627 lines
26 KiB
Python
627 lines
26 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2023 The HuggingFace 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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# limitations under the License.
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"""
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Training a CLIP like dual encoder models using text and vision encoders in the library.
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The script can be used to train CLIP like models for languages other than English by using
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a text encoder pre-trained in the desired language. Currently this script supports the following vision
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and text models:
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Vision models: ViT(https://huggingface.co/models?filter=vit), CLIP (https://huggingface.co/models?filter=clip)
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Text models: BERT, ROBERTa (https://huggingface.co/models?filter=fill-mask)
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"""
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import logging
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import os
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import sys
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import warnings
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from dataclasses import dataclass, field
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from typing import Optional
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import tensorflow as tf
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from datasets import load_dataset
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from PIL import Image
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import transformers
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from transformers import (
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AutoImageProcessor,
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AutoTokenizer,
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HfArgumentParser,
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PushToHubCallback,
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TFAutoModel,
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TFTrainingArguments,
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TFVisionTextDualEncoderModel,
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create_optimizer,
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)
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from transformers.utils import check_min_version, send_example_telemetry
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from transformers.utils.versions import require_version
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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("4.40.0.dev0")
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require_version(
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"datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/contrastive-image-text/requirements.txt"
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)
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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, or train from scratch.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, default=None
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)
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vision_model_name_or_path: str = field(
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metadata={"help": "Path to pretrained image model or model identifier from huggingface.co/models"},
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default=None,
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)
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text_model_name_or_path: str = field(
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metadata={"help": "Path to pretrained text model or model identifier from huggingface.co/models"}, default=None
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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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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
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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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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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)
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},
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)
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use_auth_token: bool = field(
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default=None,
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metadata={
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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)
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},
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)
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freeze_vision_model: bool = field(
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default=False, metadata={"help": "Whether to freeze the vision model parameters or not."}
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)
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freeze_text_model: bool = field(
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default=False, metadata={"help": "Whether to freeze the text model parameters or not."}
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)
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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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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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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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data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input files."})
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image_column: Optional[str] = field(
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default="image_path",
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metadata={"help": "The name of the column in the datasets containing the full image file paths."},
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)
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caption_column: Optional[str] = field(
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default="caption",
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metadata={"help": "The name of the column in the datasets containing the image captions."},
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)
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train_file: Optional[str] = field(
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default=None, metadata={"help": "The input training data file (a jsonlines file)."}
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)
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
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)
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test_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input testing data file (a jsonlines file)."},
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)
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max_seq_length: Optional[int] = field(
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default=128,
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metadata={
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"help": (
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"The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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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": (
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"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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)
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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": (
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"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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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension == "json", "`validation_file` should be a json file."
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dataset_name_mapping = {
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"image_caption_dataset.py": ("image_path", "caption"),
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}
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def crop_to_square(image):
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height, width = tf.shape(image)[0], tf.shape(image)[1]
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if height > width:
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image = tf.image.crop_to_bounding_box(image, (height - width) // 2, 0, width, width)
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elif width > height:
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image = tf.image.crop_to_bounding_box(image, 0, (width - height) // 2, height, height)
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return image
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def load_as_tf_dataset(dataset, image_column, image_size, mean, std, batch_size, shuffle):
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dataset = dataset.with_format("tensorflow")[:] # Load the dataset as tensor slices, but not the images yet!
