ResNet50 v1.5 For PaddlePaddle

This repository provides a script and recipe to train the ResNet50 model to achieve state-of-the-art accuracy. The content of this repository is tested and maintained by NVIDIA.

Table Of Contents

Model overview
Setup
- Requirements
Quick Start Guide
Advanced
Performance
- Benchmarking
  - Training performance benchmark
  - Inference performance benchmark
- Results
Release notes
- Changelog
- Known issues

Model overview

The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model.

The difference between v1 and v1.5 is that in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.

This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1 but comes with a small performance drawback (~5% imgs/sec).

The model is initialized as described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification

This model is trained with mixed precision using Tensor Cores on the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x 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.

Default configuration

The following sections highlight the default configurations for the ResNet50 model.

Optimizer

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

Momentum (0.875)
Learning rate (LR) = 0.256 for 256 global batch size, for other batch sizes we linearly scale the learning rate. For example, default LR = 2.048 for 2048 global batch size on 8xA100. (256 batch size per GPU)
Learning rate schedule - we use cosine LR schedule
Linear warmup of the learning rate during the first 5 epochs according to Training ImageNet in 1 hour.
Weight decay (WD)= 3.0517578125e-05 (1/32768).
We do not apply WD on Batch Norm trainable parameters (gamma/bias)
Label smoothing = 0.1
We train for:
- 50 Epochs -> configuration that reaches 75.9% top1 accuracy
- 90 Epochs -> configuration that reaches 76.9% top1 accuracy (90 epochs is a standard for ImageNet networks)

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

Other training recipes

This script does not target any specific benchmark. There are changes that others have made which can speed up convergence and/or increase accuracy.

One of the more popular training recipes is provided by fast.ai.

The fast.ai recipe introduces many changes to the training procedure, one of which is progressive resizing of the training images.

The first part of training uses 128px images, the middle part uses 224px images, and the last part uses 288px images. The final validation is performed on 288px images.

The training script in this repository performs validation on 224px images, just like the original paper described.

These two approaches can't be directly compared, since the fast.ai recipe requires validation on 288px images, and this recipe keeps the original assumption that validation is done on 224px images.

Using 288px images means that more FLOPs are needed during inference to reach the same accuracy.

Feature support matrix

This model supports the following features:

Feature	ResNet50
DALI	Yes
Paddle AMP	Yes
Paddle ASP	Yes
PaddleSlim QAT	Yes
Paddle-TRT	Yes

Features

NVIDIA DALI - DALI is a library accelerating the 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.
Paddle AMP is a PaddlePaddle built-in module that provides functions to construct AMP workflow. The details can be found in Automatic Mixed Precision (AMP), which requires minimal network code changes to leverage Tensor Cores performance. Refer to the Enabling mixed precision section for more details.
Paddle ASP is a PaddlePaddle built-in module that provides functions to enable automatic sparsity workflow with only a few code line insertions. The full APIs can be found in Paddle.static.sparsity. Paddle ASP support, currently, static graph mode only (Dynamic graph support is under development). Refer to the Enable Automatic SParsity section for more details.
PaddleSlim is a set of tools based on PaddlePaddle for model acceleration, quantization, pruning, and knowledge distillation. For model quantization, PaddleSlim offers simple and user-friendly APIs for quantization aware training. The full APIs can be found in Quantization aware training. PaddleSlim currently supports updating gradients and scales simultaneously during quantization aware training (Training with fixed scales is still under development). Refer to the Enable quantization aware training section for more details.
Paddle-TRT is a PaddlePaddle inference integration with TensorRT. It selects subgraphs to be accelerated by TensorRT, while leaving the rest of the operations to be executed natively by PaddlePaddle. Refer to the Inference with TensorRT section for more details.

DALI

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

For data loaders, we only support DALI as data loader for now.

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 NVIDIA Volta, and following with both the NVIDIA Turing and NVIDIA 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 in PaddlePaddle, refer to the Mixed Precision Training paper and Automatic Mixed Precision Training documentation.
Techniques used for mixed precision training, refer to the Mixed-Precision Training of Deep Neural Networks blog.

Enabling mixed precision

Mixed precision is enabled in Paddle by using the Automatic Mixed Precision (AMP) 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 PaddlePaddle, loss scaling can be easily applied by passing in arguments to GradScaler(). The scaling value to be used can be dynamic or fixed.

For an in-depth walk through on AMP, check out sample usage here. Paddle AMP is a PaddlePaddle built-in module that provides functions to construct AMP workflow. The details can be found in Automatic Mixed Precision (AMP), which requires minimal network code changes to leverage Tensor Cores performance.

Code example to enable mixed precision for static graph:

Use paddle.static.amp.decorate to wrap optimizer

import paddle.static.amp as amp
mp_optimizer = amp.decorate(optimizer=optimizer, init_loss_scaling=8.0)

Minimize loss , and get scaled_loss, which is useful when you need customized loss.

ops, param_grads = mp_optimizer.minimize(loss)
scaled_loss = mp_optimizer.get_scaled_loss()

For distributed training, it is recommended to use Fleet to enable amp, which is a unified API for distributed training of PaddlePaddle. For more information, refer to Fleet

import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.amp = True # by default this is false
optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)

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 a 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.

