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import os
import shutil
import uuid
from dataclasses import dataclass
from typing import Optional
import pandas as pd
from datasets import Features, Image, Value, load_dataset
from sklearn.model_selection import train_test_split
ALLOWED_EXTENSIONS = ("jpeg", "png", "jpg", "JPG", "JPEG", "PNG")
@dataclass
class VLMPreprocessor:
"""
VLMPreprocessor is a class for preprocessing visual language model (VLM) datasets. It handles tasks such as
validating data paths, ensuring the presence of required files, splitting datasets, and preparing data for
training and validation.
Attributes:
train_data (str): Path to the training data directory.
username (str): Username for the Hugging Face Hub.
project_name (str): Name of the project.
token (str): Authentication token for the Hugging Face Hub.
column_mapping (dict): Mapping of column names.
valid_data (Optional[str]): Path to the validation data directory. Default is None.
test_size (Optional[float]): Proportion of the dataset to include in the validation split. Default is 0.2.
seed (Optional[int]): Random seed for dataset splitting. Default is 42.
local (Optional[bool]): Flag indicating whether to save data locally or push to the Hugging Face Hub. Default is False.
Methods:
_process_metadata(data_path):
Processes the metadata.jsonl file in the given data path and ensures it contains the required columns.
__post_init__():
Validates the existence of training and validation data paths, checks for required files, and ensures
the presence of a minimum number of image files.
split(df):
Splits the given DataFrame into training and validation sets based on the specified test size and seed.
prepare():
Prepares the dataset for training and validation by copying data to a cache directory, processing metadata,
and either saving the dataset locally or pushing it to the Hugging Face Hub.
"""
train_data: str
username: str
project_name: str
token: str
column_mapping: dict
valid_data: Optional[str] = None
test_size: Optional[float] = 0.2
seed: Optional[int] = 42
local: Optional[bool] = False
def _process_metadata(self, data_path):
metadata = pd.read_json(os.path.join(data_path, "metadata.jsonl"), lines=True)
# make sure that the metadata.jsonl file contains the required columns: file_name, objects
if "file_name" not in metadata.columns:
raise ValueError(f"{data_path}/metadata.jsonl should contain 'file_name' column.")
col_names = list(self.column_mapping.values())
for col in col_names:
if col not in metadata.columns:
raise ValueError(f"{data_path}/metadata.jsonl should contain '{col}' column.")
return metadata
def __post_init__(self):
# Check if train data path exists
if not os.path.exists(self.train_data):
raise ValueError(f"{self.train_data} does not exist.")
# check if self.train_data contains at least 5 image files in jpeg, png or jpg format only
train_image_files = [f for f in os.listdir(self.train_data) if f.endswith(ALLOWED_EXTENSIONS)]
if len(train_image_files) < 5:
raise ValueError(f"{self.train_data} should contain at least 5 jpeg, png or jpg files.")
# check if self.train_data contains a metadata.jsonl file
if "metadata.jsonl" not in os.listdir(self.train_data):
raise ValueError(f"{self.train_data} should contain a metadata.jsonl file.")
# Check if valid data path exists
if self.valid_data:
if not os.path.exists(self.valid_data):
raise ValueError(f"{self.valid_data} does not exist.")
# check if self.valid_data contains at least 5 image files in jpeg, png or jpg format only
valid_image_files = [f for f in os.listdir(self.valid_data) if f.endswith(ALLOWED_EXTENSIONS)]
if len(valid_image_files) < 5:
raise ValueError(f"{self.valid_data} should contain at least 5 jpeg, png or jpg files.")