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tf_dataset = tf.data.Dataset.from_tensor_slices(dataset)
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def load_image(sample):
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image_path = sample[image_column]
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image = tf.io.read_file(image_path)
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image = tf.image.decode_image(image, channels=3, expand_animations=False)
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image = crop_to_square(image)
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image = tf.image.resize(image, [image_size, image_size], method="bicubic", antialias=True)
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image = image / 255.0
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image = (image - mean) / std
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image = tf.transpose(image, perm=[2, 0, 1]) # Convert to channels-first
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sample["pixel_values"] = image
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del sample[image_column]
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return sample
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if shuffle:
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tf_dataset = tf_dataset.shuffle(len(tf_dataset))
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tf_dataset = tf_dataset.map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
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tf_dataset = tf_dataset.batch(batch_size, drop_remainder=shuffle)
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tf_dataset = tf_dataset.prefetch(tf.data.experimental.AUTOTUNE)
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return tf_dataset
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def main():
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# 1. Parse input arguments
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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, TFTrainingArguments))
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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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if model_args.use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
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FutureWarning,
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)
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if model_args.token is not None:
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raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
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model_args.token = model_args.use_auth_token
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if model_args.model_name_or_path is not None:
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if model_args.vision_model_name_or_path is not None or model_args.text_model_name_or_path is not None:
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raise ValueError(
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"If using model_name_or_path, you cannot specify separate image/text model paths as well!"
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)
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if model_args.vision_model_name_or_path is not None or model_args.text_model_name_or_path is not None:
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if model_args.model_name_or_path is not None:
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raise ValueError(
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"If using separate image/text model paths, you cannot specify model_name_or_path as well!"
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)
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if not (model_args.vision_model_name_or_path is not None and model_args.text_model_name_or_path is not None):
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raise ValueError(
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"If using separate image/text model paths, you must specify both vision_model_name_or_path "
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"and text_model_name_or_path!"
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)
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/TensorFlow versions.
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send_example_telemetry("run_clip", model_args, data_args, framework="tensorflow")
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# 2. 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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# The default of training_args.log_level is passive, so we set log level at info here to have that default.
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transformers.utils.logging.set_verbosity_info()
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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.info(f"Training/evaluation parameters {training_args}")
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# 3. Detecting last checkpoint and eventually continue from 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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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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# 4. Load dataset
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# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub).
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#
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# For CSV/JSON files this script will use the first column for the full image path and the second column for the
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# captions (unless you specify column names for this with the `image_column` and `caption_column` arguments).
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#
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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dataset = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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keep_in_memory=False,
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data_dir=data_args.data_dir,
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token=model_args.token,
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)
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else:
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data_files = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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extension = data_args.train_file.split(".")[-1]
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = data_args.validation_file.split(".")[-1]
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if data_args.test_file is not None:
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data_files["test"] = data_args.test_file
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extension = data_args.test_file.split(".")[-1]
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dataset = load_dataset(
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extension,
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.
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# 5. Load pretrained model, tokenizer, and image processor
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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elif model_args.text_model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.text_model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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else:
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raise ValueError(
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"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
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"You can do it from another script, save it, and load it from here, using --tokenizer_name."
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)
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if model_args.model_name_or_path:
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# Load image_processor, in this script we only use this to get the mean and std for normalization.
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image_processor = AutoImageProcessor.from_pretrained(
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model_args.image_processor_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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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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with training_args.strategy.scope():
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model = TFAutoModel.from_pretrained(
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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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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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else:
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# Load image_processor, in this script we only use this to get the mean and std for normalization.
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image_processor = AutoImageProcessor.from_pretrained(
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model_args.image_processor_name or model_args.vision_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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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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with training_args.strategy.scope():
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model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
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vision_model_name_or_path=model_args.vision_model_name_or_path,
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text_model_name_or_path=model_args.text_model_name_or_path,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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config = model.config
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if model_args.freeze_vision_model:
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model.vision_model.trainable = False
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if model_args.freeze_text_model:
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model.text_model.trainable = False
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# Preprocessing the datasets.
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# We need to tokenize inputs and targets.
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if training_args.do_train:
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column_names = dataset["train"].column_names
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elif training_args.do_eval:
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column_names = dataset["validation"].column_names
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elif training_args.do_predict:
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column_names = dataset["test"].column_names
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else:
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logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
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return
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# 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,
|
|
)
|
|
# Transformers models compute the right loss for their task by default when labels are passed, and will
|
|
# use this for training unless you specify your own loss function in compile().
|
|
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()
|