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ResNeXt101-32x4d For PyTorch

This repository provides a script and recipe to train the ResNeXt101-32x4d model to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA.

Model overview
Setup
- Requirements
Quick Start Guide
Advanced
- Scripts and sample code
Performance
Release notes
- Changelog
- Known issues

Model overview

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.

Model architecture

_ 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.

Default configuration

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

Optimizer

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 lineary 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.

Data augmentation

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

Feature support matrix

The following features are supported by this model:

Feature	ResNeXt101-32x4d
DALI	Yes
APEX AMP	Yes

Features

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.

DALI

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 ResNeXt101-32x4d, for DGX1 and DGX2 we recommend --data-backends dali-cpu.

Mixed precision training

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 the Volta and Turing architecture, 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.
How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.
APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch.

Enabling mixed precision

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, for example:
```
from apex import amp
```

Initialize an AMP handle, for example:

amp_handle = amp.init(enabled=True, verbose=True)

Wrap your optimizer with the AMP handle, for example:
```
optimizer = amp_handle.wrap_optimizer(optimizer)
```
Scale loss before backpropagation (assuming loss is stored in a variable called losses)
- Default backpropagate for FP32:
```
losses.backward()
```
- Scale loss and backpropagate with AMP:
```
with optimizer.scale_loss(losses) as scaled_losses:
   scaled_losses.backward()
```

Setup

The following section lists the requirements that you need to meet in order to start training the ResNeXt101-32x4d model.

Requirements

This repository contains Dockerfile which extends the PyTorch NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:

NVIDIA Docker
PyTorch 19.10-py3 NGC container or newer
NVIDIA Volta or Turing based GPU

For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning DGX Documentation:

For those unable to use the PyTorch NGC container, to set up the required environment or create your own container, see the versioned NVIDIA Container Support Matrix.

Quick Start Guide

1. Clone the repository.

git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/PyTorch/Classification/

2. Download and preprocess the dataset.

The ResNeXt101-32x4d script operates on ImageNet 1k, a widely popular image classification dataset from the ILSVRC challenge.

PyTorch can work directly on JPEGs, therefore, preprocessing/augmentation is not needed.

Download the images.
Extract the training data:

mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
cd ..

Extract the validation data and move the images to subfolders:

mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash

The directory in which the train/ and val/ directories are placed, is referred to as <path to imagenet> in this document.

3. Build the RNXT101-32x4d PyTorch NGC container.

docker build . -t nvidia_rnxt101-32x4d

4. Start an interactive session in the NGC container to run training/inference.

nvidia-docker run --rm -it -v <path to imagenet>:/imagenet --ipc=host nvidia_rnxt101-32x4d

5. Start training

To run training for a standard configuration (DGX1V, AMP/FP32, 90/250 Epochs), run one of the scripts in the ./resnext101-32x4d/training directory called ./resnext101-32x4d/training/{AMP,FP32}/{DGX1}_RNXT101-32x4d_{AMP, FP32}_{90,250}E.sh.

Ensure ImageNet is mounted in the /imagenet directory.

Example: bash ./resnext101-32x4d/training/DGX1_RNXT101-32x4d_FP16_250E.sh <path were to store checkpoints and logs>

6. Start inference

To run inference on ImageNet on a checkpointed model, run:

python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --resume <path to checkpoint> -b <batch size> <path to imagenet>

To run inference on JPEG image, you have to first extract the model weights from checkpoint:

python checkpoint2model.py --checkpoint-path <path to checkpoint> --weight-path <path where weights will be stored>

Then run classification script:

python classify.py --arch resnext101-32x4d -c fanin --weights <path to weights from previous step> --precision AMP|FP16|FP32 --image <path to JPEG image>

Advanced

The following sections provide greater details of the dataset, running training and inference, and the training results.

Scripts and sample code

To run a non standard configuration use:

For 1 GPU
- FP32 python ./main.py --arch resnext101-32x4d -c fanin --label-smoothing 0.1 <path to imagenet> python ./main.py --arch resnext101-32x4d -c fanin --label-smoothing 0.1 --amp --static-loss-scale 256 <path to imagenet>
For multiple GPUs
- FP32 python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d -c fanin --label-smoothing 0.1 <path to imagenet>
- AMP python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d -c fanin --label-smoothing 0.1 --amp --static-loss-scale 256 <path to imagenet>

Use python ./main.py -h to obtain the list of available options in the main.py script.

