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README.md |
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.
Table Of Contents
- Model overview
- Setup
- Quick Start Guide
- Advanced
- Performance
- Release notes
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.
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.
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 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.
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 DGXA100, 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.
- 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:
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()
Enabling TF32
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.
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 20.06-py3 NGC container or newer
- Supported GPUs:
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:
- Getting Started Using NVIDIA GPU Cloud
- Accessing And Pulling From The NGC Container Registry
- Running PyTorch
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.
To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the resnext101-32x4d model on the ImageNet dataset. For the specifics concerning training and inference, see the Advanced section.
-
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 (DGXA100/DGX1/DGX2, AMP/TF32/FP32, 90/250 Epochs),
run one of the scripts in the ./resnext101-32x4d/training
directory
called ./resnext101-32x4d/training/{AMP, TF32, FP32}/{DGXA100, DGX1, DGX2}_RNXT101-32x4d_{AMP, TF32, FP32}_{90,250}E.sh
.
Ensure ImageNet is mounted in the /imagenet
directory.
Example:
bash ./resnext101-32x4d/training/AMP/DGX1_RNXT101-32x4d_AMP_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|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>
- FP32
-
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>
- FP32
Use python ./main.py -h
to obtain the list of available options in the main.py
script.
Command-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] [--num-classes N] [-j N] [--epochs N]
[--run-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] [--seed SEED] [--gather-checkpoints]
[--raport-file RAPORT_FILE] [--evaluate] [--training-only]
[--no-checkpoints] [--checkpoint-filename CHECKPOINT_FILENAME]
[--workspace DIR] [--memory-format {nchw,nhwc}]
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)
--num-classes N number of classes in the dataset
-j N, --workers N number of data loading workers (default: 5)
--epochs N number of total epochs to run
--run-epochs N run only N epochs, used for checkpointing runs
-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.
--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
--checkpoint-filename CHECKPOINT_FILENAME
--workspace DIR path to directory where checkpoints will be stored
--memory-format {nchw,nhwc}
memory layout, nchw or nhwc
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 losstrain.total_ips
- training speed measured in images/secondtrain.compute_ips
- training speed measured in images/second, not counting data loadingtrain.data_time
- time spent on waiting on datatrain.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 lossval.top1
- validation top1 accuracyval.top5
- validation top5 accuracyval.total_ips
- inference speed measured in images/secondval.compute_ips
- inference speed measured in images/second, not counting data loadingval.data_time
- time spent on waiting on dataval.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 --precision AMP|
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 -b <batch_size> --training-only -p 1 --raport-file benchmark.json --epochs 1 --prof 100 <path to imagenet>
- AMP
python ./main.py --arch resnext101-32x4d -b <batch_size> --training-only -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --amp --static-loss-scale 256 --memory-format nhwc <path to imagenet>
- FP32
- For multiple GPUs
- FP32
python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d -b <batch_size> --training-only -p 1 --raport-file benchmark.json --epochs 1 --prof 100 <path to imagenet>
- AMP
python ./multiproc.py --nproc_per_node 8 ./main.py --arch resnext101-32x4d -b <batch_size> --training-only -p 1 --raport-file benchmark.json --amp --static-loss-scale 256 --epochs 1 --prof 100 --memory-format nhwc <path to imagenet>
- FP32
Each of these scripts will run 100 iterations and save results in the benchmark.json
file.
Batch size should be picked appropriately depending on the hardware configuration.
Platform | Precision | Batch Size |
---|---|---|
DGXA100 | AMP | 128 |
DGXA100 | TF32 | 128 |
DGX-1 | AMP | 128 |
DGX-1 | FP32 | 64 |
Inference performance benchmark
To benchmark inference, run:
- FP32
python ./main.py --arch resnext101-32x4d -b <batch_size> -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --evaluate <path to imagenet>
- AMP
python ./main.py --arch resnext101-32x4d -b <batch_size> -p 1 --raport-file benchmark.json --epochs 1 --prof 100 --evaluate --amp --memory-format nhwc <path to imagenet>
Each of these scripts will run 100 iterations and save results in the benchmark.json
file.
Batch size should be picked appropriately depending on the hardware configuration.
Platform | Precision | Batch Size |
---|---|---|
DGXA100 | AMP | 128 |
DGXA100 | TF32 | 128 |
DGX-1 | AMP | 128 |
DGX-1 | FP32 | 64 |
Results
Our results were obtained by running the applicable training script in the pytorch-20.06 NGC container.
To achieve these same results, follow the steps in the Quick Start Guide.
Training accuracy results
Training accuracy: NVIDIA DGX A100 (8x A100 40GB)
epochs | Mixed Precision Top1 | TF32 Top1 |
---|---|---|
90 | 79.37 +/- 0.13 | 79.38 +/- 0.13 |
Training accuracy: NVIDIA DGX-1 (8x V100 16GB)
epochs | Mixed Precision Top1 | FP32 Top1 |
---|---|---|
90 | 79.43 +/- 0.04 | 79.40 +/- 0.10 |
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
Training performance: NVIDIA DGX A100 (8x A100 40GB)
GPUs | Mixed Precision | TF32 | Mixed Precision Speedup | Mixed Precision Strong Scaling | Mixed Precision Training Time (90E) | TF32 Strong Scaling | TF32 Training Time (90E) |
---|---|---|---|---|---|---|---|
1 | 908.40 img/s | 300.42 img/s | 3.02x | 1.00x | ~37 hours | 1.00x | ~111 hours |
8 | 6887.59 img/s | 2380.51 img/s | 2.89x | 7.58x | ~5 hours | 7.92x | ~14 hours |
Training performance: NVIDIA DGX-1 16GB (8x V100 16GB)
GPUs | Mixed Precision | FP32 | Mixed Precision Speedup | Mixed Precision Strong Scaling | Mixed Precision Training Time (90E) | FP32 Strong Scaling | FP32 Training Time (90E) |
---|---|---|---|---|---|---|---|
1 | 534.91 img/s | 150.05 img/s | 3.56x | 1.00x | ~62 hours | 1.00x | ~222 hours |
8 | 4000.79 img/s | 1151.01 img/s | 3.48x | 7.48x | ~9 hours | 7.67x | ~29 hours |
Training performance: NVIDIA DGX-1 32GB (8x V100 32GB)
GPUs | Mixed Precision | FP32 | Mixed Precision Speedup | Mixed Precision Strong Scaling | Mixed Precision Training Time (90E) | FP32 Strong Scaling | FP32 Training Time (90E) |
---|---|---|---|---|---|---|---|
1 | 516.07 img/s | 139.80 img/s | 3.69x | 1.00x | ~65 hours | 1.00x | ~238 hours |
8 | 3861.95 img/s | 1070.94 img/s | 3.61x | 7.48x | ~9 hours | 7.66x | ~31 hours |
Inference performance results
Inference performance: NVIDIA DGX-1 (1x V100 16GB)
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
- July 2020
- Added A100 scripts
- Updated README
Known issues
There are no known issues with this model.