# check if self.valid_data contains a metadata.jsonl file
if "metadata.jsonl" not in os.listdir(self.valid_data):
raise ValueError(f"{self.valid_data} should contain a metadata.jsonl file.")
def split(self, df):
train_df, valid_df = train_test_split(
df,
test_size=self.test_size,
random_state=self.seed,
)
train_df = train_df.reset_index(drop=True)
valid_df = valid_df.reset_index(drop=True)
return train_df, valid_df
def prepare(self):
random_uuid = uuid.uuid4()
cache_dir = os.environ.get("HF_HOME")
if not cache_dir:
cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "huggingface")
data_dir = os.path.join(cache_dir, "autotrain", str(random_uuid))
if self.valid_data:
shutil.copytree(self.train_data, os.path.join(data_dir, "train"))
shutil.copytree(self.valid_data, os.path.join(data_dir, "validation"))
train_metadata = self._process_metadata(os.path.join(data_dir, "train"))
valid_metadata = self._process_metadata(os.path.join(data_dir, "validation"))
train_metadata.to_json(os.path.join(data_dir, "train", "metadata.jsonl"), orient="records", lines=True)
valid_metadata.to_json(
os.path.join(data_dir, "validation", "metadata.jsonl"), orient="records", lines=True
)
features = Features(
{
"image": Image(),
}
)
for _, col_map in self.column_mapping.items():
features[col_map] = Value(dtype="string")
dataset = load_dataset("imagefolder", data_dir=data_dir, features=features)
rename_dict = {
"image": "autotrain_image",
}
for col, col_map in self.column_mapping.items():
if col == "text_column":
rename_dict[col_map] = "autotrain_text"
elif col == "prompt_text_column":
rename_dict[col_map] = "autotrain_prompt"
dataset = dataset.rename_columns(rename_dict)
if self.local:
dataset.save_to_disk(f"{self.project_name}/autotrain-data")
else:
dataset.push_to_hub(
f"{self.username}/autotrain-data-{self.project_name}",
private=True,
token=self.token,
)
else:
metadata = pd.read_json(os.path.join(self.train_data, "metadata.jsonl"), lines=True)
train_df, valid_df = self.split(metadata)
# create train and validation folders
os.makedirs(os.path.join(data_dir, "train"), exist_ok=True)
os.makedirs(os.path.join(data_dir, "validation"), exist_ok=True)
# move images to train and validation folders
for row in train_df.iterrows():
shutil.copy(
os.path.join(self.train_data, row[1]["file_name"]),
os.path.join(data_dir, "train", row[1]["file_name"]),
)
for row in valid_df.iterrows():
shutil.copy(
os.path.join(self.train_data, row[1]["file_name"]),
os.path.join(data_dir, "validation", row[1]["file_name"]),
)
# save metadata.jsonl file to train and validation folders
train_df.to_json(os.path.join(data_dir, "train", "metadata.jsonl"), orient="records", lines=True)
valid_df.to_json(os.path.join(data_dir, "validation", "metadata.jsonl"), orient="records", lines=True)
train_metadata = self._process_metadata(os.path.join(data_dir, "train"))
valid_metadata = self._process_metadata(os.path.join(data_dir, "validation"))
train_metadata.to_json(os.path.join(data_dir, "train", "metadata.jsonl"), orient="records", lines=True)
valid_metadata.to_json(
os.path.join(data_dir, "validation", "metadata.jsonl"), orient="records", lines=True
)
features = Features(
{
"image": Image(),
}
)
for _, col_map in self.column_mapping.items():
features[col_map] = Value(dtype="string")
dataset = load_dataset("imagefolder", data_dir=data_dir, features=features)
rename_dict = {
"image": "autotrain_image",
}
for col, col_map in self.column_mapping.items():
if col == "text_column":
rename_dict[col_map] = "autotrain_text"
elif col == "prompt_text_column":
rename_dict[col_map] = "autotrain_prompt"
dataset = dataset.rename_columns(rename_dict)
if self.local:
dataset.save_to_disk(f"{self.project_name}/autotrain-data")
else:
dataset.push_to_hub(
f"{self.username}/autotrain-data-{self.project_name}",
private=True,
token=self.token,
)
if self.local:
return f"{self.project_name}/autotrain-data"
return f"{self.username}/autotrain-data-{self.project_name}"
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