Automatic SParsity

Automatic SParsity (ASP) provides a workflow to transfer deep learning models from dense to 2:4 structured sparse, that allows that inference leverage NVIDIA's Sparse Tensor Core, introduced in Ampere architecture, to theoretically reach 2x speedup and save almost 50% memory usage. The workflow of ASP generally includes two steps:

Prune well-trained dense models to 2:4 sparse.
Retrain sparse model with same hyper-parameters to recover accuracy.

For more information, refer to

Enable Automatic SParsity

There is a built-in module in PaddlePaddle to enable ASP training, which only needs to insert a couple of lines in the original codebase optimizer decoration and model pruning.

optimizer = sparsity.decorate(optimizer)
...
sparsity.prune_model(main_program)

Moreover, ASP is also compatible with mixed precision training.

Note that currently ASP only supports static graphs (Dynamic graph support is under development).

Quantization Aware Training

Quantization aware training (QAT) is a technique to train models with the awareness of the quantization process. Quantization refers to reducing the precision of numerical values in a model, typically from floating-point to lower-bit fixed-point representations. In QAT, during the training process, the model is trained to accommodate the effects of quantization, enabling it to maintain performance even when deployed with reduced precision. Through PaddleSlim QAT, we can quantize models by the following steps:

quantize and dequantize the weights and inputs before feeding them into weighted-layers (ex. Convolution and Fullyconnected)
record the scale of each tensor for use in low precision inference

For more information, refer to

INTEGER QUANTIZATION FOR DEEP LEARNING INFERENCE: PRINCIPLES AND EMPIRICAL EVALUATION

Enable Quantization Aware Training

PaddlePaddle integrates some QAT modules from PaddleSlim, a toolkit for deep learning model compression, to enable QAT training. The APIs can quantize a train program and also convert it into an INT8 inference model.

quant_program = quanter.quant_aware(program)
...
quant_infer_program = quanter.convert(quant_program)

The detailed information on QAT API can be found in quantization_aware_tutorial.

Moreover, QAT is also compatible with mixed precision training.

Setup

The following section lists the requirements you need to meet to start training the ResNet50 model.

Requirements

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

NVIDIA Docker
PaddlePaddle 23.12-py3 NGC container or newer
Supported GPUs:
- NVIDIA Ampere architecture

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

Quick Start Guide

1. Clone the repository.

git clone https://github.com/NVIDIA/DeepLearningExamples.git
cd DeepLearningExamples/PaddlePaddle/Classification/RN50v1.5

2. Download and preprocess the dataset.

The ResNet50 script operates on ImageNet 1k, a widely popular image classification dataset from the ILSVRC challenge.

Paddle 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 resnet50 model on the ImageNet dataset. For the specifics concerning training and inference, refer to the Advanced section.

Download the images.
Extract the training data:

cd <path to imagenet>
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 ResNet50 PaddlePaddle NGC container.

docker build . -t nvidia_resnet50

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 --e FLAGS_apply_pass_to_program=1  nvidia_resnet50

5. Start training

To run training for a standard configuration (DGXA100, AMP/TF32), use one of the scripts in scripts/training to launch training. (Please ensure ImageNet is mounted in the /imagenet directory.)

Example:

# For TF32 and 8 GPUs training in 90 epochs
bash scripts/training/train_resnet50_TF32_90E_DGXA100.sh

# For AMP and 8 GPUs training in 90 epochs
bash scripts/training/train_resnet50_AMP_90E_DGXA100.sh

Or you can manually launch training by paddle.distributed.launch. paddle.distributed.launch is a built-in module in PaddlePaddle that spawns up multiple distributed training processes on each of the training nodes.

Example:

# For single GPU training with AMP
python -m paddle.distributed.launch --gpus=0 train.py \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC

# For 8 GPUs training with AMP
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC

Note that for initializing training with checkpoints or pretrained parameters, refer to Training process for more details.

6. Start validation/evaluation.

To evaluate the validation dataset located in /imagenet/val, you need to specify the pretrained weights by --from-pretrained-params and set eval_only to --run-scope.

You can download pretrained weights from NGC:

wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/dle/resnet_50_paddle_ckpt/versions/22.05.0_amp/zip -O resnet_50_paddle_ckpt_22.05.0_amp.zip
unzip -d <path_to_downloaded_ckpt> resnet_50_paddle_ckpt_22.05.0_amp.zip

This checkpoint is well pretrained on the ImageNet dataset with AMP mode. It achieves 77.11% top 1 accuracy on the test dataset.

Example:

TF32

# For single GPU evaluation
python -m paddle.distributed.launch --gpus=0 train.py \
  --from-pretrained-params <path_to_downloaded_ckpt> \
  --run-scope eval_only

# For 8 GPUs evaluation
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --from-pretrained-params <path_to_downloaded_ckpt> \
  --run-scope eval_only

AMP

# For single GPU evaluation
python -m paddle.distributed.launch --gpus=0 train.py \
  --from-pretrained-params <path_to_downloaded_ckpt> \
  --run-scope eval_only \
  --amp \
  --data-layout NHWC

# For 8 GPUs evaluation
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --from-pretrained-params <path_to_downloaded_ckpt> \
  --run-scope eval_only \
  --amp \
  --data-layout NHWC

We also provide scripts to inference with TensorRT that could reach better performance. Refer to Inference process in Advanced for more details.

