comparison pytorch_embedding.py @ 0:38333676a029 draft default tip

planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93

0:38333676a029

"""
This module provides functionality to extract image embeddings
using a specified
pretrained model from the torchvision library. It includes functions to:
- List image files directly from a ZIP file without extraction.
- Apply model-specific preprocessing and transformations.
- Extract embeddings using various models.
- Save the resulting embeddings into a CSV file.
Modules required:
- argparse: For command-line argument parsing.
- os, csv, zipfile: For file handling (ZIP file reading, CSV writing).
- inspect: For inspecting function signatures and models.
- torch, torchvision: For loading and using pretrained models
to extract embeddings.
- PIL, cv2: For image processing tasks such as resizing, normalization,
and conversion.
"""

import argparse
import csv
import inspect
import logging
import os
import zipfile
from inspect import signature

import cv2
import numpy as np
import torch
import torchvision.models as models
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms

# Configure logging
logging.basicConfig(
    filename="/tmp/ludwig_embeddings.log",
    filemode="a",
    format="%(asctime)s - %(levelname)s - %(message)s",
    level=logging.DEBUG,
)

# Create a cache directory in the current working directory
cache_dir = os.path.join(os.getcwd(), 'hf_cache')
try:
    os.makedirs(cache_dir, exist_ok=True)
    logging.info(f"Cache directory created: {cache_dir}, writable: {os.access(cache_dir, os.W_OK)}")
except OSError as e:
    logging.error(f"Failed to create cache directory {cache_dir}: {e}")
    raise

# Available models from torchvision
AVAILABLE_MODELS = {
    name: getattr(models, name)
    for name in dir(models)
    if callable(
        getattr(models, name)
    ) and "weights" in signature(getattr(models, name)).parameters
}

# Default resize and normalization settings for models
MODEL_DEFAULTS = {
    "default": {"resize": (224, 224), "normalize": (
        [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
    )},
    "efficientnet_b1": {"resize": (240, 240)},
    "efficientnet_b2": {"resize": (260, 260)},
    "efficientnet_b3": {"resize": (300, 300)},
    "efficientnet_b4": {"resize": (380, 380)},
    "efficientnet_b5": {"resize": (456, 456)},
    "efficientnet_b6": {"resize": (528, 528)},
    "efficientnet_b7": {"resize": (600, 600)},
    "inception_v3": {"resize": (299, 299)},
    "swin_b": {"resize": (224, 224), "normalize": (
        [0.5, 0.0, 0.5], [0.5, 0.5, 0.5]
    )},
    "swin_s": {"resize": (224, 224), "normalize": (
        [0.5, 0.0, 0.5], [0.5, 0.5, 0.5]
    )},
    "swin_t": {"resize": (224, 224), "normalize": (
        [0.5, 0.0, 0.5], [0.5, 0.5, 0.5]
    )},
    "vit_b_16": {"resize": (224, 224), "normalize": (
        [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
    )},
    "vit_b_32": {"resize": (224, 224), "normalize": (
        [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
    )},
}

for model, settings in MODEL_DEFAULTS.items():
    if "normalize" not in settings:
        settings["normalize"] = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])


# Custom transform classes
class CLAHETransform:
    def __init__(self, clip_limit=2.0, tile_grid_size=(8, 8)):
        self.clahe = cv2.createCLAHE(
            clipLimit=clip_limit,
            tileGridSize=tile_grid_size
        )

    def __call__(self, img):
        img = np.array(img.convert("L"))
        img = self.clahe.apply(img)
        return Image.fromarray(img).convert("RGB")


class CannyTransform:
    def __init__(self, threshold1=100, threshold2=200):
        self.threshold1 = threshold1
        self.threshold2 = threshold2

    def __call__(self, img):
        img = np.array(img.convert("L"))
        edges = cv2.Canny(img, self.threshold1, self.threshold2)
        return Image.fromarray(edges).convert("RGB")


