Train_SimCLR_model.md

Train SimCLR Model

Introduction

SimCLR is a simple framework for contrastive learning of visual representations. SimCLR learns representations by maximizing agreement between differently augmented views of the same data example via a contrastive loss in the latent space(https://arxiv.org/abs/2002.05709).

Installation

• See INSTALL.md

Data Preparation

• See GETTING_STARTED.md

Implemented Models

Models are all trained with ResNet-50 backbone.

	epochs	official results	passl results	Backbone	Model
SimCLR	100	64.5	64.8	ResNet-50	download

Getting Started

1. Train SimCLR

single gpu

ImageNet

python3 tools/train.py -c configs/simclr/simclr_r50_IM.yaml

Cifar10

python3 tools/train.py -c configs/simclr/simclr_r18_cifar10.yaml

multiple gpus

ImageNet

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/simclr/simclr_r50_IM.yaml

Cifar10

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/simclr/simclr_r50_IM.yaml

Pretraining models with 200 epochs can be found at SimCLR. Note: The default learning rate in config files is for 8 GPUs. If using differnt number GPUs, the total batch size will change in proportion, you have to scale the learning rate following new_lr = old_lr * new_ngpus / old_ngpus.

2. Extract backbone weights

python tools/extract_weight.py ${CHECKPOINT} --output ${WEIGHT_FILE}

• Support PaddleClas

Convert the format of the extracted weights to the corresponding format of paddleclas to facilitate training on paddleclas

python tools/passl2ppclas/convert.py --type res50 --checkpoint ${CHECKPOINT} --output ${WEIGHT_FILE}

Note: It must be ensured that the weights are extracted

3. Evaluation on ImageNet Linear Classification

Train:

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/moco/moco_clas_r50.yaml --pretrained ${WEIGHT_FILE}

Evaluate:

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/moco/moco_clas_r50.yaml --load ${CLS_WEGHT_FILE} --evaluate-only

The trained linear weights in conjuction with the backbone weights can be found at SimCLR linear.