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 single GPU evaluation
python -m paddle.distributed.launch --gpus=0 train.py

# For 8 GPUs evaluation
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py

Command-line options:

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

python train.py -h

PaddlePaddle RN50v1.5 training script

optional arguments:
  -h, --help            show this help message and exit

General:
  --checkpoint-dir CHECKPOINT_DIR
                        A path to store trained models. (default: ./checkpoint)
  --inference-dir INFERENCE_DIR
                        A path to store inference model once the training is finished. (default: ./inference/)
  --run-scope {train_eval,train_only,eval_only}
                        Running scope. It should be one of {train_eval, train_only, eval_only}. (default: train_eval)
  --epochs EPOCHS       The number of epochs for training. (default: 90)
  --save-interval SAVE_INTERVAL
                        The iteration interval to save checkpoints. (default: 1)
  --eval-interval EVAL_INTERVAL
                        The iteration interval to test trained models on a given validation dataset. Ignored when --run-scope is
                        train_only. (default: 1)
  --print-interval PRINT_INTERVAL
                        The iteration interval to show a training/evaluation message. (default: 10)
  --report-file REPORT_FILE
                        A file in which to store JSON experiment reports. (default: ./train.json)
  --benchmark           To enable benchmark mode. (default: False)
  --benchmark-steps BENCHMARK_STEPS
                        Steps for benchmark run, only be applied when --benchmark is set. (default: 100)
  --benchmark-warmup-steps BENCHMARK_WARMUP_STEPS
                        Warmup steps for benchmark run, only be applied when --benchmark is set. (default: 100)
  --model-prefix MODEL_PREFIX
                        The prefix name of model files to save/load. (default: resnet_50_paddle)
  --from-pretrained-params FROM_PRETRAINED_PARAMS
                        A folder path which contains pretrained parameters, that is a file in name --model-prefix + .pdparams. It should
                        not be set with --from-checkpoint at the same time. (default: None)
  --from-checkpoint FROM_CHECKPOINT
                        A checkpoint path to resume training. It should not be set with --from-pretrained-params at the same time. The
                        path provided could be a folder contains < epoch_id/ckpt_files > or < ckpt_files >. (default: None)
  --last-epoch-of-checkpoint LAST_EPOCH_OF_CHECKPOINT
                        The epoch id of the checkpoint given by --from-checkpoint. It should be None, auto or integer >= 0. If it is set
                        as None, then training will start from 0-th epoch. If it is set as auto, then it will search largest integer-
                        convertible folder --from-checkpoint, which contains the required checkpoint. Default is None. (default: None)
  --show-config SHOW_CONFIG
                        To show arguments. (default: True)
  --enable-cpu-affinity ENABLE_CPU_AFFINITY
                        To enable in-built GPU-CPU affinity. (default: True)

Dataset:
  --image-root IMAGE_ROOT
                        A root folder of train/val images. It should contain train and val folders, which store corresponding images.
                        (default: /imagenet)
  --image-shape IMAGE_SHAPE
                        The image shape. Its shape should be [channel, height, width]. (default: [4, 224, 224])
  --batch-size BATCH_SIZE
                        The batch size for both training and evaluation. (default: 256)
  --dali-random-seed DALI_RANDOM_SEED
                        The random seed for DALI data loader. (default: 42)
  --dali-num-threads DALI_NUM_THREADS
                        The number of threads applied to the DALI data loader. (default: 4)
  --dali-output-fp16    Output FP16 data from DALI data loader. (default: False)

Data Augmentation:
  --crop-size CROP_SIZE
                        The size to crop input images. (default: 224)
  --rand-crop-scale RAND_CROP_SCALE
                        Range from which to choose a random area fraction. (default: [0.08, 1.0])
  --rand-crop-ratio RAND_CROP_RATIO
                        Range from which to choose a random aspect ratio (width/height). (default: [0.75, 1.3333333333333333])
  --normalize-scale NORMALIZE_SCALE
                        A scalar to normalize images. (default: 0.00392156862745098)
  --normalize-mean NORMALIZE_MEAN
                        The mean values to normalize RGB images. (default: [0.485, 0.456, 0.406])
  --normalize-std NORMALIZE_STD
                        The std values to normalize RGB images. (default: [0.229, 0.224, 0.225])
  --resize-short RESIZE_SHORT
                        The length of the shorter dimension of the resized image. (default: 256)

Model:
  --model-arch-name MODEL_ARCH_NAME
                        The model architecture name. It should be one of {ResNet50}. (default: ResNet50)
  --num-of-class NUM_OF_CLASS
                        The number classes of images. (default: 1000)
  --data-layout {NCHW,NHWC}
                        Data format. It should be one of {NCHW, NHWC}. (default: NCHW)
  --bn-weight-decay     Apply weight decay to BatchNorm shift and scale. (default: False)

Training:
  --label-smoothing LABEL_SMOOTHING
                        The ratio of label smoothing. (default: 0.1)
  --optimizer OPTIMIZER
                        The name of optimizer. It should be one of {Momentum}. (default: Momentum)
  --momentum MOMENTUM   The momentum value of an optimizer. (default: 0.875)
  --weight-decay WEIGHT_DECAY
                        The coefficient of weight decay. (default: 3.0517578125e-05)
  --lr-scheduler LR_SCHEDULER
                        The name of the learning rate scheduler. It should be one of {Cosine}. (default: Cosine)
  --lr LR               The initial learning rate. (default: 0.256)
  --warmup-epochs WARMUP_EPOCHS
                        The number of epochs for learning rate warmup. (default: 5)
  --warmup-start-lr WARMUP_START_LR
                        The initial learning rate for warm up. (default: 0.0)

