mindspore/model_zoo/official/cv/simclr

• Contents
• • SimCLR Description
  • Model Architecture
  • Dataset
  • Environment Requirements
  • Quick Start
  • Script Description
  • • Script and Sample Code
    • Script Parameters
    • Training Process
    • • Training
    • Evaluation Process
    • • Evaluation
  • Model Description
  • • Performance
    • • Evaluation Performance
  • Description of Random Situation
  • ModelZoo Homepage

Contents

• SimCLR Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
    • Training
  • Evaluation Process
    • Evaluation
• Model Description
  • Performance
    • Evaluation Performance
• ModelZoo Homepage

SimCLR Description

SimCLR: a simple framework for contrastive learning of visual representations.
Paper: Ting Chen and Simon Kornblith and Mohammad Norouzi and Geoffrey Hinton. A Simple Framework for Contrastive Learning of Visual Representations. arXiv preprint arXiv:2002.05709. 2020.

Model Architecture

SimCLR learns representations by maximizing agreement between differently augmented views of the same data example via a contrastive loss in the latent space. This framework comprises the following four major components: a stochastic data augmentation module, a neural network base encoder, a small neural network projection head and a contrastive loss function.

Dataset

In the following sections, we will introduce how to run the scripts using the related dataset below.

Dataset used: CIFAR-10

• Dataset size：175M，60,000 32*32 colorful images in 10 classes
  • Train：146M，50,000 images
  • Test：29.3M，10,000 images
• Data format：binary files
  • Note：Data will be processed in dataset.py
• Download the dataset, the directory structure is as follows:

├─cifar-10-batches-bin
│
└─cifar-10-verify-bin

Environment Requirements

• Hardware（Ascend）
  • Prepare hardware environment with Ascend processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Quick Start

After installing MindSpore via the official website, you can start training and evaluation as follows:

# enter script dir, train SimCLR
sh run_standalone_train_ascend.sh [cifar10] [TRAIN_DATASET_PATH] [DEVICE_ID]
or
sh run_distribution_ascend.sh [DEVICENUM] [RANK_TABLE_FILE] [cifar10] [TRAIN_DATASET_PATH]
# enter script dir, evaluate SimCLR
sh run_standalone_eval_ascend.sh [cifar10] [DEVICE_ID] [SIMCLR_MODEL_PATH] [TRAIN_DATASET_PATH] [EVAL_DATASET_PATH]

Script Description

Script and Sample Code

├── cv
    ├── SimCLR
        ├── README.md                    // descriptions about SimCLR
        ├── requirements.txt             // package needed
        ├── scripts
        │   ├──run_distribution_train_ascend.sh         // train in ascend
        │   ├──run_standalone_train_ascend.sh          // train in ascend
        │   ├──run_standalone_eval_ascend.sh          //  evaluate in ascend
        ├── src
        │   ├──dataset.py             // creating dataset
        │   ├──lr_generator.py             // generating learning rate
        │   ├──nt_xent.py             // contrastive cross entropy loss
        │   ├──optimizer.py             // generating optimizer
        │   ├──resnet.py             // base encoder network
        │   ├──simclr_model.py              // simclr architecture
        ├── train.py               // training script
        ├── linear_eval.py               //  linear evaluation script
        ├── export.py             // export model for inference

Script Parameters

Major parameters in train.py as follows:
--device_target: Device target, Currently only Ascend is supported.
--run_cloudbrain: Whether it is running on CloudBrain platform.
--run_distribute: Run distributed training.
--device_num: Device num.
--device_id: Device id, default is 0.
--dataset_name: Dataset, Currently only cifar10 is supported.
--train_url: Cloudbrain Location of training outputs.This parameter needs to be set when running on the cloud brain platform.
--data_url: Cloudbrain Location of data. This parameter needs to be set when running on the cloud brain platform.
--train_dataset_path: Dataset path for training classifier. This parameter needs to be set when running on the host.
--train_output_path: Location of ckpt and log. This parameter needs to be set when running on the host.
--batch_size: Batch size, default is 128.
--epoch_size: Epoch size for training, default is 100.
--projection_dimension: Projection output dimensionality, default is 128.
--width_multiplier: Width multiplier for ResNet50, default is 1.
--temperature: Temperature for contrastive cross entropy loss.
--pre_trained_path: Pretrained checkpoint path.
--pretrain_epoch_size: real_epoch_size = epoch_size - pretrain_epoch_size.
save_checkpoint_epochs: Save checkpoint epochs, default is 1.
--save_graphs: Whether save graphs, default is False.
--optimizer: Optimizer, Currently only Adam is supported.
--weight_decay: Weight decay.
--warmup_epochs: Warmup epochs.

