Train_DenseCL_model.md

Train DenseCL Model

Introduction

PASSL reproduces DenseCL, a self-supervised model for dense prediction tasks.

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
DenseCL	200	63.62	64.61	ResNet-50	download

Getting Started

1. Train DenseCL

single gpu

python tools/train.py -c configs/densecl/densecl_r50.yaml

multiple gpus

python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c configs/densecl/densecl_r50.yaml

Pretraining models with 200 epochs can be found at DenseCL

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} --remove_prefix

• 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 DenseCL linear