class RGBAtoRGBTransform:
    def __call__(self, img):
        if img.mode == "RGBA":
            background = Image.new("RGBA", img.size, (255, 255, 255, 255))
            img = Image.alpha_composite(background, img).convert("RGB")
        else:
            img = img.convert("RGB")
        return img


def get_image_files_from_zip(zip_file):
    """Returns a list of image file names in the ZIP file."""
    try:
        with zipfile.ZipFile(zip_file, "r") as zip_ref:
            file_list = [
                f for f in zip_ref.namelist() if f.lower().endswith(
                    (".png", ".jpg", ".jpeg", ".bmp", ".gif")
                )
            ]
        return file_list
    except zipfile.BadZipFile as exc:
        raise RuntimeError("Invalid ZIP file.") from exc
    except Exception as exc:
        raise RuntimeError("Error reading ZIP file.") from exc


def load_model(model_name, device):
    """Loads a specified torchvision model and
    modifies it for feature extraction."""
    if model_name not in AVAILABLE_MODELS:
        raise ValueError(
            f"Unsupported model: {model_name}. \
            Available models: {list(AVAILABLE_MODELS.keys())}")
    try:
        if "weights" in inspect.signature(
                AVAILABLE_MODELS[model_name]).parameters:
            model = AVAILABLE_MODELS[model_name](weights="DEFAULT").to(device)
        else:
            model = AVAILABLE_MODELS[model_name]().to(device)
        logging.info("Model loaded")
    except Exception as e:
        logging.error(f"Failed to load model {model_name}: {e}")
        raise

    if hasattr(model, "fc"):
        model.fc = torch.nn.Identity()
    elif hasattr(model, "classifier"):
        model.classifier = torch.nn.Identity()
    elif hasattr(model, "head"):
        model.head = torch.nn.Identity()

    model.eval()
    return model


def write_csv(output_csv, list_embeddings, ludwig_format=False):
    """Writes embeddings to a CSV file, optionally in Ludwig format."""
    with open(output_csv, mode="w", encoding="utf-8", newline="") as csv_file:
        csv_writer = csv.writer(csv_file)
        if list_embeddings:
            if ludwig_format:
                header = ["sample_name", "embedding"]
                formatted_embeddings = []
                for embedding in list_embeddings:
                    sample_name = embedding[0]
                    vector = embedding[1:]
                    embedding_str = " ".join(map(str, vector))
                    formatted_embeddings.append([sample_name, embedding_str])
                csv_writer.writerow(header)
                csv_writer.writerows(formatted_embeddings)
                logging.info("CSV created in Ludwig format")
            else:
                header = ["sample_name"] + [f"vector{i + 1}" for i in range(
                    len(list_embeddings[0]) - 1
                )]
                csv_writer.writerow(header)
                csv_writer.writerows(list_embeddings)
                logging.info("CSV created")
        else:
            csv_writer.writerow(["sample_name"] if not ludwig_format
                                else ["sample_name", "embedding"])
            logging.info("No valid images found. Empty CSV created.")


def extract_embeddings(
        model_name,
        apply_normalization,
        zip_file,
        file_list,
        transform_type="rgb"):
    """Extracts embeddings from images
    using batch processing or sequential fallback."""

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = load_model(model_name, device)
    model_settings = MODEL_DEFAULTS.get(model_name, MODEL_DEFAULTS["default"])
    resize = model_settings["resize"]
    normalize = model_settings.get("normalize", (
        [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
    ))

