PyTorch Tutorial: DALI Data Loader for TFRecord Data

In this tutorial, we'll explore how to use NVIDIA DALI (Data Loading Library) with PyTorch to efficiently load and preprocess TFRecord data for training deep learning models. DALI is an optimized data pipeline library designed for high-throughput data loading and preprocessing, making it particularly useful when dealing with large datasets.

Installation

Before we begin, make sure to install the required libraries:

pip install torch torchvision
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cudaXX (Replace XX with your CUDA version, e.g., nvidia-dali-cuda110 for CUDA 11.0)

Code Examples

1. Importing Libraries

import torch
import torchvision.transforms as transforms
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.fn as fn
import nvidia.dali.types as types

2. Defining the DALI Pipeline

class TFRecordPipeline(Pipeline):
    def __init__(self, batch_size, num_threads, device_id, data_path):
        super().__init__(batch_size, num_threads, device_id, seed=12)
        self.input = fn.readers.tfrecord(file_root=data_path, index_path=data_path+"/idx_files")
        self.decode = fn.decoders.image(output_type=types.RGB)
        self.resize = fn.resize(device="gpu", resize_x=224, resize_y=224)
        self.transpose = fn.transpose(device="gpu", perm=[2, 0, 1])
        self.normalize = fn.crop_mirror_normalize(
            device="gpu",
            dtype=types.FLOAT,
            mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
            std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
        )

    def define_graph(self):
        inputs, labels = self.input(name="Reader")
        images = self.decode(inputs)
        images = self.resize(images)
        images = self.transpose(images)
        images = self.normalize(images)
        return images, labels

3. Creating the Data Loader

def create_dali_data_loader(batch_size, num_threads, device_id, data_path):
    pipeline = TFRecordPipeline(batch_size=batch_size, num_threads=num_threads, device_id=device_id, data_path=data_path)
    pipeline.build()
    data_loader = DALIGenericIterator(
        pipelines=pipeline,
        output_map=["data", "label"],
        size=pipeline.epoch_size("Reader"),
        auto_reset=True,
        fill_last_batch=False,
    )
    return data_loader

4. Using the Data Loader in PyTorch

# Set your data path
data_path = "path/to/your/tfrecord_data"

# Create the DALI data loader
batch_size = 32
num_threads = 4
device_id = 0
data_loader = create_dali_data_loader(batch_size, num_threads, device_id, data_path)

# Iterate over the data loader in your training loop
for i, data in enumerate(data_loader):
    inputs = data["data"]
    labels = data["label"]
    # Your training logic here

Conclusion

By using NVIDIA DALI with PyTorch, you can significantly improve data loading and preprocessing performance for training your deep learning models with TFRecord data. This tutorial provides a basic example to get you started, but you can further customize the DALI pipeline to suit your specific data and model requirements.

Happy training with DALI and PyTorch!