Advanced Training:
  --amp                 Enable automatic mixed precision training (AMP). (default: False)
  --scale-loss SCALE_LOSS
                        The loss scalar for AMP training, only be applied when --amp is set. (default: 1.0)
  --use-dynamic-loss-scaling
                        Enable dynamic loss scaling in AMP training, only be applied when --amp is set. (default: False)
  --use-pure-fp16       Enable pure FP16 training, only be applied when --amp is set. (default: False)
  --fuse-resunit        Enable CUDNNv8 ResUnit fusion, only be applied when --amp is set. (default: False)
  --asp                 Enable automatic sparse training (ASP). (default: False)
  --prune-model         Prune model to 2:4 sparse pattern, only be applied when --asp is set. (default: False)
  --mask-algo {mask_1d,mask_2d_greedy,mask_2d_best}
                        The algorithm to generate sparse masks. It should be one of {mask_1d, mask_2d_greedy, mask_2d_best}. This only
                        be applied when --asp and --prune-model is set. (default: mask_1d)
  --qat                 Enable quantization aware training (QAT). (default: False)

python inference.py -h

Paddle-TRT:
  --device DEVICE_ID
                        The GPU device id for Paddle-TRT inference. (default: 0)
  --inference-dir INFERENCE_DIR
                        A path to load inference models. (default: ./inference)
  --batch-size BATCH_SIZE
                        The batch size for Paddle-TRT. (default: 256)
  --image-shape IMAGE_SHAPE
                        The image shape. Its shape should be [channel, height, width]. (default: [4, 224, 224])
  --data-layout {NCHW,NHWC}
                        Data format. It should be one of {NCHW, NHWC}. (default: NCHW)
  --precision {FP32,FP16,INT8}
                        The precision of TensorRT. It should be one of {FP32, FP16, INT8}. (default: FP32)
  --workspace-size WORKSPACE_SIZE
                        The memory workspace of TensorRT in MB. (default: 1073741824)
  --min-subgraph-size MIN_SUBGRAPH_SIZE
                        The minimal subgraph size to enable PaddleTRT. (default: 3)
  --use-static USE_STATIC
                        Fix TensorRT engine at first running. (default: False)
  --use-calib-mode USE_CALIB_MODE
                        Use the PTQ calibration of PaddleTRT int8. (default: False)
  --report-file REPORT_FILE
                        A file in which to store JSON experiment report. (default: ./inference.json)
  --use-synthetic USE_SYNTHAT
                        Apply synthetic data for benchmark. (default: False)
  --benchmark-steps BENCHMARK_STEPS
                        Steps for benchmark run, only be applied when --benchmark is set. (default: 100)
  --benchmark-warmup-steps BENCHMARK_WARMUP_STEPS
                        Warmup steps for benchmark run, only be applied when --benchmark is set. (default: 100)
  --show-config SHOW_CONFIG
                        To show arguments. (default: True)

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 the number of classes in your dataset is not 1000, you need to specify it to --num-of-class.

Training process

The checkpoint will be stored in the directory specified with --checkpoint-dir and --model-arch-name, including three files:

.pdparams: The parameters contain all the trainable tensors and will save to a file with the suffix “.pdparams”.
.pdopts: The optimizer information contains all the Tensors used by the optimizer. For Adam optimizer, it contains beta1, beta2, momentum, and so on. All the information will be saved to a file with the suffix “.pdopt”. (If the optimizer has no Tensor need to save (like SGD), the file will not be generated).
.pdmodel: The network description is the description of the program. It’s only used for deployment. The description will save to a file with the suffix “.pdmodel”.

The prefix of model files is specified by --model-prefix, whose default value is resnet_50_paddle. Model of each epoch would be stored in directory ./checkpoint/ResNet50/epoch_id/ with three files by default, including resnet_50_paddle.pdparams, resnet_50_paddle.pdopts, resnet_50_paddle.pdmodel. Note that epoch_id is 0-based, which means epoch_id is from 0 to 89 for a total of 90 epochs. For example, the model of the 89th epoch would be stored in ./output/ResNet50/89/resnet_50_paddle

When the training phase is done, the inference model will be stored in the directory specified with --inference-dir and --model-arch-name, and it includes .pdmodel and .pdparams two files.

Assume you want to train the ResNet50 for 90 epochs, but the training process aborts during the 50th epoch due to infrastructure faults. To resume training from the checkpoint, specify --from-checkpoint and --last-epoch-of-checkpoint with following these steps:

Set ./checkpoint/ResNet50/49 to --from-checkpoint.
Set --last-epoch-of-checkpoint to 49. Then rerun the training to resume training from the 50th epoch to the 89th epoch.

Example:

# Resume AMP training from checkpoint of 50-th epoch
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC \
  --model-prefix resnet_50_paddle \
  --from-checkpoint ./checkpoint/ResNet50/49 \
  --last-epoch-of-checkpoint 49

We also provide automatic searching for the checkpoint from last epoch. You can enable this by setting --last-epoch-of-checkpoint as auto. Note that if you enable automatic searching, --from-checkpoint should be a folder containing checkpoint files or <epoch_id>/<ckpt_files>. In previous example, it should be ./checkpoint/ResNet50.

