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ResNeXt101-32x4d for PyTorch

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Description

ResNet with bottleneck 3x3 Convolutions substituted by 3x3 Grouped Convolutions.Publisher

NVIDIA Deep Learning ExamplesLatest Version

21.03.1Modified

November 4, 2022Compressed Size

35.4 KBThis resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from NGC

The ResNeXt101-32x4d is a model introduced in the Aggregated Residual Transformations for Deep Neural Networks paper.

It is based on regular ResNet model, substituting 3x3 convolutions inside the bottleneck block for 3x3 grouped convolutions.

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 3x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

We use NHWC data layout when training using Mixed Precision.

*Image source: Aggregated Residual Transformations for Deep Neural Networks*

Image shows difference between ResNet bottleneck block and ResNeXt bottleneck block.

ResNeXt101-32x4d model's cardinality equals to 32 and bottleneck width equals to 4.

The following sections highlight the default configurations for the ResNeXt101-32x4d model.

This model uses SGD with momentum optimizer with the following hyperparameters:

- Momentum (0.875)
- Learning rate (LR) = 0.256 for 256 batch size, for other batch sizes we linearly scale the learning rate.
- Learning rate schedule - we use cosine LR schedule
- For bigger batch sizes (512 and up) we use linear warmup of the learning rate during the first couple of epochs according to Training ImageNet in 1 hour. Warmup length depends on the total training length.
- Weight decay (WD)= 6.103515625e-05 (1/16384).
- We do not apply WD on Batch Norm trainable parameters (gamma/bias)
- Label smoothing = 0.1
- We train for:
- 90 Epochs -> 90 epochs is a standard for ImageNet networks
- 250 Epochs -> best possible accuracy.

- For 250 epoch training we also use MixUp regularization.

This model uses the following data augmentation:

- For training:
- Normalization
- Random resized crop to 224x224
- Scale from 8% to 100%
- Aspect ratio from 3/4 to 4/3

- Random horizontal flip

- For inference:
- Normalization
- Scale to 256x256
- Center crop to 224x224

The following features are supported by this model:

Feature | ResNeXt101-32x4d |
---|---|

DALI | Yes |

APEX AMP | Yes |

NVIDIA DALI - DALI is a library accelerating data preparation pipeline. To accelerate your input pipeline, you only need to define your data loader with the DALI library. For more information about DALI, refer to the DALI product documentation.

APEX is a PyTorch extension that contains utility libraries, such as Automatic Mixed Precision (AMP), which require minimal network code changes to leverage Tensor Cores performance. Refer to the Enabling mixed precision section for more details.

We use NVIDIA DALI, which speeds up data loading when CPU becomes a bottleneck. DALI can use CPU or GPU, and outperforms the PyTorch native dataloader.

Run training with `--data-backends dali-gpu`

or `--data-backends dali-cpu`

to enable DALI.
For DGXA100 and DGX1 we recommend `--data-backends dali-cpu`

.

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:

- Porting the model to use the FP16 data type where appropriate.
- Adding loss scaling to preserve small gradient values.

The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.

For information about:

- How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
- Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
- APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch.

Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), a library from APEX that casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients. In PyTorch, loss scaling can be easily applied by using scale_loss() method provided by AMP. The scaling value to be used can be dynamic or fixed.

For an in-depth walk through on AMP, check out sample usage here. APEX is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage tensor cores performance.

To enable mixed precision, you can:

Import AMP from APEX:

`from apex import amp`

Wrap model and optimizer in amp.initialize:

`model, optimizer = amp.initialize(model, optimizer, opt_level="O1", loss_scale="dynamic")`

Scale loss before backpropagation:

`with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward()`

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.

TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.