Dynamic Mode

Data loading: MXNet recordIO

Overview

This example shows you how to use the data that is stored in the MXNet recordIO format with DALI.

Creating an Index

To use data that is stored in the recordIO format, we need to use the readers.MXNet operator. In addition to the arguments that are common to all readers, such as random_shuffle, this operator takes the path and index_path arguments:

• path is the list of paths to recordIO files

• index_path is a list (of size 1) that contains the path to the index file. This file, with .idx extension, is automatically created when you use MXNet’s im2rec.py utility; and can also be obtained from the recordIO file by using the rec2idx utility that is included with DALI.

The DALI_EXTRA_PATH environment variable should point to the location where data from DALI extra repository is downloaded.

Important: Ensure that you check out the correct release tag that corresponds to the installed version of DALI.

import os
from pathlib import Path

import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np

import nvidia.dali.experimental.dynamic as ndd

dali_extra_dir = Path(os.environ["DALI_EXTRA_PATH"])
base = dali_extra_dir / "db" / "recordio"
batch_size = 16

idx_files = [base / "train.idx"]
rec_files = [base / "train.rec"]

Reading and Processing Images

1. Read the images stored in the recordIO format, decode them and prepare them for ingestion in a DL framework.

   Processing images involves cropping, normalizing, and HWC -> CHW conversion process.

reader = ndd.readers.MXNet(path=rec_files, index_path=idx_files)

for jpegs, labels in reader.next_epoch(batch_size=batch_size):
    images = ndd.decoders.image(jpegs, device="gpu")
    output = ndd.crop_mirror_normalize(
        images,
        dtype=ndd.float32,
        crop=(224, 224),
        mean=[0.0, 0.0, 0.0],
        std=[1.0, 1.0, 1.0],
    )
    break  # Run once

2. To visualize the results, use the matplotlib library, which expects images in HWC format, but the output is in CHW.

   Note: CHW is the preferred format for most Deep Learning frameworks.

3. For the visualization purposes, transpose the images back to the HWC layout.

def show_images(image_batch: ndd.Batch):
    columns = 4
    rows = (batch_size + 1) // columns
    fig = plt.figure(figsize=(20, (20 // columns) * rows))
    gs = gridspec.GridSpec(rows, columns)
    for j, img in enumerate(image_batch):
        plt.subplot(gs[j])
        plt.axis("off")
        img_chw = np.asarray(img.cpu())
        img_hwc = np.transpose(img_chw, (1, 2, 0)) / 255.0
        plt.imshow(img_hwc)
    plt.show()

show_images(output)