5. Load pretrained model, tokenizer, and image processor
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
elif model_args.text_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.text_model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
# Load image_processor, in this script we only use this to get the mean and std for normalization.
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,
use_auth_token=True if model_args.use_auth_token else None,
)
with training_args.strategy.scope():
model = TFAutoModel.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
# Load image_processor, in this script we only use this to get the mean and std for normalization.
image_processor = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.vision_model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
with training_args.strategy.scope():
model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
vision_model_name_or_path=model_args.vision_model_name_or_path,
text_model_name_or_path=model_args.text_model_name_or_path,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
config = model.config
if model_args.freeze_vision_model:
model.vision_model.trainable = False
if model_args.freeze_text_model:
model.text_model.trainable = False
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
if training_args.do_train:
column_names = dataset["train"].column_names
elif training_args.do_eval:
column_names = dataset["validation"].column_names
elif training_args.do_predict:
column_names = dataset["test"].column_names
else:
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
return
# 6. Get the column names for input/target.
dataset_columns = dataset_name_mapping.get(data_args.dataset_name, None)
if data_args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = data_args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if data_args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = data_args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# # 7. Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples):
captions = list(examples[caption_column])
text_inputs = tokenizer(captions, max_length=data_args.max_seq_length, padding="max_length", truncation=True)
examples["input_ids"] = text_inputs.input_ids
examples["attention_mask"] = text_inputs.attention_mask
return examples
def filter_corrupt_images(examples):
"""remove problematic images"""
valid_images = []
for image_file in examples[image_column]:
try:
Image.open(image_file)
valid_images.append(True)
except Exception:
valid_images.append(False)
return valid_images
if training_args.do_train:
if "train" not in dataset:
raise ValueError("--do_train requires a train dataset")
train_dataset = dataset["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
train_dataset = train_dataset.filter(
filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
)
train_dataset = train_dataset.map(
function=tokenize_captions,
batched=True,
remove_columns=[col for col in column_names if col != image_column],
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
tf_train_dataset = load_as_tf_dataset(
dataset=train_dataset,
batch_size=training_args.per_device_train_batch_size,
image_column=image_column,
image_size=config.vision_config.image_size,
mean=image_processor.image_mean,
std=image_processor.image_std,
shuffle=True,
)
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError("--do_eval requires a train validation")
eval_dataset = dataset["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
eval_dataset = eval_dataset.filter(
filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
)
eval_dataset = eval_dataset.map(
function=tokenize_captions,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[col for col in column_names if col != image_column],
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
tf_eval_dataset = load_as_tf_dataset(
dataset=eval_dataset,
batch_size=training_args.per_device_eval_batch_size,
image_column=image_column,
image_size=config.vision_config.image_size,
mean=image_processor.image_mean,
std=image_processor.image_std,
shuffle=False,
)
# 8. Preparing push_to_hub and model card
push_to_hub_model_id = training_args.push_to_hub_model_id
if model_args.model_name_or_path is not None:
model_name = model_args.model_name_or_path.split("/")[-1]
else:
vision_name = model_args.vision_model_name_or_path.split("/")[-1]
text_name = model_args.text_model_name_or_path.split("/")[-1]
model_name = f"{vision_name}-{text_name}"
if not push_to_hub_model_id:
if data_args.dataset_name is not None:
push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}"
else:
push_to_hub_model_id = f"{model_name}-finetuned-contrastive-image-text-modeling"
model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "contrastive-image-text-modeling"}
if data_args.dataset_name is not None:
model_card_kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
model_card_kwargs["dataset_args"] = data_args.dataset_config_name
model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
model_card_kwargs["dataset"] = data_args.dataset_name
if training_args.push_to_hub:
callbacks = [
PushToHubCallback(
output_dir=training_args.output_dir,
hub_model_id=push_to_hub_model_id,
hub_token=training_args.push_to_hub_token,
tokenizer=tokenizer,
**model_card_kwargs,
)
]
else:
callbacks = []
# # 9. Training
if training_args.do_train:
num_train_steps = int(len(tf_train_dataset) * int(training_args.num_train_epochs))
if training_args.warmup_steps > 0:
num_warmup_steps = training_args.warmup_steps
elif training_args.warmup_ratio > 0:
num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
else:
num_warmup_steps = 0
optimizer, lr_schedule = create_optimizer(
init_lr=training_args.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
adam_beta1=training_args.adam_beta1,
adam_beta2=training_args.adam_beta2,
adam_epsilon=training_args.adam_epsilon,
weight_decay_rate=training_args.weight_decay,
adam_global_clipnorm=training_args.max_grad_norm,
)
model.compile(optimizer=optimizer, jit_compile=training_args.xla)
if not training_args.do_eval:
tf_eval_dataset = None
model.fit(
tf_train_dataset,
validation_data=tf_eval_dataset,
epochs=int(training_args.num_train_epochs),
callbacks=callbacks,
)
# # 10. Evaluation
if training_args.do_eval and not training_args.do_train:
model.evaluate(tf_eval_dataset)
if __name__ == "__main__":
main()