Example:

# Resume AMP training from checkpoint with automatic searching
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC \
  --model-prefix resnet_50_paddle \
  --from-checkpoint ./checkpoint/ResNet50 \
  --last-epoch-of-checkpoint auto

To start training from pretrained weights, set --from-pretrained-params to ./checkpoint/ResNet50/<epoch_id>/<--model-prefix>.

Example:

# Train AMP with model initialization by <./your_own_path_to/resnet_50_paddle>
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC \
  --model-prefix resnet_50_paddle \
  --from-pretrained-params ./checkpoint/ResNet50/<epoch_id>

Make sure:

Resume from checkpoints: Both <--model-prefix>.pdopts and <--model-prefix>.pdparams must be in the given path.
Start from pretrained weights: <--model-prefix>.pdparams must be in the given path.
Don't set --from-checkpoint and --from-pretrained-params at the same time.

The difference between those two is that --from-pretrained-params contain only model weights, and --from-checkpoint, apart from model weights, contain the optimizer state, and LR scheduler state.

--from-checkpoint is suitable for dividing the training into parts, for example, in order to divide the training job into shorter stages, or restart training after infrastructure faults.

--from-pretrained-params can be used as a base for fine tuning the model to a different dataset or as a backbone to detection models.

Metrics gathered through both training and evaluation:

[train|val].loss - loss
[train|val].top1 - top 1 accuracy
[train|val].top5 - top 5 accuracy
[train|val].data_time - time spent on waiting on data
[train|val].compute_time - time spent on computing
[train|val].batch_time - time spent on a mini-batch
[train|val].ips - speed measured in images per second

Metrics gathered through training only

train.lr - learning rate

Automatic SParsity training process:

To enable automatic sparsity training workflow, turn on --asp and --prune-mode when training launches. Refer to Command-line options

Note that automatic sparsity (ASP) requires a pretrained model to initialize parameters.

You can apply scripts/training/train_resnet50_AMP_ASP_90E_DGXA100.sh we provided to launch ASP + AMP training.

# Default path to pretrained parameters is ./checkpoint/ResNet50/89/resnet_50_paddle
bash scripts/training/train_resnet50_AMP_ASP_90E_DGXA100.sh <pretrained_parameters>

Or following steps below to manually launch ASP + AMP training.

First, set --from-pretrained-params to a pretrained model file. For example, if you have trained the ResNet50 for 90 epochs following Training process, the final pretrained weights would be stored in ./checkpoint/ResNet50/89/resnet_50_paddle.pdparams by default, and set --from-pretrained-params to ./checkpoint/ResNet50/89.

Then run following command to run AMP + ASP:

python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --from-pretrained-params ./checkpoint/ResNet50/89 \
  --model-prefix resnet_50_paddle \
  --epochs 90 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC \
  --asp \
  --prune-model \
  --mask-algo mask_1d

Quantization Aware Training Process

Quantization aware training requires a fine-tuned model. Quantize / dequantize OPs will be inserted into the model and then a smaller number of epochs of training will be taken to update the parameters in the model.

To enable quantization aware training workflow, turn on --qat when training launches. Refer to Command-line options.

You can apply the script scripts/training/train_resnet50_AMP_QAT_10E_DGXA100.sh we provided to launch AMP + QAT training.

# Default path to pretrained parameters is ./output/ResNet50/89/resnet_50_paddle
bash scripts/training/train_resnet50_AMP_QAT_10E_DGXA100.sh <pretrained_parameters>

Or following steps below to manually launch AMP + QAT training.

First, set --from-pretrained-params to a pretrained model file. For example, if you have trained the ResNet50 for 90 epochs following Training process, the final pretrained weights would be stored in ./output/ResNet50/89/resnet_50_paddle.pdparams by default, and set --from-pretrained-params to ./output/ResNet50/89.

Then run following command to run AMP + QAT:

python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --from-pretrained-params ./output/ResNet50/89 \
  --model-prefix resnet_50_paddle \
  --epochs 10 \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC \
  --qat

Inference process

Inference on your own datasets.

To run inference on a single example with pretrained parameters,

Set --from-pretrained-params to your pretrained parameters.
Set --image-root to the root folder of your own dataset.

Note that the validation dataset should be in image-root/val.

Set --run-scope to eval_only.

# For single GPU evaluation
python -m paddle.distributed.launch --gpus=0 train.py \
  --from-pretrained-params <path_to_pretrained_params> \
  --image-root <your_own_dataset> \
  --run-scope eval_only

# For 8 GPUs evaluation
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --from-pretrained-params <path_to_pretrained_params> \
  --image-root <your_own_dataset> \
  --run-scope eval_only

Inference with TensorRT

For inference with TensorRT, we provide two scopes to benchmark with or without data preprocessing.

The default scripts in scripts/inference use synthetic input to run inference without data preprocessing.

For example,

Run bash scripts/inference/export_resnet50_AMP.sh <your_checkpoint> to export an inference model.

The default path of the checkpoint is ./output/ResNet50/89.

Run bash scripts/inference/infer_resnet50_AMP.sh to infer with TensorRT.

Or you could manually run export_model.py and inference.py with specific arguments, refer to Command-line options.