Commmand-line options:

To see the full list of available options and their descriptions, use the -h or --help command-line option, for example:

python main.py -h

usage: main.py [-h] [--data-backend BACKEND] [--arch ARCH]
               [--model-config CONF] [-j N] [--epochs N] [-b N]
               [--optimizer-batch-size N] [--lr LR] [--lr-schedule SCHEDULE]
               [--warmup E] [--label-smoothing S] [--mixup ALPHA]
               [--momentum M] [--weight-decay W] [--bn-weight-decay]
               [--nesterov] [--print-freq N] [--resume PATH]
               [--pretrained-weights PATH] [--fp16]
               [--static-loss-scale STATIC_LOSS_SCALE] [--dynamic-loss-scale]
               [--prof N] [--amp] [--local_rank LOCAL_RANK] [--seed SEED]
               [--gather-checkpoints] [--raport-file RAPORT_FILE] [--evaluate]
               [--training-only] [--no-checkpoints] [--workspace DIR]
               DIR

PyTorch ImageNet Training

positional arguments:
  DIR                   path to dataset

optional arguments:
  -h, --help            show this help message and exit
  --data-backend BACKEND
                        data backend: pytorch | syntetic | dali-gpu | dali-cpu
                        (default: dali-cpu)
  --arch ARCH, -a ARCH  model architecture: resnet18 | resnet34 | resnet50 |
                        resnet101 | resnet152 | resnext101-32x4d | se-
                        resnext101-32x4d (default: resnet50)
  --model-config CONF, -c CONF
                        model configs: classic | fanin | grp-fanin | grp-
                        fanout(default: classic)
  -j N, --workers N     number of data loading workers (default: 5)
  --epochs N            number of total epochs to run
  -b N, --batch-size N  mini-batch size (default: 256) per gpu
  --optimizer-batch-size N
                        size of a total batch size, for simulating bigger
                        batches using gradient accumulation
  --lr LR, --learning-rate LR
                        initial learning rate
  --lr-schedule SCHEDULE
                        Type of LR schedule: step, linear, cosine
  --warmup E            number of warmup epochs
  --label-smoothing S   label smoothing
  --mixup ALPHA         mixup alpha
  --momentum M          momentum
  --weight-decay W, --wd W
                        weight decay (default: 1e-4)
  --bn-weight-decay     use weight_decay on batch normalization learnable
                        parameters, (default: false)
  --nesterov            use nesterov momentum, (default: false)
  --print-freq N, -p N  print frequency (default: 10)
  --resume PATH         path to latest checkpoint (default: none)
  --pretrained-weights PATH
                        load weights from here
  --fp16                Run model fp16 mode.
  --static-loss-scale STATIC_LOSS_SCALE
                        Static loss scale, positive power of 2 values can
                        improve fp16 convergence.
  --dynamic-loss-scale  Use dynamic loss scaling. If supplied, this argument
                        supersedes --static-loss-scale.
  --prof N              Run only N iterations
  --amp                 Run model AMP (automatic mixed precision) mode.
  --local_rank LOCAL_RANK
                        Local rank of python process. Set up by distributed
                        launcher
  --seed SEED           random seed used for numpy and pytorch
  --gather-checkpoints  Gather checkpoints throughout the training, without
                        this flag only best and last checkpoints will be
                        stored
  --raport-file RAPORT_FILE
                        file in which to store JSON experiment raport
  --evaluate            evaluate checkpoint/model
  --training-only       do not evaluate
  --no-checkpoints      do not store any checkpoints, useful for benchmarking
  --workspace DIR       path to directory where checkpoints will be stored

Dataset guidelines

To use your own dataset, divide it in directories as in the following scheme:

Training images - train/<class id>/<image>
Validation images - val/<class id>/<image>

If your dataset's has number of classes different than 1000, you need to add a custom config in the image_classification/resnet.py file.

resnet_versions = {
    ...
    'resnext101-32x4d-custom' : {
        'net' : ResNet,
        'block' : Bottleneck,
        'cardinality' : 32,
        'layers' : [3, 4, 23, 3],
        'widths' : [128, 256, 512, 1024],
        'expansion' : 2,
        'num_classes' : <custom number of classes>,
    }
}

After adding the config, run the training script with --arch resnext101-32x4d-custom flag.

Training process

All the results of the training will be stored in the directory specified with --workspace argument. Script will store:

most recent checkpoint - checkpoint.pth.tar (unless --no-checkpoints flag is used).
checkpoint with best validation accuracy - model_best.pth.tar (unless --no-checkpoints flag is used).
JSON log - in the file specified with --raport-file flag.

