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

Contents

• Prototypical-Network 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

protonet-Description

PyTorch code for NeuralIPS 2017 paper: Prototypical Networks for Few-shot Learning

Model Architecture

Proto-Net contains 2 parts named Encoder and Relation. The former one has 4 convolution layers, the latter one has 2 convolution layers and 2 linear layers.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

Dataset used: omniglot

• Dataset size 4.02M，32462 28*28 in 1622 classes

  • Train 1,200 classes
  • Test 422 classes

• Data format .png files

  • Note Data has been processed in omniglot_resized

• The directory structure is as follows:

└─Data
    ├─raw
    ├─spilts
    │     vinyals
    │         test.txt
    │         train.txt
    │         val.txt
    │         trainval.txt 
    └─data
           Alphabet_of_the_Magi
           Angelic

Environment Requirements

• Hardware（Ascend/GPU/CPU）
  • Prepare hardware environment with Ascend, GPU, or CPU 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 RelationNet
sh run_train_ascend.sh  
# enter script dir, evaluate RelationNet
sh run_eval_ascend.sh

Script and Sample Code

├── cv
    ├── ProtoNet
        ├── README.md                    // descriptions about lenet
        ├── scripts
        │   ├──run_train_ascend.sh          // train in ascend
        │   ├──run_eval_ascend.sh          //  evaluate in ascend
        ├── src
        │   ├──parser_util.py            // parameter configuration
        │   ├──dataset.py               // creating dataset
        │   ├──IterDatasetGenerator.py // generate dataset
        │   ├──protonet.py             // relationnet architecture
        │   ├──PrototypicalLoss.py    // loss function
        ├── train.py                // training script
        ├── eval.py                 //  evaluation script  

Script Parameters

Major parameters in train.py and config.py as follows:

--class_num: the number of class we use in one step.
--sample_num_per_class: the number of quert data we extract from one class.
--batch_num_per_class: the number of support data we extract from one class.
--data_path: The absolute full path to the train and evaluation datasets.
--episode: Total training epochs.
--test_episode: Total testing episodes
--learning_rate: Learning rate
--device_target: Device where the code will be implemented.
--save_dir: The absolute full path to the checkpoint file saved
                   after training.
--data_path: Path where the dataset is saved

Training Process

Training

python train.py --data_path Data --ckpt_path ckpt > log.txt 2>&1 &
# or enter script dir, and run the script
sh run_train_ascend.sh
python train.py

The model checkpoint will be saved in the current directory.

Evaluation Process

Evaluation

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

python eval.py --data_path Data > log.txt 2>&1 &  
# or enter script dir, and run the script
sh run_eval_ascend.sh
python train.py

Test Acc: 0.9954400658607483  Loss: 0.02102319709956646

Model Description

Performance

Evaluation Performance

Parameters	ProtoNet
Resource	Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G
uploaded Date	03/26/2021 (month/day/year)
MindSpore Version	1.1.1
Dataset	OMNIGLOT
Training Parameters	episode=500, class_num = 5, lr=0.001
Optimizer	Adam
Loss Function	Prototypicalloss
outputs	Accuracy
Loss	0.002
Speed	215 ms/step
Total time	3 h 23m (8p)
Checkpoint for Fine tuning	440 KB (.ckpt file)
Scripts	https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/pro1

ModelZoo Homepage

Please check the official homepage.