    # Define transform pipeline
    if transform_type == "grayscale":
        initial_transform = transforms.Grayscale(num_output_channels=3)
    elif transform_type == "clahe":
        initial_transform = CLAHETransform()
    elif transform_type == "edges":
        initial_transform = CannyTransform()
    elif transform_type == "rgba_to_rgb":
        initial_transform = RGBAtoRGBTransform()
    else:
        initial_transform = transforms.Lambda(lambda x: x.convert("RGB"))

    transform_list = [initial_transform,
                      transforms.Resize(resize),
                      transforms.ToTensor()]
    if apply_normalization:
        transform_list.append(transforms.Normalize(mean=normalize[0],
                                                   std=normalize[1]))
    transform = transforms.Compose(transform_list)

    class ImageDataset(Dataset):
        def __init__(self, zip_file, file_list, transform=None):
            self.zip_file = zip_file
            self.file_list = file_list
            self.transform = transform

        def __len__(self):
            return len(self.file_list)

        def __getitem__(self, idx):
            with zipfile.ZipFile(self.zip_file, "r") as zip_ref:
                with zip_ref.open(self.file_list[idx]) as file:
                    try:
                        image = Image.open(file)
                        if self.transform:
                            image = self.transform(image)
                        return image, os.path.basename(self.file_list[idx])
                    except Exception as e:
                        logging.warning(
                            "Skipping %s: %s", self.file_list[idx], e
                        )
                        return None, os.path.basename(self.file_list[idx])

    # Custom collate function
    def collate_fn(batch):
        batch = [item for item in batch if item[0] is not None]
        if not batch:
            return None, None
        images, names = zip(*batch)
        return torch.stack(images), names

    list_embeddings = []
    with torch.inference_mode():
        try:
            # Try DataLoader with reduced resource usage
            dataset = ImageDataset(zip_file, file_list, transform=transform)
            dataloader = DataLoader(
                dataset,
                batch_size=16,  # Reduced for lower memory usage
                num_workers=1,  # Reduced to minimize shared memory
                shuffle=False,
                pin_memory=True if device == "cuda" else False,
                collate_fn=collate_fn,
            )
            for images, names in dataloader:
                if images is None:
                    continue
                images = images.to(device)
                embeddings = model(images).cpu().numpy()
                for name, embedding in zip(names, embeddings):
                    list_embeddings.append([name] + embedding.tolist())
        except RuntimeError as e:
            logging.warning(
                f"DataLoader failed: {e}. \
                Falling back to sequential processing."
            )
            # Fallback to sequential processing
            for file in file_list:
                with zipfile.ZipFile(zip_file, "r") as zip_ref:
                    with zip_ref.open(file) as img_file:
                        try:
                            image = Image.open(img_file)
                            image = transform(image)
                            input_tensor = image.unsqueeze(0).to(device)
                            embedding = model(
                                input_tensor
                            ).squeeze().cpu().numpy()
                            list_embeddings.append(
                                [os.path.basename(file)] + embedding.tolist()
                            )
                        except Exception as e:
                            logging.warning("Skipping %s: %s", file, e)

    return list_embeddings


def main(zip_file, output_csv, model_name, apply_normalization=False,
         transform_type="rgb", ludwig_format=False):
    """Main entry point for processing the zip file and
    extracting embeddings."""
    file_list = get_image_files_from_zip(zip_file)
    logging.info("Image files listed from ZIP")

    list_embeddings = extract_embeddings(
        model_name,
        apply_normalization,
        zip_file,
        file_list,
        transform_type
    )
    logging.info("Embeddings extracted")
    write_csv(output_csv, list_embeddings, ludwig_format)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Extract image embeddings.")
    parser.add_argument(
        "--zip_file",
        required=True,
        help="Path to the ZIP file containing images."
    )
    parser.add_argument(
        "--model_name",
        required=True,
        choices=AVAILABLE_MODELS.keys(),
        help="Model for embedding extraction."
    )
    parser.add_argument(
        "--normalize",
        action="store_true",
        help="Whether to apply normalization."
    )
    parser.add_argument(
        "--transform_type",
        required=True,
        help="Image transformation type."
    )
    parser.add_argument(
        "--output_csv",
        required=True,
        help="Path to the output CSV file"
    )
    parser.add_argument(
        "--ludwig_format",
        action="store_true",
        help="Prepare CSV file in Ludwig input format"
    )

    args = parser.parse_args()
    main(
        args.zip_file,
        args.output_csv,
        args.model_name,
        args.normalize,
        args.transform_type,
        args.ludwig_format
    )