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reader.py
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reader.py
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"""Reader module provides files and webdataset readers"""
from pathlib import Path
from PIL import Image, UnidentifiedImageError
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
import io
def folder_to_keys(folder, enable_text=True, enable_image=True, enable_metadata=False):
"""returns a list of keys from a folder of images and text"""
path = Path(folder)
text_files = None
metadata_files = None
image_files = None
if enable_text:
text_files = [*path.glob("**/*.txt")]
text_files = {text_file.relative_to(path).as_posix(): text_file for text_file in text_files}
if enable_image:
image_files = [
*path.glob("**/*.png"),
*path.glob("**/*.jpg"),
*path.glob("**/*.jpeg"),
*path.glob("**/*.bmp"),
*path.glob("**/*.webp"),
*path.glob("**/*.PNG"),
*path.glob("**/*.JPG"),
*path.glob("**/*.JPEG"),
*path.glob("**/*.BMP"),
*path.glob("**/*.WEBP"),
]
image_files = {image_file.relative_to(path).as_posix(): image_file for image_file in image_files}
if enable_metadata:
metadata_files = [*path.glob("**/*.json")]
metadata_files = {metadata_file.relative_to(path).as_posix(): metadata_file for metadata_file in metadata_files}
keys = None
def join(new_set):
return new_set & keys if keys is not None else new_set
if enable_text:
keys = join(text_files.keys())
elif enable_image:
keys = join(image_files.keys())
elif enable_metadata:
keys = join(metadata_files.keys())
keys = list(sorted(keys))
return keys, text_files, image_files, metadata_files
def get_image_dataset():
"""retrieve image dataset module without importing torch at the top level"""
from torch.utils.data import Dataset # pylint: disable=import-outside-toplevel
class ImageDataset(Dataset):
"""ImageDataset is a pytorch Dataset exposing image and text tensors from a folder of image and text"""
def __init__(
self,
preprocess,
tokenizer,
folder,
enable_text=True,
enable_image=True,
enable_metadata=False,
input_sampler=lambda a: a,
):
super().__init__()
self.keys, text_files, image_files, metadata_files = folder_to_keys(
folder, enable_text, enable_image, enable_metadata
)
self.keys = input_sampler(self.keys)
self.enable_text = enable_text
self.enable_image = enable_image
self.enable_metadata = enable_metadata
keys_set = set(self.keys)
if self.enable_text:
self.tokenizer = lambda text: tokenizer([text])[0]
self.text_files = {k: v for k, v in text_files.items() if k in keys_set}
if self.enable_image:
self.image_files = {k: v for k, v in image_files.items() if k in keys_set}
self.image_transform = preprocess
if self.enable_metadata:
self.metadata_files = {k: v for k, v in metadata_files.items() if k in keys_set}
def __len__(self):
return len(self.keys)
def __getitem__(self, ind):
key = self.keys[ind]
output = {}
if self.enable_image:
image_file = self.image_files[key]
try:
image_tensor = self.image_transform(Image.open(image_file))
except (UnidentifiedImageError, OSError) as e:
print(f"Failed to load image {image_file}. Error: {e}. Skipping.")
return None # return None to be filtered in the batch collate_fn
output["image_filename"] = str(image_file)
output["image_tensor"] = image_tensor
if self.enable_text:
text_file = self.text_files[key]
caption = text_file.read_text()
tokenized_text = self.tokenizer(caption)
output["text_tokens"] = tokenized_text
output["text"] = caption
if self.enable_metadata:
metadata_file = self.metadata_files[key]
metadata = metadata_file.read_text()
output["metadata"] = metadata
return output
return ImageDataset
def create_webdataset(
urls,
image_transform,
tokenizer,
enable_text=True,
enable_image=True,
image_key="jpg",
caption_key="txt",
enable_metadata=False,
cache_path=None,
input_sampler=lambda a: a,
):
"""Create a WebDataset reader, it can read a webdataset of image, text and json"""
import webdataset as wds # pylint: disable=import-outside-toplevel
urls = input_sampler(urls)
dataset = wds.WebDataset(urls, cache_dir=cache_path, cache_size=10**10, handler=wds.handlers.warn_and_continue)
def _tokenizer(text):
return tokenizer([text])[0]
def filter_dataset(item):
if enable_text and caption_key not in item:
return False
if enable_image and image_key not in item:
return False
if enable_metadata and "json" not in item:
return False
return True
filtered_dataset = dataset.select(filter_dataset)
def preprocess_dataset(item):
output = {}
if enable_image:
image_data = item[image_key]
image = Image.open(io.BytesIO(image_data))
image_tensor = image_transform(image)
output["image_filename"] = item["__key__"]
output["image_tensor"] = image_tensor
if enable_text:
text = item[caption_key]
caption = text.decode("utf-8")
tokenized_text = _tokenizer(caption)
output["text_tokens"] = tokenized_text
output["text"] = caption
if enable_metadata:
metadata_file = item["json"]
metadata = metadata_file.decode("utf-8")
output["metadata"] = metadata
return output
transformed_dataset = filtered_dataset.map(preprocess_dataset, handler=wds.handlers.warn_and_continue)
return transformed_dataset
def dataset_to_dataloader(dataset, batch_size, num_prepro_workers, input_format):
"""Create a pytorch dataloader from a dataset"""
def collate_fn(batch):
batch = list(filter(lambda x: x is not None, batch))
return default_collate(batch)
data = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_prepro_workers,
pin_memory=True,
prefetch_factor=2,
collate_fn=collate_fn if input_format == "files" else None,
)
return data
class FilesReader:
"""FilesReader is a reader that reads files from a folder"""
def __init__(
self,
sampler,
preprocess,
tokenizer,
input_dataset,
batch_size,
num_prepro_workers,
enable_text=True,
enable_image=True,
enable_metadata=False,
) -> None:
super().__init__()
dataset = get_image_dataset()(
preprocess, tokenizer, input_dataset, enable_text, enable_image, enable_metadata, sampler
)
self.dataloader = dataset_to_dataloader(dataset, batch_size, num_prepro_workers, "files")
def __iter__(self):
for batch in self.dataloader:
yield batch
class WebdatasetReader:
"""WebdatasetReader is a reader that reads samples from a webdataset"""
def __init__(
self,
sampler,
preprocess,
tokenizer,
input_dataset,
batch_size,
num_prepro_workers,
enable_text=True,
enable_image=True,
enable_metadata=False,
wds_image_key="jpg",
wds_caption_key="txt",
cache_path=None,
):
self.batch_size = batch_size
dataset = create_webdataset(
input_dataset,
preprocess,
tokenizer,
enable_text=enable_text,
enable_image=enable_image,
image_key=wds_image_key,
caption_key=wds_caption_key,
enable_metadata=enable_metadata,
cache_path=cache_path,
input_sampler=sampler,
)
self.dataloader = dataset_to_dataloader(dataset, batch_size, num_prepro_workers, "webdataset")
def __iter__(self):
for batch in self.dataloader:
yield batch