Note that arguments passed to export_model.py and inference.py should be the same with arguments used in training.

To run inference with data preprocessing, set the option --use-synthetic to false and --image-root to the path of your own dataset. For example,

python inference.py --inference-dir <path_to_model> \
  --image-root <your_own_data_set> \
  --use-synthetic False

Performance

The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to NVIDIA Data Center Deep Learning Product 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 (A100 GPUs only for now), set --benchmark, --benchmark-steps and --benchmark-warmup-steps, then run training with --run-scope train_only. Refer to Command-line options.

Example:

# For 8 GPUs benchmark for AMP
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --run-scope train_only \
  --amp \
  --scale-loss 128.0 \
  --use-dynamic-loss-scaling \
  --data-layout NHWC \
  --benchmark \
  --benchmark-steps 100 \
  --benchmark-warmup-steps 300

Benchmark will run 300 iterations for warmup and 100 iterations for benchmark, then save benchmark results in the --report-file file.

Inference performance benchmark

Benchmark

To benchmark evaluation (A100 GPUs only for now), set --benchmark, --benchmark-steps and --benchmark-warmup-steps, then run training with --run-scope eval_only. Refer to Command-line options.

Example:

# For 8 GPUs benchmark for AMP
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --run-scope eval_only \
  --amp \
  --data-layout NHWC \
  --benchmark \
  --benchmark-steps 100 \
  --benchmark-warmup-steps 300

Benchmark will run 300 iterations for warmup and 100 iterations for benchmark, then save benchmark results in the --report-file file.

It is also allowed to set batch size for benchmark by adding --batch-size <batch_size> in launching commands.

# For 8 GPUs benchmark for AMP
python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 train.py \
  --run-scope eval_only \
  --batch-size 32 \
  --amp \
  --data-layout NHWC \
  --benchmark \
  --benchmark-steps 100 \
  --benchmark-warmup-steps 300

Benchmark with TensorRT

To benchmark the inference performance with TensorRT on a specific batch size, run inference.py with --use-synthetic True. The benchmark uses synthetic input without data preprocessing.

FP32 / TF32

python inference.py \
    --inference-dir <path_to_exported_model> \
    --precision FP32 \
    --batch-size <batch_size> \
    --benchmark-steps 1024 \
    --benchmark-warmup-steps 16 \
    --use-synthetic True

FP16

python inference.py \
    --inference-dir <path_to_exported_model> \
    --precision FP16 \
    --batch-size <batch_size>
    --benchmark-steps 1024 \
    --benchmark-warmup-steps 16 \
    --use-synthetic True

Note that arguments passed to inference.py should be the same with arguments used in training.

Results

Training accuracy results

Our results were obtained by running the applicable training script in the PaddlePaddle NGC container.

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

Training accuracy: NVIDIA DGX A100 (8x A100 80GB)

Epochs	Mixed Precision Top1	TF32 Top1
50	75.96 +/- 0.09	76.17 +/- 0.11
90	76.93 +/- 0.14	76.91 +/- 0.13

Example plots

The following images show the 90 epoch configuration on a DGX-A100.

Accuracy recovering of Automatic SParsity: NVIDIA DGX A100 (8x A100 80GB)

Epochs	Mixed Precision Top1 (Baseline)	Mixed Precision+ASP Top1
90	76.92	76.72

Training performance results

Our results were obtained by running the applicable training script in the PaddlePaddle NGC container.

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

Training performance: NVIDIA DGX A100 (8x A100 80GB)

GPUs	Throughput - TF32	Throughput - mixed precision	Throughput speedup (TF32 to mixed precision)	TF32 Scaling	Mixed Precision Scaling	Mixed Precision Training Time (90E)	TF32 Training Time (90E)
1	1024 img/s	2897 img/s	2.83 x	1.0 x	1.0 x	~13 hours	~40 hours
8	8013 img/s	23874 img/s	2.98 x	7.83 x	8.24 x	~2 hours	~4 hours

Training performance of Automatic SParsity: NVIDIA DGX A100 (8x A100 80GB)

GPUs	Throughput - mixed precision	Throughput - mixed precision+ASP	Overhead
1	2711 img/s	2686 img/s	1.0%
8	20267 img/s	20144 img/s	0.6%

Note that the train.py would enable CPU affinity binding to GPUs by default, that is designed and guaranteed to be optimal for NVIDIA DGX-series. You could disable binding via launch train.py with --enable-cpu-affinity false.

Inference performance results

Inference performance: NVIDIA DGX A100 (1x A100 80GB)

Our results were obtained by running the applicable training script with --run-scope eval_only argument in the PaddlePaddle NGC container.

