Бензина / Benzina
Description of the project
Benzina is an image loading library that accelerates image loading and preprocessing by making use of the hardware decoder in NVIDIA's GPUs.
Since it minimize the use of the CPU and of the GPU computing units, it's easier to reach saturation of GPU computing power / CPU. In our tests using ResNet18 models in PyTorch on the ImageNet 2012 dataset, we could observe an increase by 1.8x the amount of images loaded, preprocessed then processed by the model when using a single CPU and GPU:
| Data Loader | CPU | CPU Workers | CPU Usage | GPU | Batch Size | Pipeline Speed |
|---|---|---|---|---|---|---|
| Benzina | Intel Xeon 2698* | 1 | 33% | Tesla V100* | 256 | 525 img/s |
| PyTorch ImageFolder | Intel Xeon 2698* | 2 | 100% | Tesla V100* | 256 | 290 img/s |
| PyTorch ImageFolder | Intel Xeon 2698* | 4 | 100% | Tesla V100* | 256 | 395 img/s |
| PyTorch ImageFolder | Intel Xeon 2698* | 6 | 100% | Tesla V100* | 256 | 425 img/s |
| DALI | Intel Xeon 2698* | 1 | 100% | Tesla V100* | 256 | 575 img/s |
Note
- Intel Xeon 2698 is the Intel Xeon E5-2698 v4 @ 2.20GHz version
- Tesla V100 is the Tesla V100 SXM2 16GB version
While DALI currently outperforms Benzina, the speedup can only be seen on JPEGs through the nvJPEG decoder. Benzina requires to transcode the input dataset to H.265 but then the gain can be seen on all type of images as well as providing the dataset in a format that is easier to distribute.
The name "Benzina" is a phonetic transliteration of the Ukrainian word "Бензина", meaning "gasoline" (or "petrol").
ImageNet loading in PyTorch
As long as your dataset is converted into Benzina's data format, you can load it to train a PyTorch model in a few lines of code. Here is an example demonstrating how this can be done with an ImageNet dataset. It is based on the ImageNet example from PyTorch
import torch
import benzina.torch as bz
import benzina.torch.operations as ops
seed = 1234
torch.manual_seed(seed)
# Dataset
train_dataset = bz.dataset.ImageNet("path/to/dataset", split="train")
val_dataset = bz.dataset.ImageNet("path/to/dataset", split="val")
# Dataloaders
bias = ops.ConstantBiasTransform(bias=(0.485 * 255, 0.456 * 255, 0.406 * 255))
std = ops.ConstantNormTransform(norm=(0.229 * 255, 0.224 * 255, 0.225 * 255))
train_loader = bz.DataLoader(
train_dataset,
shape=(224, 224),
batch_size=256,
shuffle=True,
seed=seed,
bias_transform=bias,
norm_transform=std,
warp_transform=ops.SimilarityTransform(scale=(0.08, 1.0),
ratio=(3./4., 4./3.),
flip_h=0.5,
random_crop=True))
val_loader = bz.DataLoader(
val_dataset,
shape=(224, 224),
batch_size=256,
shuffle=False,
seed=seed,
bias_transform=bias,
norm_transform=std,
warp_transform=ops.CenterResizedCrop(224/256)))
for epoch in range(1, 10):
# train for one epoch
train(train_dataloader, ...)
# evaluate on validation set
accuracy = validate(valid_dataloader, ...)