Major parameters in linear_eval.py as follows:
--device_target: Device target, Currently only Ascend is supported.
--run_cloudbrain: Whether it is running on CloudBrain platform.
--run_distribute: Run distributed training.
--device_num: Device num.
--device_id: Device id, default is 0.
--dataset_name: Dataset, Currently only cifar10 is supported.
--train_url: Cloudbrain Location of training outputs.This parameter needs to be set when running on the cloud brain platform.
--data_url: Cloudbrain Location of data. This parameter needs to be set when running on the cloud brain platform.
--train_dataset_path: Dataset path for training classifier. This parameter needs to be set when running on the host.
--eval_dataset_path: Dataset path for evaluating classifier.This parameter needs to be set when running on the host.
--train_output_path: Location of ckpt and log. This parameter needs to be set when running on the host.
--class_num: dataset classification number, default is 10 for cifar10.
--batch_size: Batch size, default is 128.
--epoch_size: Epoch size for training, default is 100.
--projection_dimension: Projection output dimensionality, default is 128.
--width_multiplier: Width multiplier for ResNet50, default is 1.
--pre_classifier_checkpoint_path: Classifier Checkpoint file path.
--encoder_checkpoint_path: Encoder Checkpoint file path.
--save_checkpoint_epochs: Save checkpoint epochs, default is 10.
--print_iter: Log print iter, default is 100.
--save_graphs: whether save graphs, default is False.

Training Process

Training

• running on Ascend

  sh run_distribution_ascend.sh [DEVICENUM] [RANK_TABLE_FILE] [cifar10] [TRAIN_DATASET_PATH]
  

  After training, the loss value will be achieved as follows:

  # grep "loss is " log
  epoch: 1 step: 48, loss is 9.5758915
  epoch time: 253236.075 ms, per step time: 5275.752 ms
  epoch: 1 step: 48, loss is 9.363186
  epoch time: 253739.376 ms, per step time: 5286.237 ms
  epoch: 1 step: 48, loss is 9.36029
  epoch time: 253711.625 ms, per step time: 5285.659 ms
  ...
  epoch: 100 step: 48, loss is 7.453776
  epoch time: 12341.851 ms, per step time: 257.122 ms
  epoch: 100 step: 48, loss is 7.499168
  epoch time: 12420.060 ms, per step time: 258.751 ms
  epoch: 100 step: 48, loss is 7.442362
  epoch time: 12725.863 ms, per step time: 265.122 ms
  ...
  

  The model checkpoint will be saved in the outputs directory.

Evaluation Process

Evaluation

Before running the command below, please check the checkpoint path used for evaluation.

• running on Ascend

  sh run_standalone_eval_ascend.sh [cifar10] [DEVICE_ID] [SIMCLR_MODEL_PATH] [TRAIN_DATASET_PATH] [EVAL_DATASET_PATH]
  

  You can view the results through the file "eval_log". The accuracy of the test dataset will be as follows:

  # grep "Average accuracy: " eval_log
  'Accuracy': 0.84505
  

Model Description

Performance

Evaluation Performance

Parameters	Ascend
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G
uploaded Date	30/03/2021 (month/day/year)
MindSpore Version	1.1.1
Dataset	CIFAR-10
Training Parameters	epoch=100, batch_size=128, device_num=8
Optimizer	Adam
Loss Function	NT-Xent Loss
linear eval	84.505%
Total time	25m04s
Scripts	SimCLR Script

Description of Random Situation

We set the seed inside dataset.py. We also use random seed in train.py.

ModelZoo Homepage

Please check the official homepage.