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README.md

AlexNet

Training Alexnet on imagenette Dataset using OneFlow

Usage

0. Requirements

Experiment environment:

  • oneflow
  • tqdm
  • tensorboardX (optional)

1. Prepare Traning Data And Pretrain Models

Download Ofrecord

wget https://oneflow-public.oss-cn-beijing.aliyuncs.com/datasets/imagenette_ofrecord.tar.gz
tar zxf imagenette_ofrecord.tar.gz

Download Pretrain Models

wget https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/cv/classification/alexnet/alexnet_oneflow_model.tar.gz

2. Run Oneflow Training Script

Eager Training Scripts

bash eager/train.sh

Graph Training Scripts

bash graph/train.sh

3. Inference on Single Image

Eager Inference

bash eager/infer.sh

Graph Inference

bash graph/infer.sh

Util

1. Model Compare

Compare Alexnet model on different training mode (Graph / Eager)

bash check/check.sh

Compare results will be saved to results/check_info

Compare Results Picture

bash check/draw.sh

The pictures will be saved to results/pictures

2. Convert Pretrained Model Weight

convert pytorch pretrained model to oneflow pretrained model

wget https://download.pytorch.org/models/alexnet-owt-7be5be79.pth
import torch
import oneflow as flow 
from models.alexnet import alexnet

parameters = torch.load("alexnet-owt-7be5be79.pth")
new_parameters = dict()
for key,value in parameters.items():
     if "num_batches_tracked" not in key:
          val = value.detach().cpu().numpy()
          new_parameters[key] = val




alexnet_module = alexnet()
alexnet_module.load_state_dict(new_parameters)
flow.save(alexnet_module.state_dict(), "alexnet_oneflow_model")