Metrics gathered through training:

train.loss - training loss
train.total_ips - training speed measured in images/second
train.compute_ips - training speed measured in images/second, not counting data loading
train.data_time - time spent on waiting on data
train.compute_time - time spent in forward/backward pass

Inference process

Validation is done every epoch, and can be also run separately on a checkpointed model.

python ./main.py --arch resnext101-32x4d --evaluate --epochs 1 --resume <path to checkpoint> -b <batch size> <path to imagenet>

Metrics gathered through training:

val.loss - validation loss
val.top1 - validation top1 accuracy
val.top5 - validation top5 accuracy
val.total_ips - inference speed measured in images/second
val.compute_ips - inference speed measured in images/second, not counting data loading
val.data_time - time spent on waiting on data
val.compute_time - time spent on inference

To run inference on JPEG image, you have to first extract the model weights from checkpoint:

python checkpoint2model.py --checkpoint-path <path to checkpoint> --weight-path <path where weights will be stored>

Then run classification script:

python classify.py --arch resnext101-32x4d -c fanin --weights <path to weights from previous step> --precision AMP|FP16|FP32 --image <path to JPEG image>

Example output:

Performance

Benchmarking

The following section shows how to run benchmarks measuring the model performance in training and inference modes.

Training performance benchmark

To benchmark training, run:

For 1 GPU
- FP32 python ./main.py --arch resnext101-32x4d --training-only -p 1 --raport-file benchmark.json --epochs 1 --prof 100 <path to imagenet>
- FP16 python ./main.py --arch resnext101-32x4d --training-only -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --fp16 --static-loss-scale 256 <path to imagenet>
- AMP python ./main.py --arch resnext101-32x4d --training-only -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --amp --static-loss-scale 256 <path to imagenet>
For multiple GPUs
- FP32 python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d --training-only -p 1 --raport-file benchmark.json --epochs 1 --prof 100 <path to imagenet>
- FP16 python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d --training-only -p 1 --raport-file benchmark.json --fp16 --static-loss-scale 256 --epochs 1 --prof 100 <path to imagenet>
- AMP python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d --training-only -p 1 --raport-file benchmark.json --amp --static-loss-scale 256 --epochs 1 --prof 100 <path to imagenet>

Each of these scripts will run 100 iterations and save results in the benchmark.json file.

Inference performance benchmark

To benchmark inference, run:

FP32

python ./main.py --arch resnext101-32x4d -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --evaluate <path to imagenet>

FP16

python ./main.py --arch resnext101-32x4d -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --evaluate --fp16 <path to imagenet>

python ./main.py --arch resnext101-32x4d -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --evaluate --amp <path to imagenet>

Each of these scripts will run 100 iterations and save results in the benchmark.json file.

Results

Our results were obtained by running the applicable training script in the pytorch-19.10 NGC container.

To achieve these same results, follow the steps in the Quick Start Guide.

Training accuracy results

Training accuracy: NVIDIA DGX-1 (8x V100 16G)

epochs	Mixed Precision Top1	FP32 Top1
90	79.23 +/- 0.09	79.23 +/- 0.09
250	79.92 +/- 0.13	80.06 +/- 0.06

Example plots

The following images show a 250 epochs configuration on a DGX-1V.

Training performance results

Traininig performance: NVIDIA DGX1-16G (8x V100 16G)

GPUs	Mixed Precision	FP32	Mixed Precision speedup	Mixed Precision Strong Scaling	FP32 Strong Scaling
1	313.43 img/s	146.66 img/s	2.14x	1.00x	1.00x
8	2384.85 img/s	1116.58 img/s	2.14x	7.61x	7.61x

Traininig performance: NVIDIA DGX1-32G (8x V100 32G)

GPUs	Mixed Precision	FP32	Mixed Precision speedup	Mixed Precision Strong Scaling	FP32 Strong Scaling
1	297.83 img/s	143.27 img/s	2.08x	1.00x	1.00x
8	2270.85 img/s	1104.62 img/s	2.06x	7.62x	7.71x

Traininig performance: NVIDIA DGX2 (16x V100 32G)

GPUs	Mixed Precision	FP32	Mixed Precision speedup	Mixed Precision Strong Scaling	FP32 Strong Scaling
1	308.42 img/s	151.67 img/s	2.03x	1.00x	1.00x
16	4473.37 img/s	2261.97 img/s	1.98x	14.50x	14.91x

Training Time for 90 Epochs

Training time: NVIDIA DGX-1 (8x V100 16G)

GPUs	Mixed Precision training time	FP32 training time
1	~ 114 h	~ 242 h
8	~ 17 h	~ 34 h

Training time: NVIDIA DGX-2 (16x V100 32G)

GPUs	Mixed Precision training time	FP32 training time
1	~ 116 h	~ 234 h
16	~ 10 h	~ 18 h

Inference performance results

Inference performance: NVIDIA DGX-1 (1x V100 16G)