TF32 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	138.90 img/s	7.19 ms	7.25 ms	7.70 ms	17.05 ms
2	263.20 img/s	7.59 ms	7.61 ms	8.27 ms	18.17 ms
4	442.47 img/s	9.04 ms	9.31 ms	10.10 ms	20.41 ms
8	904.99 img/s	8.83 ms	9.27 ms	10.08 ms	18.16 ms
16	1738.12 img/s	9.20 ms	9.75 ms	10.16 ms	18.06 ms
32	2423.74 img/s	13.20 ms	16.09 ms	18.10 ms	28.01 ms
64	2890.31 img/s	22.14 ms	22.10 ms	22.79 ms	30.62 ms
128	2676.88 img/s	47.81 ms	68.94 ms	77.97 ms	92.41 ms
256	3283.94 img/s	77.95 ms	79.02 ms	80.88 ms	98.36 ms

Mixed Precision Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	127.12 img/s	7.86 ms	8.24 ms	8.52 ms	14.17 ms
2	239.49 img/s	8.35 ms	9.08 ms	9.78 ms	9.89 ms
4	519.19 img/s	7.70 ms	7.44 ms	7.69 ms	14.20 ms
8	918.01 img/s	8.71 ms	8.39 ms	9.08 ms	21.23 ms
16	1795.41 img/s	8.91 ms	9.73 ms	10.36 ms	11.39 ms
32	3201.59 img/s	9.99 ms	12.04 ms	15.29 ms	23.23 ms
64	4919.89 img/s	13.00 ms	13.66 ms	14.06 ms	24.75 ms
128	4361.36 img/s	29.34 ms	47.47 ms	157.49 ms	77.42 ms
256	5742.03 img/s	44.58 ms	52.78 ms	356.58 ms	78.99 ms

Paddle-TRT performance results

Paddle-TRT performance: NVIDIA DGX A100 (1x A100 80GB)

Our results for Paddle-TRT were obtained by running the inference.py script on NVIDIA DGX A100 with (1x A100 80G) GPU.

Note that the benchmark does not include data preprocessing. Refer to Benchmark with TensorRT.

TF32 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	969.11 img/s	1.03 ms	1.03 ms	1.13 ms	1.14 ms
2	1775.33 img/s	1.13 ms	1.13 ms	1.22 ms	1.23 ms
4	3088.02 img/s	1.29 ms	1.30 ms	1.39 ms	1.40 ms
8	4552.29 img/s	1.76 ms	1.76 ms	1.85 ms	1.87 ms
16	6059.48 img/s	2.64 ms	2.64 ms	2.73 ms	2.75 ms
32	7264.92 img/s	4.40 ms	4.41 ms	4.49 ms	4.52 ms
64	8022.82 img/s	7.98 ms	8.03 ms	8.05 ms	8.11 ms
128	8436.27 img/s	15.17 ms	15.20 ms	15.27 ms	15.30 ms
256	8623.08 img/s	29.69 ms	29.82 ms	29.86 ms	29.97 ms

FP16 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	1306.28 img/s	0.76 ms	0.77 ms	0.86 ms	0.87 ms
2	2453.18 img/s	0.81 ms	0.82 ms	0.91 ms	0.92 ms
4	4295.75 img/s	0.93 ms	0.95 ms	1.03 ms	1.04 ms
8	7036.09 img/s	1.14 ms	1.15 ms	1.23 ms	1.25 ms
16	10376.70 img/s	1.54 ms	1.56 ms	1.64 ms	1.66 ms
32	13078.23 img/s	2.45 ms	2.45 ms	2.54 ms	2.56 ms
64	14992.88 img/s	4.27 ms	4.27 ms	4.36 ms	4.38 ms
128	16386.96 img/s	7.81 ms	7.83 ms	7.89 ms	7.93 ms
256	17363.79 img/s	14.74 ms	14.80 ms	14.82 ms	14.90 ms

INT8 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	1430.17 img/s	0.70 ms	0.70 ms	0.79 ms	0.80 ms
2	2683.75 img/s	0.74 ms	0.75 ms	0.84 ms	0.85 ms
4	4792.51 img/s	0.83 ms	0.84 ms	0.93 ms	0.94 ms
8	8366.92 img/s	0.96 ms	0.96 ms	1.05 ms	1.06 ms
16	13083.56 img/s	1.22 ms	1.22 ms	1.32 ms	1.33 ms
32	18171.90 img/s	1.76 ms	1.76 ms	1.86 ms	1.87 ms
64	22578.08 img/s	2.83 ms	2.84 ms	2.93 ms	2.95 ms
128	25730.51 img/s	4.97 ms	4.98 ms	5.07 ms	5.08 ms
256	27935.10 img/s	9.16 ms	9.26 ms	9.30 ms	9.34 ms

Paddle-TRT performance: NVIDIA A30 (1x A30 24GB)

Our results for Paddle-TRT were obtained by running the inference.py script on NVIDIA A30 with (1x A30 24G) GPU.

Note that the benchmark does not include data preprocessing. Refer to Benchmark with TensorRT.

TF32 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	860.08 img/s	1.16 ms	1.16 ms	1.27 ms	1.29 ms
2	1422.02 img/s	1.40 ms	1.41 ms	1.52 ms	1.53 ms
4	2058.41 img/s	1.94 ms	1.94 ms	2.06 ms	2.10 ms
8	2748.94 img/s	2.91 ms	2.93 ms	3.03 ms	3.22 ms
16	3329.39 img/s	4.80 ms	4.90 ms	4.93 ms	5.09 ms
32	3729.45 img/s	8.58 ms	8.68 ms	8.74 ms	8.84 ms
64	3946.74 img/s	16.21 ms	16.34 ms	16.41 ms	16.51 ms
128	4116.98 img/s	31.09 ms	31.26 ms	31.38 ms	31.43 ms
256	4227.52 img/s	60.55 ms	60.93 ms	61.01 ms	61.25 ms

