/NCTU_DLSR_final_project-1

2018 Fall NCTU Deep Learning and System Realization Final Project Benchmark

Primary LanguagePythonMIT LicenseMIT

評分環境

  • 機器: NCHC Aitrain container
  • Python environment as LAB5

使用方法

  • Download this repository to benchmark your project
$ git clone https://github.com/nctu-arch/NCTU_DLSR_final_project.git
  • Preparation: install requirements
$ cd NCTU_DLSR_final_project
$ pip3 install -r requirements.txt
  • import benchmark
from benchmark import benchmarking
  • Benchmarking function usage - benchmarking(team, task, model, preprocess_fn, *pre_args, **pre_kwargs)
    • team:
      • 1~12
    • task:
      • 0: classification
      • 1: super resolution
      • 2: objection detection
    • model: pytorch
      • 目前計算 pytorch model weight 數量及大小
    • preprocess_fn, *pre_args, **pre_kwargs:
      • 前處理 function， 可以轉換 data format
      • 參數 可自定義, 無則 None
  • 撰寫 Inference code
net = resnet18() # define model 
@benchmarking(team=12, task=0, model=net, preprocess_fn=None)
def inference_fn(*args, **kwargs):
    dev = kwargs['device']
    if dev == 'cpu':
        metric = do_cpu_inference()
        ...
    elif dev == 'cuda':
        metric = do_gpu_inference()
        ...
    return metric

Test Categories

  • CINIC-10
    • Baseline
      • CINIC-10 test data
    • Accuracy Ranking
      • private test data
    • Model size
    • CPU inference time
    • GPU inference time
  • DIV2K
    • Baseline
      • DIV2K x2 validtion data
    • PSNR Ranking
      • private test data
    • Model size
    • CPU inference time
    • GPU inference time
  • Clothes
    • Baseline:
      • Validation data
    • F-score Ranking
      • ITRI test data
    • Model size
    • CPU inference time
    • GPU inference time

What to Submit?

  • Any source code you used in your project.
  • Create a team directory named teamX including 'Classification','Object Detection' and 'Super Resolution' to push each task respectively.
e.g..
.
├── team11
└── team12
    ├── Classification
    ├── Object Detection
    └── Super Resolution

How to Submit?

  • As a student, you can apply for a GitHub Student Developer Pack, which offers unlimited private repositories.
  • Fork this repository, and then make your forked repo duplicated. (Settings -> Danger Zone)
  • Add nctu-arch as collaborator. (Settings -> collaborator)
  • After deadline we will pull your source code for open review.
  • Please describe the external plugins you used and its usage precisely.

Example usage

TESTDATADIR="./path/to/cifar10/data" python3 example.py
  • default: overide team 12 data

Score sheet link

http://140.113.213.76/