FP32 Inference Latency

batch size	Throughput Avg	Latency Avg	Latency 90%	Latency 95%	Latency 99%
1	47.34 img/s	21.02ms	23.41ms	24.55ms	26.00ms
2	89.68 img/s	22.14ms	22.90ms	24.86ms	26.59ms
4	175.92 img/s	22.57ms	24.96ms	25.53ms	26.03ms
8	325.69 img/s	24.35ms	25.17ms	25.80ms	28.52ms
16	397.04 img/s	40.04ms	40.01ms	40.08ms	40.32ms
32	431.77 img/s	73.71ms	74.05ms	74.09ms	74.26ms
64	485.70 img/s	131.04ms	131.38ms	131.53ms	131.81ms
128	N/A	N/A	N/A	N/A	N/A

Mixed Precision Inference Latency

batch size	Throughput Avg	Latency Avg	Latency 90%	Latency 95%	Latency 99%
1	43.11 img/s	23.05ms	25.19ms	25.41ms	26.63ms
2	83.29 img/s	23.82ms	25.11ms	26.25ms	27.29ms
4	173.67 img/s	22.82ms	24.38ms	25.26ms	25.92ms
8	330.18 img/s	24.05ms	26.45ms	27.37ms	27.74ms
16	634.82 img/s	25.00ms	26.93ms	28.12ms	28.73ms
32	884.91 img/s	35.71ms	35.96ms	36.01ms	36.13ms
64	998.40 img/s	63.43ms	63.63ms	63.75ms	63.96ms
128	1079.10 img/s	117.74ms	118.02ms	118.11ms	118.35ms

Inference performance: NVIDIA T4

FP32 Inference Latency

batch size	Throughput Avg	Latency Avg	Latency 90%	Latency 95%	Latency 99%
1	55.64 img/s	17.88ms	19.21ms	20.35ms	22.29ms
2	109.22 img/s	18.24ms	19.00ms	20.43ms	22.51ms
4	217.27 img/s	18.26ms	18.88ms	19.51ms	21.74ms
8	294.55 img/s	26.74ms	27.35ms	27.62ms	28.93ms
16	351.30 img/s	45.34ms	45.72ms	46.10ms	47.43ms
32	401.97 img/s	79.10ms	79.37ms	79.44ms	81.83ms
64	449.30 img/s	140.30ms	140.73ms	141.26ms	143.57ms
128	N/A	N/A	N/A	N/A	N/A

Mixed Precision Inference Latency

batch size	Throughput Avg	Latency Avg	Latency 90%	Latency 95%	Latency 99%
1	51.14 img/s	19.48ms	20.16ms	21.40ms	26.21ms
2	102.29 img/s	19.44ms	19.77ms	20.42ms	24.51ms
4	209.44 img/s	18.93ms	19.52ms	20.23ms	21.95ms
8	408.69 img/s	19.47ms	21.12ms	23.15ms	25.77ms
16	641.78 img/s	24.54ms	25.19ms	25.64ms	27.31ms
32	800.26 img/s	39.28ms	39.43ms	39.54ms	41.96ms
64	883.66 img/s	71.76ms	71.87ms	71.94ms	72.78ms
128	948.27 img/s	134.19ms	134.40ms	134.58ms	134.81ms

Release notes

Changelog

October 2019

Initial release

Known issues

There are no known issues with this model.

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ResNeXt101-32x4d For PyTorch

Table Of Contents

Model overview

Model architecture

Default configuration

Optimizer

Data augmentation

Feature support matrix

Features

DALI

Mixed precision training

Enabling mixed precision

Setup

Requirements

Quick Start Guide

1. Clone the repository.

2. Download and preprocess the dataset.

3. Build the RNXT101-32x4d PyTorch NGC container.

4. Start an interactive session in the NGC container to run training/inference.

5. Start training

6. Start inference

Advanced

Scripts and sample code

Commmand-line options:

Dataset guidelines

Training process

Inference process

Performance

Benchmarking

Training performance benchmark

Inference performance benchmark

Results

Training accuracy results

Training accuracy: NVIDIA DGX-1 (8x V100 16G)

Example plots

Training performance results

Traininig performance: NVIDIA DGX1-16G (8x V100 16G)

Traininig performance: NVIDIA DGX1-32G (8x V100 32G)

Traininig performance: NVIDIA DGX2 (16x V100 32G)

Training Time for 90 Epochs

Training time: NVIDIA DGX-1 (8x V100 16G)

Training time: NVIDIA DGX-2 (16x V100 32G)

Inference performance results

Inference performance: NVIDIA DGX-1 (1x V100 16G)

FP32 Inference Latency

Mixed Precision Inference Latency

Inference performance: NVIDIA T4

FP32 Inference Latency

Mixed Precision Inference Latency

Release notes

Changelog

Known issues

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