FP16 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	1195.76 img/s	0.83 ms	0.84 ms	0.95 ms	0.96 ms
2	2121.44 img/s	0.94 ms	0.95 ms	1.05 ms	1.10 ms
4	3498.59 img/s	1.14 ms	1.14 ms	1.26 ms	1.30 ms
8	5139.91 img/s	1.55 ms	1.56 ms	1.67 ms	1.72 ms
16	6322.78 img/s	2.53 ms	2.54 ms	2.64 ms	2.83 ms
32	7093.70 img/s	4.51 ms	4.61 ms	4.64 ms	4.70 ms
64	7682.36 img/s	8.33 ms	8.44 ms	8.48 ms	8.58 ms
128	8072.73 img/s	15.85 ms	15.98 ms	16.04 ms	16.14 ms
256	8393.37 img/s	30.50 ms	30.67 ms	30.70 ms	30.84 ms

INT8 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	1346.83 img/s	0.74 ms	0.74 ms	0.85 ms	0.87 ms
2	2415.06 img/s	0.83 ms	0.83 ms	0.94 ms	0.99 ms
4	4152.29 img/s	0.96 ms	0.97 ms	1.07 ms	1.11 ms
8	6684.53 img/s	1.20 ms	1.20 ms	1.31 ms	1.37 ms
16	9336.11 img/s	1.71 ms	1.72 ms	1.82 ms	1.89 ms
32	11544.88 img/s	2.77 ms	2.77 ms	2.88 ms	3.09 ms
64	12954.16 img/s	4.94 ms	5.04 ms	5.08 ms	5.23 ms
128	13914.60 img/s	9.20 ms	9.27 ms	9.34 ms	9.45 ms
256	14443.15 img/s	17.72 ms	17.87 ms	17.92 ms	18.00 ms

Paddle-TRT performance: NVIDIA A10 (1x A10 24GB)

Our results for Paddle-TRT were obtained by running the inference.py script on NVIDIA A10 with (1x A10 24G) GPU.

Note that the benchmark does not include data preprocessing. Refer to Benchmark with TensorRT.

TF32 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	601.39 img/s	1.66 ms	1.66 ms	1.82 ms	1.85 ms
2	962.31 img/s	2.08 ms	2.13 ms	2.23 ms	2.38 ms
4	1338.26 img/s	2.99 ms	3.04 ms	3.14 ms	3.32 ms
8	1650.56 img/s	4.85 ms	4.93 ms	5.01 ms	5.14 ms
16	2116.53 img/s	7.56 ms	7.64 ms	7.71 ms	7.84 ms
32	2316.43 img/s	13.81 ms	14.00 ms	14.07 ms	14.26 ms
64	2477.26 img/s	25.83 ms	26.05 ms	26.15 ms	26.35 ms
128	2528.92 img/s	50.61 ms	51.24 ms	51.37 ms	51.72 ms
256	2576.08 img/s	99.37 ms	100.45 ms	100.66 ms	101.05 ms

FP16 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	1109.59 img/s	0.90 ms	0.90 ms	1.06 ms	1.08 ms
2	1901.53 img/s	1.05 ms	1.05 ms	1.22 ms	1.23 ms
4	2733.20 img/s	1.46 ms	1.48 ms	1.62 ms	1.65 ms
8	3494.23 img/s	2.29 ms	2.32 ms	2.44 ms	2.48 ms
16	4113.53 img/s	3.89 ms	3.99 ms	4.10 ms	4.17 ms
32	4714.63 img/s	6.79 ms	6.98 ms	7.14 ms	7.30 ms
64	5054.70 img/s	12.66 ms	12.78 ms	12.83 ms	13.08 ms
128	5261.98 img/s	24.32 ms	24.58 ms	24.71 ms	24.96 ms
256	5397.53 img/s	47.43 ms	47.83 ms	47.95 ms	48.17 ms

INT8 Inference Latency

Batch Size	Avg throughput	Avg latency	90% Latency	95% Latency	99% Latency
1	1285.15 img/s	0.78 ms	0.78 ms	0.93 ms	0.95 ms
2	2293.43 img/s	0.87 ms	0.88 ms	1.03 ms	1.05 ms
4	3508.39 img/s	1.14 ms	1.15 ms	1.29 ms	1.32 ms
8	5907.02 img/s	1.35 ms	1.36 ms	1.51 ms	1.60 ms
16	7416.99 img/s	2.16 ms	2.19 ms	2.31 ms	2.36 ms
32	8337.02 img/s	3.84 ms	3.91 ms	4.01 ms	4.14 ms
64	9039.71 img/s	7.08 ms	7.24 ms	7.40 ms	7.66 ms
128	9387.23 img/s	13.63 ms	13.84 ms	13.92 ms	14.11 ms
256	9598.97 img/s	26.67 ms	27.12 ms	27.24 ms	27.48 ms

Release notes

Changelog

December 2021

Initial release
Cosine LR schedule
DALI support
DALI-CPU dataloader
Added A100 scripts
Paddle AMP

January 2022

Added options Label Smoothing, fan-in initialization, skipping weight decay on batch norm gamma and bias.
Updated README
A100 convergence benchmark

December 2023

Add quantization aware training
Add INT8 inference for Paddle-TRT
Simplify the inference process

Known issues

Allreduce issues to top1 and top5 accuracy in evaluation. Workaround: use build_strategy.fix_op_run_order = True for eval program. (refer to Paddle-issue-39567 for details)