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.
The documentation for this project can be found here.
Since it minimizes the use of the CPU and of the GPU computing units, it is 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 2.4x the amount of images loaded, preprocessed then processed by the model when using a single CPU and GPU:
| Data loader | CPU | CPU Workers | GPU | GPU compute speed | Pipeline effective speed |
|---|---|---|---|---|---|
| PyTorch ImageFolder | Intel Xeon E5-2623* | 2 | Tesla V100* | 1050 img/s | 400 img/s |
| Benzina | Intel Xeon E5-2623* | 1 | Tesla V100* | 1050 img/s | 960 img/s |
Note
- Intel Xeon E5-2623 is the Xeon E5-2623 v3 @ 3.00 GHz version
- Tesla V100 is the Tesla V100 PCIE 16GB version
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
dataset = bz.ImageNet("path/to/data")
indices = list(range(len(dataset)))
n_valid = 50000
n_test = 100000
n_train = len(dataset) - n_valid - n_test
train_sampler = torch.utils.data.SubsetRandomSampler(indices[:n_train])
valid_sampler = torch.utils.data.SubsetRandomSampler(indices[n_train:-n_test])
# Dataloaders
bias = ops.ConstantBiasTransform(bias=(123.675, 116.28 , 103.53))
std = ops.ConstantNormTransform(norm=(58.395, 57.12 , 57.375))
train_dataloader = bz.DataLoader(
dataset,
batch_size=256,
sampler=train_sampler,
seed=seed,
shape=(224, 224),
bias_transform=bias,
norm_transform=std,
warp_transform=ops.SimilarityTransform(flip_h=0.5))
valid_dataloader = bz.DataLoader(
dataset,
batch_size=512,
sampler=valid_sampler,
seed=seed,
shape=(224, 224),
bias_transform=bias,
norm_transform=std,
warp_transform=ops.SimilarityTransform())
for epoch in range(1, 10):
# train for one epoch
train(train_dataloader, ...)
# evaluate on validation set
accuracy = validate(valid_dataloader, ...)
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