MSML_Project

Python code for paper "Multi-stage Meta-Learning for Few-Shot with Lie Group Network Constraint"

1. Environment

1.1. Requirements

1. Python 3.X
2. PyTorch (ver>=0.4)
3. Numpy
4. mini-ImageNet dataset
5. zipfile

1.2. Testing Environment

1.2.1 Software:

• Ubuntu 16.04
• Python 3.6.1
• PyTorch 1.0.1
• Numpy 1.17.2

1.2.2 Hardware:

• CPU: Intel Xeon E5-2620 v4 @2.10GHz with 8 Cores
• GPU: NVIDIA TITAN Xp with CUDA 8.0.61

2. File Structure

MSML_Project

└─data
 └─miniimagenet
   ├─images
    ├─nxx.jpg
    ├─...
   ├─train.csv
   ├─val.csv
   ├─test.csv
 ─proc_images.py
└─meta
  ├─main.py
  ├─model.py
  ├─net_meta.py
  ├─data_provider_meta.py
└─pretrain
  ├─pretrain.py
  ├─data_provider.py
  ├─net.py

3. Experiment Details

3.1. Computing resource usage

	RAM	GPU Memory
Pretrain Phase	1500MB	6267MB
5-way 1-shot	1800MB	8767MB
5-way 5-shot	1800MB	10157MB

3.2 Processes

3.2.1 prepare dataset

1. download mini-ImageNet dataset, images are croped to 84* 84 pixels.
2. put mini-ImageNet.zip in folder MSML_project/data/.
3. run proc_dataset.py to unzip file and copy all images to folder MSML_Project/data/miniimagenet/images/.

3.2.2 Pretrain

1. run MSML_Project/pretrain/pretrain.py to get pretrain weight.

3.3.3 Meta-Train & Meta-Test

1. finish pretrain phase
2. run MSML_Project/meta/main.py

3.3. Speed

	iter/s	total time
Pretrain	2.64	5h-10m-46s
5-way 1-shot	0.7	6h-51m-34s
5-way 5-shot	0.26	19h-23m-45s